NRAM_Controller_12_Dataset_4.ipynb

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    "colab": {
      "name": "NRAM_Controller_12-Final.ipynb",
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      "mimetype": "text/x-python",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Jiig7MT1g6X7"
      },
      "source": [
        "# NRAM Implementation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mzV_aXjjAT9G",
        "colab_type": "text"
      },
      "source": [
        "### --------------------Dataset 4 Implemenation--------------------"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "my1537BG-tt8"
      },
      "source": [
        "### Status\n",
        "Completed\n",
        "*   Base Framework\n",
        "*   Code Cleanup\n",
        "*   Neural Network model - MLP, LSTM, LSTM + MLP\n",
        "*  Dataset 1\n",
        "*  Not Gate\n",
        "* Update model to run more gates\n",
        "* Add read/write module\n",
        "* Dataset 2 for read/write module \n",
        "* Dataset 3 for copy\n",
        "* Dataset 4 for basic bucket sort\n",
        "* Dataset 5 larger bucket sort list\n",
        "* Dataset 6 larger bucket sort list\n",
        "\n",
        "To-Do - Priority -2\n",
        "* LSTM - check hidden states\n",
        "* Check Final Error Summation\n",
        "\n",
        "\n",
        "To-Do - Priority -3\n",
        "* Gradient Clipping\n",
        "* Gradient Noise\n",
        "* Entropy\n",
        "* Limit the value of log\n",
        "* Enforcing Distribution Constraint\n",
        "\n",
        "\n",
        "#### Errors\n",
        "* Training\n",
        "* Testing - same size problem\n",
        "* Testing - different sized problem\n",
        "\n",
        "\n",
        "#### 5 Different parameters\n",
        "* Layer size\n",
        "* Learning rate\n",
        "\n",
        "\n",
        "#### Plot\n",
        "Y-axis error\n",
        "X-axis training iteration\n",
        "\n",
        "### Ablation Study\n",
        "After training, perturb the model (added noise, removed layers, etc.) or the data (added noise, shuffled input, etc). Show the impacts of the ablation on the three types of errors, for the 5 different hyper-parameter settings. Randomize the ablations and show aggregated error results (e.g., with histograms or boxplots)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "rGVz5HwlhDtX"
      },
      "source": [
        "## Initial Setup"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "OjRq1i1ngydI"
      },
      "source": [
        "### Import libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "j5SUYTG7mkYz",
        "colab": {}
      },
      "source": [
        "from __future__ import print_function\n",
        "\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "tf.enable_eager_execution()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "T4ApKdVugtiT"
      },
      "source": [
        "### Create Modules / Gates"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "zGWfXinzlzuM",
        "colab": {}
      },
      "source": [
        "def zeroModule(a, M):\n",
        "    zero = np.asarray([0])\n",
        "    return tf.one_hot(zero, M)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "vMr3C4Bpm_F5",
        "colab": {}
      },
      "source": [
        "def oneModule(a, M):\n",
        "    one = np.asarray([1])\n",
        "    return tf.one_hot(one, M)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "nha3eY8X1IhT",
        "colab": {}
      },
      "source": [
        "def twoModule(a, M):\n",
        "    two = np.asarray([2])\n",
        "    return tf.one_hot(two, M)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "kMZFHeLn1NTQ",
        "colab": {}
      },
      "source": [
        "def incModule(a, M):\n",
        "  return tf.roll(a, shift=1, axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "5YjJ8iexM5bC",
        "colab": {}
      },
      "source": [
        "def decModule(a, M):\n",
        "  return tf.roll(a, shift=-1, axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "mVGHQM0eDGfu",
        "colab": {}
      },
      "source": [
        "# def incModule1(a, M):\n",
        "#   aVal = tf.argmax(a, axis=1)\n",
        "#   addVal = tf.mod(tf.add(aVal, 1), a.shape[1])\n",
        "#   return tf.one_hot(addVal, a.shape[1])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_p8cSsZSF5bq",
        "colab": {}
      },
      "source": [
        "def addModule(a, b, M):\n",
        "  aVal = tf.argmax(a, axis=1)\n",
        "  bVal = tf.argmax(b, axis=1)\n",
        "  addVal = tf.mod(tf.add(aVal, bVal), a.shape[1])\n",
        "  return tf.one_hot(addVal, a.shape[1])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NGTn_A4w9UCi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# def addModule2(a, b, M):\n",
        "#     bVal = tf.roll(b, shift=-M, axis=1)\n",
        "    \n",
        "#     rows = [tf.roll(b, shift=shift+1, axis=1) for shift in range(M)]\n",
        "#     print(rows)\n",
        "#     bVal = tf.stack(rows)\n",
        "#     print(bVal)\n",
        "#     bVal = tf.reshape(bVal, shape=[4,4,1])\n",
        "#     print(bVal)\n",
        "# #     return bVal\n",
        "#     return tf.tensordot(a, bVal, axes=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2KSYBdSx9UCm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# addModule2(tf.one_hot([2], M), tf.one_hot([1], M), M)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ntBMShoxLb_E",
        "colab": {}
      },
      "source": [
        "def subModule(a, b, M):\n",
        "  aVal = tf.argmax(a, axis=1)\n",
        "  bVal = tf.argmax(b, axis=1)\n",
        "  subtractVal = tf.mod(tf.subtract(aVal, bVal), M)\n",
        "  return tf.one_hot(subtractVal, M)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "vDTe2xahNgaz",
        "colab": {}
      },
      "source": [
        "def lessModule(a, b, M):\n",
        "  aVal = tf.argmax(a, axis=1)\n",
        "  bVal = tf.argmax(b, axis=1)\n",
        "  lessVal = tf.less(aVal, bVal)\n",
        "  return tf.one_hot(tf.cast(lessVal, tf.int64), M)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "2g3ZQKmUQpJM",
        "colab": {}
      },
      "source": [
        "def lessOrEqualThenModule(a, b, M):\n",
        "  aVal = tf.argmax(a, axis=1)\n",
        "  bVal = tf.argmax(b, axis=1)\n",
        "  lessVal = tf.less_equal(aVal, bVal)\n",
        "  return tf.one_hot(tf.cast(lessVal, tf.int64), M)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "vHjdqWraQ0iw",
        "colab": {}
      },
      "source": [
        "def equalModule(a, b, M):\n",
        "  aVal = tf.argmax(a, axis=1)\n",
        "  bVal = tf.argmax(b, axis=1)\n",
        "  equalVal = tf.equal(aVal, bVal)\n",
        "  return tf.one_hot(tf.cast(equalVal, tf.int64), M)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "33Gou21oRRYD",
        "colab": {}
      },
      "source": [
        "def minimumModule(a, b, M):\n",
        "  aVal = tf.argmax(a, axis=1)\n",
        "  bVal = tf.argmax(b, axis=1)\n",
        "  minimumVal = tf.minimum(aVal, bVal)\n",
        "  return tf.one_hot(tf.cast(minimumVal, tf.int64), M)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "sNFEl44sRrpi",
        "colab": {}
      },
      "source": [
        "def maximumModule(a, b, M):\n",
        "  aVal = tf.argmax(a, axis=1)\n",
        "  bVal = tf.argmax(b, axis=1)\n",
        "  maximumVal = tf.maximum(aVal, bVal)\n",
        "  return tf.one_hot(tf.cast(maximumVal, tf.int64), M)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "3fdYDwRbgclC"
      },
      "source": [
        "### Test Modules"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "609r8cUKwgF2",
        "colab": {}
      },
      "source": [
        "#Note : Temporary M Values\n",
        "M = 20"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Qdf9Kx1aDtpN",
        "colab": {}
      },
      "source": [
        "# incModule1(tf.one_hot([1, 2], M), M)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ArqxHhD0UcKJ",
        "outputId": "8b372918-487e-4310-9905-88d33620258e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "incModule(tf.one_hot([1, 2], M), M)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=7, shape=(2, 20), dtype=float32, numpy=\n",
              "array([[0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.],\n",
              "       [0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "fK0ApcZ6VJcN",
        "outputId": "b030e974-cd33-487b-dde4-2c15d36627c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "decModule(tf.one_hot([4, 9], M), M)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=16, shape=(2, 20), dtype=float32, numpy=\n",
              "array([[0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ycjL_7EpI-t-",
        "outputId": "541d5220-6d26-4cd7-d4b7-d326de6c13bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "addModule(tf.one_hot([1, 2], M), tf.one_hot([1, 2], M), M)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=38, shape=(2, 20), dtype=float32, numpy=\n",
              "array([[0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DhQoWIsD9UDu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# addModule2(tf.one_hot([2], M), tf.one_hot([1], M), M)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "dax-xXeMgufM",
        "outputId": "16343e68-d065-49b1-8d75-30d66325cc95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "addModule(np.array([[0., 0., 0., 0.5, 0.5]]), np.array([[0., 0., 0., 1., 0.]]), M)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=52, shape=(1, 5), dtype=float32, numpy=array([[0., 1., 0., 0., 0.]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "PvK-yReagQkw"
      },
      "source": [
        "**Note: One hot of values greater than M is 0**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "M8TZ2g58gRgR",
        "outputId": "327d3f64-a98a-48b8-b68d-caed37fb06b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "tf.one_hot([7, 8], M)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=58, shape=(2, 20), dtype=float32, numpy=\n",
              "array([[0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "IvnYQqPsLl20",
        "outputId": "bc70d8eb-69d2-4cae-df39-2f06489a90ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "subModule(tf.one_hot([4, 8], M), tf.one_hot([6, 7], M), M)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=80, shape=(2, 20), dtype=float32, numpy=\n",
              "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 1., 0.],\n",
              "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "KRczko6xMStv",
        "outputId": "72196cf6-c808-4734-853c-b5757383ac7c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "incModule(tf.one_hot([4, 9], M), M)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=89, shape=(2, 20), dtype=float32, numpy=\n",
              "array([[0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "7FmUCjtIND3u",
        "outputId": "e9c1d666-4928-45a5-8c36-a68a3dd39699",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "decModule(tf.one_hot([4, 9], M), M)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=98, shape=(2, 20), dtype=float32, numpy=\n",
              "array([[0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_SkTggdrOe64",
        "outputId": "e43abb9d-1f98-486a-b8f1-dc3042796747",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "lessModule(tf.one_hot([8, 5], M), tf.one_hot([6, 7], M), M)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=119, shape=(2, 20), dtype=float32, numpy=\n",
              "array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.],\n",
              "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "k0d33tqhRDgU",
        "outputId": "86b672a1-0711-4d29-e1b7-9cd72aea9951",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "lessOrEqualThenModule(tf.one_hot([8, 5], M), tf.one_hot([8, 7], M), M)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=140, shape=(2, 20), dtype=float32, numpy=\n",
              "array([[0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.],\n",
              "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "7fK2kzaWRDsD",
        "outputId": "0ddba222-3fca-49ec-a4f0-2c907b4f6377",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "equalModule(tf.one_hot([8, 5], M), tf.one_hot([8, 7], M), M)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=161, shape=(2, 20), dtype=float32, numpy=\n",
              "array([[0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.],\n",
              "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "pigT9MXeRnGU",
        "outputId": "1b660d82-ab27-42d9-fdfe-3e58d8120938",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "minimumModule(tf.one_hot([8, 5], M), tf.one_hot([8, 7], M), M)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=181, shape=(2, 20), dtype=float32, numpy=\n",
              "array([[0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "3BXUJ9lmiEja"
      },
      "source": [
        "### Circuit Implementation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "5M1uj2sjWZ5u",
        "colab": {}
      },
      "source": [
        "# def dotProduct(a, b):\n",
        "#   print(\"a \",a.shape)\n",
        "#   print(\"b \", b.shape)\n",
        "#   if len(a.shape) == 2:\n",
        "#     return tf.tensordot(a.T, b, axes=1)\n",
        "#   else:\n",
        "#     output = []\n",
        "#     for i in range(a.shape[0]):\n",
        "#       aT = tf.transpose(a[i])\n",
        "#       output.append(tf.tensordot(aT, b[i], axes=1))\n",
        "#     return tf.stack(output)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "geOH2iAy1BAj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def dotProduct(a, b):\n",
        "  if len(b.shape) > 1:\n",
        "    print(\"error dotProduct shape: \", b.shape)\n",
        "  aT = tf.transpose(a)\n",
        "  aT = tf.reshape(aT, [a.shape[0],a.shape[2],a.shape[1]])\n",
        "  return tf.tensordot(aT, b, axes=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "NwIjw_u3YfAm",
        "outputId": "fbfbe39b-209e-46ed-8189-f33e273197f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "a = np.array([[\n",
        "    [1., 2., 3.],\n",
        "    [4., 5., 6.],\n",
        "]])\n",
        "\n",
        "b = np.array([1., 0.])\n",
        "dotProduct(a, b)\n",
        "\n",
        "\n",
        "a = np.array([[\n",
        "    [1., 2., 3.],\n",
        "    [4., 5., 6.],\n",
        "]])\n",
        "\n",
        "b = np.array([0., 1.])\n",
        "dotProduct(a, b)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=216, shape=(1, 3), dtype=float64, numpy=array([[4., 5., 6.]])>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZD4UW1C5nBIY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# a = np.array([\n",
        "#     [\n",
        "#       [1., 2., 3.],\n",
        "#       [4., 5., 6.],\n",
        "#     ],\n",
        "#     [\n",
        "#       [1., 2., 3.],\n",
        "#       [4., 5., 6.],\n",
        "#     ],\n",
        "# ])\n",
        "\n",
        "# b = np.array([\n",
        "#       [1., 0.],\n",
        "#       [0., 1.]\n",
        "#     ])\n",
        "# dotProduct(a, b)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "u6c9aQ_vidJ2"
      },
      "source": [
        "#### Gates List"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "szypKQYPHhz_",
        "colab": {}
      },
      "source": [
        "gates = [incModule, decModule, addModule]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "3t2_sRNQLxQA",
        "outputId": "515e7389-7381-4a9d-fe77-b69e28a76cb8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "gates[0](tf.one_hot([1, 9], M), M)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=225, shape=(2, 20), dtype=float32, numpy=\n",
              "array([[0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ufUl6gMukbfv"
      },
      "source": [
        "#### Gate Controller Output"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "nkJ0U4JQkx6Y"
      },
      "source": [
        "Runs the input which is selected by controller values into the gate passed and returns the output from the gate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "zzzDSRfSMCG3",
        "colab": {}
      },
      "source": [
        "def gateOutput(gate, inputs, controllerValues, M):\n",
        "  if(controllerValues.shape[0] > 1):\n",
        "    gateInputA = dotProduct(inputs, controllerValues[0])\n",
        "    gateInputB = dotProduct(inputs, controllerValues[1])\n",
        "    output = gate(gateInputA, gateInputB, M)\n",
        "  else:\n",
        "    gateInputA = dotProduct(inputs, controllerValues[0])\n",
        "    output = gate(gateInputA, M)\n",
        "  \n",
        "  return output"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "WJtRToqMb-t6",
        "colab": {}
      },
      "source": [
        "ip = np.array([[\n",
        "  [1., 0., 0., 0., 0.],\n",
        "  [0., 1., 0., 0., 0.],\n",
        "  [0., 0., 0., 1., 0.],\n",
        "  [0., 1., 0., 0., 0.]\n",
        " ]])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "tHk-AU-JcPG1",
        "outputId": "43ccb129-1c00-48ba-c865-f48c888c8f6b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "cp = np.array([[0., 0., 0., 1.],\n",
        "              [0., 0., 1., 0.]])\n",
        "cp.shape"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "-v88H8xMIJNb"
      },
      "source": [
        "Run gate to check output - Adds ip 4th value with 3rd value"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-QwYscutXBAM",
        "colab": {}
      },
      "source": [
        "outputVal = gateOutput(gates[2], ip, cp, M)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "2Yib6hWAprpV",
        "outputId": "c6657a73-e6ff-4694-b8a6-aabe45021cbf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "outputVal"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=271, shape=(1, 5), dtype=float32, numpy=array([[0., 0., 0., 0., 1.]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "v6GT03BdqqW3",
        "outputId": "fd46d329-80e7-45cf-b2dc-87c5b9d85f04",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "tf.dtypes.cast(ip, tf.float32)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=274, shape=(1, 4, 5), dtype=float32, numpy=\n",
              "array([[[1., 0., 0., 0., 0.],\n",
              "        [0., 1., 0., 0., 0.],\n",
              "        [0., 0., 0., 1., 0.],\n",
              "        [0., 1., 0., 0., 0.]]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "SFsUtDGcytne"
      },
      "source": [
        "For adding the output to the registers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "mw9hvmJKp4Y0",
        "outputId": "63802a50-d76e-480e-c4de-aa239a0f0b4f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "source": [
        "tf.concat([ip, [outputVal]], axis=1)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=280, shape=(1, 5, 5), dtype=float64, numpy=\n",
              "array([[[1., 0., 0., 0., 0.],\n",
              "        [0., 1., 0., 0., 0.],\n",
              "        [0., 0., 0., 1., 0.],\n",
              "        [0., 1., 0., 0., 0.],\n",
              "        [0., 0., 0., 0., 1.]]])>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "wtlsZZwqzQDH",
        "outputId": "246162be-b8dd-4b50-8a1c-eedf8ef00465",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "zeroModule([], M)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=286, shape=(1, 20), dtype=float32, numpy=\n",
              "array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "0XIeyQKgA7rR"
      },
      "source": [
        "#### Input Register"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "g6LRxD2_0Dpf",
        "colab": {}
      },
      "source": [
        "def registerValueModule(a):\n",
        "    aVal = np.asarray([a])\n",
        "    return tf.one_hot(aVal, M)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "M2eRTYos0Lib",
        "outputId": "10336c44-bd1d-4933-f28b-b7fa19841ca9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "registerValueModule(2)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=292, shape=(1, 20), dtype=float32, numpy=\n",
              "array([[0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0.]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "rIsnozvvyq1X",
        "colab": {}
      },
      "source": [
        "registers = tf.concat([registerValueModule(0), registerValueModule(1), registerValueModule(3)], axis=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "K_Y2_qNYBMOY"
      },
      "source": [
        "**Registers = [0,1,3]**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "o0-ZLeDezsj9",
        "colab": {}
      },
      "source": [
        "registers = tf.reshape(registers, shape=[1, registers.shape[0], registers.shape[1]])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "yFAr3khI06Cs",
        "outputId": "2a753e13-e1eb-4b34-d5fa-2b85bb97c3b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        }
      },
      "source": [
        "registers"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=312, shape=(1, 3, 20), dtype=float32, numpy=\n",
              "array([[[1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "         0., 0., 0., 0.],\n",
              "        [0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "         0., 0., 0., 0.],\n",
              "        [0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "         0., 0., 0., 0.]]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "eyRqTvVqBf34"
      },
      "source": [
        "#### Update data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-aMLqPU7h049",
        "colab": {}
      },
      "source": [
        "def runTimeStep(gates, registers, controllerValues_, registercontrollerValues_):\n",
        "  updatedRegisters = registers\n",
        "  for i in range(len(gates)):\n",
        "    output = gateOutput(gates[i], updatedRegisters, controllerValues_[i], M)\n",
        "    updatedRegisters = tf.concat([updatedRegisters, [output]], axis=1)\n",
        "\n",
        "  output_registers = []\n",
        "\n",
        "  for i in range(registers.shape[1]):\n",
        "    output = dotProduct(updatedRegisters, registercontrollerValues_[i])\n",
        "    output_registers.append(output)\n",
        "  \n",
        "  output_registers = tf.stack(output_registers, axis=1)\n",
        "  \n",
        "  return output_registers"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "6g-9SmRHGEbw"
      },
      "source": [
        "**Circuit Testing 1**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "JAQOP0RvF7GV",
        "outputId": "224cd193-af3d-4ac3-82c8-1f5604607b6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        }
      },
      "source": [
        "# registers = [2, 1, 3]\n",
        "registers = tf.concat([registerValueModule(2), registerValueModule(1), registerValueModule(3)], axis=0)\n",
        "registers = tf.reshape(registers, shape=[1, registers.shape[0], registers.shape[1]])\n",
        "\n",
        "print(\"Input registers: \", tf.argmax(registers, axis=2))\n",
        "\n",
        "# Gatest -> Increment, Decrement, Addition\n",
        "gates = [incModule, decModule, addModule]\n",
        "\n",
        "# Increment input r1 -> decrement output o1 -> add output o2 + input r1\n",
        "controllerValues_ = [\n",
        "    [[0, 1, 0]],\n",
        "    [[0, 0, 0, 1]],\n",
        "    [[0, 0, 0, 0, 1],\n",
        "     [0, 1, 0, 0, 0]]\n",
        "]\n",
        "\n",
        "# Save the input r2 -> output r0, input r0 -> output r1, last gate output -> output r2\n",
        "registerControllerValues_ = [\n",
        "    [0, 0, 1, 0, 0, 0],\n",
        "    [1, 0, 0, 0, 0, 0],\n",
        "    [0, 0, 0, 0, 0, 1]\n",
        "]\n",
        "\n",
        "controllerValues = []\n",
        "for value in controllerValues_:\n",
        "  controllerValues.append(tf.dtypes.cast(value, tf.float32))\n",
        "\n",
        "registerControllerValues = []\n",
        "for value in registerControllerValues_:\n",
        "  registerControllerValues.append(tf.dtypes.cast(value, tf.float32))\n",
        "\n",
        "# Calling the function to run the complete circuit\n",
        "output_registers = runTimeStep(gates, registers, controllerValues, registerControllerValues)\n",
        "\n",
        "print(\"\\noutput_registers:\\n\", output_registers)\n",
        "print(\"\\noutput_registers:\\n\", tf.argmax(output_registers, axis=2))"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Input registers:  tf.Tensor([[2 1 3]], shape=(1, 3), dtype=int64)\n",
            "\n",
            "output_registers:\n",
            " tf.Tensor(\n",
            "[[[0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            "  [0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            "  [0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]], shape=(1, 3, 20), dtype=float32)\n",
            "\n",
            "output_registers:\n",
            " tf.Tensor([[3 2 2]], shape=(1, 3), dtype=int64)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "7niScH_Df2Oo"
      },
      "source": [
        "**Circuit Testing 2**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Joyn2Q0kuAKE",
        "outputId": "9ab1971c-fab3-439f-db5d-e93408473475",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        }
      },
      "source": [
        "gates = [addModule]\n",
        "\n",
        "controllerValues_ = [\n",
        "    [\n",
        "        [1, 0, 0],\n",
        "        [0, 1, 0]\n",
        "    ]\n",
        "]\n",
        "\n",
        "registerControllerValues_ = [\n",
        "    [0, 0, 0, 1],\n",
        "    [0, 1, 0, 0],\n",
        "    [0, 0, 1, 0]\n",
        "]\n",
        "\n",
        "\n",
        "controllerValues = []\n",
        "for value in controllerValues_:\n",
        "  controllerValues.append(tf.dtypes.cast(value, tf.float32))\n",
        "\n",
        "registerControllerValues = []\n",
        "for value in registerControllerValues_:\n",
        "  registerControllerValues.append(tf.dtypes.cast(value, tf.float32))\n",
        "\n",
        "# First set of input values\n",
        "registers = tf.concat([registerValueModule(1), registerValueModule(1), registerValueModule(3)], axis=0)\n",
        "registers = tf.reshape(registers, shape=[1, registers.shape[0], registers.shape[1]])\n",
        "\n",
        "print(\"Input register set 1: \", tf.argmax(registers, axis=2))\n",
        "\n",
        "print(\"\\nRunning input r0 , r1 -> add -> output r0\")\n",
        "\n",
        "# Calling the function to run the complete circuit\n",
        "output_registers = runTimeStep(gates, registers, controllerValues, registerControllerValues)\n",
        "\n",
        "print(\"\\noutput_registers:\\n\", output_registers)\n",
        "print(\"\\noutput_registers:\\n\", tf.argmax(output_registers, axis=2))\n",
        "\n",
        "\n",
        "# Second set of input values\n",
        "registers = tf.concat([registerValueModule(2), registerValueModule(1), registerValueModule(3)], axis=0)\n",
        "registers = tf.reshape(registers, shape=[1, registers.shape[0], registers.shape[1]])\n",
        "\n",
        "print(\"\\n\\nInput register set 2: \", tf.argmax(registers, axis=2))\n",
        "\n",
        "print(\"\\nRunning input r0 , r1 -> add -> output r0\")\n",
        "# Calling the function to run the complete circuit\n",
        "output_registers = runTimeStep(gates, registers, controllerValues, registerControllerValues)\n",
        "\n",
        "print(\"\\noutput_registers:\\n\", output_registers)\n",
        "print(\"\\noutput_registers:\\n\", tf.argmax(output_registers, axis=2))"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Input register set 1:  tf.Tensor([[1 1 3]], shape=(1, 3), dtype=int64)\n",
            "\n",
            "Running input r0 , r1 -> add -> output r0\n",
            "\n",
            "output_registers:\n",
            " tf.Tensor(\n",
            "[[[0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            "  [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            "  [0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]], shape=(1, 3, 20), dtype=float32)\n",
            "\n",
            "output_registers:\n",
            " tf.Tensor([[2 1 3]], shape=(1, 3), dtype=int64)\n",
            "\n",
            "\n",
            "Input register set 2:  tf.Tensor([[2 1 3]], shape=(1, 3), dtype=int64)\n",
            "\n",
            "Running input r0 , r1 -> add -> output r0\n",
            "\n",
            "output_registers:\n",
            " tf.Tensor(\n",
            "[[[0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            "  [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            "  [0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]], shape=(1, 3, 20), dtype=float32)\n",
            "\n",
            "output_registers:\n",
            " tf.Tensor([[3 1 3]], shape=(1, 3), dtype=int64)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CqOyepkSHrSB",
        "colab_type": "text"
      },
      "source": [
        "### Memory"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aUfwkdVhHtc2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def readMemory(pointer, memory, M):\n",
        "  # pointer -> 1,M\n",
        "  # memory -> M, M\n",
        "  # output -> 1, M\n",
        "  val = tf.tensordot(pointer, memory, axes=1)\n",
        "  return val\n",
        "\n",
        "def registerModule(a, M):\n",
        "    aVal = np.asarray([a])\n",
        "    return tf.one_hot(aVal, M)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pLUCpxF9HuW2",
        "colab_type": "code",
        "outputId": "9d0bd13b-4908-4ecd-ecc9-2a044dca87c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        }
      },
      "source": [
        "registers = []\n",
        "for _ in range(10):\n",
        "  registers.append(registerModule(_+2, 10))\n",
        "registers = tf.concat(registers, axis=0)\n",
        "print(registers)\n",
        "\n",
        "pointer = np.array([[0., 0., 0., 0., 0., 0.5, 0.5, 0., 0., 0.]], dtype=\"float32\")\n",
        "\n",
        "print(pointer)\n",
        "\n",
        "val = readMemory(pointer, registers, 10)\n",
        "\n",
        "print(val)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor(\n",
            "[[0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]], shape=(10, 10), dtype=float32)\n",
            "[[0.  0.  0.  0.  0.  0.5 0.5 0.  0.  0. ]]\n",
            "tf.Tensor([[0.  0.  0.  0.  0.  0.  0.  0.5 0.5 0. ]], shape=(1, 10), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yprjj7u-HzAm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def writeMemory(pointer, value, memory, M):\n",
        "  # newValToWrite -> M, M\n",
        "  # mask -> M, M\n",
        "  # M:=(J−p)JT ·M+paT\n",
        "  \n",
        "  newValToWrite = tf.tensordot(tf.transpose(pointer), value, axes=1)\n",
        "\n",
        "  J = np.ones([1,M], dtype=\"float32\")\n",
        "  erase = tf.transpose(J-pointer)\n",
        "  \n",
        "  mask = tf.tensordot(erase, J, axes=1)\n",
        "  \n",
        "  oldMemory = mask * memory\n",
        "\n",
        "  return oldMemory + newValToWrite"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kj3rl2guH195",
        "colab_type": "code",
        "outputId": "410f6d4e-fc03-44c2-9cf6-0334c2102362",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        }
      },
      "source": [
        "registers = []\n",
        "for _ in range(10):\n",
        "  registers.append(registerModule(_, 10))\n",
        "memory = tf.concat(registers, axis=0)\n",
        "memory = np.array(memory, dtype=\"float32\")\n",
        "print(memory)\n",
        "\n",
        "pointer = np.array([[0., 1., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=\"float32\")\n",
        "value    = np.array([[0., 0., 0., 0., 0., 0., 0., 1., 0., 0.]], dtype=\"float32\")\n",
        "\n",
        "outMemory = writeMemory(pointer, value, memory, 10)\n",
        "print(outMemory)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n",
            "tf.Tensor(\n",
            "[[1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
            " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]], shape=(10, 10), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "bfhbghdOjijr"
      },
      "source": [
        "## NRAM Controller Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "zlBQ73u7R0eh"
      },
      "source": [
        "#### NRAM Controller MLP Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "A5bIwnH5zNie",
        "colab": {}
      },
      "source": [
        "def gateOutputNN(gate, registers, controllerValues, M):\n",
        "  if(len(controllerValues) > 1):\n",
        "    gateInputA = dotProduct(registers, controllerValues[0][0])\n",
        "    gateInputB = dotProduct(registers, controllerValues[1][0])\n",
        "    output = gate(gateInputA, gateInputB, M)\n",
        "  else:\n",
        "    gateInputA = dotProduct(registers, controllerValues[0][0])\n",
        "    output = gate(gateInputA, M)\n",
        "  return output"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QFI7ujB2jPeZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Handle extra b size\n",
        "def dotProductMemory(a, b):\n",
        "  aT = tf.transpose(a)\n",
        "  aT = tf.reshape(aT, [a.shape[0],a.shape[2],a.shape[1]])\n",
        "  return tf.tensordot(aT, b[0], axes=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "GjfXD0Jbt-da",
        "colab": {}
      },
      "source": [
        "def runTimeStepNN(gates, registers, memory, readModule, writeModule, controllerValues_, registercontrollerValues_):\n",
        "  updatedRegisters = registers\n",
        "\n",
        "  M = memory.shape[0]\n",
        "  pointer = dotProductMemory(updatedRegisters, controllerValues_[0][0])\n",
        "  value = readModule(pointer, memory, M)\n",
        "\n",
        "  value= tf.reshape(value, shape=[value.shape[0], 1, value.shape[1]])\n",
        "  updatedRegisters = tf.concat([updatedRegisters, value], axis=1)\n",
        "  \n",
        "  for i in range(len(gates)):\n",
        "    output = gateOutputNN(gates[i], updatedRegisters, controllerValues_[i+1], M)\n",
        "    output= tf.reshape(output, shape=[updatedRegisters.shape[0], 1, updatedRegisters.shape[2]])\n",
        "    updatedRegisters = tf.concat([updatedRegisters, output], axis=1)\n",
        "  \n",
        "  # controllerValues_[-1] => [Pointer, Value]\n",
        "  writePointer = dotProductMemory(updatedRegisters, controllerValues_[-1][0])\n",
        "  writeValue = dotProductMemory(updatedRegisters, controllerValues_[-1][1])\n",
        "  \n",
        "  outputMemory = writeMemory(writePointer, writeValue, memory, M)\n",
        "  writePointer = tf.reshape(writePointer, shape=[writePointer.shape[0], 1, writePointer.shape[1]])\n",
        "  updatedRegisters = tf.concat([updatedRegisters, writePointer], axis=1)\n",
        "\n",
        "  outputRegisters = []\n",
        "  for i in range(registers.shape[1]):\n",
        "    output = dotProduct(updatedRegisters, registercontrollerValues_[i][0])\n",
        "    outputRegisters.append(output)\n",
        "  \n",
        "  outputRegisters = tf.stack(outputRegisters, axis=1)\n",
        "  \n",
        "  return outputRegisters, outputMemory"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "bH6eayDVWVg3",
        "colab": {}
      },
      "source": [
        "class NRAMController(tf.keras.Model):\n",
        "   def __init__(self, hidden_dimensions=100, gateControllerDimensions=[[3], [4,4]], registerControllerDimensions=[4, 4, 4]):\n",
        "      super(NRAMController, self).__init__()\n",
        "      self.Hidden1 = tf.keras.layers.Dense(hidden_dimensions, input_shape=(3,), use_bias=True, activation=\"relu\")\n",
        "      self.Hidden2 = tf.keras.layers.Dense(hidden_dimensions, input_shape=(hidden_dimensions,), use_bias=True, activation=\"relu\")\n",
        "\n",
        "      self.gateControllerDimensions = gateControllerDimensions\n",
        "      self.registerControllerDimensions = registerControllerDimensions\n",
        "\n",
        "      # Gate Inputs Controller values \n",
        "      self.GateControllerOutput = []\n",
        "      for gateControllerValue in gateControllerDimensions:\n",
        "        GateController = []\n",
        "        for dim in gateControllerValue:\n",
        "          GateController.append(tf.keras.layers.Dense(dim, input_shape=(hidden_dimensions,), use_bias=True, activation=\"softmax\"))\n",
        "        self.GateControllerOutput.append(GateController)\n",
        "\n",
        "      # Register Output Controller Values\n",
        "      self.RegisterController = []\n",
        "      for i in registerControllerDimensions:\n",
        "        self.RegisterController.append(tf.keras.layers.Dense(i, input_shape=(hidden_dimensions,), use_bias=True, activation=\"softmax\"))\n",
        "\n",
        "      # Complete status\n",
        "      self.CompletedStatus = tf.keras.layers.Dense(1, input_shape=(hidden_dimensions,), use_bias=True, activation=\"sigmoid\")\n",
        "   \n",
        "   def call(self, input_registers):\n",
        "      # hidden layers\n",
        "      hidden1 = self.Hidden1(input_registers)\n",
        "      hidden2 = self.Hidden2(hidden1)\n",
        "      \n",
        "      # To get gate controller values\n",
        "      gateControllerOutput = []\n",
        "      for i in range(len(self.gateControllerDimensions)):\n",
        "        gateController = []\n",
        "        for j in range(len(self.gateControllerDimensions[i])):\n",
        "          gateController.append(self.GateControllerOutput[i][j](hidden2))\n",
        "        gateControllerOutput.append(gateController)\n",
        "         \n",
        "      registerControllerOutput = []\n",
        "      # To get register controller values\n",
        "      for i in range(len(self.registerControllerDimensions)):\n",
        "        registerControllerOutput.append(self.RegisterController[i](hidden2))\n",
        "\n",
        "      # final completed status\n",
        "      completed = self.CompletedStatus(hidden2)\n",
        "\n",
        "      return gateControllerOutput, registerControllerOutput, completed"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Hnd_YcNF1HyU"
      },
      "source": [
        "#### Example Implementation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "RNaT8HpdoCZ4",
        "colab": {}
      },
      "source": [
        "# Creating instance of the contoller network\n",
        "nramController = NRAMController(hidden_dimensions=100, gateControllerDimensions=[[3], [4], [5], [6,6], [7, 7]], registerControllerDimensions=[8, 8, 8]);"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "X4F4zm_G_hUB",
        "outputId": "35cf5746-7e60-481c-88e8-6e63e90363a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        }
      },
      "source": [
        "# Running the controller\n",
        "gateControllerOutput, registerControllerOutput, completedStatus = nramController(tf.Variable(tf.ones((1,3))))\n",
        "\n",
        "print(\"\\nThe output from the neural network controller without training \\n\")\n",
        "gateControllerOutput"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:642: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "\n",
            "The output from the neural network controller without training \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[<tf.Tensor: id=1000, shape=(1, 3), dtype=float32, numpy=array([[0.37230447, 0.28697336, 0.34072214]], dtype=float32)>],\n",
              " [<tf.Tensor: id=1027, shape=(1, 4), dtype=float32, numpy=array([[0.30113864, 0.2007511 , 0.2653489 , 0.23276134]], dtype=float32)>],\n",
              " [<tf.Tensor: id=1054, shape=(1, 5), dtype=float32, numpy=\n",
              "  array([[0.17710035, 0.19932449, 0.22247809, 0.17420568, 0.2268914 ]],\n",
              "        dtype=float32)>],\n",
              " [<tf.Tensor: id=1081, shape=(1, 6), dtype=float32, numpy=\n",
              "  array([[0.20189562, 0.15323657, 0.18456745, 0.14026861, 0.16120267,\n",
              "          0.15882906]], dtype=float32)>,\n",
              "  <tf.Tensor: id=1108, shape=(1, 6), dtype=float32, numpy=\n",
              "  array([[0.20499408, 0.17442021, 0.14447783, 0.1348918 , 0.17228264,\n",
              "          0.16893345]], dtype=float32)>],\n",
              " [<tf.Tensor: id=1135, shape=(1, 7), dtype=float32, numpy=\n",
              "  array([[0.11742136, 0.11574272, 0.14552216, 0.16923682, 0.19149885,\n",
              "          0.14980012, 0.11077798]], dtype=float32)>,\n",
              "  <tf.Tensor: id=1162, shape=(1, 7), dtype=float32, numpy=\n",
              "  array([[0.14281586, 0.11714788, 0.1827183 , 0.13221374, 0.12947857,\n",
              "          0.1326855 , 0.1629402 ]], dtype=float32)>]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "MZfmwP8Jx4Oq",
        "outputId": "3ed18e69-3d7f-4453-bbd5-6fd8fde86f82",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        }
      },
      "source": [
        "registerControllerOutput"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<tf.Tensor: id=1189, shape=(1, 8), dtype=float32, numpy=\n",
              " array([[0.14613116, 0.14055979, 0.14297901, 0.11615644, 0.14677796,\n",
              "         0.10847977, 0.0845544 , 0.11436144]], dtype=float32)>,\n",
              " <tf.Tensor: id=1216, shape=(1, 8), dtype=float32, numpy=\n",
              " array([[0.13265939, 0.11767452, 0.1222621 , 0.13187745, 0.13280205,\n",
              "         0.09305298, 0.16974987, 0.09992165]], dtype=float32)>,\n",
              " <tf.Tensor: id=1243, shape=(1, 8), dtype=float32, numpy=\n",
              " array([[0.1408189 , 0.09443189, 0.11637634, 0.15165898, 0.10242932,\n",
              "         0.11085888, 0.11570625, 0.16771947]], dtype=float32)>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Hnfjd145x7dC",
        "outputId": "f4af4d9a-1b12-4652-9aac-24d705f348e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "completedStatus"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=1270, shape=(1, 1), dtype=float32, numpy=array([[0.43601167]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "fEINnQwk_SbF"
      },
      "source": [
        "##### Input Controller Values is the output of NN being applied to the circuit for one time step"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fzX2cCz7RFlP",
        "colab_type": "code",
        "outputId": "c1679776-6872-41ce-8862-bb00618deeb2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        }
      },
      "source": [
        "r_ = []\n",
        "for _ in range(M):\n",
        "  r_.append(registerModule(_, M))\n",
        "memory = tf.concat(r_, axis=0)\n",
        "memory = np.array(memory, dtype=\"float32\")\n",
        "print(memory)"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "pMgQHz42llTj",
        "outputId": "1321e543-4d3d-4150-c4e1-435fe10a924e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1805
        }
      },
      "source": [
        "gates = [incModule, decModule, addModule]\n",
        "\n",
        "# First set of input values\n",
        "registers = tf.concat([registerValueModule(0), registerValueModule(0), registerValueModule(0)], axis=0)\n",
        "registers = tf.reshape(registers, shape=[1, registers.shape[0], registers.shape[1]])\n",
        "\n",
        "print(\"Input register set 1: \", tf.argmax(registers, axis=2))\n",
        "\n",
        "print(\"\\nRunning input r0 -> increment -> output r0\")\n",
        "\n",
        "# Calling the function to run the complete circuit\n",
        "output_registers, memory = runTimeStepNN(gates, registers, memory, readMemory, writeMemory, gateControllerOutput, registerControllerOutput)\n",
        "\n",
        "print(\"\\nOutput Memory\")\n",
        "print(memory)\n",
        "\n",
        "print(\"\\noutput_registers:\\n\", output_registers)\n",
        "print(\"\\noutput_registers:\\n\", tf.argmax(output_registers, axis=2))"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Input register set 1:  tf.Tensor([[0 0 0]], shape=(1, 3), dtype=int64)\n",
            "\n",
            "Running input r0 -> increment -> output r0\n",
            "\n",
            "Output Memory\n",
            "tf.Tensor(\n",
            "[[0.83925533 0.08968843 0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.07105629]\n",
            " [0.14705984 0.8332962  0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.01964401]\n",
            " [0.         0.         1.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.        ]\n",
            " [0.         0.         0.         1.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.        ]\n",
            " [0.         0.         0.         0.         1.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         1.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  1.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         1.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         1.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         1.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         1.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         1.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  1.         0.         0.         0.         0.         0.\n",
            "  0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         1.         0.         0.         0.         0.\n",
            "  0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         1.         0.         0.         0.\n",
            "  0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         1.         0.         0.\n",
            "  0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         1.         0.\n",
            "  0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         1.\n",
            "  0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  1.         0.        ]\n",
            " [0.08893663 0.01499514 0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.8960682 ]], shape=(20, 20), dtype=float32)\n",
            "\n",
            "output_registers:\n",
            " tf.Tensor(\n",
            "[[[0.7342109  0.16867803 0.         0.         0.         0.\n",
            "   0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.         0.         0.         0.\n",
            "   0.         0.09711104]\n",
            "  [0.7645509  0.15193692 0.         0.         0.         0.\n",
            "   0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.         0.         0.         0.\n",
            "   0.         0.08351216]\n",
            "  [0.76032275 0.1345474  0.         0.         0.         0.\n",
            "   0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.         0.         0.         0.\n",
            "   0.         0.10512984]]], shape=(1, 3, 20), dtype=float32)\n",
            "\n",
            "output_registers:\n",
            " tf.Tensor([[0 0 0]], shape=(1, 3), dtype=int64)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "10bMXmezUw_f",
        "colab_type": "code",
        "outputId": "03ae95cf-0ac5-401d-b180-aba63bc546b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1431
        }
      },
      "source": [
        "memory"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=1616, shape=(20, 20), dtype=float32, numpy=\n",
              "array([[0.83925533, 0.08968843, 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.07105629],\n",
              "       [0.14705984, 0.8332962 , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.01964401],\n",
              "       [0.        , 0.        , 1.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        , 1.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        , 0.        , 1.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        1.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ],\n",
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              "        0.        , 1.        , 0.        , 0.        , 0.        ,\n",
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              "        0.        , 0.        , 0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 1.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 1.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
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              "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 1.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        1.        , 0.        , 0.        , 0.        , 0.        ,\n",
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              "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 1.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 1.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 1.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 1.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        1.        , 0.        , 0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 1.        , 0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 1.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 1.        , 0.        ],\n",
              "       [0.08893663, 0.01499514, 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.8960682 ]],\n",
              "      dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Q5Yp8kFGREb4"
      },
      "source": [
        "#### NRAM Controller with LSTM V1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "99U-TGkmDqCF",
        "colab": {}
      },
      "source": [
        "class NRAMControllerLSTM(tf.keras.Model):\n",
        "  def __init__(self, hidden_dimensions=256, gateControllerDimensions=[[3], [4,4]], registerControllerDimensions=[4, 4, 4]):\n",
        "    super(NRAMControllerLSTM, self).__init__()\n",
        "    self.lstm = layers.LSTM(hidden_dimensions, return_sequences=True, return_state=True)\n",
        "\n",
        "    self.gateControllerDimensions = gateControllerDimensions\n",
        "    self.registerControllerDimensions = registerControllerDimensions\n",
        "\n",
        "    # Gate Inputs Controller values \n",
        "    self.GateControllerOutput = []\n",
        "    for gateControllerValue in gateControllerDimensions:\n",
        "      GateController = []\n",
        "      for dim in gateControllerValue:\n",
        "        GateController.append(tf.keras.layers.Dense(dim, input_shape=(hidden_dimensions,), use_bias=True, activation=\"softmax\"))\n",
        "      self.GateControllerOutput.append(GateController)\n",
        "\n",
        "    # Register Output Controller Values\n",
        "    self.RegisterController = []\n",
        "    for i in registerControllerDimensions:\n",
        "      self.RegisterController.append(tf.keras.layers.Dense(i, input_shape=(hidden_dimensions,), use_bias=True, activation=\"softmax\"))\n",
        "\n",
        "    self.CompletedStatus = tf.keras.layers.Dense(1, input_shape=(hidden_dimensions,), use_bias=True, activation=\"sigmoid\")\n",
        "\n",
        "  def call(self, input_registers):\n",
        "    inputValue = tf.reshape(input_registers, shape=[input_registers.shape[0], 1, input_registers.shape[1]])\n",
        "    lstm_output, state_h, state_c  = self.lstm(inputValue)\n",
        "\n",
        "    # To get gate controller values\n",
        "    gateControllerOutput = []\n",
        "    for i in range(len(self.gateControllerDimensions)):\n",
        "      gateController = []\n",
        "      for j in range(len(self.gateControllerDimensions[i])):\n",
        "        o = self.GateControllerOutput[i][j](lstm_output)\n",
        "        gateController.append(tf.reshape(o, shape=[o.shape[0],o.shape[2]]))\n",
        "      gateControllerOutput.append(gateController)\n",
        "\n",
        "    registerControllerOutput = []\n",
        "    # To get register controller values\n",
        "    for i in range(len(self.registerControllerDimensions)):\n",
        "      o = self.RegisterController[i](lstm_output)\n",
        "      registerControllerOutput.append(tf.reshape(o, shape=[o.shape[0],o.shape[2]]))\n",
        "\n",
        "    # final completed status\n",
        "    completed = self.CompletedStatus(lstm_output)\n",
        "\n",
        "    return gateControllerOutput, registerControllerOutput, completed"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "lSJKOt7tQ8dj",
        "outputId": "ddd39c0d-3b6d-4a23-872e-1e57ccb1a714",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        }
      },
      "source": [
        "# Creating instance of the contoller network\n",
        "nramControllerLSTM = NRAMControllerLSTM();\n",
        "\n",
        "# Running the controller\n",
        "gateControllerOutput, registerControllerOutput, completedStatus = nramControllerLSTM(tf.Variable(tf.ones((1,3))))\n",
        "\n",
        "print(\"\\nThe output from the neural network controller without training \\n\")\n",
        "gateControllerOutput"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9f0dbff60>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "\n",
            "The output from the neural network controller without training \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[<tf.Tensor: id=2069, shape=(1, 3), dtype=float32, numpy=array([[0.32622147, 0.34377876, 0.32999974]], dtype=float32)>],\n",
              " [<tf.Tensor: id=2114, shape=(1, 4), dtype=float32, numpy=array([[0.25935197, 0.25328338, 0.23664114, 0.25072348]], dtype=float32)>,\n",
              "  <tf.Tensor: id=2159, shape=(1, 4), dtype=float32, numpy=array([[0.25244626, 0.25777104, 0.24896346, 0.24081925]], dtype=float32)>]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VnHsOkcDAwJO",
        "colab_type": "code",
        "outputId": "a5418dd5-0671-47ea-d7a8-26a838e25a4d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "registerControllerOutput"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<tf.Tensor: id=2204, shape=(1, 4), dtype=float32, numpy=array([[0.24601799, 0.25457376, 0.25704366, 0.24236459]], dtype=float32)>,\n",
              " <tf.Tensor: id=2249, shape=(1, 4), dtype=float32, numpy=array([[0.24842866, 0.25543913, 0.24552289, 0.25060934]], dtype=float32)>,\n",
              " <tf.Tensor: id=2294, shape=(1, 4), dtype=float32, numpy=array([[0.25713637, 0.23989758, 0.25396597, 0.2490001 ]], dtype=float32)>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "HFGqpuDkRIw9"
      },
      "source": [
        "#### NRAM Controller with LSTM V2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "F7uo7cYrSdSQ",
        "colab": {}
      },
      "source": [
        "class NRAMControllerLSTM2(tf.keras.Model):\n",
        "   def __init__(self, hidden_dimensions=256, gateControllerDimensions=[[3], [4,4]], registerControllerDimensions=[4, 4, 4]):\n",
        "      super(NRAMControllerLSTM2, self).__init__()\n",
        "#       self.lstmHidden = layers.CuDNNLSTM(hidden_dimensions, return_sequences=True, return_state=True)\n",
        "      self.lstmHidden = layers.LSTM(hidden_dimensions, return_sequences=True, return_state=True)\n",
        "\n",
        "#       self.output_dimensions = output_dimensions\n",
        "\n",
        "#       self.Output = []\n",
        "#       self.SoftmaxOutput = []\n",
        "#       # Gate Inputs Controller values and Register Output Controller Values\n",
        "#       for dimension in output_dimensions:\n",
        "#         self.Output.append(layers.LSTM(dimension, return_sequences=True, return_state=True))\n",
        "#         self.SoftmaxOutput.append(layers.Activation(\"softmax\"))\n",
        "\n",
        "      self.gateControllerDimensions = gateControllerDimensions\n",
        "      self.registerControllerDimensions = registerControllerDimensions\n",
        "\n",
        "      # Gate Inputs Controller values \n",
        "      self.GateControllerOutput = []\n",
        "      self.SoftmaxGateOutput= []\n",
        "      for gateControllerValue in gateControllerDimensions:\n",
        "        GateController = []\n",
        "        SoftmaxLayer = []\n",
        "        for dim in gateControllerValue:\n",
        "          GateController.append(layers.LSTM(dim, return_sequences=True, return_state=True))\n",
        "          SoftmaxLayer.append(layers.Activation(\"softmax\"))\n",
        "        self.GateControllerOutput.append(GateController)\n",
        "        self.SoftmaxGateOutput.append(SoftmaxLayer)\n",
        "\n",
        "      # Register Output Controller Values\n",
        "      self.RegisterController = []\n",
        "      self.SoftmaxRegisterOutput = []\n",
        "      for i in registerControllerDimensions:\n",
        "        self.RegisterController.append(layers.LSTM(i, return_sequences=True, return_state=True))\n",
        "        self.SoftmaxRegisterOutput.append(layers.Activation(\"softmax\"))\n",
        "\n",
        "      self.CompletedStatus = tf.keras.layers.Dense(1, input_shape=(hidden_dimensions,), use_bias=True, activation=\"sigmoid\")\n",
        "   \n",
        "   def call(self, input_registers):\n",
        "      inputValue = tf.reshape(input_registers, shape=[input_registers.shape[0], 1, input_registers.shape[1]])\n",
        "      lstm_output, state_h, state_c  = self.lstmHidden(inputValue)\n",
        "\n",
        "      # To get gate controller values\n",
        "      gateControllerOutput = []\n",
        "      for i in range(len(self.gateControllerDimensions)):\n",
        "        gateController = []\n",
        "        for j in range(len(self.gateControllerDimensions[i])):\n",
        "          o, h, c = self.GateControllerOutput[i][j](lstm_output)\n",
        "          gateController.append(self.SoftmaxGateOutput[i][j](tf.reshape(o, shape=[o.shape[0],o.shape[2]])))\n",
        "        gateControllerOutput.append(gateController)\n",
        "\n",
        "      registerControllerOutput = []\n",
        "      # To get register controller values\n",
        "      for i in range(len(self.registerControllerDimensions)):\n",
        "        o, h, c = self.RegisterController[i](lstm_output)\n",
        "        registerControllerOutput.append(self.SoftmaxRegisterOutput[i](tf.reshape(o, shape=[o.shape[0],o.shape[2]])))\n",
        "\n",
        "#       # To get controller values\n",
        "#       output = []\n",
        "#       for i in range(len(self.output_dimensions)):\n",
        "#         o, h, c = self.Output[i](lstm_output)\n",
        "#         lstmOp = tf.reshape(o, shape=[o.shape[0],o.shape[2]])\n",
        "#         output.append(self.SoftmaxOutput[i](lstmOp))\n",
        "\n",
        "      # final completed status\n",
        "      completed = self.CompletedStatus(lstm_output)\n",
        "\n",
        "      return gateControllerOutput, registerControllerOutput, completed"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "kMGbwAJxSgis",
        "outputId": "6594993c-af1a-46ef-c64a-1a1e5901fab7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        }
      },
      "source": [
        "# Example with one gate and 3 input/output registers\n",
        "outputVals = [3, 4, 4, 4]\n",
        "\n",
        "# Creating instance of the contoller network\n",
        "nramControllerLSTM = NRAMControllerLSTM2();\n",
        "\n",
        "# Running the controller\n",
        "gateControllerOutput, registerControllerOutput, completedStatus = nramControllerLSTM(tf.Variable(tf.ones((1,3))))\n",
        "\n",
        "print(\"\\nThe output from the neural network controller without training \\n\")\n",
        "gateControllerOutput"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9f0dbf710>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9e45420f0>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9e4542390>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9e45424a8>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9e45426d8>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9e45427f0>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9e4542b70>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "\n",
            "The output from the neural network controller without training \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[<tf.Tensor: id=3008, shape=(1, 3), dtype=float32, numpy=array([[0.33481416, 0.33363506, 0.33155075]], dtype=float32)>],\n",
              " [<tf.Tensor: id=3339, shape=(1, 4), dtype=float32, numpy=array([[0.25054097, 0.2500583 , 0.25190574, 0.247495  ]], dtype=float32)>,\n",
              "  <tf.Tensor: id=3670, shape=(1, 4), dtype=float32, numpy=array([[0.25033322, 0.25077695, 0.24978833, 0.24910156]], dtype=float32)>]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pJT2qRHHHFlC",
        "colab_type": "code",
        "outputId": "4a23aa82-f4b9-41d3-8bd1-5592250236fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "registerControllerOutput"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<tf.Tensor: id=4001, shape=(1, 4), dtype=float32, numpy=array([[0.2525751 , 0.24935223, 0.24682142, 0.25125128]], dtype=float32)>,\n",
              " <tf.Tensor: id=4332, shape=(1, 4), dtype=float32, numpy=array([[0.24965625, 0.24924602, 0.24948908, 0.25160867]], dtype=float32)>,\n",
              " <tf.Tensor: id=4663, shape=(1, 4), dtype=float32, numpy=array([[0.25000688, 0.24983045, 0.25046328, 0.24969937]], dtype=float32)>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "vhZ8G5qeRZSW"
      },
      "source": [
        "## Dataset Implementation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ORziqbqo7Mg_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# To select dataset\n",
        "dataSelected = 4"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hLhcTRDPsmzq",
        "colab_type": "text"
      },
      "source": [
        "##### Bucketsort"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JBDJWrXBf-vg",
        "colab_type": "code",
        "outputId": "4758f8be-d1fe-4590-af7d-478345f4b1f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "source": [
        "# Python3 program to sort an array \n",
        "# using bucket sort \n",
        "def insertionSort(b): \n",
        "\tfor i in range(1, len(b)): \n",
        "\t\tup = b[i] \n",
        "\t\tj = i - 1\n",
        "\t\twhile j >=0 and b[j] > up: \n",
        "\t\t\tb[j + 1] = b[j] \n",
        "\t\t\tj -= 1\n",
        "\t\tb[j + 1] = up\t \n",
        "\treturn b\t \n",
        "\t\t\t\n",
        "def bucketSort(x, M): \n",
        "\tarr = [] \n",
        "\tslot_num = 3 # 10 means 10 slots, each \n",
        "\t\t\t\t# slot's size is 0.1 \n",
        "\tfor i in range(slot_num): \n",
        "\t\tarr.append([]) \n",
        "\t\t\n",
        "\t# Put array elements in different buckets \n",
        "\tfor j in x: \n",
        "\t\tindex_b = int((slot_num * j)/M) \n",
        "\t\tarr[index_b].append(j) \n",
        "\t\n",
        "\t# Sort individual buckets \n",
        "\tfor i in range(slot_num): \n",
        "\t\tarr[i] = insertionSort(arr[i]) \n",
        "\t\n",
        "\t# concatenate the result \n",
        "\tk = 0\n",
        "\tfor i in range(slot_num): \n",
        "\t\tfor j in range(len(arr[i])): \n",
        "\t\t\tx[k] = arr[i][j] \n",
        "\t\t\tk += 1\n",
        "\treturn x, arr\n",
        "\n",
        "# Driver Code \n",
        "x = [1, 4, 3, 6, \n",
        "\t2, 5, 7, 9, 8] \n",
        "print(\"Sorted Array is\") \n",
        "print(bucketSort(x, 10))\n",
        "\n",
        "\n",
        "# Driver Code \n",
        "x = [1, 14, 13, 6, \n",
        "\t12, 15, 7, 19, 8] \n",
        "print(\"Sorted Array is\") \n",
        "print(bucketSort(x, 20))"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sorted Array is\n",
            "([1, 2, 3, 4, 5, 6, 7, 8, 9], [[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n",
            "Sorted Array is\n",
            "([1, 6, 7, 8, 12, 13, 14, 15, 19], [[1, 6], [7, 8, 12, 13], [14, 15, 19]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nh3ousI_stj8",
        "colab_type": "text"
      },
      "source": [
        "##### Test data Generator"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OuamNiOVsr9g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# # Initalize the memory\n",
        "# inputMemory = np.zeros((M,M), dtype=\"float32\")\n",
        "# targetMemory = np.zeros((M,M), dtype=\"float32\")\n",
        "\n",
        "# # Pointer to the buckets\n",
        "# p1 = 8\n",
        "# p2 = 10\n",
        "# p3 = 12\n",
        "\n",
        "# # Bucket 1\n",
        "# inputMemory[0] = registerModule(p1, M)\n",
        "# targetMemory[0] = registerModule(p1, M)\n",
        "\n",
        "# # Bucket 2\n",
        "# inputMemory[1] = registerModule(p2, M)\n",
        "# targetMemory[1] = registerModule(p2, M)\n",
        "\n",
        "# # Bucket 3\n",
        "# inputMemory[2] = registerModule(p3, M)\n",
        "# targetMemory[2] = registerModule(p3, M)\n",
        "\n",
        "# # We can give sorted values into the function bucketSort\n",
        "# _, bucketsArray = bucketSort(np.arange(1, 20), 20)\n",
        "# # print(bucketsArray)\n",
        "\n",
        "\n",
        "# print(\"\\nindex:\")\n",
        "# randIndex = np.arange(3)\n",
        "# np.random.shuffle(randIndex)\n",
        "# print(randIndex)\n",
        "\n",
        "# for i in range(3):\n",
        "#   # memory index starts from 4 to 6 -> 4,5,6\n",
        "#   bucketIndex = randIndex[i]\n",
        "#   val = np.random.choice(bucketsArray[bucketIndex], 1)[0]\n",
        "#   print(val)\n",
        "#   inputMemory[i+4] = registerModule(val, M)\n",
        "#   targetMemory[i+4] = registerModule(val, M)\n",
        "  \n",
        "#   if bucketIndex == 0:\n",
        "#     #bucket1\n",
        "#     targetMemory[p1] = registerModule(val, M)\n",
        "#   elif bucketIndex == 1:\n",
        "#     #bucket2\n",
        "#     targetMemory[p2] = registerModule(val, M)    \n",
        "#   elif bucketIndex == 2:\n",
        "#     #bucket3\n",
        "#     targetMemory[p3] = registerModule(val, M)\n",
        "\n",
        "# print(np.argmax(inputMemory, axis=1))\n",
        "# print(np.argmax(targetMemory, axis=1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n8L5FeUwOh9N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# ## [20x20] -> 3 Pointers -> 4 Input Elements -> 2 element in each bucket -> 3,  1,4,  1,2, 1,2, 1,2\n",
        "# # Initalize the memory\n",
        "# inputMemory = np.zeros((M,M), dtype=\"float32\")\n",
        "# targetMemory = np.zeros((M,M), dtype=\"float32\")\n",
        "\n",
        "# # Pointer to the buckets\n",
        "# p1 = 9\n",
        "# p2 = 12\n",
        "# p3 = 15\n",
        "\n",
        "# # Bucket 1\n",
        "# inputMemory[0] = registerModule(p1, M)\n",
        "# targetMemory[0] = registerModule(p1, M)\n",
        "\n",
        "# # Bucket 2\n",
        "# inputMemory[1] = registerModule(p2, M)\n",
        "# targetMemory[1] = registerModule(p2, M)\n",
        "\n",
        "# # Bucket 3\n",
        "# inputMemory[2] = registerModule(p3, M)\n",
        "# targetMemory[2] = registerModule(p3, M)\n",
        "\n",
        "# # We can give sorted values into the function bucketSort\n",
        "# _, bucketsArray = bucketSort(np.arange(1, 20), 20)\n",
        "# # print(bucketsArray)\n",
        "\n",
        "\n",
        "# print(\"\\nindex:\")\n",
        "# randIndex = np.arange(3)\n",
        "# np.random.shuffle(randIndex)\n",
        "# print(randIndex)\n",
        "\n",
        "# for i in range(3):\n",
        "#   # memory index starts from 4 to 6 -> 4,5,6,7\n",
        "#   bucketIndex = randIndex[i]\n",
        "#   val = np.random.choice(bucketsArray[bucketIndex], 1)[0]\n",
        "#   print(val)\n",
        "#   inputMemory[i+4] = registerModule(val, M)\n",
        "#   targetMemory[i+4] = registerModule(val, M)\n",
        "  \n",
        "#   if bucketIndex == 0:\n",
        "#     #bucket1\n",
        "#     targetMemory[p1] = registerModule(val, M)\n",
        "#   elif bucketIndex == 1:\n",
        "#     #bucket2\n",
        "#     targetMemory[p2] = registerModule(val, M)    \n",
        "#   elif bucketIndex == 2:\n",
        "#     #bucket3\n",
        "#     targetMemory[p3] = registerModule(val, M)\n",
        "\n",
        "# # 4th element\n",
        "# print(\"\\nindex:\")\n",
        "# randIndex = np.arange(3)\n",
        "# np.random.shuffle(randIndex)\n",
        "# print(randIndex)\n",
        "\n",
        "# for i in range(1):\n",
        "#   # memory index starts from 4 to 6 -> 4,5,6,7\n",
        "#   bucketIndex = randIndex[i]\n",
        "#   val = np.random.choice(bucketsArray[bucketIndex], 1)[0]\n",
        "#   print(val)\n",
        "#   inputMemory[i+7] = registerModule(val, M)\n",
        "#   targetMemory[i+7] = registerModule(val, M)\n",
        "  \n",
        "#   if bucketIndex == 0:\n",
        "#     #bucket1\n",
        "#     targetMemory[p1+1] = registerModule(val, M)\n",
        "#   elif bucketIndex == 1:\n",
        "#     #bucket2\n",
        "#     targetMemory[p2+1] = registerModule(val, M)    \n",
        "#   elif bucketIndex == 2:\n",
        "#     #bucket3\n",
        "#     targetMemory[p3+1] = registerModule(val, M)\n",
        "\n",
        "# print(np.argmax(inputMemory, axis=1))\n",
        "# print(np.argmax(targetMemory, axis=1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wyzaNXZKwpqR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# ## [20x20] -> 3 Pointers -> 6 Input Elements -> 2 element in each bucket -> 3,  1,6,  1,2, 1,2, 1,2\n",
        "# # Initalize the memory\n",
        "# inputMemory = np.zeros((M,M), dtype=\"float32\")\n",
        "# targetMemory = np.zeros((M,M), dtype=\"float32\")\n",
        "\n",
        "# # Pointer to the buckets\n",
        "# p1 = 11\n",
        "# p2 = 14\n",
        "# p3 = 17\n",
        "\n",
        "# # Bucket 1\n",
        "# inputMemory[0] = registerModule(p1, M)\n",
        "# targetMemory[0] = registerModule(p1, M)\n",
        "\n",
        "# # Bucket 2\n",
        "# inputMemory[1] = registerModule(p2, M)\n",
        "# targetMemory[1] = registerModule(p2, M)\n",
        "\n",
        "# # Bucket 3\n",
        "# inputMemory[2] = registerModule(p3, M)\n",
        "# targetMemory[2] = registerModule(p3, M)\n",
        "\n",
        "# # We can give sorted values into the function bucketSort\n",
        "# _, bucketsArray = bucketSort(np.arange(1, 20), 20)\n",
        "# # print(bucketsArray)\n",
        "\n",
        "\n",
        "# print(\"\\nindex:\")\n",
        "# randIndex = np.arange(3)\n",
        "# np.random.shuffle(randIndex)\n",
        "# print(randIndex)\n",
        "\n",
        "# for i in range(3):\n",
        "#   # memory index starts from 4 to 6 -> 4,5,6,7\n",
        "#   bucketIndex = randIndex[i]\n",
        "#   val = np.random.choice(bucketsArray[bucketIndex], 1)[0]\n",
        "#   print(val)\n",
        "#   inputMemory[i+4] = registerModule(val, M)\n",
        "#   targetMemory[i+4] = registerModule(val, M)\n",
        "  \n",
        "#   if bucketIndex == 0:\n",
        "#     #bucket1\n",
        "#     targetMemory[p1] = registerModule(val, M)\n",
        "#   elif bucketIndex == 1:\n",
        "#     #bucket2\n",
        "#     targetMemory[p2] = registerModule(val, M)    \n",
        "#   elif bucketIndex == 2:\n",
        "#     #bucket3\n",
        "#     targetMemory[p3] = registerModule(val, M)\n",
        "\n",
        "# # 4th element\n",
        "# print(\"\\nindex:\")\n",
        "# randIndex = np.arange(3)\n",
        "# np.random.shuffle(randIndex)\n",
        "# print(randIndex)\n",
        "\n",
        "# for i in range(3):\n",
        "#   # memory index starts from 4 to 6 -> 4,5,6,7\n",
        "#   bucketIndex = randIndex[i]\n",
        "#   val = np.random.choice(bucketsArray[bucketIndex], 1)[0]\n",
        "#   print(val)\n",
        "#   inputMemory[i+7] = registerModule(val, M)\n",
        "#   targetMemory[i+7] = registerModule(val, M)\n",
        "  \n",
        "#   if bucketIndex == 0:\n",
        "#     #bucket1\n",
        "#     targetMemory[p1+1] = registerModule(val, M)\n",
        "#   elif bucketIndex == 1:\n",
        "#     #bucket2\n",
        "#     targetMemory[p2+1] = registerModule(val, M)    \n",
        "#   elif bucketIndex == 2:\n",
        "#     #bucket3\n",
        "#     targetMemory[p3+1] = registerModule(val, M)\n",
        "\n",
        "# print(np.argmax(inputMemory, axis=1))\n",
        "# print(np.argmax(targetMemory, axis=1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8qRl86D6O1zu",
        "colab_type": "text"
      },
      "source": [
        "### Dataset 4"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HZQqDkzgs0R2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def repeat(size, inputSet, targetSet):\n",
        "  inputRepeatSet = []\n",
        "  targetRepeatSet = []\n",
        "  \n",
        "  for _ in range(size):\n",
        "    arrIndex = np.arange(len(inputSet))\n",
        "    np.random.shuffle(arrIndex)\n",
        "    \n",
        "    for j in arrIndex:\n",
        "      inputRepeatSet.append(inputSet[j])\n",
        "      targetRepeatSet.append(targetSet[j])\n",
        "\n",
        "  return inputRepeatSet, targetRepeatSet\n",
        "\n",
        "def dataset4(repeats, M):\n",
        "  inputSet = []\n",
        "  targetSet = []\n",
        "  \n",
        "  for _ in range(repeats):\n",
        "    # Initalize the memory\n",
        "    inputMemory = np.zeros((M,M), dtype=\"float32\")\n",
        "    targetMemory = np.zeros((M,M), dtype=\"float32\")\n",
        "\n",
        "    # Pointer to the buckets\n",
        "    p1 = 8\n",
        "    p2 = 10\n",
        "    p3 = 12\n",
        "\n",
        "    # Bucket 1\n",
        "    inputMemory[0] = registerModule(p1, M)\n",
        "    targetMemory[0] = registerModule(p1, M)\n",
        "\n",
        "    # Bucket 2\n",
        "    inputMemory[1] = registerModule(p2, M)\n",
        "    targetMemory[1] = registerModule(p2, M)\n",
        "\n",
        "    # Bucket 3\n",
        "    inputMemory[2] = registerModule(p3, M)\n",
        "    targetMemory[2] = registerModule(p3, M)\n",
        "\n",
        "    # We can give sorted values into the function bucketSort\n",
        "    _, bucketsArray = bucketSort(np.arange(1, 20), 20)\n",
        "    # print(bucketsArray)\n",
        "\n",
        "\n",
        "    print(\"\\nindex:\")\n",
        "    randIndex = np.arange(3)\n",
        "    np.random.shuffle(randIndex)\n",
        "    print(randIndex)\n",
        "\n",
        "    for i in range(3):\n",
        "      # memory index starts from 4 to 6 -> 4,5,6\n",
        "      bucketIndex = randIndex[i]\n",
        "      val = np.random.choice(bucketsArray[bucketIndex], 1)[0]\n",
        "      inputMemory[i+4] = registerModule(val, M)\n",
        "      targetMemory[i+4] = registerModule(val, M)\n",
        "\n",
        "      if bucketIndex == 0:\n",
        "        #bucket1\n",
        "        targetMemory[p1] = registerModule(val, M)\n",
        "      elif bucketIndex == 1:\n",
        "        #bucket2\n",
        "        targetMemory[p2] = registerModule(val, M)    \n",
        "      elif bucketIndex == 2:\n",
        "        #bucket3\n",
        "        targetMemory[p3] = registerModule(val, M)\n",
        "\n",
        "    print(np.argmax(inputMemory, axis=1))\n",
        "    print(np.argmax(targetMemory, axis=1))\n",
        "\n",
        "    inputSet.append(inputMemory)\n",
        "    targetSet.append(targetMemory)\n",
        "  return inputSet, targetSet"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ioGw30_ky17i",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 42605
        },
        "outputId": "f8618dfb-8853-41ee-f0b9-e94b575039f2"
      },
      "source": [
        "# dataSelected = 4\n",
        "if dataSelected == 4:\n",
        "  ## Generate dataset\n",
        "  inputSet, targetSet = dataset4(500, M)\n",
        "\n",
        "  inputTrainSet, targetTrainSet = repeat(20, inputSet[0:40], targetSet[0:40])\n",
        "\n",
        "  print(len(inputTrainSet))\n",
        "  print(inputSet[0].shape)\n",
        "  print(targetSet[0].shape)\n",
        "\n",
        "  inputTestSet, targetTestSet = inputSet[40:500], targetSet[40:500]\n",
        "\n",
        "  print(len(inputTestSet))\n",
        "  # for (input_data, target_data) in zip(inputSet, targetSet):\n",
        "  #   print(input_data)\n",
        "  #   print(target_data)"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "index:\n",
            "[1 0 2]\n",
            "[ 8 10 12  0 10  5 16  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
            "[ 8 10 12  0 10  5 16  0  5  0 10  0 16  0  0  0  0  0  0  0]\n",
            "800\n",
            "(20, 20)\n",
            "(20, 20)\n",
            "460\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zitwyuZFQzyA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "outputId": "6051e5b9-5c69-4b7f-feda-ee62fafefd7d"
      },
      "source": [
        "if dataSelected == 4:\n",
        "  print(inputSet[0])"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mqyynvABRTwm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "be81ef2d-e162-4f49-a205-bfca0fba38c3"
      },
      "source": [
        "if dataSelected == 4:\n",
        "  print(np.argmax(inputSet[0], axis=1))"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 8 10 12  0  3 12 18  0  0  0  0  0  0  0  0  0  0  0  0  0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-DNUIFCmRbUh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "0cb92ca9-7976-42ae-926f-f27bb92198fb"
      },
      "source": [
        "if dataSelected == 4:\n",
        "  print(np.argmax(targetSet[0], axis=1))"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 8 10 12  0  3 12 18  0  3  0 12  0 18  0  0  0  0  0  0  0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hlz_8dAmRDxh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "outputId": "6264fd7d-5ed4-4d31-a1d6-4757ccdd5bc5"
      },
      "source": [
        "if dataSelected == 4:\n",
        "  print(targetSet[0])"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "THPWe7-J1jNf",
        "colab_type": "text"
      },
      "source": [
        "### Dataset 5"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HOMOe-Zx01UT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def repeat(size, inputSet, targetSet):\n",
        "  inputRepeatSet = []\n",
        "  targetRepeatSet = []\n",
        "  \n",
        "  for _ in range(size):\n",
        "    arrIndex = np.arange(len(inputSet))\n",
        "    np.random.shuffle(arrIndex)\n",
        "    \n",
        "    for j in arrIndex:\n",
        "      inputRepeatSet.append(inputSet[j])\n",
        "      targetRepeatSet.append(targetSet[j])\n",
        "\n",
        "  return inputRepeatSet, targetRepeatSet\n",
        "\n",
        "def dataset5(repeats, M):\n",
        "  inputSet = []\n",
        "  targetSet = []\n",
        "  \n",
        "  for _ in range(repeats):\n",
        "    ## [20x20] -> 3 Pointers -> 4 Input Elements -> 2 element in each bucket -> 3,  1,4,  1,2, 1,2, 1,2\n",
        "    # Initalize the memory\n",
        "    inputMemory = np.zeros((M,M), dtype=\"float32\")\n",
        "    targetMemory = np.zeros((M,M), dtype=\"float32\")\n",
        "\n",
        "    # Pointer to the buckets\n",
        "    p1 = 9\n",
        "    p2 = 12\n",
        "    p3 = 15\n",
        "\n",
        "    # Bucket 1\n",
        "    inputMemory[0] = registerModule(p1, M)\n",
        "    targetMemory[0] = registerModule(p1, M)\n",
        "\n",
        "    # Bucket 2\n",
        "    inputMemory[1] = registerModule(p2, M)\n",
        "    targetMemory[1] = registerModule(p2, M)\n",
        "\n",
        "    # Bucket 3\n",
        "    inputMemory[2] = registerModule(p3, M)\n",
        "    targetMemory[2] = registerModule(p3, M)\n",
        "\n",
        "    # We can give sorted values into the function bucketSort\n",
        "    _, bucketsArray = bucketSort(np.arange(1, 20), 20)\n",
        "    # print(bucketsArray)\n",
        "\n",
        "\n",
        "    print(\"\\nindex:\")\n",
        "    randIndex = np.arange(3)\n",
        "    np.random.shuffle(randIndex)\n",
        "    print(randIndex)\n",
        "\n",
        "    for i in range(3):\n",
        "      # memory index starts from 4 to 6 -> 4,5,6,7\n",
        "      bucketIndex = randIndex[i]\n",
        "      val = np.random.choice(bucketsArray[bucketIndex], 1)[0]\n",
        "      print(val)\n",
        "      inputMemory[i+4] = registerModule(val, M)\n",
        "      targetMemory[i+4] = registerModule(val, M)\n",
        "\n",
        "      if bucketIndex == 0:\n",
        "        #bucket1\n",
        "        targetMemory[p1] = registerModule(val, M)\n",
        "      elif bucketIndex == 1:\n",
        "        #bucket2\n",
        "        targetMemory[p2] = registerModule(val, M)    \n",
        "      elif bucketIndex == 2:\n",
        "        #bucket3\n",
        "        targetMemory[p3] = registerModule(val, M)\n",
        "\n",
        "    # 4th element\n",
        "    print(\"\\nindex:\")\n",
        "    randIndex = np.arange(3)\n",
        "    np.random.shuffle(randIndex)\n",
        "    print(randIndex)\n",
        "\n",
        "    for i in range(1):\n",
        "      # memory index starts from 4 to 6 -> 4,5,6,7\n",
        "      bucketIndex = randIndex[i]\n",
        "      val = np.random.choice(bucketsArray[bucketIndex], 1)[0]\n",
        "      print(val)\n",
        "      inputMemory[i+7] = registerModule(val, M)\n",
        "      targetMemory[i+7] = registerModule(val, M)\n",
        "\n",
        "      if bucketIndex == 0:\n",
        "        #bucket1\n",
        "        targetMemory[p1+1] = registerModule(val, M)\n",
        "      elif bucketIndex == 1:\n",
        "        #bucket2\n",
        "        targetMemory[p2+1] = registerModule(val, M)    \n",
        "      elif bucketIndex == 2:\n",
        "        #bucket3\n",
        "        targetMemory[p3+1] = registerModule(val, M)\n",
        "\n",
        "    print(np.argmax(inputMemory, axis=1))\n",
        "    print(np.argmax(targetMemory, axis=1))\n",
        "\n",
        "    inputSet.append(inputMemory)\n",
        "    targetSet.append(targetMemory)\n",
        "  return inputSet, targetSet"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CO15-Dol6z4N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# dataSelected = 5\n",
        "if dataSelected == 5:\n",
        "  ## Generate dataset\n",
        "  inputSet, targetSet = dataset5(500, M)\n",
        "  inputTrainSet, targetTrainSet = repeat(20, inputSet[0:40], targetSet[0:40])\n",
        "\n",
        "  print(len(inputTrainSet))\n",
        "  print(inputSet[0].shape)\n",
        "  print(targetSet[0].shape)\n",
        "\n",
        "  inputTestSet, targetTestSet = inputSet[40:500], targetSet[40:500]\n",
        "\n",
        "  # for (input_data, target_data) in zip(inputSet, targetSet):\n",
        "  #   print(input_data)\n",
        "  #   print(target_data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ESmNV7HR1wj1",
        "colab_type": "text"
      },
      "source": [
        "### Dataset 6"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cv9IDi-C1tf0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def repeat(size, inputSet, targetSet):\n",
        "  inputRepeatSet = []\n",
        "  targetRepeatSet = []\n",
        "  \n",
        "  for _ in range(size):\n",
        "    arrIndex = np.arange(len(inputSet))\n",
        "    np.random.shuffle(arrIndex)\n",
        "    \n",
        "    for j in arrIndex:\n",
        "      inputRepeatSet.append(inputSet[j])\n",
        "      targetRepeatSet.append(targetSet[j])\n",
        "\n",
        "  return inputRepeatSet, targetRepeatSet\n",
        "\n",
        "def dataset6(repeats, M):\n",
        "  inputSet = []\n",
        "  targetSet = []\n",
        "  \n",
        "  for _ in range(repeats):\n",
        "    ## [20x20] -> 3 Pointers -> 6 Input Elements -> 2 element in each bucket -> 3,  1,6,  1,2, 1,2, 1,2\n",
        "    # Initalize the memory\n",
        "    inputMemory = np.zeros((M,M), dtype=\"float32\")\n",
        "    targetMemory = np.zeros((M,M), dtype=\"float32\")\n",
        "\n",
        "    # Pointer to the buckets\n",
        "    p1 = 11\n",
        "    p2 = 14\n",
        "    p3 = 17\n",
        "\n",
        "    # Bucket 1\n",
        "    inputMemory[0] = registerModule(p1, M)\n",
        "    targetMemory[0] = registerModule(p1, M)\n",
        "\n",
        "    # Bucket 2\n",
        "    inputMemory[1] = registerModule(p2, M)\n",
        "    targetMemory[1] = registerModule(p2, M)\n",
        "\n",
        "    # Bucket 3\n",
        "    inputMemory[2] = registerModule(p3, M)\n",
        "    targetMemory[2] = registerModule(p3, M)\n",
        "\n",
        "    # We can give sorted values into the function bucketSort\n",
        "    _, bucketsArray = bucketSort(np.arange(1, 20), 20)\n",
        "    # print(bucketsArray)\n",
        "\n",
        "\n",
        "    print(\"\\nindex:\")\n",
        "    randIndex = np.arange(3)\n",
        "    np.random.shuffle(randIndex)\n",
        "    print(randIndex)\n",
        "\n",
        "    for i in range(3):\n",
        "      # memory index starts from 4 to 6 -> 4,5,6,7\n",
        "      bucketIndex = randIndex[i]\n",
        "      val = np.random.choice(bucketsArray[bucketIndex], 1)[0]\n",
        "      print(val)\n",
        "      inputMemory[i+4] = registerModule(val, M)\n",
        "      targetMemory[i+4] = registerModule(val, M)\n",
        "\n",
        "      if bucketIndex == 0:\n",
        "        #bucket1\n",
        "        targetMemory[p1] = registerModule(val, M)\n",
        "      elif bucketIndex == 1:\n",
        "        #bucket2\n",
        "        targetMemory[p2] = registerModule(val, M)    \n",
        "      elif bucketIndex == 2:\n",
        "        #bucket3\n",
        "        targetMemory[p3] = registerModule(val, M)\n",
        "\n",
        "    # 4th element\n",
        "    print(\"\\nindex:\")\n",
        "    randIndex = np.arange(3)\n",
        "    np.random.shuffle(randIndex)\n",
        "    print(randIndex)\n",
        "\n",
        "    for i in range(3):\n",
        "      # memory index starts from 4 to 6 -> 4,5,6,7\n",
        "      bucketIndex = randIndex[i]\n",
        "      val = np.random.choice(bucketsArray[bucketIndex], 1)[0]\n",
        "      print(val)\n",
        "      inputMemory[i+7] = registerModule(val, M)\n",
        "      targetMemory[i+7] = registerModule(val, M)\n",
        "\n",
        "      if bucketIndex == 0:\n",
        "        #bucket1\n",
        "        targetMemory[p1+1] = registerModule(val, M)\n",
        "      elif bucketIndex == 1:\n",
        "        #bucket2\n",
        "        targetMemory[p2+1] = registerModule(val, M)    \n",
        "      elif bucketIndex == 2:\n",
        "        #bucket3\n",
        "        targetMemory[p3+1] = registerModule(val, M)\n",
        "\n",
        "    print(np.argmax(inputMemory, axis=1))\n",
        "    print(np.argmax(targetMemory, axis=1))\n",
        "\n",
        "    inputSet.append(inputMemory)\n",
        "    targetSet.append(targetMemory)\n",
        "  return inputSet, targetSet"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lschFQKT6kq2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# dataSelected = 6"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EAk1RaXf6gQN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "if dataSelected == 6:\n",
        "  ## Generate dataset\n",
        "  inputSet, targetSet = dataset6(500, M)\n",
        "  inputTrainSet, targetTrainSet = repeat(40, inputSet[0:40], targetSet[0:40])\n",
        "\n",
        "  print(len(inputTrainSet))\n",
        "  print(inputSet[0].shape)\n",
        "  print(targetSet[0].shape)\n",
        "\n",
        "  inputTestSet, targetTestSet = inputSet[40:500], targetSet[40:500]\n",
        "\n",
        "  # for (input_data, target_data) in zip(inputSet, targetSet):\n",
        "  #   print(input_data)\n",
        "  #   print(target_data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "D_9C5PA4RLA8"
      },
      "source": [
        "## Train Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k76s7cjDNoB4",
        "colab_type": "text"
      },
      "source": [
        "### Training Function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "lUGX_C7Juetl",
        "colab": {}
      },
      "source": [
        "optimizer = tf.train.AdamOptimizer(\n",
        "    learning_rate=0.001,\n",
        "    beta1=0.9,\n",
        "    beta2=0.999,\n",
        "    epsilon=1e-08\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "q8eUsG9T3WBO",
        "colab": {}
      },
      "source": [
        "def trainModel(inputSet, targetSet, gates, nramController, readMemory, writeMemory, epochs=2, max_timesteps=5, min_error=1e-5):\n",
        "  loss_history = []\n",
        "\n",
        "  for epoch in range(epochs):\n",
        "      batch = 0\n",
        "      for (input_data, target_data) in zip(inputSet, targetSet):\n",
        "          cost = 0\n",
        "          total_prob_complete = 0\n",
        "          prob_incomplete = 1\n",
        "          \n",
        "          with tf.GradientTape() as tape:\n",
        "            registers = tf.concat([registerValueModule(0), registerValueModule(0), registerValueModule(0)], axis=0)\n",
        "            registers = tf.reshape(registers, shape=[1, registers.shape[0], registers.shape[1]])\n",
        "            memory = input_data\n",
        "           \n",
        "            for timestep in range(max_timesteps):\n",
        "              register_data = []\n",
        "              registersNumpy = registers.numpy()\n",
        "              for i in range(registersNumpy.shape[1]):\n",
        "                register_data.append(registersNumpy[0][i][0])\n",
        "              register_data = np.array([register_data], dtype=\"float32\")\n",
        "              \n",
        "              gateControllerOutput, registerControllerOutput, status = nramController(register_data)\n",
        "              output_registers, memory = runTimeStepNN(gates, registers, memory, readMemory, writeMemory, gateControllerOutput, registerControllerOutput)\n",
        "              \n",
        "              registers = output_registers\n",
        "              \n",
        "              # update the probability when its the last time step\n",
        "              if timestep == max_timesteps:\n",
        "                  prob_complete = 1 - total_prob_complete\n",
        "              else:\n",
        "                  prob_complete = status * prob_incomplete\n",
        "\n",
        "              # Accumulate the probability that a result has been produced.\n",
        "              total_prob_complete += prob_complete\n",
        "\n",
        "              # update the probability for incomplete status\n",
        "              prob_incomplete *= 1 - status\n",
        "\n",
        "              # Cost for this timestep.\n",
        "              cost_current_timestep_registers = 0\n",
        "\n",
        "              # Add epsilon to every log to avoid having inf in costs.\n",
        "              epsilon = tf.constant(1e-35, shape=[output_registers.shape[0], output_registers.shape[2]])\n",
        "\n",
        "              for r in range(memory.shape[0]):\n",
        "                  y = target_data[r]\n",
        "                  yPredicted = memory[r]\n",
        "                  loss = tf.math.reduce_sum((y * tf.math.log(tf.math.add(yPredicted, epsilon))), axis=1)\n",
        "                  cost_current_timestep_registers += tf.reshape(loss, [loss.shape[0], 1])\n",
        "\n",
        "              cost_now = prob_complete * cost_current_timestep_registers\n",
        "              cost -= cost_now\n",
        "            final_cost = tf.math.reduce_sum(cost)\n",
        "          \n",
        "          # store the loss history \n",
        "          loss_history.append(final_cost.numpy())\n",
        "\n",
        "          # fetch the trainable variables\n",
        "          variables = nramController.variables\n",
        "\n",
        "          # calculate the gradient\n",
        "          grads = tape.gradient(final_cost, variables)\n",
        "\n",
        "          # update the weights of the network\n",
        "          optimizer.apply_gradients(zip(grads, variables), global_step=tf.train.get_or_create_global_step())\n",
        "        \n",
        "          batch += 1\n",
        "          print(\"\\nEpoch:\", epoch, \" batch:\", batch, \" final cost:\", final_cost.numpy())\n",
        "          \n",
        "          if final_cost.numpy() < min_error:\n",
        "            print(\"\\nExit: Epoch:\", epoch, \" batch:\", batch, \" final cost:\", final_cost.numpy())\n",
        "            return loss_history, nramController\n",
        "          \n",
        "  return loss_history, nramController"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "1MZlsNaiRRiZ"
      },
      "source": [
        "### Run the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "pTOo1kur3AMO"
      },
      "source": [
        "##### LSTM Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "VF01xnaTWuVq",
        "outputId": "9b3c8545-1a68-43c0-e118-21285ad1090d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 22375
        }
      },
      "source": [
        "## Run me\n",
        "gateDimensions = [[3], [4], [5], [6], [7], [8,8], [9,9], [10], [11,11], [12,12], [13,13], [14,14], [15,15], [16,16]]\n",
        "registerDimensions = [17, 17, 17]\n",
        "\n",
        "nramController = NRAMControllerLSTM(hidden_dimensions=256, gateControllerDimensions=gateDimensions, registerControllerDimensions=registerDimensions)\n",
        "\n",
        "gates = [zeroModule, oneModule, twoModule, incModule, addModule, subModule, decModule, lessModule, lessOrEqualThenModule, equalModule, minimumModule, maximumModule]\n",
        "\n",
        "loss_history, model = trainModel(inputTrainSet, targetTrainSet, gates, nramController, readMemory, writeMemory, min_error=1e-1)\n"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9f0dcc438>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_grad.py:102: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Deprecated in favor of operator or tf.math.divide.\n",
            "\n",
            "Epoch: 0  batch: 1  final cost: 183.04047\n",
            "\n",
            "Epoch: 0  batch: 2  final cost: 114.428154\n",
            "\n",
            "Epoch: 0  batch: 3  final cost: 153.98615\n",
            "\n",
            "Epoch: 0  batch: 4  final cost: 135.46187\n",
            "\n",
            "Epoch: 0  batch: 5  final cost: 151.31842\n",
            "\n",
            "Epoch: 0  batch: 6  final cost: 90.647194\n",
            "\n",
            "Epoch: 0  batch: 7  final cost: 148.50003\n",
            "\n",
            "Epoch: 0  batch: 8  final cost: 148.04964\n",
            "\n",
            "Epoch: 0  batch: 9  final cost: 146.71375\n",
            "\n",
            "Epoch: 0  batch: 10  final cost: 163.63252\n",
            "\n",
            "Epoch: 0  batch: 11  final cost: 108.837715\n",
            "\n",
            "Epoch: 0  batch: 12  final cost: 151.1057\n",
            "\n",
            "Epoch: 0  batch: 13  final cost: 120.0504\n",
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            "Epoch: 0  batch: 14  final cost: 149.70117\n",
            "\n",
            "Epoch: 0  batch: 15  final cost: 137.65627\n",
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            "Epoch: 0  batch: 16  final cost: 103.356804\n",
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            "Epoch: 0  batch: 17  final cost: 100.75308\n",
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            "Epoch: 0  batch: 18  final cost: 99.40822\n",
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            "Epoch: 0  batch: 19  final cost: 132.22475\n",
            "\n",
            "Epoch: 0  batch: 20  final cost: 98.99054\n",
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            "Epoch: 0  batch: 21  final cost: 96.36674\n",
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            "Epoch: 0  batch: 22  final cost: 95.84903\n",
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            "Epoch: 0  batch: 497  final cost: 0.55268514\n",
            "\n",
            "Epoch: 0  batch: 498  final cost: 0.6303439\n",
            "\n",
            "Epoch: 0  batch: 499  final cost: 0.62612706\n",
            "\n",
            "Epoch: 0  batch: 500  final cost: 0.56824964\n",
            "\n",
            "Epoch: 0  batch: 501  final cost: 0.76517344\n",
            "\n",
            "Epoch: 0  batch: 502  final cost: 0.23258749\n",
            "\n",
            "Epoch: 0  batch: 503  final cost: 0.5447855\n",
            "\n",
            "Epoch: 0  batch: 504  final cost: 0.5988318\n",
            "\n",
            "Epoch: 0  batch: 505  final cost: 0.6090334\n",
            "\n",
            "Epoch: 0  batch: 506  final cost: 0.5749107\n",
            "\n",
            "Epoch: 0  batch: 507  final cost: 0.7453258\n",
            "\n",
            "Epoch: 0  batch: 508  final cost: 0.5308469\n",
            "\n",
            "Epoch: 0  batch: 509  final cost: 0.2057735\n",
            "\n",
            "Epoch: 0  batch: 510  final cost: 0.50710756\n",
            "\n",
            "Epoch: 0  batch: 511  final cost: 0.5511162\n",
            "\n",
            "Epoch: 0  batch: 512  final cost: 0.18118958\n",
            "\n",
            "Epoch: 0  batch: 513  final cost: 0.5565385\n",
            "\n",
            "Epoch: 0  batch: 514  final cost: 0.18172657\n",
            "\n",
            "Epoch: 0  batch: 515  final cost: 0.17022744\n",
            "\n",
            "Epoch: 0  batch: 516  final cost: 0.20745492\n",
            "\n",
            "Epoch: 0  batch: 517  final cost: 0.22596355\n",
            "\n",
            "Epoch: 0  batch: 518  final cost: 0.5919424\n",
            "\n",
            "Epoch: 0  batch: 519  final cost: 0.17910475\n",
            "\n",
            "Epoch: 0  batch: 520  final cost: 0.73356724\n",
            "\n",
            "Epoch: 0  batch: 521  final cost: 0.6655228\n",
            "\n",
            "Epoch: 0  batch: 522  final cost: 0.48735994\n",
            "\n",
            "Epoch: 0  batch: 523  final cost: 0.16174598\n",
            "\n",
            "Epoch: 0  batch: 524  final cost: 1.0354581\n",
            "\n",
            "Epoch: 0  batch: 525  final cost: 0.24815965\n",
            "\n",
            "Epoch: 0  batch: 526  final cost: 0.5598334\n",
            "\n",
            "Epoch: 0  batch: 527  final cost: 0.5578935\n",
            "\n",
            "Epoch: 0  batch: 528  final cost: 0.17283805\n",
            "\n",
            "Epoch: 0  batch: 529  final cost: 0.5063405\n",
            "\n",
            "Epoch: 0  batch: 530  final cost: 0.5179639\n",
            "\n",
            "Epoch: 0  batch: 531  final cost: 0.15370409\n",
            "\n",
            "Epoch: 0  batch: 532  final cost: 0.19008498\n",
            "\n",
            "Epoch: 0  batch: 533  final cost: 0.67419475\n",
            "\n",
            "Epoch: 0  batch: 534  final cost: 0.5506824\n",
            "\n",
            "Epoch: 0  batch: 535  final cost: 0.5386243\n",
            "\n",
            "Epoch: 0  batch: 536  final cost: 0.1578756\n",
            "\n",
            "Epoch: 0  batch: 537  final cost: 0.4927251\n",
            "\n",
            "Epoch: 0  batch: 538  final cost: 0.51002365\n",
            "\n",
            "Epoch: 0  batch: 539  final cost: 0.5248912\n",
            "\n",
            "Epoch: 0  batch: 540  final cost: 0.6021853\n",
            "\n",
            "Epoch: 0  batch: 541  final cost: 0.82911956\n",
            "\n",
            "Epoch: 0  batch: 542  final cost: 0.48433867\n",
            "\n",
            "Epoch: 0  batch: 543  final cost: 0.16371861\n",
            "\n",
            "Epoch: 0  batch: 544  final cost: 0.18715775\n",
            "\n",
            "Epoch: 0  batch: 545  final cost: 0.16015571\n",
            "\n",
            "Epoch: 0  batch: 546  final cost: 0.464883\n",
            "\n",
            "Epoch: 0  batch: 547  final cost: 0.6632703\n",
            "\n",
            "Epoch: 0  batch: 548  final cost: 0.6407174\n",
            "\n",
            "Epoch: 0  batch: 549  final cost: 0.46065935\n",
            "\n",
            "Epoch: 0  batch: 550  final cost: 0.5254521\n",
            "\n",
            "Epoch: 0  batch: 551  final cost: 0.5262394\n",
            "\n",
            "Epoch: 0  batch: 552  final cost: 0.44393033\n",
            "\n",
            "Epoch: 0  batch: 553  final cost: 0.4934436\n",
            "\n",
            "Epoch: 0  batch: 554  final cost: 0.48102742\n",
            "\n",
            "Epoch: 0  batch: 555  final cost: 0.457143\n",
            "\n",
            "Epoch: 0  batch: 556  final cost: 0.18999743\n",
            "\n",
            "Epoch: 0  batch: 557  final cost: 0.1948347\n",
            "\n",
            "Epoch: 0  batch: 558  final cost: 0.137701\n",
            "\n",
            "Epoch: 0  batch: 559  final cost: 0.4420021\n",
            "\n",
            "Epoch: 0  batch: 560  final cost: 0.50642264\n",
            "\n",
            "Epoch: 0  batch: 561  final cost: 0.46447867\n",
            "\n",
            "Epoch: 0  batch: 562  final cost: 0.1854066\n",
            "\n",
            "Epoch: 0  batch: 563  final cost: 0.4429174\n",
            "\n",
            "Epoch: 0  batch: 564  final cost: 0.61257625\n",
            "\n",
            "Epoch: 0  batch: 565  final cost: 0.5422788\n",
            "\n",
            "Epoch: 0  batch: 566  final cost: 0.43556035\n",
            "\n",
            "Epoch: 0  batch: 567  final cost: 0.40626764\n",
            "\n",
            "Epoch: 0  batch: 568  final cost: 0.13405879\n",
            "\n",
            "Epoch: 0  batch: 569  final cost: 0.4204647\n",
            "\n",
            "Epoch: 0  batch: 570  final cost: 0.47912356\n",
            "\n",
            "Epoch: 0  batch: 571  final cost: 0.43706757\n",
            "\n",
            "Epoch: 0  batch: 572  final cost: 0.147385\n",
            "\n",
            "Epoch: 0  batch: 573  final cost: 0.13084017\n",
            "\n",
            "Epoch: 0  batch: 574  final cost: 0.20760362\n",
            "\n",
            "Epoch: 0  batch: 575  final cost: 0.46145588\n",
            "\n",
            "Epoch: 0  batch: 576  final cost: 0.7195586\n",
            "\n",
            "Epoch: 0  batch: 577  final cost: 0.13596013\n",
            "\n",
            "Epoch: 0  batch: 578  final cost: 0.5664519\n",
            "\n",
            "Epoch: 0  batch: 579  final cost: 0.5286622\n",
            "\n",
            "Epoch: 0  batch: 580  final cost: 0.13277926\n",
            "\n",
            "Epoch: 0  batch: 581  final cost: 0.4481187\n",
            "\n",
            "Epoch: 0  batch: 582  final cost: 0.4658345\n",
            "\n",
            "Epoch: 0  batch: 583  final cost: 0.16225527\n",
            "\n",
            "Epoch: 0  batch: 584  final cost: 0.551852\n",
            "\n",
            "Epoch: 0  batch: 585  final cost: 0.46600014\n",
            "\n",
            "Epoch: 0  batch: 586  final cost: 0.3984795\n",
            "\n",
            "Epoch: 0  batch: 587  final cost: 0.81358707\n",
            "\n",
            "Epoch: 0  batch: 588  final cost: 0.40678144\n",
            "\n",
            "Epoch: 0  batch: 589  final cost: 0.44050148\n",
            "\n",
            "Epoch: 0  batch: 590  final cost: 0.42703265\n",
            "\n",
            "Epoch: 0  batch: 591  final cost: 0.43250927\n",
            "\n",
            "Epoch: 0  batch: 592  final cost: 0.17257781\n",
            "\n",
            "Epoch: 0  batch: 593  final cost: 0.13526332\n",
            "\n",
            "Epoch: 0  batch: 594  final cost: 0.39067298\n",
            "\n",
            "Epoch: 0  batch: 595  final cost: 0.40724325\n",
            "\n",
            "Epoch: 0  batch: 596  final cost: 0.3685205\n",
            "\n",
            "Epoch: 0  batch: 597  final cost: 0.42550603\n",
            "\n",
            "Epoch: 0  batch: 598  final cost: 0.1485908\n",
            "\n",
            "Epoch: 0  batch: 599  final cost: 0.1335027\n",
            "\n",
            "Epoch: 0  batch: 600  final cost: 0.3756953\n",
            "\n",
            "Epoch: 0  batch: 601  final cost: 0.35627738\n",
            "\n",
            "Epoch: 0  batch: 602  final cost: 0.11916709\n",
            "\n",
            "Epoch: 0  batch: 603  final cost: 0.42259046\n",
            "\n",
            "Epoch: 0  batch: 604  final cost: 0.1173816\n",
            "\n",
            "Epoch: 0  batch: 605  final cost: 0.12168303\n",
            "\n",
            "Epoch: 0  batch: 606  final cost: 0.37787747\n",
            "\n",
            "Epoch: 0  batch: 607  final cost: 0.4102773\n",
            "\n",
            "Epoch: 0  batch: 608  final cost: 0.36623296\n",
            "\n",
            "Epoch: 0  batch: 609  final cost: 0.35636804\n",
            "\n",
            "Epoch: 0  batch: 610  final cost: 0.12880605\n",
            "\n",
            "Epoch: 0  batch: 611  final cost: 0.12968159\n",
            "\n",
            "Epoch: 0  batch: 612  final cost: 0.42007712\n",
            "\n",
            "Epoch: 0  batch: 613  final cost: 0.7439157\n",
            "\n",
            "Epoch: 0  batch: 614  final cost: 0.50114954\n",
            "\n",
            "Epoch: 0  batch: 615  final cost: 0.46756533\n",
            "\n",
            "Epoch: 0  batch: 616  final cost: 0.40492058\n",
            "\n",
            "Epoch: 0  batch: 617  final cost: 0.39968532\n",
            "\n",
            "Epoch: 0  batch: 618  final cost: 0.36313784\n",
            "\n",
            "Epoch: 0  batch: 619  final cost: 0.4061171\n",
            "\n",
            "Epoch: 0  batch: 620  final cost: 0.14426258\n",
            "\n",
            "Epoch: 0  batch: 621  final cost: 0.18383743\n",
            "\n",
            "Epoch: 0  batch: 622  final cost: 0.20278928\n",
            "\n",
            "Epoch: 0  batch: 623  final cost: 0.15362856\n",
            "\n",
            "Epoch: 0  batch: 624  final cost: 0.48165053\n",
            "\n",
            "Epoch: 0  batch: 625  final cost: 0.3909222\n",
            "\n",
            "Epoch: 0  batch: 626  final cost: 0.15122333\n",
            "\n",
            "Epoch: 0  batch: 627  final cost: 0.119854674\n",
            "\n",
            "Epoch: 0  batch: 628  final cost: 0.49051052\n",
            "\n",
            "Epoch: 0  batch: 629  final cost: 0.3673109\n",
            "\n",
            "Epoch: 0  batch: 630  final cost: 0.34807974\n",
            "\n",
            "Epoch: 0  batch: 631  final cost: 0.34765542\n",
            "\n",
            "Epoch: 0  batch: 632  final cost: 0.35917598\n",
            "\n",
            "Epoch: 0  batch: 633  final cost: 0.112135984\n",
            "\n",
            "Epoch: 0  batch: 634  final cost: 0.33318332\n",
            "\n",
            "Epoch: 0  batch: 635  final cost: 0.34799063\n",
            "\n",
            "Epoch: 0  batch: 636  final cost: 0.38565972\n",
            "\n",
            "Epoch: 0  batch: 637  final cost: 0.3596634\n",
            "\n",
            "Epoch: 0  batch: 638  final cost: 0.5729959\n",
            "\n",
            "Epoch: 0  batch: 639  final cost: 0.3346299\n",
            "\n",
            "Epoch: 0  batch: 640  final cost: 0.4073851\n",
            "\n",
            "Epoch: 0  batch: 641  final cost: 0.13220933\n",
            "\n",
            "Epoch: 0  batch: 642  final cost: 0.5606892\n",
            "\n",
            "Epoch: 0  batch: 643  final cost: 0.108083755\n",
            "\n",
            "Epoch: 0  batch: 644  final cost: 0.34651434\n",
            "\n",
            "Epoch: 0  batch: 645  final cost: 0.35301635\n",
            "\n",
            "Epoch: 0  batch: 646  final cost: 0.14256392\n",
            "\n",
            "Epoch: 0  batch: 647  final cost: 0.34895265\n",
            "\n",
            "Epoch: 0  batch: 648  final cost: 0.312\n",
            "\n",
            "Epoch: 0  batch: 649  final cost: 0.42081612\n",
            "\n",
            "Epoch: 0  batch: 650  final cost: 0.15877308\n",
            "\n",
            "Epoch: 0  batch: 651  final cost: 0.35657513\n",
            "\n",
            "Epoch: 0  batch: 652  final cost: 0.3423441\n",
            "\n",
            "Epoch: 0  batch: 653  final cost: 0.13447392\n",
            "\n",
            "Epoch: 0  batch: 654  final cost: 0.09994805\n",
            "\n",
            "Exit: Epoch: 0  batch: 654  final cost: 0.09994805\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "U54F8NuW3EBr"
      },
      "source": [
        "##### LSTM V2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "DTHSBdO7V0Er",
        "outputId": "c2478cd6-64f4-487d-d362-c5feea0516cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 10645
        }
      },
      "source": [
        "## Run me\n",
        "gateDimensions = [[3], [4], [5], [6], [7], [8,8], [9,9], [10], [11,11], [12,12], [13,13], [14,14], [15,15], [16,16]]\n",
        "registerDimensions = [17, 17, 17]\n",
        "\n",
        "nramController2 = NRAMControllerLSTM2(hidden_dimensions=256, gateControllerDimensions=gateDimensions, registerControllerDimensions=registerDimensions)\n",
        "\n",
        "gates = [zeroModule, oneModule, twoModule, incModule, addModule, subModule, decModule, lessModule, lessOrEqualThenModule, equalModule, minimumModule, maximumModule]\n",
        "\n",
        "loss_history2, model2 = trainModel(inputTrainSet, targetTrainSet, gates, nramController2, readMemory, writeMemory, min_error=1e-1)\n"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9f0fbd630>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9f0fbd6d8>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9f0fbd1d0>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9f0fb9d68>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9f0fb9048>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9f0ffdf28>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9f0ffdf98>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9e4326780>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9e4326ef0>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9e4326630>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9e4326400>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9e4326dd8>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9e4326f98>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9e4326198>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9f0e50b00>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9e42fc6d8>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9e42fc748>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9e42fcfd0>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9e42fceb8>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9e42fc320>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9e42fc0f0>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9e42fc5c0>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9e4475c50>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9e4475b00>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9e44751d0>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.LSTM object at 0x7fe9e4475320>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
            "\n",
            "Epoch: 0  batch: 1  final cost: 181.67044\n",
            "\n",
            "Epoch: 0  batch: 2  final cost: 112.27288\n",
            "\n",
            "Epoch: 0  batch: 3  final cost: 148.14182\n",
            "\n",
            "Epoch: 0  batch: 4  final cost: 125.86653\n",
            "\n",
            "Epoch: 0  batch: 5  final cost: 138.28441\n",
            "\n",
            "Epoch: 0  batch: 6  final cost: 78.243576\n",
            "\n",
            "Epoch: 0  batch: 7  final cost: 126.10696\n",
            "\n",
            "Epoch: 0  batch: 8  final cost: 120.45961\n",
            "\n",
            "Epoch: 0  batch: 9  final cost: 113.21962\n",
            "\n",
            "Epoch: 0  batch: 10  final cost: 123.98554\n",
            "\n",
            "Epoch: 0  batch: 11  final cost: 76.75162\n",
            "\n",
            "Epoch: 0  batch: 12  final cost: 100.299324\n",
            "\n",
            "Epoch: 0  batch: 13  final cost: 61.438736\n",
            "\n",
            "Epoch: 0  batch: 14  final cost: 85.106735\n",
            "\n",
            "Epoch: 0  batch: 15  final cost: 64.70558\n",
            "\n",
            "Epoch: 0  batch: 16  final cost: 46.939922\n",
            "\n",
            "Epoch: 0  batch: 17  final cost: 40.86342\n",
            "\n",
            "Epoch: 0  batch: 18  final cost: 36.025627\n",
            "\n",
            "Epoch: 0  batch: 19  final cost: 39.42536\n",
            "\n",
            "Epoch: 0  batch: 20  final cost: 29.582626\n",
            "\n",
            "Epoch: 0  batch: 21  final cost: 25.532972\n",
            "\n",
            "Epoch: 0  batch: 22  final cost: 23.005919\n",
            "\n",
            "Epoch: 0  batch: 23  final cost: 20.855358\n",
            "\n",
            "Epoch: 0  batch: 24  final cost: 25.419703\n",
            "\n",
            "Epoch: 0  batch: 25  final cost: 10.54617\n",
            "\n",
            "Epoch: 0  batch: 26  final cost: 21.178652\n",
            "\n",
            "Epoch: 0  batch: 27  final cost: 25.108025\n",
            "\n",
            "Epoch: 0  batch: 28  final cost: 10.874855\n",
            "\n",
            "Epoch: 0  batch: 29  final cost: 9.59355\n",
            "\n",
            "Epoch: 0  batch: 30  final cost: 8.574293\n",
            "\n",
            "Epoch: 0  batch: 31  final cost: 11.28627\n",
            "\n",
            "Epoch: 0  batch: 32  final cost: 5.997225\n",
            "\n",
            "Epoch: 0  batch: 33  final cost: 5.4495864\n",
            "\n",
            "Epoch: 0  batch: 34  final cost: 10.1481085\n",
            "\n",
            "Epoch: 0  batch: 35  final cost: 4.3149776\n",
            "\n",
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            "\n",
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            "\n",
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            "\n",
            "Epoch: 0  batch: 41  final cost: 5.2182827\n",
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            "Epoch: 0  batch: 42  final cost: 5.8709993\n",
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            "Epoch: 0  batch: 44  final cost: 3.1125288\n",
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            "\n",
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            "Epoch: 0  batch: 86  final cost: 1.6352663\n",
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            "Epoch: 0  batch: 98  final cost: 1.5048783\n",
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            "Epoch: 0  batch: 100  final cost: 0.873649\n",
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            "\n",
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            "Epoch: 0  batch: 109  final cost: 0.57935256\n",
            "\n",
            "Epoch: 0  batch: 110  final cost: 1.2588358\n",
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            "\n",
            "Epoch: 0  batch: 112  final cost: 0.4562714\n",
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            "Epoch: 0  batch: 113  final cost: 0.42420322\n",
            "\n",
            "Epoch: 0  batch: 114  final cost: 0.8354802\n",
            "\n",
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            "\n",
            "Epoch: 0  batch: 116  final cost: 0.5702238\n",
            "\n",
            "Epoch: 0  batch: 117  final cost: 0.539407\n",
            "\n",
            "Epoch: 0  batch: 118  final cost: 0.5451242\n",
            "\n",
            "Epoch: 0  batch: 119  final cost: 0.6123919\n",
            "\n",
            "Epoch: 0  batch: 120  final cost: 0.8377697\n",
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            "Epoch: 0  batch: 121  final cost: 0.3056149\n",
            "\n",
            "Epoch: 0  batch: 122  final cost: 0.75231755\n",
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            "\n",
            "Epoch: 0  batch: 125  final cost: 0.5025166\n",
            "\n",
            "Epoch: 0  batch: 126  final cost: 0.3994853\n",
            "\n",
            "Epoch: 0  batch: 127  final cost: 0.5681908\n",
            "\n",
            "Epoch: 0  batch: 128  final cost: 0.56577706\n",
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            "\n",
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            "\n",
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            "\n",
            "Epoch: 0  batch: 133  final cost: 0.2660726\n",
            "\n",
            "Epoch: 0  batch: 134  final cost: 0.47000813\n",
            "\n",
            "Epoch: 0  batch: 135  final cost: 0.3461169\n",
            "\n",
            "Epoch: 0  batch: 136  final cost: 0.33381885\n",
            "\n",
            "Epoch: 0  batch: 137  final cost: 0.27427733\n",
            "\n",
            "Epoch: 0  batch: 138  final cost: 0.490196\n",
            "\n",
            "Epoch: 0  batch: 139  final cost: 0.4157676\n",
            "\n",
            "Epoch: 0  batch: 140  final cost: 0.42715928\n",
            "\n",
            "Epoch: 0  batch: 141  final cost: 0.27069637\n",
            "\n",
            "Epoch: 0  batch: 142  final cost: 0.4808327\n",
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            "Epoch: 0  batch: 143  final cost: 0.47659385\n",
            "\n",
            "Epoch: 0  batch: 144  final cost: 0.577816\n",
            "\n",
            "Epoch: 0  batch: 145  final cost: 0.32036564\n",
            "\n",
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            "\n",
            "Epoch: 0  batch: 147  final cost: 0.4584896\n",
            "\n",
            "Epoch: 0  batch: 148  final cost: 0.33432794\n",
            "\n",
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            "\n",
            "Epoch: 0  batch: 150  final cost: 0.46916574\n",
            "\n",
            "Epoch: 0  batch: 151  final cost: 0.81216764\n",
            "\n",
            "Epoch: 0  batch: 152  final cost: 0.8721857\n",
            "\n",
            "Epoch: 0  batch: 153  final cost: 0.464895\n",
            "\n",
            "Epoch: 0  batch: 154  final cost: 0.3501751\n",
            "\n",
            "Epoch: 0  batch: 155  final cost: 0.30275893\n",
            "\n",
            "Epoch: 0  batch: 156  final cost: 0.37878767\n",
            "\n",
            "Epoch: 0  batch: 157  final cost: 0.3539137\n",
            "\n",
            "Epoch: 0  batch: 158  final cost: 0.35535735\n",
            "\n",
            "Epoch: 0  batch: 159  final cost: 0.2036652\n",
            "\n",
            "Epoch: 0  batch: 160  final cost: 0.55503154\n",
            "\n",
            "Epoch: 0  batch: 161  final cost: 0.26302612\n",
            "\n",
            "Epoch: 0  batch: 162  final cost: 0.35082665\n",
            "\n",
            "Epoch: 0  batch: 163  final cost: 0.4804681\n",
            "\n",
            "Epoch: 0  batch: 164  final cost: 0.48281416\n",
            "\n",
            "Epoch: 0  batch: 165  final cost: 0.41019455\n",
            "\n",
            "Epoch: 0  batch: 166  final cost: 0.4165186\n",
            "\n",
            "Epoch: 0  batch: 167  final cost: 0.47120494\n",
            "\n",
            "Epoch: 0  batch: 168  final cost: 0.26595834\n",
            "\n",
            "Epoch: 0  batch: 169  final cost: 0.45976642\n",
            "\n",
            "Epoch: 0  batch: 170  final cost: 0.45540422\n",
            "\n",
            "Epoch: 0  batch: 171  final cost: 0.27663374\n",
            "\n",
            "Epoch: 0  batch: 172  final cost: 0.8532168\n",
            "\n",
            "Epoch: 0  batch: 173  final cost: 0.26190615\n",
            "\n",
            "Epoch: 0  batch: 174  final cost: 0.37152818\n",
            "\n",
            "Epoch: 0  batch: 175  final cost: 0.4447328\n",
            "\n",
            "Epoch: 0  batch: 176  final cost: 0.65927714\n",
            "\n",
            "Epoch: 0  batch: 177  final cost: 0.31995338\n",
            "\n",
            "Epoch: 0  batch: 178  final cost: 0.70923406\n",
            "\n",
            "Epoch: 0  batch: 179  final cost: 0.37397468\n",
            "\n",
            "Epoch: 0  batch: 180  final cost: 0.42474425\n",
            "\n",
            "Epoch: 0  batch: 181  final cost: 0.34298995\n",
            "\n",
            "Epoch: 0  batch: 182  final cost: 0.52613133\n",
            "\n",
            "Epoch: 0  batch: 183  final cost: 0.34668246\n",
            "\n",
            "Epoch: 0  batch: 184  final cost: 0.6136683\n",
            "\n",
            "Epoch: 0  batch: 185  final cost: 0.35817397\n",
            "\n",
            "Epoch: 0  batch: 186  final cost: 0.20571172\n",
            "\n",
            "Epoch: 0  batch: 187  final cost: 0.21200122\n",
            "\n",
            "Epoch: 0  batch: 188  final cost: 0.32298356\n",
            "\n",
            "Epoch: 0  batch: 189  final cost: 0.63809395\n",
            "\n",
            "Epoch: 0  batch: 190  final cost: 0.25954103\n",
            "\n",
            "Epoch: 0  batch: 191  final cost: 0.32556415\n",
            "\n",
            "Epoch: 0  batch: 192  final cost: 0.22175252\n",
            "\n",
            "Epoch: 0  batch: 193  final cost: 0.26801\n",
            "\n",
            "Epoch: 0  batch: 194  final cost: 0.36366612\n",
            "\n",
            "Epoch: 0  batch: 195  final cost: 0.319003\n",
            "\n",
            "Epoch: 0  batch: 196  final cost: 0.36761254\n",
            "\n",
            "Epoch: 0  batch: 197  final cost: 0.36515912\n",
            "\n",
            "Epoch: 0  batch: 198  final cost: 0.3066259\n",
            "\n",
            "Epoch: 0  batch: 199  final cost: 0.5533721\n",
            "\n",
            "Epoch: 0  batch: 200  final cost: 0.24818215\n",
            "\n",
            "Epoch: 0  batch: 201  final cost: 0.30953687\n",
            "\n",
            "Epoch: 0  batch: 202  final cost: 0.1814153\n",
            "\n",
            "Epoch: 0  batch: 203  final cost: 0.28619534\n",
            "\n",
            "Epoch: 0  batch: 204  final cost: 0.29353297\n",
            "\n",
            "Epoch: 0  batch: 205  final cost: 0.2859988\n",
            "\n",
            "Epoch: 0  batch: 206  final cost: 0.34332418\n",
            "\n",
            "Epoch: 0  batch: 207  final cost: 0.3004493\n",
            "\n",
            "Epoch: 0  batch: 208  final cost: 0.33077988\n",
            "\n",
            "Epoch: 0  batch: 209  final cost: 0.2861628\n",
            "\n",
            "Epoch: 0  batch: 210  final cost: 0.24570376\n",
            "\n",
            "Epoch: 0  batch: 211  final cost: 0.6193767\n",
            "\n",
            "Epoch: 0  batch: 212  final cost: 0.4118636\n",
            "\n",
            "Epoch: 0  batch: 213  final cost: 0.32267624\n",
            "\n",
            "Epoch: 0  batch: 214  final cost: 0.27037773\n",
            "\n",
            "Epoch: 0  batch: 215  final cost: 0.21480088\n",
            "\n",
            "Epoch: 0  batch: 216  final cost: 0.1931678\n",
            "\n",
            "Epoch: 0  batch: 217  final cost: 0.31265765\n",
            "\n",
            "Epoch: 0  batch: 218  final cost: 0.16754384\n",
            "\n",
            "Epoch: 0  batch: 219  final cost: 0.3047484\n",
            "\n",
            "Epoch: 0  batch: 220  final cost: 0.17984688\n",
            "\n",
            "Epoch: 0  batch: 221  final cost: 0.21991417\n",
            "\n",
            "Epoch: 0  batch: 222  final cost: 0.21629637\n",
            "\n",
            "Epoch: 0  batch: 223  final cost: 0.24580711\n",
            "\n",
            "Epoch: 0  batch: 224  final cost: 0.29753366\n",
            "\n",
            "Epoch: 0  batch: 225  final cost: 0.28816044\n",
            "\n",
            "Epoch: 0  batch: 226  final cost: 0.17390335\n",
            "\n",
            "Epoch: 0  batch: 227  final cost: 0.443828\n",
            "\n",
            "Epoch: 0  batch: 228  final cost: 0.28826398\n",
            "\n",
            "Epoch: 0  batch: 229  final cost: 0.20235166\n",
            "\n",
            "Epoch: 0  batch: 230  final cost: 0.25163764\n",
            "\n",
            "Epoch: 0  batch: 231  final cost: 0.43287325\n",
            "\n",
            "Epoch: 0  batch: 232  final cost: 0.16317464\n",
            "\n",
            "Epoch: 0  batch: 233  final cost: 0.28316846\n",
            "\n",
            "Epoch: 0  batch: 234  final cost: 0.46451694\n",
            "\n",
            "Epoch: 0  batch: 235  final cost: 0.15054953\n",
            "\n",
            "Epoch: 0  batch: 236  final cost: 0.4263566\n",
            "\n",
            "Epoch: 0  batch: 237  final cost: 0.23149176\n",
            "\n",
            "Epoch: 0  batch: 238  final cost: 0.2323745\n",
            "\n",
            "Epoch: 0  batch: 239  final cost: 0.44532922\n",
            "\n",
            "Epoch: 0  batch: 240  final cost: 0.26741466\n",
            "\n",
            "Epoch: 0  batch: 241  final cost: 0.22746897\n",
            "\n",
            "Epoch: 0  batch: 242  final cost: 0.43993676\n",
            "\n",
            "Epoch: 0  batch: 243  final cost: 0.39884016\n",
            "\n",
            "Epoch: 0  batch: 244  final cost: 0.18881978\n",
            "\n",
            "Epoch: 0  batch: 245  final cost: 0.26052442\n",
            "\n",
            "Epoch: 0  batch: 246  final cost: 0.22494885\n",
            "\n",
            "Epoch: 0  batch: 247  final cost: 0.2184994\n",
            "\n",
            "Epoch: 0  batch: 248  final cost: 0.2521679\n",
            "\n",
            "Epoch: 0  batch: 249  final cost: 0.24736111\n",
            "\n",
            "Epoch: 0  batch: 250  final cost: 0.13570072\n",
            "\n",
            "Epoch: 0  batch: 251  final cost: 0.24692363\n",
            "\n",
            "Epoch: 0  batch: 252  final cost: 0.21856093\n",
            "\n",
            "Epoch: 0  batch: 253  final cost: 0.14435469\n",
            "\n",
            "Epoch: 0  batch: 254  final cost: 0.20594458\n",
            "\n",
            "Epoch: 0  batch: 255  final cost: 0.20087364\n",
            "\n",
            "Epoch: 0  batch: 256  final cost: 0.3720918\n",
            "\n",
            "Epoch: 0  batch: 257  final cost: 0.1471526\n",
            "\n",
            "Epoch: 0  batch: 258  final cost: 0.19449684\n",
            "\n",
            "Epoch: 0  batch: 259  final cost: 0.1660758\n",
            "\n",
            "Epoch: 0  batch: 260  final cost: 0.122719124\n",
            "\n",
            "Epoch: 0  batch: 261  final cost: 0.15874904\n",
            "\n",
            "Epoch: 0  batch: 262  final cost: 0.23319155\n",
            "\n",
            "Epoch: 0  batch: 263  final cost: 0.22407351\n",
            "\n",
            "Epoch: 0  batch: 264  final cost: 0.22601727\n",
            "\n",
            "Epoch: 0  batch: 265  final cost: 0.19203329\n",
            "\n",
            "Epoch: 0  batch: 266  final cost: 0.13470942\n",
            "\n",
            "Epoch: 0  batch: 267  final cost: 0.19691953\n",
            "\n",
            "Epoch: 0  batch: 268  final cost: 0.16806822\n",
            "\n",
            "Epoch: 0  batch: 269  final cost: 0.34007925\n",
            "\n",
            "Epoch: 0  batch: 270  final cost: 0.22001407\n",
            "\n",
            "Epoch: 0  batch: 271  final cost: 0.1800538\n",
            "\n",
            "Epoch: 0  batch: 272  final cost: 0.12720871\n",
            "\n",
            "Epoch: 0  batch: 273  final cost: 0.273764\n",
            "\n",
            "Epoch: 0  batch: 274  final cost: 0.21650046\n",
            "\n",
            "Epoch: 0  batch: 275  final cost: 0.35689464\n",
            "\n",
            "Epoch: 0  batch: 276  final cost: 0.21413527\n",
            "\n",
            "Epoch: 0  batch: 277  final cost: 0.17992872\n",
            "\n",
            "Epoch: 0  batch: 278  final cost: 0.40006274\n",
            "\n",
            "Epoch: 0  batch: 279  final cost: 0.15202342\n",
            "\n",
            "Epoch: 0  batch: 280  final cost: 0.114008054\n",
            "\n",
            "Epoch: 0  batch: 281  final cost: 0.17504655\n",
            "\n",
            "Epoch: 0  batch: 282  final cost: 0.1461001\n",
            "\n",
            "Epoch: 0  batch: 283  final cost: 0.25744244\n",
            "\n",
            "Epoch: 0  batch: 284  final cost: 0.12103854\n",
            "\n",
            "Epoch: 0  batch: 285  final cost: 0.20017375\n",
            "\n",
            "Epoch: 0  batch: 286  final cost: 0.16538513\n",
            "\n",
            "Epoch: 0  batch: 287  final cost: 0.19992304\n",
            "\n",
            "Epoch: 0  batch: 288  final cost: 0.33332857\n",
            "\n",
            "Epoch: 0  batch: 289  final cost: 0.19990091\n",
            "\n",
            "Epoch: 0  batch: 290  final cost: 0.30114943\n",
            "\n",
            "Epoch: 0  batch: 291  final cost: 0.30452162\n",
            "\n",
            "Epoch: 0  batch: 292  final cost: 0.19431487\n",
            "\n",
            "Epoch: 0  batch: 293  final cost: 0.16378136\n",
            "\n",
            "Epoch: 0  batch: 294  final cost: 0.1935891\n",
            "\n",
            "Epoch: 0  batch: 295  final cost: 0.13965115\n",
            "\n",
            "Epoch: 0  batch: 296  final cost: 0.13018273\n",
            "\n",
            "Epoch: 0  batch: 297  final cost: 0.16552755\n",
            "\n",
            "Epoch: 0  batch: 298  final cost: 0.09899085\n",
            "\n",
            "Exit: Epoch: 0  batch: 298  final cost: 0.09899085\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "zzDVr5bL3HPD"
      },
      "source": [
        "##### MLP Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "TNi_n42GWhfl",
        "outputId": "b8be3751-f1b4-4476-96fb-341e1a2e1049",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        }
      },
      "source": [
        "## Run me\n",
        "\n",
        "gateDimensions = [[3], [4], [5], [6], [7], [8,8], [9,9], [10], [11,11], [12,12], [13,13], [14,14], [15,15], [16,16]]\n",
        "registerDimensions = [17, 17, 17]\n",
        "nramController3 = NRAMController(hidden_dimensions=400, gateControllerDimensions=gateDimensions, registerControllerDimensions=registerDimensions);\n",
        "\n",
        "# gates = [zeroModule, oneModule, addModule]\n",
        "\n",
        "gates = [zeroModule, oneModule, twoModule, incModule, addModule, subModule, decModule, lessModule, lessOrEqualThenModule, equalModule, minimumModule, maximumModule]\n",
        "\n",
        "loss_history3, model3 = trainModel(inputTrainSet, targetTrainSet, gates, nramController3, readMemory, writeMemory, min_error=1e-5)\n"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch: 0  batch: 1  final cost: 180.66592\n",
            "\n",
            "Epoch: 0  batch: 2  final cost: 91.30688\n",
            "\n",
            "Epoch: 0  batch: 3  final cost: 87.52073\n",
            "\n",
            "Epoch: 0  batch: 4  final cost: 46.259922\n",
            "\n",
            "Epoch: 0  batch: 5  final cost: 38.799885\n",
            "\n",
            "Epoch: 0  batch: 6  final cost: 10.333138\n",
            "\n",
            "Epoch: 0  batch: 7  final cost: 25.803755\n",
            "\n",
            "Epoch: 0  batch: 8  final cost: 21.753414\n",
            "\n",
            "Epoch: 0  batch: 9  final cost: 15.849615\n",
            "\n",
            "Epoch: 0  batch: 10  final cost: 14.367812\n",
            "\n",
            "Epoch: 0  batch: 11  final cost: 3.7789419\n",
            "\n",
            "Epoch: 0  batch: 12  final cost: 0.7991698\n",
            "\n",
            "Epoch: 0  batch: 13  final cost: 0.07597877\n",
            "\n",
            "Epoch: 0  batch: 14  final cost: 0.02794069\n",
            "\n",
            "Epoch: 0  batch: 15  final cost: 0.02953581\n",
            "\n",
            "Epoch: 0  batch: 16  final cost: 0.0072044716\n",
            "\n",
            "Epoch: 0  batch: 17  final cost: 0.001989522\n",
            "\n",
            "Epoch: 0  batch: 18  final cost: 0.00078110443\n",
            "\n",
            "Epoch: 0  batch: 19  final cost: 0.0003824249\n",
            "\n",
            "Epoch: 0  batch: 20  final cost: 1.3536442e-08\n",
            "\n",
            "Exit: Epoch: 0  batch: 20  final cost: 1.3536442e-08\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "3mkEhLYs3Lhz"
      },
      "source": [
        "### Plots "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "KEASgZnQhfk7",
        "outputId": "54f49f72-a95d-4948-a53b-21a242577d4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.plot(loss_history, label='LSTM V1')\n",
        "plt.plot(loss_history2, label='LSTM V2')\n",
        "plt.plot(loss_history3, label='MLP')\n",
        "\n",
        "plt.legend(loc='upper right')\n",
        "plt.ylabel('loss value')\n",
        "plt.xlabel('batches')\n",
        "plt.title(\"Training Loss\")\n",
        "# plt.ylim(0, 10)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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P5HWpQxFbq9oYYzLZzfVO4CSgUkSqgW8DJ4lIFc4f75uBzwOo6rsicg+wGggBX1TVcKbK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\nwhhjjCcLEMYYYzxZgDDGGOPp/wOGB+W8XxhLPAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "AEX1zI9rT4E9"
      },
      "source": [
        "## Test Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZoxIdVzC0cDl",
        "colab_type": "text"
      },
      "source": [
        "#### Test Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kLBDPqwDlWdP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def testModel(inputSet, targetSet, gates, nramController, readMemory, writeMemory, epochs=2, max_timesteps=5, debug=False):\n",
        "  loss_history = []\n",
        "\n",
        "  for (input_data, target_data) in zip(inputSet, targetSet):\n",
        "        cost = 0\n",
        "        cum_prob_complete = 0\n",
        "        prob_incomplete = 1\n",
        "\n",
        "        registers = tf.concat([registerValueModule(0), registerValueModule(0), registerValueModule(0)], axis=0)\n",
        "        registers = tf.reshape(registers, shape=[1, registers.shape[0], registers.shape[1]])\n",
        "        memory = input_data\n",
        "\n",
        "        for timestep in range(max_timesteps):\n",
        "          register_data = []\n",
        "          registersNumpy = registers.numpy()\n",
        "          for i in range(registersNumpy.shape[1]):\n",
        "            register_data.append(registersNumpy[0][i][0])\n",
        "          register_data = np.array([register_data], dtype=\"float32\")\n",
        "\n",
        "          gateControllerOutput, registerControllerOutput, status = nramController(register_data)\n",
        "          output_registers, memory = runTimeStepNN(gates, registers, memory, readMemory, writeMemory, gateControllerOutput, registerControllerOutput)\n",
        "\n",
        "          if debug:\n",
        "            print(\"\\n\\noutputs\")\n",
        "            print(\"Timestep: \",timestep)\n",
        "\n",
        "            for values in gateControllerOutput:\n",
        "              print(\"\\n Gate Controller vals:\")\n",
        "              for v in values:\n",
        "                print(v.numpy())\n",
        "            \n",
        "            for i in range(len(registerControllerOutput)):\n",
        "              print(\"\\nRegister controller \", i)\n",
        "              print(registerControllerOutput[i].numpy())\n",
        "\n",
        "            print(\"\\nOutput registers\")\n",
        "            print(output_registers.numpy())\n",
        "            \n",
        "            print(\"\\nOutput memory\")\n",
        "            print(memory.numpy())\n",
        "\n",
        "          registers = output_registers\n",
        "          if timestep == max_timesteps:\n",
        "              prob_complete = 1 - cum_prob_complete\n",
        "          else:\n",
        "              prob_complete = status * prob_incomplete\n",
        "\n",
        "          # Update the probability that the computation isn't complete\n",
        "          prob_incomplete *= 1 - status\n",
        "\n",
        "          # Accumulate the probability that a result has been produced.\n",
        "          cum_prob_complete += prob_complete\n",
        "\n",
        "          # Cost for this timestep.\n",
        "          unscaled_cost = 0\n",
        "\n",
        "          # Add epsilon to every log to avoid having inf in costs.\n",
        "          epsilon = tf.constant(1e-35, shape=[output_registers.shape[0], output_registers.shape[2]])\n",
        "\n",
        "          for r in range(memory.shape[0]):\n",
        "              y = target_data[r]\n",
        "              yPredicted = memory[r]\n",
        "              loss = tf.math.reduce_sum((y * tf.math.log(tf.math.add(yPredicted, epsilon))), axis=1)\n",
        "              unscaled_cost += tf.reshape(loss, [loss.shape[0], 1])\n",
        "\n",
        "          scaled_cost = prob_complete * unscaled_cost\n",
        "          cost -= scaled_cost\n",
        "        final_cost = tf.math.reduce_sum(cost)\n",
        "\n",
        "        # store the loss history \n",
        "        loss_history.append(final_cost.numpy())\n",
        "        if debug:\n",
        "          print(\"final cost:\", final_cost.numpy())\n",
        "  return loss_history, memory"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-D7DThMRmnmh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## Test\n",
        "test_loss_history3, outputMemory = testModel(inputTestSet, targetTestSet, gates, model3, readMemory, writeMemory)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PMOYgIBwoXoD",
        "colab_type": "code",
        "outputId": "aecebaa0-c167-41e3-b16e-00c3cce6a89b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "# plt.plot(loss_history, label='LSTM V1')\n",
        "# plt.plot(loss_history2, label='LSTM V2')\n",
        "plt.plot(test_loss_history3, label='MLP')\n",
        "\n",
        "plt.legend(loc='upper right')\n",
        "plt.ylabel('loss value')\n",
        "plt.xlabel('batches')\n",
        "plt.title(\"Testing Loss\")\n",
        "# plt.ylim(0, 10)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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oSuZkjRaiLHPoBnnjOWRGw2dtEkND3YTMBrFPoRNysq4KIN9z+buvKnzYhbrM\nhQH6wx9pVzPRF1m/tpimUNw3IKfrl1V7fUpn99pcYTIf2RLl9K5tYaTd0KH/Lpq7UdinoJgX/AQE\nGyN54NkN+OjP7kS7ZHqvyZndraYgI29cepuPDPvanXIEdsI0+iijKfXgpk2Z+lVjm2MKgyMd/Oau\n5TkTmNt85EIxP0H35/rakXPbByTzkf91MlyPKZkceijvbBgaxYoNQ1G7lklPoSHDBPTj5WnJZjRH\nv50+hQJ7rW14nm4rrCa/j4///C5ctvhZPLpqE8rApIv4aEwmGBMwPTQFF9ZuHsHzm4aN53VjMm0E\nlMlTYJbHVxdlLryqsPYW21zto69e+QB+fPOT2GnGAF4Xh2Dq0B+9iyl0I21abd+Ocw0UWK8pZspi\nqYpm2Q8kXyrupbTz5rOvx4oNw3H7WSk1L/rINpGXga1tH5OOD0PLa8O3L59rykbNmEx4vZzHuh07\nB33lWgDAzz70qsyx7stcqGOwwyz97pH5qNYUqsHKeBJZPzhqPacbTaHIiyvy4ZSp51OMFiQhqWWj\nOHxqH/VyYAuGEPWNzLY+ueWVteiGNpsW4lNOvCvzkac/whp91EXfOtjAbNhwrHT7OZOrv9M9u69s\nSCpz5E9oaHkKIfdGJ96qylxMVAi7eZHa/yPtiphCEZ+ChQTTbVAZiZ+BZsIUyn8gjuaTrSqiKJiB\nB5/bgOc3jtjp13bnOZ6L9q/+zjebmI50s/Ka61JrnoKBvrK1s4wMxsD4yiLv/fj7FLIndkr6FEI2\nl7nokUtBrZLafXNe2GaZQpGPdaTAgCljPvK5xu5otl9bNDw2fTb+18nwickPGRgtu8yVAwzGkefe\nAAA45/gDABRPXutGErNpIX7ak58JyNVG1KdjLFh9CtntshZwk/motE/BsC+vjfHQFMJYUwgCVdgJ\nmSV6JtfKa9uc+UhMfK5oA/3ZFwlJtZt5svsTZ6THy7YKv57SYR5EEk7UZnmpyYbEnMBcOtLDBblr\n8bpyq6Rqr7UbqjJti3frUfqjO/OR37U+yWtJ1FbJOcxklkrNR+XalJGrKXi3Y/gWuyiIZypzoTbX\nG2FjQvkUiOgFRPTX8fYUIppRLVnVQUx8rok441MoYD4qUrjOFGNvb9fGbDwJywEjNR+V1RR8JiVG\nMR+Nf9+qlAZkq6Tm+RR66mhOktf8x1nUTjEafDNerb4Hi9ZQBiZNoZd5CrlMwXNYmdopXSU11rAD\nzXwUMveIEbJxu0rkMgUi+nsAlwD4XrxrLoDfVklUlWh6+BT0Q8UczTYzj6kff9W6iPkoWXjGc6Bz\nPICrrX2Umo9GKmAKqsTs9w501dCAAAAgAElEQVR66VOwRTIV9ikU1hT8Luhopo2UBtP15VQFpV0t\no7snE2RenoIn6+n1Gs1ymYukj7A4gzehY6luWyV8NIWPAHgdgA0AwMyPANipSqKqRODjaNZepsun\nkJ1ozOcZY6NDYWLwYQqW/j1NBi6INgTD9IUuiXtF2jBnSpH3AmrtIxsNOhNw/y5IgNZWTIuP8GEx\nAfnQ43u+7byqJprshFiwI4MNK49W3/FucmmVNh9xXOaCKKMp9SKeYizKk+jwYQrDzDwifhBRE2Pn\nCO85mj7ScBeaQhEzTxEpqsiEVTT6SJxXVFPISwazoYg5zhcm00XG0azRl0le66J/a/Kaa5glphWz\n5F7UrOhtPrKZjLr8qtV3oJn0evDK88al7zeia6qNgEqv0SzKXBhDUnttPhqjadeHKVxPRJ8FMIWI\n3gzgl4jWQZiU8HI0a79HOyH6muZHlVez37W/mPnItj97wOTAdEGc16BufQouqTi913YF0Uf2Mg72\nSdbmHC4Dm2mqqPZhYm4u+DoiO8qEJfdnYirlTSlyuyrz6X5Cy2vBd9wOtzvK775GgE7JMSmij3Tz\nUci9uWfbuK4SPkzhMwBWAbgXwD8AuALA56skqkoE5BGSatAU5JXJlHNzrnXt74lPwdGu74Qkzm8W\nTF7LtmM/lpqPitWS8oc6IQGiIJt8hlma12ksA1tbPmXWbZK7Dzm+BdNsSVBsOKe0UKDR1evImbw2\nfCfhoVGVATQbVNqnENU+EgXxJFoUpriVlblg5hDA9+P/kx7Nho+jWT023A4xta+JtVvULGhTC0Uc\nwj5hi/ntZiHuzd8MFP3t1tHsjOhCOgFWE32Ubuvx4qZz9GOm32X7B9L34sso031m2u1tKBdbz7OF\nrrqEiqLIaArSsaItmtevdrfiS/bgiElT8CRMQ8jR4lQBs/buZHrLj6vxCEnNZQpE9AQMd8XMe1VC\nUcUQmoIrBE0/Mtphq/lIh93MY99X1KGo7s8e6GiRH75tC/PRf139EA7aYzu81lIbyt5OvvYVMlfC\nFEwOOQI5J6Y8c1IR2BLhfKKPVJ9CMXr8natmidPUd1nrnq7xFDWF5bYf/121cRgbh7Jlany7GNLM\nR80GlXY0M8fJa5X5FNLtiVTmYoG0PQDg3QB2qIac6pE4mj2ybwVG2yFaDT8VsFDyWgGJvoimUDSL\nVJzXkO7xM7++F3/+1GFe1+fRGB2L/jJQTfSR4ePRzUfZPAXtdzdMQfudaoH5jFLdV0xTcDma9QJt\n6TXySVl6yk8+Ku1KOz2cIF/z1T8ahTpfumXzUUBAMwi6ylMwlbkIuTdu4Qm58lq8jrL4/zQznwvg\n6DGgrRIIE0kxTcHsaDayiUKaQjxxeLxt2+Ri+g468gzsAd2nAPhltWYmQqeEmdJUjaYgb1skYY1g\nnd6uylxo78cnec3oU8gx7djaMJ1vNRl17VK20WLur1f9iHu1fbu+fQyPpppCVKKiO5MZGddT6I1k\nr2sfYwEf89HB0s8AkeYw6WsmOXMDtEMjnRCthq/5qICmwPZj2eutRzJ7hFTorylEf4Oy9Q2SdvKl\nYtl81GV3avuGPAWC/ty0iTujOXTTv/ZbvAMPCdQiuHv6FOzHbBNKHrMoO/noWpnNV+LVVk77Jvia\ngIZkphBEFU67WY5ThKS67r8s2DY4KoTP5H6WtN0GsBTAeyqhZgwgBqerKJv+UYwWYgq2fu39dGM+\nMmogiVkqt9mYuOjPWDAFRqopNHrLFTJ0kPah5vkQutIULFK629GcPahOLPn9quYFth6zRbGYAyDy\n+zVBl5S70khMz6ZHs+KwlCfTIIrzFEoyhZDRbAYIKPu8xa9umMOE1BSYuZhheYJDvLhRR1ik/uxH\nO4wpLXNIqo5CeQoVZTQXjT5KktekOdpnus7maOT3wcxJhnijYAa1CyYzCsGsQSTn9dKnYHE0uyKy\nTM9LjSbK79dlPvIxS5m2e6EpROYTmc5ibZmfTd41xTWFRtAlU2BOrs/kKRjMg0XRcbzfqmBlCkT0\nCdeFzHx278mpHuLdO1dT036PdkLMHMg+KobpQzS/OdNeH2kyPdfSrmF/kbWf5bblshV6CYsi7ZjA\nyTlI1gAuWlbDBbnnxIxAmqkkYz7S2ujiq7PZ8720JwsfKGo+0k9XncvmbfkSEz1FoDJgNUSzuPnI\nrUUZr/HsQnY0iwqn3ZTOFmUudG0s1Y57o4GOVfKaS1PoqhIqEQ0A+DOA/rifS5j5i9o5/QB+DOAV\nAFYDOJ6Zl3bTbx7EB1Fkic2RdvfmI1dGs59PwZ/ZdAq0K7fR7RztFZMPyXzUU03BvO3KaO6tT0HX\nOmJtzavGVv7kbYMr+sjLj2A0YRV/EusHR7F5OJXAWaOnaIsmEnq18pocktoIqMuQ1Dh5LVCj6uSM\n5m4m8wm18hozf7nLtocBHM7Mm4ioBeBGIrqSmW+Rzvk7AGuZeW8iOgHA1wAc32W/Tghu7qrUqT/6\nkU6Ilq3MhTavlck8rs58lNusQoeiKfhdqrbjMwFK5qOmJ6P1gV/GrnpNxg/Qxddrq6NUNHlN9Y1E\nJRn6m3bTpY0BAur9WKukGselnWYbDvjyNVobbL0vH7jyemzw7UIxH8VLaZYPSc3PU+jKpzBW6oEE\nn9LZA0T0ESL6NhH9QPzPu44jbIp/tuL/+h2+DcCP4u1LALyJytgtCiDVFFxqvXpstBOizzKB5U00\ntvMASGGLVlKkdm2agl0DKZrRXPTJuyRTWx8hpwXxunVs22hJ3h/rjlt95tYm0a4kOvW3X54CCzLT\nfdL2Xx57Hi/9/FVYtHSNtQ3XM7c5l/PusyeJZrr5qCBXMJuP3G3kHReaqWo+IgQBlb5nV55CGhle\n/nmKaUoPea0SPqLa/wHYBcDfALge0XoKG30aJ6IGEd0NYCWAa5n5Vu2U3QEsAwBmbgNYD2C2oZ1T\niGgRES1atWqVT9dWCM7rqtSpP/vRNqPVIBy53y657dsGZrfmI+uH7NAUfIdQ6mguNkn7MsToXEFT\nGpLaU5+CYdJjuJ9tnuO5CDIlM8L8d5tqE2bJ/fqHo7F+6xN2puB7f+q92rTZYsKEC6wz5KKpKQYS\n8qjKI1skoKqOZnQVktoJozyFBmVNlXJpl7IQbTaDYMwczT5MYW9m/gKAzcz8I0SJa6/yaZyZO8x8\nICJGcggRvbwMkcx8HjMvYOYFc+bMKdNEgrKO5r5mgO/+7StwxL47y3QZ2rdJ9HZa/JbjtDEbw77Q\nfU227eivMkf7JK/pNnmPCZA5TT7qpU9B7lr2qbgcsRmfQg8/OvZ4t7L2lOxTUozz+3EljMn3Z6+S\nauiuB88hsqnLbRfVFAz7cl5Q3rzeCqLprpchqcxRG7qzOuT0AXQTwJAIbF0k2BWFD1MQRUbWxZP6\nLBRcZIeZ1wFYCOBI7dDTAPYAknUaZiFyOPccnZCxfnA0YQZOn4L27OXkNVmYdpmEsvvtDERc0+6E\n+N71jymSjH5uhlZX8pqndFbWp6CT5FPSIeTIcQ9EA71XUCZAyafimpiy0Udd9K9nNCfmI/s1qSRp\n1hRSRurSBqRrtfuT7eSX3fMMfnbrU85rTEyqLBhs1YB8YPpe8trIZTzxoJZLtwc9CEkNgth8FMr7\npfdXquUIYvw0g2Cscte8mMJ5RLQ9gC8AuBTAEkQOYSeIaA4RbRdvTwHwZgAPaqddCuAD8fa7APyJ\nK6oPe/m9z+KAL1+DR1dGbg63pqCSIPsUSJouXSahTJsmBqKp67+8Yzm+euWD+PbCR7PnWsh1O5rV\ng8PtDv704AprG0Xl9ozJxPHmZFOZePZVSOZAulaGPjHlhaB2I4npV/pkq5vCVhUmVtCsqE9s8sR6\nzZIV+Oxv7o3blfswtdn9iwlDixbiCaOm4NHnlpG2dYIX41teSyUQmkIXPgVTmYuQWTIhlmo6aQeI\ntOqxKp3twxR+yMxrmfl6Zt6LmXdi5u/lX4ZdASwkosUAbkfkU7iMiE4nomPjcy4AMJuIHgXwCURr\nN1QCsR7CpuE2ALejWR9Tox1ONAVZujV9PLb35socFddsiUv6boxpVM+1aQpZpMxG3f+1Kx/CyRcu\nwh1Pqjbq1HxULE8hm8Xr0BSk7XYPPhZX+2LBlMzElGMuWr52EMvWbCnVf5ZBCk3BQ3tSJMwsE3M9\nJ1fIrS2ixsqEkhDK7l8Mx/9MdHpdb9TC3W2EzHjj16/DxbcvMx4XY1p+Lo0gjhzqwnwkoo/0sSZ+\n6s/zkRUb8fymYc/2o2ubAeGi25bh/25eWorOIvApc/EEEV0F4GIUkOSZeTGAgwz7T5O2hxBVXa0c\nU/uiW904JJiCX55CJ2R0wpQpyJqC6UnYVFhX9qqQUijZbzo3n1aZZhMtS1dvBgCs3ayWHZbtlkVg\ni823nByfk0pqvbSR2iqCukot6P0LSXrpmf71Hm3ORB9TTHKtxY/g83SU0goaEbbVxGyMUmz24q0w\nqw0VfdXG6KOca0Y7IZ7fNIwVG4ac57Wlbz+qklo+JLXDaZ5CNqPZTPebz/kzZgw0ce+X/ia3fUGW\nKOT5XM699QI+08A+AP4A4CMAlhLRt4jo9dWS1XtM6480BVGHfcRznWDBPFrNeMqWBGiz+cjcjmvy\n7qb2kWlvXkazLbeCChqQ9Hv1sZ9HoXpVMAWZDql9bWJa+OBKrNk8Eh/vXb8ZLcRD6jYxDpN07SJT\nD4OUYa8mameUcr++sAVcqPdVDD5mLX0cC+3fmugZ79fNR0G3PgWiOCRVYgpy2QtD00I4zYOgS0Tq\nNXrpiLPAp3T2Fmb+BTMfB+BAADMRhaZOKkzrjzWF2DTj87ECaaRC6lNIEUkDZrOBDnMyjjp5i0Hu\nqqiaJdbebja6xs1Y5I/Mz9Hsd++AFBGF1NHn+g5/cOMTuN0Rn59pX+o78Smw2seWkTY+eOHt+MAP\nbotp6Z4rsPY3pSdLV+Zayc+S7oNx2wZ1IlKP2dYhl88z2f2Lho+amE/Ej7MMzhfG83OaEAJcXq6Q\nXAyzEVdJLZ2nEMZ+iUyegrxdfpwxs/Jd9jKM2wYvtkNEf0VE3wZwB6KFdiZdldRpsfmoiFoPpBpF\nGn2U52i2tOk4V0gDrtdtZzYuDQRYvnYLHnxuQ0RDfFyXsARtqk/BQUzSt7kdE+RIm1STsZ9/+mVL\n8O7v3pxPRNJ+Ctl8ZiqI99CKjXH/3s3b+9UYe9oXK7SYr1Xp0tvxYVquMgg+PgXTTFt0EjMxHz2j\nubCm4LFPJzNlCu77lt9JQIRGQ9UUHlu1CUuf3+xHpzAfZdZTYOvYKAKRMS3o62UYtw0+Gc1LAfwL\ngBsA7M/M72HmX1VNWK8xtV8tFeAj1QJRxA6AZJEd+Z2Ybf9uaVztxyLRm663qsSGdoXWyozXf20h\njjz3Bvxy0TIpyoiM5xdNXisSfZSaWWSfQqHunJBJaUtMUd6vTwp5zsXNw2286LNX4Or7n0v23fnU\nWsX0mE7s+oSQpStDs0aXvE++1tWG03xkseeZ+pC3i74XU3g3a/0UbdPHfKRjNGdcpTlK6QlCU5CZ\nwpvOuh6HfuM6LzrFpJ0tcyFrkeUHusiYFuRNFE1hPjO/g5kvYmY/9jkBMVUrfe2u05NiWNcU5DZM\n0r+XdKbuS81HpPxWz82nVUCYZ+R2/v2SxUk4ro0O1XzkHnx6YpjcjotORvHS3j4w5SlE5iNpf0dj\nCjndP7l6Czoh45xrHwYQSZDHffsvOP2y+zP9MoCf3PIkrrrvOaN/w0WzqilkJX/XpKLet3rMFmGn\nrNesHGHj3jyYmA8zw7YutA+KRPYJpKHO/t9gQOg6JNWUp9BhznzfZdBhVvwVY6Ep+KynsKFyKsYA\nzUaAgVaQ1D1xS7XpQZFIJlLky5uPsvvEN5uYj4RPoYBKbzRLWZaC3DISO7cy5qPob5HxZr4fB1OQ\n7OdCki/r3DNBjiZLmaLKuHRzSlEJbv1gFKRw79PpJyFL+5//7X0AgEf/8y3JcZ+MZltdIqFR6eYZ\n2/n6WGhbnANtZbLOHi/6WkzMp9tXa7o8r0kxBoosXRskIanF6BNI8xQ4w9DLal4ymKH4PCaKprDV\nQPgVAD+pFnA7mououC6tIo3+saOIpmCTxAVDzC6OI5iS/4ArKsnJH0jR9R58YFplTG++iLkLkB3/\n0V+xUlxomlSltlwTtQk2G78wy+jh02xpX2dAVkezYq7K3ktRDc4U3s1c3GmuX6+je/NR9kCDCM2u\noo8Q+xQo895t+lih9kNWVnVr9LCysA3bFFOQ/QrO8SUdGx5VzUdqSGr20sGRjtFMY+rPFi1RyFfh\nkNj1duU68qY2lNJHOfzBGE3lYZJjcCXmI1n6TZPjVE1B//CLmjSE6t4JGcvWbMH5NzyeHNOzWW19\nypDLnGyIQ6VlkoRAokvi8i/5mevP35aLIzML5ui8R1ZslLQeK8lGmPqRQ4/F72IwfQR+dBQRzJqN\nKCS1fOlsjmofBVp9KSlxslvzkZwYNyE0BSL6OBHNpAgXENGdRHRE5ZRVAFlT8PlYgXQibSWOZjl5\nLdvGWdc+jL8++/pM/SLj5K1PjsKnkEOTCrsGol+i+y70/UUczUVMZ1EfKU3tHKZQJp1fnuhkn4Fr\ngi46UclM4cQLbsV/XP6ARLP5Gh/n+5JnN2D+l67BwodWKm9zpG3WFGylO7J5Ix6aAgP/cdkSvPmc\nPyf5G0Wfvz0kVf1dBCZzTp65r53rU8juazUCNLooNheGwnyklqGIzEfZcV40c5o5mhYmVPQRgJNj\nv8IRALYH8LcAzqyUqoowtS/VFEJm/PjmpTj7mocy58mvbVj3KUjHXAPJ9SEL6EzBx3yUTTzLnpu3\n8pqtJIOcF9PNcpw/vfVJvOwLV6lSrCQ1dXLyFMoIbYqmICUxseUcQYsL+iNImAIzNmnJR7YoILeZ\nUj22aOkai6ZgNx+5+hq1PEjFpwDGXx5Ta1AWnR9NiaBySKbopwiMfrUcu/+Iw3xk+xb6GkESfTTc\n7uDYb92YOefvf7wIp/76XjOdLEcfSbRy+hwXPrQKv4hLbxR1aIccrQE90XwKgoqjAPwfM98P9/w1\nYSES2IDopZ32u/vxzT9li8+ZPsw+Q5XUIpOX6dREYtYdzYaT7VJ1dl+eeaajmSN8fQphyNjsSP4T\n+0773f0YHO1o5SYEvayYd4z0leAKcgSM7LNQpLeMpuBuU5+EEpt7yNlcD0M+RLTtYAraoagSZrpz\nJNZS9ZBPX1OVbc0QdfGdLLMsnKdgeJBySKbopwiMJtSca1zmI9u7bjYIjSBAJ2Q8unITFi9fnznn\n2iUrcNFtT2FotIP3nX8LHl6RLicjQkaNeQoSxZ/61WIAxce2yJgWTU8UTeEOIroGEVO4mohmACjp\nqx9fyJqCT6IVkPUp6FVSba3o+/OSzNS2TVKSWZswS1R2iQnISitp/26cfe3D2O+LV2PD0KjZp6Bp\nKKaVvxjZ+7a1UwRyX0n0kdaHft/59fkFzSqTbccmA7Wt7HU6Xdn21d+thiptpo5mvzb027FFH+X5\nHsTh3971NOZ95nJjKXfX9UD0zFRNoRhM5+e9r7Yjo9k2poT5qBNmQ6x13PbEGtz06Gqc/vslyb4O\nM4IgLnOhacZF6LChE0bCmhi7zYlQ5gLROsqfAfBKZt6CaFnND1ZKVUWQ17rVJ4jBkQ6uXRKVlZYP\nDWnJa3nrKQjoH55pQKflHvRzDe1Z+jJqCtIE7EObOFPJaDZc95u7ngYArN8y6pTGOPlt1iZMPoUV\nG4aSCralNAXpGlFtNnrm9gk67wPVzxe/OyEnkUgCygSo1dW3Qz3YbARKO0Ig0SV+m6aQMR9ZmIke\nkqpHKYl2/uvqyLS6aqO7oqfZ0WyPkpJxx5Nr8fS6QcP1/tK+wIhW+2jN5pGkOJ7tXfc1gqj2kcdk\nLc7QrQUURx/pgoFLi/cFMys+j4miKbwGwEPMvI6ITgTweUTLZk46iIkdyE6Mn//tffj7Hy/Ckmc2\nKANYTDC+ZS4EspEu9nMyeQoOCSMjoRr6FqGnVvOMRVPIczQzp3SyQQjVHdymxeIjn0K6Ldp81Rl/\nxFH/fQMAe3kGF2TzkXhn+sTk806UNrX7ScxHsclAhtWn4IrI0g7p9mK/kNR0OxuSatEUNFt/ZlIv\n+PhNzEf359ie9Tu/8xe8/mt/yh4wnu8mTDcfHfyVa/GqM/7o7L/ZiEJSbe9JrUtlvs+GZT2FMn6R\nzPk8AaOPAHwHwBYiOgDAvwF4DMCPK6WqIrSkGF/9/T66KgojHRztKK9ybRyRYfYpOJiCZeKVMdox\nTzompI7afA0kj75VG4fxjasfSs1MGlOyXZtKSuYCYvo+3XYt2mgrzCI9/6l4LYMyte1lp6rwe+if\nZdZ2Hr3Xs959gLFNm0PeaD5SfAp26V25RjvUaqjr8AoHru5TkC+T32HWfOSnKdiCImQhwAVzRrPO\nfOzw9R/kMXGhURU2H1EUkmo6RQhYMk2qYChVWmX1uZpuoqijuROqwlqjMTGYQjteQ+FtAL7FzP8L\nYEa1ZFWDfklTsElVfdqH+fymiCmI0tl6lVQb9HdvmrzFR6+bkYy15HNMQibYpJIv/34JvrXw0STq\nxKwWm2iI/upSUXqN+tvIFJg122u2HTXe2++O5YlpU+IMd0vtITMGWgFetdcOljY1JsfpBKybdm2h\noQ53QObemw3SHM3+IakmBuBb5sLELMUxID8AwZan4GM+sqFQpeAYrlBn27Wt2HwEmCfsTfKCV/Fh\nPQLRlLym52kIFDUftcMQTYkRTBRNYSMRnYooFPVyIgoQ+RUmHRTzUcb+mq6bIA/I1Zsje6p5jWaH\npuAR6SKK7UVmDnetFNuxIiGPOnRmJEsk5oiSlAYfacy02A2zrim4PxzXWtoy5DaThdnZbV6JyhKT\n1WyWMun0fNGOfo3ctOzg9a2xBWTt0uKeMslr2uSj9ykgQn9Pf9t+2Hun6cn4VzUFtvoUfGE2H+l0\nZq9zPRvTobzxPJLkKWS/zZ/f9pTxmlaDEv+QacKWmYLoXy5xz4xkOU69/IiJ2sKRXdKqj8DE8Skc\nD2AYUb7CcwDmAvivSqmqCK2GqvbJEAM7IFJeplg2L3E0w96GDJ/sWV26tJVn0M+11d93XWNCOtlF\nfxUJyHCx2CMn5ij96YzQ4HBlqCuCmeiXn93wqB9TMH3QUQSM/RxGpPXYBGEbY+90TEzBTHMR85Et\nAibrU+DMtktTGGg2cMS+O5ujwqCuLyC3KbrJ09ZMDEm3qZtacDF80/n+5iPGhsF0Ml/6/GYl0VBG\nqxEkJhmTGWyzYWlcAdkXZ9IUdHrffPb1hTWFkU6oaAcTIvooZgQ/BTCLiI4BMMTMk9Kn0Newh6SO\nSANKfm/Pb1R9CkGOicV2LG8odMK0X5emoB/Xz53mGXZrajuQbs6kSsu+D7OmoP5W2hATDHMhTWG4\n4w6HFDDX9FfbN0UfBQ5NwRp9xIY8BenUQSmE072eQrZ90/PQk8N0CfyCG5/A/U9n61YmzEQzb+hm\nvYypM/kbbV27ZAXe8PU/4bYn1uBjF92VDWltmwUIpV3DfbkYvlmIco9nuWzMqk3pspUPPGuv6Sl8\nCoD5XckrpOnCU6phQ6lPJOjXW3tk5abi5qNOqFg4JoSmQETvAXAborWU3wPgViJ6V9WEVQH54cov\n57O/uTeJxY6ktfSYMB/1GaKPbM4kIDtB2ga0XDohid4x+hTMbeln7jC9z0qDDbKvQEBPcIvOSyVN\nn0ququ06lT7VqKQsPTZNQdQHMsEkrWarhmYlfyLKZolrUWF6uYJOmNUUZAYox/W75jH9UDs0G0gy\nmoJyD4yvXLYE7/3+LZnrxP0GsXlDZmoujHY4MY0A0YJHy9YM4j3fuxmX3vMMnt+shqjqmgYA5Xqd\nZgFbLS7b+XkQwQYhM1bFwhyQLqpkQqtByTdoygCXNYWEKYjCiJIwFWiTtS6QCAxrDF5kUlvvqcOa\npjABmAKAzyHKUfgAM78fwCEAvlAtWdXAZj762a1PYXVS90W9ZrTDaDWyLz1txzx8fcMfB2JGJddf\nN/ajqfxpu+o1O0zrz6Ut03Z8nszwjJqCdL7RXKNrChZHsyzVm0xRct/iI7ruoZWY/6VrcOvjakmG\ntB31d1QaQGOmuiQXS/z62hGif1tZDFNIqmx6UDQFw3P854vuwo9vXpq579BiiNZt9vJ7da01LpgJ\nIdUCmVlj+NkOv3LZElxw4xPJkYGWNk3o34iBhuhWsu9fRlFN4eM/vxtLnrFL/YIOZk6EOQBKBrKO\nViNIGLzpPmSfgl6OJhWmKJO3ErL5XermqO9e/xje9q2brPSNdsIJ6VMImHml9Hu1z3VEtAcRLSSi\nJUR0PxF93HDOoUS0nojujv+fVoD2wpCjj2yQ7boiA1p+KbpUaZO6bOGMOgbixX86IadtGU7V7ZUC\n+qmzp6WaQi5PSCbq6K98ayZzrywxmyajjLlFcbyZNQUODWYdxWkcTbC3PRGt17zoybWOG0oh3p38\nrORJ/r3fvwXMqepvug/d0Sz2tw2aguLozvEp/P6eZ3Da7+63aArZ813mI13yVNrrCGafBhGEjGzo\npAG/XLRc+g7UZVcykXsGAUH+jiiOVrvuoZWqFqhJyGdd81CSq2Kj67YnzEIBIJmPQmDLcNq2KPRn\nQrMRJNE9Jh+HYAqNIPU1ilcvm4/0UNHQ8i7lRL2XfO5KPL1uEMvXZpP35HuS55/mGISk5i6yA+Aq\nIroawEXx7+MBXOFxXRvAvzHznXFpjDuI6FpmXqKddwMzH+NPcnn0eTAF2UE2a0oLW0Y6ynX6ZGAL\n+/TWFCSm4OAJVp+CfrJvKY/oUlb+yvfWMZkEElrsYYjKb4N2w1AnkY7mYzju2zdht+2mJL+P/uaN\nePuBu2HPHabGdPlpP9P7m9g41Maz69MPTqbvlsfXYM8dpiIgyoRcymYi037mbJimrP3I5qOs811m\nlCrNnTA0jpP1g6PYMv5DV7EAACAASURBVNJOJmfTIlAmCJOaMB8BwJOrN2cczSbItzdFW7VQZ1Km\nyVT2jzSIcP8zG3DSD2/HPx76Inz6yH0AZBna/0h1yGxD1/X20/UUVJPMJoezuBmkPiXTmBYLU/VJ\n2eZEhLOveSipm2bySYVsnhv+6ad3JtsjnRBhyE6HeztkxcIxIcxHzPzvAM4DMD/+fx4zf9rjumeZ\n+c54eyOABwDs3h253cGXKYhvZtaUKPK2T9YUtPOt9WW0UW0LpxOqeSc0m2SS6y1MQe9HDbu1NqfS\nGt+CyhTs5qHIDmo2Gci486m1+I/Llij2ZTn0VtAvM4U7n1qHyxY/q7Tz27ufQSNQwynzGN6UmDl+\n+CfpR2iKJpInTP08F2PPmI/CrPlIXltXQPaLmHweprsaHO1g39Ouxj3L1kXXSceGnOajVFMQTOzw\ns65X1vtwP8booCxoRO2qfZqc/PK9yOPqcundumzptu/F9Y3IjuZ0GV3C5mF7P40g9SmYxnTql0m3\nCcB3rn8sOYcoa9axZTSb7mekHYKZsWFoFKf++l7FxDTSDtFUzEcTIPoIAJj5V8z8ifj/b4p2QkTz\nABwE4FbD4dcQ0T1EdCUR7We5/hQiWkREi1atWlW0+wSyGmaDbIeeMRBJZvJE61O6GshKCTaNQmgK\nsp0+L3zVFerX78jFsCH1KZj7S/qSzEfDBglVl3g+/at7cf6NT0SOS6nddidMGG2YsXGbcc4fonWS\n88puC0zryyrBNslf9ymId5Wcz9pvZCeBUUVTiBpoNoJMn+u2SEzBQJ/+zoRgAqS2cfkUl6Yg9y1P\nzLIJw1kYMj7Ur/kURrRoI7P5KFTMRwIiax1QzWz6fdjerw9TYGZpGd1AiSDS0ZSYgtEkalinQ9+W\nGYtMv8/nJ/uvFi1dg4tue0qp1NoOWRFKx0JTsJqPiGgjzNoaAWBmnunTARFNB/ArAP/C2fWe7wTw\nAmbeRERHAfgtgBfrbTDzeYi0FSxYsMBT/s2iz4MpyJE1Ql1XmYLfS8kkSlnOU81HsYRqOFm2iyoM\nQjtXLvqXN3Hq5ir51szRPOlfwQCm9TWwOa41tMkSHTTSCdPYdzA6YSTBjXSitmzalgl5C/QITOtv\nZPZl+uFoDQmRra6f5yqgp48D+VwxITWDbDmQtVtGkmNGTUG7rV1nDSRrQ+8Q+4vkNl1MQUySARHk\noS87xe3mo9SGroecZkp5G5lCynD0CXNwpINXnfGHZOwDULQXU8mOpF3He1fNR+m9bxq2R601csxH\niWYK1Ucj33JgiGAzhaSaIJ7dSDvEplijkekY7agZzePqaGbmGcw80/B/RgGG0ELEEH7KzL829LGB\nmTfF21cAaBHRjiXvJRfePoX4bQq12WU+ssG3IucUg6PZVGH11tjRKn4n29rQU8Nu8yfbH970BM68\n8kEAqjRpulS2tQspb7upqWPbJpENj3YUhtIOOVnJzhbJZENH+vBdMGsK6fZuswaSPIWBpspAOglz\nzpqbBPRxMGqIPmo1ggydQlOYMdA0J69p7cr+FXmCErA5momk6CPS3q1DqEiuRzrOBjXGkzEf2TSF\neFuPzHlk5UZsGGpjpVR9dYmUSxCFuKZt/vqfXpvSXtB8NNIJlfpFOpqNHE1BMiUmhSu1tx+QwdfI\nalTdu14x19i/XPRQmI0UptDWHM0TwadQFhSJUhcAeICZz7acs0t8HojokJgee3hBl/DRFOTJUGgK\n/SU0Bd/CdbJPQYx3fZJ8dOUmPL9pGC/eaXpEo+Ojlu/RVYdf4Mu/X4Innt8MQPMpGOhNTUCpJDZT\nMm9YmUI7zOQptBLzUbGqqHrlUhum9GU1BTGh7LXjNIzGz1sUM1PPi/66GHvWp5AeE8+hvxlkorjW\nDUaawoyBFpiBveZMS9voZG0Ou8waSLbFM1eisyyagrwYPUF1pssak488q2sj8uS5bM2WpDy1DFnr\n0T+ZB5/Nhoh+6pLFyXaUJ5Eeayi0+zCF1HzkCtkFIhu9eP8uTaETpr4v/X6IKDNZ6+aj6f1mo4wQ\nrkZsTEFzNI+FpuATfVQWr0NUL+leIro73vdZAHsCADN/F8C7APwjEbUBDAI4gYtWzioAb0dzKMxH\njcx1vqtU+kYf9QtNQepXH/jn3/AE+poBDn/ZTnhk5SZFVNSblRmYbeF2Af37ku9NmLMyyXpQoztm\nTUmH0EaL+Wi4ndqXGazYSYtWRO2EjPVbRvHt67Mr5snob5rMR1Fffc0A7S0hQmaj5pdoCppkrjIF\ne0jqBTc+nvQzOKJOqGs3S5oCoudww6cOw1/910JFuhaQQ4xNDMAmBTcCkkq3aImJFtu4DKL0vjNM\nQRpXb/j6QuP1sjlUZ7oPPmfPGwAi6Vh+1vJE6PYppAKDLQdCd/43JNOPWVOI9rVDTrb1OcAoWGjm\nIytTEBpNO0yipEYkYS4TkjpRHM1lwMw3MjMx83xmPjD+fwUzfzdmCGDmbzHzfsx8ADO/mpn/UhU9\ngB9T6Egv08gUPPvyKZ0NIDFdhJJ6qpscrl7yHN46fzfsMjOSGg84/Rp86pJ7ooPaubJT0BQVotCo\n2YgCAr741n0Tk5ZOc7oCWTqYhSN0xkATm0c6xo92uN1J2hIfrrCTCnOSL9oh47//+Ai+d/3jzvP6\nmtk3Je63rxkkkqyJyesZzcl+2Y6sTQLCTr/brIHkHvuaQWYcrIv9A9P6m0l7e+wwFTtM6zP6FBoB\n4T/e/nIA6TOXz9FNO8l1RFbzkfy8bUyFKO1Hj3DKk75FH6Ib3Xz0jGFRHRmjHZU5ykzhfwzL58rX\nAdmQVBl6sInsUxh2aApAOlmbzEf6PX79qoeS3BpAXQpYRloJN133W06ia3dYYQQTpXT2VgOf6CM5\nfFKYj5QVyeLNPFNU1qdp8Sn0paGWJvMRM2PD4Ch2nTWgDMVfLFoe9aM1K9+jK+Qv6kf9TUT44Ote\niI8c9qIMHXJfYZhGHwmmICRzfUF7AEnInYnOyKfg72juhGFSpNAF07sWPLKvEaDdiQIKTDHmtpBU\nWauxhbHOGEjNaX2NIKMJbRHhhqzmOzRic4/+nJoB4a3zdwMgmY88HM2NgCTHqBp2K3dhGyOE1BGu\nM4E8DRRQAzZkBjp7Wl/u+xvVmKOvHV01H5lp1MeFUubCUMPJlWwnEARkTCqT8yOmD9g0hdTMtXkk\naz4a6YRKIMSk9ilMRHhpCmE6gQtNQR4YQkrYfpq7eng2o9l8nuxo1pOmTv31vfjZbU8h5EgSN5Xa\nsE22ALB2iz3qAshqM6J1EQudceYKpiX5FNJcjuhqU30ikzNU0Kknr5kgEtcA4LFVm50Zqnr7MsQE\n3dcM4lpT5sQj3dGs1z6K9qnXiA9Z1tT6W43MexfnRW2n5qtmECiCgUAjCJI2xQQin2JzNDcbQVKT\niJDVbFpJZVD7s7cd8WUKogG5651nDuQzhbaqKdhKzGTpkjVZi69Fm7yDII3MGjEUXzRmqpvMRzl2\n5emGaDggfX+jHXP0UbsToiVrCpPcpzDh4OVoljSFKQamIN7JTjMGsGKDfXD7LhIv5ynoTOEiqQb8\njIGWM8tYwOceBcKQsf3UVsI8gkRqjY5f99Aq/M1+OyfSrOws1pmCiCYyOZuHR7OZuoJBm+r565CT\np+7wLHNhYgptyXw0GsY+hQLmI5ctXi5TLdBviD4SJgjBAMT3HojF4zVaGkH6TsWk5KMpBCQ5mg2T\nlvA52CZ41xxX1Hwk973zzH7cvnSL5aoIox1Vs9RNM67rgGhMWZmlZpOPwob98hREmzo1gSF5TYfJ\nxyX3OdxOHc1ijIjgE6X2ka9TswvUmoKGUFLhTfVzxNey88yBzLV6OzJsjmbBFE799b2JBGyK/Jkx\n0DRGPtkmWx90Qsb2kiNTfLzi74d/cgd+e/fTmb5EZcdGQImJTQxck7M58imw4gQXmkXI+aUrTJFE\neWgZ1Pmr718R9x0kUVDimcpque5oBqKJRrbfyxU+t5vaSiRTWVMQGokM4XsQ40xonkJT0IUHER3T\n1wiURWQE7JOf3dEMIJE+bRFqy9ZssUaTjSQOXfO1RCIkVfQvM4UBxawyta+Bk1/3Qq19VYjwlY7l\nkFQbs9THRSNIi9mZnoXqf4na1L/DKA/ETaNNk1A1hZgpaCvuyeYjX62pG2xbTMEneS12NBOlph15\nYIhXMmOgmakJo7QjXXPi+bfitN/dZzxPMIX7n9mAW+IKoKZJcvpA0yi96Wf6+E0SGlm13Yr25Ynm\n5sfSCGFxqiiI198MEvpFv6Y6M8OxOUC2t4vzh0Y7+MplejksFa7nbIPrOQitZqQTJpOlslSrpikw\ngHOufRin/e7+5Bw5qmiHqX3JM5Mlwv5mVlMQH7ooAyGeeeRTyE7wgln1NwOs2TSCr175QK6vSLQn\nO5r1iSxZWMbiz3GZHmXp1oRWEChOc1k414WpViPAzjP7lX2jHbVEhD9TSM18drOa2lYzCBL6XNFH\ngOzo18yuBkezDts9yD4F4Y8bHO3g0nueSYSA1hhEHMnYtsxHXiGpsQMQqYQqS/3i3Q+0Gpg1pWWN\n/pDn9Rsffd7an1yWWEhmJqYwc6CJ57SBNzTaMfgUonOm9jWwZSTP0czKpCCej6ixAwD3SYu3iAlO\nfHT9zSBxlIvJyzSZiCJgL9xxWmJPFpP2Rbcty618qtfe8YGLKfQ3hJQcJhJcXzNIMrPFI5HNeRfd\nvkxpQ5ait5vawrPro1h9XVPI+hTiZ5hEP6WaimnlNTGZ9LcCXLwookEPczVBOK6ByA+mS6rNHE3B\nBcFsXE7uUDKFNTRNQUarQUptH9F+GU1BQI6O02GKPhKysav2UXRcLJ+rPrO+RpBLYzMgXPjBV+Kk\nH96u7BcmwdFO6mj+wY1PYNNwG5876mUxzdVrBzK2KU3Bq/ZRHDVBRBZNIXpBA61AqUuj48M/uQP3\nSjVMbJBt0KboEoEZA62MLdPkcBUT+w6SWciGThjZ8487eHdc/rHXJx/shw99EV6803Sc+Oo98fCK\njamDMyYrDKPB3N9spCG18cFP/vIea38zpAgMMdAvsqydK2MgR1P41JEvzexzfUhiHIy0w4TJy2ND\nX0+hE2Zt77JDfWpfM3lG8vs0mY9GEk1BzZNIoo803U8wW1nLdZW2kK+TNQV96It2TUtQ5uG71z+G\nB5/bYC353GxQ7FOIzUfShDlnhqoVNIJs4pcrT8EH7ApJ1aTu3bebkjwb07Kn8vsTmoTuA5va38w3\nHwWEQ1+6U2a/nKcgfApC2166Okoq1Zlm1djGmALhHQftjuMX7GE9RziaAzI7mmVNYaolokDgG9c8\nlEuTyV5ucrzOGGhmpL01m0eMUgvgJ12HzBjtMPqbDey326xk/8F7bo9rP/FXeNmuM9EOWSniJq4b\nbnfQ3wowYHhGNuwqZecWMXO57uXM4/bH+171gsx+V/uCcT69bhDbx2U6ZAk/KbonJRPq70QOeWw1\n0qUuFU2hkTrTBWS7N5COp2ZAxuijINEU0meQ55gX16VMwexolukpgnVbRnHkuTfgmP+50Xg8yaY2\nOJp1U2AzCDImnYxPoaBz1RSSKuZs0deeO0zFPacdgV1mDST06eaj9//gNlx533PJ72GL2WxqX8NL\nUzBBCAkjkk9BQGijRYJHeoFtiikQEc45/kC8du/Z1nOEx59Aadik9KUKyaq/GWRq5ujwUcwzq1pB\nzaoWmN7fzIQ9bBnpZMwNwl7ukxDWibM0bVK1uL9sBcvUfPSKF2yP3bebgk8ekZXWdcyfu11KZ4GB\n7vIpEEWmNSCS+mQabRBMYcWGYey/e8QM5Q9PzJNJ8b3QXfNeluSU6KNWdvyMSpqCMFMCcp6C1rbk\nUxCQyzlbaZLzFJD1KYh3XsZ8lIeG8CkgS6decbXZMGgKHfYOSTVHj0XZ3/JELcabeFeNgDBraivZ\nBrKF/m54RDX7Cmagr57mwxTyQlYHRzoZRiaYxFgsrCNjm2IKAq4XyByHXlL6QSrr78YDY6DVyAxw\nvV2fih0mxqKswoZo4E/ry2oKeuYnkA5+H2lSmI9sz0OYbXTJSNhs+5sNzBxo4abPHI5D9toht799\nd0vrKBZiClJxO7lWECDWWCbc+tk34dvvOzjZ77p/2be0/9yYKUjvQXc0tw3mIxkyU1U1hWxmuEiQ\nCuN8mCDxKaQJdTIaRqYQ0eIyK8iOZtOaEaJd3wq1Rfw6jSAK5WSDpqBLvc2AMmGio1GykHKODaZj\nQmiRaRbvvBWfL1/lqzWJhM3Nmk9nal+++SjvuMkXJyL5inwrvcA2yRRcXLsTcQUQIlshAMzdPk2e\nElLzgEFT0Ae8z3oG/QYpWF9wZ3pflLimU6075GQafGzFITNGw9A66MREpGsKIiRVnqh8IiT2njM9\npdNQhsIG+ePed1e1QK94lzvPHFBMcabF5JO+pft96c4zAAD/9a75mRBk2dHsepXypKb7FOR2gFQa\nFclzavSRSVOI2pCjmgTDazkmGsXRTNkxX0R4AOy1e0xoBkG85nhKi0BGUzCYjx58dgNuW5qWiHB9\nr6ZaQCamIMZqMwmFzvos8vIvhHAkVmMTmNbXyNUE8pjCui1Z/6AwH9WO5jGA6/2IQlZEkTniO+87\nGN884cDkeMIUWo2M6Ucf8D5CmMl8pGf5islOH/966B6QTrajIeeuSS1s5TZJzK4pRKWz5fv1Gbiy\neWdKy3+Skc1HutNZJl0p+uapKQjn98t3n4Ufn3xIdK2kIUR/7UXnvv/+BcqkpkcfAeoE1JYKrDGk\nPIUGGRmZ4F+qzyNmCo732wgIowpTyB4HzEtpmuDLFMSCM0pBPGnC1JO4IvOReh/f1OobueZb09h9\neEW0NsO82dOk82JNQfh5ZJrJ71mI72DDoCrVT+lr5JafyGMKpqCRlCkEOHr+rqWi8MpgmwpJFXCV\nvxZJRWIgv2X/XZXjw5L5SJ+gIilRXlkrXwozSTqdUF2NTEwuuq7QNmgKLUlTmNbfxHDbXhIiDCPm\nYxvQYiIy+RSe2zCEg/bcPtmXN+iXnnk0AOC7J74CKzYMYc/ZU/GDm55wXiMgawA681HrUqXbo6GD\nKcg+AOkdCtu1vq6Fjbkfd9DuePO+O+OPD6xI9smMWGwrPoV2qoXIBfkaAeGup9bhrqfScOBof6C0\nBQB/fHAlAHfFTKUgnikktaimYKndo6MV+wh+c9fTybMVQ4Mo+/7klc9scDGFmVNa2GjIjdlxej/e\nftDuyTokog+55pZA4KkpjMQRTRuGdJ+Ch/koR5MwmY9Wb47Ct5uNAP/7/w7OHK8K26Sm4HpBISN2\nNJshJkg5cUtAl/o9fL2ZUEHAnj+gk22SbMS57Q4bVx9Tr48ItNmmhSlEL0O8fssolq8dxD67zEj2\nEZFXlMSRL98FH3jtvFwnvQzV0aw+BPmZyEdcRfZkTUF+h2Jc6CXMXZoCoDoC5fb6E00hvSZxNMcM\nPWEKljGZhKQatII+h3bWCEhZzyCbp1DMp+CrKbSkmH0RbiwmXZP/oNkIcrXMrOE0xeePfhn+4a/2\nyuzfZVa/8j3KUV6AmhXe9GQKQiDUI+3kSqs25PsUssKbcDzX5qMxgMv8Ley6Nm1CvKiBViNjntGZ\nRO7i8i2zLVL3KQiVW6ep3clGKSWaQsjG1cdkiFhuW3SD0BR+sWgZ5n3m8mS/qIf/sl1nKOcXiZIo\nEnouq82mWvambV9Hs/yxiu2khLn4qzUlJsh0spOZTLZt+R2NSCGpsvnIZrpIHc1ZJuoyH8nvIqr3\nb27XN/rIN6u8v5lN5BIMz7SWsWmfDtd8O2OghY8dnlnBNyoPYhgbiflIum3XcpxAOl5sCXFA/tjP\nZQqx+chkTq4dzWMAp/mIha3XjERTaAUZJ7Gec5CnKbxop2lWR5nsU0jNRyqc0UdhmKHnzOP2x47T\n5UVb3Gn0Qpq/ZskKZf8D8dKJL91FdfrapF3TUoSutXZ1yPeh92BjCq7JzvaRiesTB7OFRpG02Egm\nGrPNPGEyhjyFJCQ1vtRWZ0i0YdLCXHZsvdy7PuaLJq/p/jIdgj5ZU9BpaQZBhuZWg3InPZcUbtKC\nor5UZqPnKZgczbYxIzLgbavc5dEo92HDmlhT2GFqNum0ZgpjANcLTKqkWk4RIalTDI5m3SRiyjeQ\n8aZ9djZqLe2MphAY6V43OIqzr31Y2Sc+ztFOVlM44ZA9lTLUQvKxDVjbRCAG8Gwta9p0p6e8cS98\n490HZPb7lGoQkKVU/RnIpMuHfM1HMlJNIfpty/UQzunUfJT1I8i0hgpTkENS03NMNaPkPkw0uyYL\nefIlGMxHYpz42DiRju3X7DXbmMkvGGNfMzvxi64Dyi4S0wjyS0S4jkYML7u/GZAipAitzqQp5JmP\nEt+aQ1PoNiRVJIhuZ2AKY+VgFtgmHc0un4KYEKxVDeXoo2a+pmCTNn9+yqtxyLwdsGJjdn1beT1Y\nIJ1odJLOMmRMy5OHaTDJmokwH+Ulr+kQE3pedFPUn7ltPdbbhakSc9OfgfxbTnLydTTLELt1R3OW\nHuFAVScaQNUUEqYQRquN7TprIFlVKylzkWgK5gJ0zSDbh4DYF1BWK5UnIX2RHbndopqCLcy61QyA\nkY5RU5CZZ0ZTCCizBrFus3cJ4dH609n9zYa6RGagaXXGkFTLsxDvVGYaRGbGYoNvVrZpnZYyBSG7\nwTaqKdiPiXUNbO9QLKQ+Y6CZ62jeMtzG3/1okbGdQ+btEC3wYehopB0qkm6qKajnmdRdMcDnzZ6K\nr7z95XjDi3dUjsuaidAUbI5mm6YwONJBXzNwmuEEbNKsT/0eAcVhqB2TafANSbUxs3QSVx3NOsTS\niuLW5HwB1acQ/b3rqbV47Zl/wi8WLVN9ClLzeeajliGvQ0xwpvenryuujzMxiYly4nkQE6PN6ife\nc18jOy7EPTQMjubIp+AObXaNs4DMjuhGEKiaAqn3IWuqeaXZTeNFd7znlbT2WUaz1SCjH3CsNYVt\nkinkh6TaVdZzjz8Q33nfwZi7/dTMYNEH/CMrN+HPD6+y0BD9NamVw+0QI9LSgOkH7idt/OxDr8Iv\nPvwa7DxzAOccf6ByTO5P+BSs5iPL5Dk42vHSEgC7FnLsAbs5a1CpbcgTnNlerW87k9cstIv31w7d\nmoKQ3MRkppS5aMlO8YgeETd/6+Nr1FW1wrRKq1zRVr5Fp09BlGyQLvjRyYfg6++cjz0kM2EQZ32r\n1/qNJYFXztsBC16wPU57677JvhkDTbzz4LkKDa14/WsZciXYrE9B1R6K2s9NWhAQMeqGQVOYH2ew\ny2GsU1oNdy6E4VnN0JhCbulsDwFqWn/TODbLrCfSDbZJ85FbU0BSJdWE7ab2JbkLuqZQZP1UeW1e\nE+SsSSHd+Db/2r1T7UAfjPLEmdRrt0wQRIS+ZpCxtYZsX0lKhzXctdXA1941PykH7YLp405/y/Sm\n267oI6ujOd6drLxmEYuF5JZoCoqjWdIU9LIkcV2t/maA4XaIdseskQZESd96fL0MwSjkNv7qJXMA\nRBFjApFPQb3WleNgwoyBJi75x9cCSKPqznjH/nh8VVTJM1nVrkGZcvKi70ZAGYm62aBMSK9NawKA\no/bfBVfcmxapC8gs5Ol9iXNevNOMzLlEkYRu8+uYTMnTB5r44TteiZlT1PpJNvhUeh1oNoxMoUj4\ndi9QmaZARHsQ0UIiWkJE9xPRxw3nEBF9k4geJaLFRDQmGRquFzQ42sFfHl3tJZPr5iIf6evf/+al\nipRhUztlSUZMOvLg9y0nrLevagpxSKpjghiwSNW+mkIeo7zuk4fmtiEmxOMX7IGPvWlvvPPguVjw\ngihxTvEpSD92nK6WaJaR52j+1K8WY9Nw21r5VdRiakgSsIDiU9C6kf1RQKSRiCt1xzCS/YGV5mRc\nGGjce6e0pAhRdhwUXQDeyJSaaYkKYYrsawYZhmx6TskxzaTkKkcftaHSQWS+/2ZD1SDk7dfsNRuH\n76OWsXaZaExMZ8ZAC4ftsxNeEY9D+bv66nH746p/eYNyvs/32tcMjKbisVhtTUaVmkIbwL8x851E\nNAPAHUR0LTPLy2y9BcCL4/+vAvCd+G+lcJmPvnv9Y97t6JpCw0P6+tAbXoiPHLZ3eo2Flk2StJQu\nkxn93mFaH7aM2CctGfqHKPcnPmRXckx/qwEYJDeTv8GUl5G3sNG8Hac5jwPRByUyogHgrPccgA/+\n8La4z/Q8+S4+deRLMaUvwP8uzL5Pq6NZejbPbxy2MwWRqZvY+815CrqEmRZTDLB+UF0O9Op/fSPe\ndNb10X0YzEemSdlVEO9FUp0pV0E8X5hqVfVJOQnJ0pGGdanFPZomN92klM8U1Daigoim83SfQrQd\nMuOiU16dOX9afxPYaF5z3fSkdJ+C3NdLdp6BXWdNsR63ob8Z4OW7zwKwzCvxtSpUpikw87PMfGe8\nvRHAAwB21057G4Afc4RbAGxHRLuiYvSK8b5YksYAv8xDW117HbIqKz4qcen2U1veS/TZIkEAOSTV\n3pZNI/A2H5VYSvCAPbZTftskTEDNLpWf7UCrgeMX7Gls38ao5ElrcLRjdTQLhpjY0aV7lNvOMIVY\nUxBMZVRaDvRFc6bjjbHpR3acpovsZJ+BrEHK+SdAdnLVaclbuEiHqPgqo1/yB4hnsPOMgcxzE7zL\n9B4DUs1HeZE207TJmBDdv6l8hhJ9FKRMwQSXpmAawnrZj4ZiAgsygoePo7mvGeDN++6ce17VGBNH\nMxHNA3AQgFu1Q7sDkI3Ky5FlHCCiU4hoEREtWrXK7LgtAttEXJRZ7DRzAI+fcVTy22cC1LuwMgVF\nU4ivjT/s7af2ObNZZWSqY0rX5WU0A/bJw2U++uFJr0y2y9SCv/iUV2PR5/86+W36oEw5ANlY/PT3\nl4/dL9m2aQryvW4ebmNotGOUXMW9i7BXJXtYep/i3YoaWHLdLED4PdLzkygmT00h6ZOA6/79MNwh\nPTMZpgSvos5LlcsesAAAG31JREFUU/RTXzPNGn7d3jvi9Lfth9Peuq+xDET0N3sPROp3o4+rTx7x\nEuX3vx/5Upz8uhcmv01hwaJPU/KaTQJ3Zf/vIi0hKhiby9Hcb/AN+GgKfc0AO80YwLnHH4ivv3N+\n7vlVoXKmQETTAfwKwL8w84a8801g5vOYeQEzL5gzZ07XNNlyEHzWcM60JQ08nwkwoynoH2s8Ycg+\nBXGNsElvP63P2yasM51+6ePJy2gGXJqC/Rr5A/XRni784CsVH8BAq6H8dmsK6T7SSJKvO/6VaaST\neM96u9P7mzjn+CjRbvNIB5uG2pnlI4FUS0qZqtm5LJoXppWsTyE0mopkqlxMQYQk7zCtD9P7m5ht\n8aNE5iPzOPNFnk+BCHj/a+ZhWn8z41OQo4+yyDqaBV644zR8VCthMXOghc8etY/UdkqLjMinkDUf\n2UrPuOqEyebml8eLMmVDUtPtgZYhqzs+fuZx+1v7EcLK2w/aHfP3mGU9r2pUyhSIqIWIIfyUmX9t\nOOVpAHJc4tx4X6WwMe2iy/7p8Jmo9S50O6sYnPLqTmKAiUqK209teYfuBQS8/cDd8NMPRa6aPkVT\ncIekAg5NwbBffG5KKWkPM9OhL90JXzp2X+txE32mEhK2rF29DUGfqd194tIdm4fb2Djcxk4GppDU\nwkmYalY7kOkR0Vtp2fU09FWmIE1Gy5qPTJqhSDzLW4/blPVbNPbdpF3JPgV5rtU1hf7kvsy0qY76\ntB+9neMO2j1uJ/uM9e+hqWVKC/ptmsJUR8E/ZsY333sQ9t5pelLvSzcfqdqOPWn0hEP2xBnvMDMG\n+dsc64Q1GZU5milirxcAeICZz7acdimAjxLRzxE5mNcz87NV0SRg0xR8a8vb4McU3OdM62/i+U0j\nivlIXCIqKW4/rc9bqyEinHvCQclv+bqRnIxmQY8JvprCdlPdjkMBF0M2aTKpppC+M5cztWGaaA0T\nnZAANw+3HZpCzBTaqXNVb1vuX1/bVzYfyWMx0RQM2oPJpyBs9zamMNAKMDQaxqWz1WOFmYKxSms2\nQxnIhvKKZS9NBeUIWpkQw7oRAPDYGUcpJbgFbHkcjUBNDD37+ANw/g1PJNFCOqY5nkfIUV7NsQfs\nhi9dej8Ag6Yg0WQqaicft5XUl5nJeDKFKjWF1wH4WwCHE9Hd8f+jiOjDRPTh+JwrADwO4FEA3wfw\nTxXSk8AmGXe7Xq0rGsQXwra5yWA+euOLI9PZW+fvVjikUECezIdyMpoBYDtLNIiLKcgf6Oxp9tBQ\nGS5txRS1kiwn2bFrCi2LrV84ck0TvpgsNwy1MTjawRyDSUbc+0jy/GQtJOtoFhqFSFCTHc0yyU1D\niKmr9pFo1/aMt4/r6ASUdZZOyamgq8NmPhJ+Anmi0yX87aZEdJjyALKagrTCnMTwGwFJARfp+SLK\nKmM+CtTKsLvOmoIvHLOvdZxNdTwPU0mMmQPqdyHTZNIU5OO2rHD5mxrrhDUZlWkKzHwjclJwOTLw\nfaQqGmyoKuy3FwtsC/OR6lOI/r5891lJaGbZyomqphBPao4HYpP0XdEr8nP4/+2de5wVxZXHf+c+\nmRfMC2FwYIYBBAbkIQwwQCIaeYqKK/FNRNyoWXfjrq6uGN/5mDXZfJLdjWtcoqxmoyZxYzQaXT9G\nE02MKxA0ihAiER8xCIgKqIg8av/oqr7Vfat7+vZ9zvT5fj7zmXu7+3ZXV1fXqXPq1DmmWC4m1MvW\nk0uifbxhotmtbHhN/A+oTuKrS8Zn+aoDGc1oxx4rJpVRU0g65xS8F8OR47jdMmPXqCNq8RiUS6pe\n3uxOzzSn8MqN83HHr7dCQGDN6++hsdasKTRUp7Bt9yf45MDhrNFrkJHo3M5B+NXmHThwSJg1hUTM\nmA/EHU9pQFVG+zKhtz+9vQVxuVblcjfhRDzmaREw4TenoJdDPQ+/pEM9zaN53ZVXno9SUxLvo0oj\nSMyeMPQ0endHFTWhOqUPtQBppsYdNvGGybXQT5h5ddJBzUcNhqiPJlQH2hDU3GSYaPZKJLN4QraX\n87IZbY70oAqVD2C7FAoNhmfmNh95PXe1WTdLNlQnMVLmhT54WLgEgFyh7LgHa5tepzXpBC49YZQd\nWdOrXak4XXv3H8hq80HMR6dMGoIBcpRvam+peEZT0MnSFGQb+Gh/drwrgtNL6KLPjsDMEU3G8+jM\n6xzkiL6rnr0dfC/Wc54GHT+3bFPuBb+kQ+66ntbe6H1CjZTDQaN8XXNEw1wUSyg4H+Swxmq8+d7H\n9vdBmmubiapk3Gg+Mgkxd6P5xtIJ2PiXnp27TCM+P1dab6Hg3amEadxq5G8KHWzCnToTMORaiBHW\nfuWEwNoHoEIexPGOFApu10PAIBTkPboTxcdd5iMAmNLW4Ll62e54DTZzUz2umDUca7a+hyWTs7y4\nAVht4nvPvIbpw5vwxq6PHPuCmCesjlWWTWsjk4Y14Jk/7nTELdL7Ofc6BTWnYJqzsybBMzdclYpj\n5cKxOOnW33iuEwGAVV+Y6viuC4NDh0WgbGg67nUeOk7zkfU/SHrSLTcvlAmOnOVw31UiRjh4WGQt\nCJ05oglLJpmfbTGJpFDI18vIC/eI+5krj3NkLOsc0t/9Ewc1aSvvM5FznYKpX3Vf6/SAweX8wiWY\n8OqkzSua5fkMPu09oUxZQSemT5k4BPc+/ya6OzKjMFMnYDL/9ERNOoEde6zVraaXX9mxVZwhJQSV\n8Flz9eeQSsSwaZuVoU6fYG1tqM4Ka63wc0k1ef8Ma6rGo5d+Jmu7ork2jZWLxgLIrpsgmoKKNJp0\nhaG+7Zxj8Or2vahJJ4ym2Ow5Be9namopqm0HMR/Z58lTUzijayi6O5qw/L/W4u0P9jn2OYSCvI5p\nsODGa67OrShUJePYu/9g1jO+94vZK69LQSSFQqFlwswRTaivTvqOuE+c0IKbThnnuR8Azp81HGNb\n6vDYhm2OOYWRrpXTQGHmFBR+L09/jxGRn/kozCrmD6S9Xe9ALjluBH663uyhPL2jyRH6Aijcc61J\nJ/AX2TGYzARDG6vx4nVzbSGgrqu+HyE1woz3UcZs0lCdcgxKnJqCtI8bFl2FEbQ6YYSCWhXsbmu1\n6QQmD1NePD17H/lpf2YtWDoR5CAUVD3ZIWFi5rD0XqQTcYwalB0sD7DyYSiUmSmIpuCFe61EWgqF\nQjiqFIJICoVCB5hSEv1Ha9/0POb8me2+Hg4A7JhIVck4dsmcrauWTTEufQ8rFNKG3/mdy9slNZj5\nKCiqI9Bj9lwxfwyumD/G6ydZFGquqCYVtz2F6jxefr2j2yMFWnZoCeu/rik01qYcK7T1pmjSFNQ9\n5WtjdsvpIBOZygQT5Np6N+ceCXvVoRdqUOGXtdCNEgakCQd1z/m+7rpWbpuPAmgKXrjvyiuJVrmI\nplAwVP79F3fj87c/l9d5fZOB5NAy9VH41PZG39FUrpjnFLzPFSbMRSJO+OGFM7Lc9vw4acIQHDgk\nsGTSkMC/MdFcm8LfGRK554I+CvQSijrHtDXg2KMG4trFYx3bbe8jbU6hqcalKWif1SI4S+A6M7GF\nEbSOsmRNwvd8PmWC8RMKpiZ/xfzR+JfHM1kBg47YT55oPftEGE3BJVBjpK/7CN/bLp3Sisu1UBvz\nxw3GocN5CgXXbRVKeBWKSAoFdyPtl4yhy+0hEAK/iS2/jvdrpx6N9uZMUhS94/Yy7YRVNU2duV8H\n0TLAPDluEi4Lxg/GT194G8l4DDM6mnIqVyxGWDqlNaffmFh3zdy8z6F7TDVUp3D3imlIxAjn3OEO\n3WXRLxnH3SumZW231ym4zEeHPSbHlWnCNF8TJgSLqSz2dT2a471fnI6zv/e8LI+16C1ImHS9o7vk\nuJFYPrMd465/3HGtjoHZEXHVPt0UGEYrss1Hsczcgju6cBiuXdzp0ABHDarDpR5mpqC4RZ2t5QRM\nolVsIikUiuWS6tf4/Oz2Z093RvPUOwAvYRJ25GjUFHy0jiH1VfjFZcfihG897dhuskl//bQJuGrh\nmLw7sHKjVghXp6yJfzWhnCtq8KEnKWqqTeE9aRoEnG1RPQe/fMxhcTcjryc+c0Qzxrb0x6Zte5CI\nK00h9/dFFyQEwvpr5xpX+po6wjALM22XVG2BW0z7HJageUNywT2nUCxvyLD07rc3JNkvSGEeil/H\nn8vkaxBNoVRCATBPdJuEQioR69Httjeg5gv8vGaCoB65Y06hJuXyPsocnwmTnZuHWBCyOkaf06lO\nKx6LBZ5TcKNrshSz7ts0p2bqD8Nowe4QGA7zUQ7ncZenGELB65qVIhsiKhSKU/t+I5Jc3OP0TsFr\n1DSof+6ulu5zK4LmZtDJNUxCb6JRusUGiYHvhxoI6PmX66uSTqGgHW+vSUjEcNxop3ZSeE2h53tL\nSBOMn+aXOYu3/d/vSqZ9YQSgWyvQYx/l87rnqmV0BEga5RXmokJkQjTNR+5J30LJCP20y2a0Ofbl\nJBQCaAqtWmL2XHC/4EThvLFyDajWm1CrmPPVINWaC5Wz+OZTxyPhCiKnPw/V8afihDvO63L46ecr\nFPR25A7vcf1Jnbjx4UxCRNVpxYiy8hK4CdJp+h1j1BRCDFLc8wfk8D4qTXe78ab5OWe0AzKh1StF\nVYioplCs81onXjBuML66ZLxjXy520pR094yR9ws1tKEwQiGMlgD0baGg5hTybSf6hPWIgTU4Z7o1\nUNDdeU0DgGTcCrURZHAQFNWOalJxrF7e5eh/zteS1gCZ4HaxmDVgyN/zyXufKR1rKE3BJQD0a5aq\nq61OJQKFindHSf3UED22nERTU3B7YhTsvNZ/U8q/MOYjvxHT0MbsuD1BcNtIwwbx62nNRW9Gdeb5\nOiSkEjHUpRPYu/+gY6Svd/ZOTcF7ojlfSBtBA/5tXm++delEoDUNXiYR61rmq31/xTR8ZlRz9vEh\n6t39TutzIV3Dg3sWlmKw7va0VXNOlaEnRFQo5Dvq8iKTIjJ7Xy6aguq4/co5OOSErjsgXti66Mua\ngrq3MCEy3DTWpnyFgr6YULmketnwr140JrTrtOq01dP2z4amjiXcctrRvm1EzW2NafF20/TqaGeP\nbC6YJ6B699TahjgR+iXj+PmXZ6O9qWc7fzn5q8mtePIP23F6V7BQNcUmkkKhWPil/MvFbu+VLlIn\nEY/h9nOn4OIf/C6nMrp94MOOSvuyUBjeXIPrT+rEiUdnR1fNlcaaFN7Y9bFDI0t5CAg/7yMAuPCz\nI/Iuj5o8V6VR5Xr6ijm24FCtl8iK1eTHhNZ6/ORL3ZjYWp9zWQoZWUCdSoXtVrJm3JDypbX04hgZ\nImT0oDps3r4XzXUpPH3FcWUuVYZICgXVeXe29MfGbXsKN1qR77I79guQm6agzAg9+fsvGD84eOHs\nc7vMR6E1hb7bdIgoy84elkZpitLnbnTBrD9jZXbM1/3URP9+CVx87AgsmWytGlZNXpWrTRtNq0FN\n0FJMafPXXsJM9F742Q47hHYQ3JpCpfn+60wb3oiXb5iHVc+8hs3b9+LAwfySexWaSE40pxIxPHTJ\nLNx1fhcAK8R1ISAf81EY76Nqn8QfijO7hhrzCHvhLkdYTaFYJri+hpq01gPaOTQF7bPyQinOnALh\nqoVj7BzUyrPJNKekawqFuXbuv7l60VjMGZ2dBMn7GlIoHFJrLMIVvlSriuv6JbFIaqIndAa/z1LQ\nd4d7PTBxqKXurlo2Bcd45G3NFT/zUU6L16TdvybAaPyW0yYEPi+Qbfbhzj043/z8xJwXMzXJdJ76\nyFUXBGltEld5oZRiRbgSCqbkMitmDcc1D24o2ELEUrQwVaXqvsIKtFIsVlOMbemfFem3EoikpqAz\nb9xgNBvy8IZh+vBGzBrZhGsXd2btC6UpFMFun4zHsPWfF+GyuVaQrwrWsgvO8WOOMK7ODsrSKa04\naWJuAfvGDLYmYF/bmUlyo9vSdQHx6SFlPir+a6mC/p3jCrECAOfOaMPrt5yIuhwCGvrhNs9edGxH\nQc6rozQypfmENR/deV6XPZcU1QFTZDWFYtAvGcc9f+1MjNHWVI03dn0cSigUK3k3aatU/VwJdVYt\nm4J4jHDB3euKUqZSsHp5V8mvOXmYpZG6E7codK1AmY9KoSlUpxLYcvPCknR87kusXDgWKxeONR8c\nkmtO7MTII+qwZusuPP7K9tBCYVhTNS6bdxR+/vK2vNdn9FZYKGhMGlqP7Xs+QUN1Cnv3H+j5BwH4\n8UXdWP/G+zm9fEqFLeaIUU0wm9ZUmJg3LvdJbSYzX+UZ2FATACrWUlh341wpVVKXYgWg1KlJJ3DB\n7OFYs3UXAHO2wqAogdLbAzuGpWhCgYhWA1gMYIcQYrxh/xwADwHYKjc9IIS4qVjlCcKDl8wq+DkH\n9e+HhTm6NgZZvJYvUW3wpYaIcP/F3bZ5w43+HE6fOhTpZAwnTyx9Xt6+gnLyyEcQKfkd1XekmJrC\nXQBuBfB9n2N+LYRYXMQy9EpUY0zlmYLRD6WFBDUfKe5eMc1OBsMEw2/Bmb54LRYjnDo5/5wSUeaG\nk8flFe4cyHggsfmowAghniGi9mKdvy+TWbxWfPNRruTzsjHZRHU0WiyOrK/Cd86anNc5PpVzO6X0\nRKokyn3X3UT0eyJ6jIg8s9oT0YVEtI6I1u3cubOU5SsLav4hbFyiIGQmmitr4UzU6MtCwR3+u7dQ\nStfgSqScE83rAbQJIT4kokUAHgRgTK4rhFgFYBUATJ06tc/3YsrXOmwE0yDY5qOiXYEJQl/ueG5f\nNgW79xXGYaOUDJAhz2eNzA7WFwXKJhSEEHu0z48S0W1E1CyEeLdcZaoUVPyWfJO8+KHMR6wolJe+\nbLdOJ+I4oq73xcg6sr4KT11+bMEiHfQ2ytYiiWgwSRcBIpomy7KrXOWpJA6oRUxFnNBNKvMR6wpl\npS9rCr2ZjoG1JXPZrTSK6ZJ6H4A5AJqJ6M8ArgeQBAAhxO0AlgL4EhEdBLAPwJmCDdwAgIOHLU2h\nmI1Smaa4xstLkKQsDFNKiul9dFYP+2+F5bLKuOjusGyZczsHFe0aKhIny4TywpoCU2nwiuYK5OjW\nAUUPlJXMMcwFUxyi6vbIVC7cIiNKxrOJpUI56csTzUzvhFtkREkWcbU0E5xCZh9jmELAQiGihA1z\nwRQGU8J6hqkEeE4hoijzUdAoqUxhueO8qfjwk4PlLgbDZMFCIaKw+ai8pBNxpGvZHZWpPNh8FFE4\nzAXDMCZYKESUOHGYC4ZhsmGhEFGU10sx8kAzDNN74TmFiDKgKokrF4zGAk6zyTCMBguFCPM3c0aW\nuwgMw1QYbD5iGIZhbFgoMAzDMDYsFBiGYRgbFgoMwzCMDQsFhmEYxoaFAsMwDGPDQoFhGIaxYaHA\nMAzD2JDoZcFviGgngDdC/rwZwLsFLE5vhuvCguvBgushQ1+tizYhxMCeDup1QiEfiGidEGJquctR\nCXBdWHA9WHA9ZIh6XbD5iGEYhrFhocAwDMPYRE0orCp3ASoIrgsLrgcLrocMka6LSM0pMAzDMP5E\nTVNgGIZhfGChwDAMw9hERigQ0QIi2kxEW4joqnKXp5gQ0Woi2kFEG7RtjUT0BBG9Kv83yO1ERP8u\n6+UlIjqmfCUvLEQ0lIh+SUQbiegVIrpUbo9iXfQjojVE9HtZFzfK7cOJ6Hl5zz8iopTcnpbft8j9\n7eUsf6EhojgRvUBEj8jvkawHE5EQCkQUB/AfABYC6ARwFhF1lrdUReUuAAtc264C8KQQYhSAJ+V3\nwKqTUfLvQgDfLVEZS8FBAJcLIToBzABwiXzuUayL/QCOF0JMBDAJwAIimgHg6wC+LYQYCeB9ABfI\n4y8A8L7c/m15XF/iUgCbtO9RrYdshBB9/g9AN4DHte8rAawsd7mKfM/tADZo3zcDaJGfWwBslp//\nE8BZpuP62h+AhwDMjXpdAKgGsB7AdFgrdxNyu/2eAHgcQLf8nJDHUbnLXqD7b4U1GDgewCMAKIr1\n4PUXCU0BwJEA3tK+/1luixKDhBDb5Od3AAySnyNRN1LtnwzgeUS0LqTJ5EUAOwA8AeBPAD4QQhyU\nh+j3a9eF3L8bQFNpS1w0/hXAlQAOy+9NiGY9GImKUGA0hDXsiYwvMhHVAvgJgL8XQuzR90WpLoQQ\nh4QQk2CNlKcBGFPmIpUcIloMYIcQ4nflLkulEhWh8DaAodr3VrktSmwnohYAkP93yO19um6IKAlL\nINwjhHhAbo5kXSiEEB8A+CUsM0k9ESXkLv1+7bqQ+wcA2FXiohaDWQBOJqLXAfwQlgnp3xC9evAk\nKkJhLYBR0sMgBeBMAD8rc5lKzc8AnCc/nwfLvq62f0F63swAsFszrfRqiIgA3AlgkxDiW9quKNbF\nQCKql5+rYM2tbIIlHJbKw9x1oepoKYCnpFbVqxFCrBRCtAoh2mH1A08JIc5BxOrBl3JPapTqD8Ai\nAH+EZUf9SrnLU+R7vQ/ANgAHYNlHL4BlB30SwKsAfgGgUR5LsDyz/gTgZQBTy13+AtbDbFimoZcA\nvCj/FkW0LiYAeEHWxQYA18ntHQDWANgC4H4Aabm9n/y+Re7vKPc9FKFO5gB4JOr14P7jMBcMwzCM\nTVTMRwzDMEwAWCgwDMMwNiwUGIZhGBsWCgzDMIwNCwWGYRjGhoUCE3mIqF2PKBvg+OVENCTAMbfm\nXzqGKS0sFBgmd5YD8BUKDNNbYaHAMBYJIrqHiDYR0f8QUTURXUdEa4loAxGtkiudlwKYCuAeInqR\niKqIqIuIfitzFawhojp5ziFE9L8yb8M31IWIaB4RPUdE64nofhmbCUR0i8z98BIRfbMMdcAwvHiN\nYWQE1a0AZgshniWi1QA2AlgthHhPHvPfAH4shHiYiH4F4B+FEOtk2JQ/ADhDCLGWiPoD+BjAuQCu\ngxWZdT+sMNyzAewD8ACAhUKIj4jonwCkYa2k/i2AMUIIQUT1wopRxDAlJdHzIQwTCd4SQjwrP/8A\nwJcBbCWiK2HlH2gE8AqAh12/Gw1gmxBiLQAIGYXVCruEJ4UQu+X3jQDaANTDSvT0rDwmBeA5WCGZ\nPwFwp8wG9khxbpNh/GGhwDAWbpVZALgNVvyjt4joBlhxcHJhv/b5EKz3jQA8IYQ4y30wEU0D8DlY\ngdf+FlYET4YpKTynwDAWw4ioW34+G8Bv5Od3pc1/qXbsXgBq3mAzgBYi6gIAIqrTQjCb+D8As4ho\npDy+hoiOktcYIIR4FMA/AJhYkLtimBxhTYFhLDbDyuGs5hO+C6ABVkTRd2CFX1fcBeB2ItoHKyfB\nGQC+I0NS7wNwgtdFhBA7iWg5gPuIKC03XwNL0DxERP1gaROXFe7WGCY4PNHMMAzD2LD5iGEYhrFh\nocAwDMPYsFBgGIZhbFgoMAzDMDYsFBiGYRgbFgoMwzCMDQsFhmEYxub/AXf0lWPVBiO1AAAAAElF\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C58ZQcNzMGhB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "06525b37-d14b-4b35-b929-42246dcf847d"
      },
      "source": [
        "## Test\n",
        "test_loss_history2, outputMemory = testModel(inputTestSet, targetTestSet, gates, model2, readMemory, writeMemory)\n",
        "# plt.plot(loss_history, label='LSTM V1')\n",
        "plt.plot(test_loss_history2, label='LSTM V2')\n",
        "\n",
        "plt.legend(loc='upper right')\n",
        "plt.ylabel('loss value')\n",
        "plt.xlabel('batches')\n",
        "plt.title(\"Testing Loss\")\n",
        "# plt.ylim(0, 10)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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eOqylFR3yfRU/80dDCV4oZuorzAyFSrMYEYTZzRnuu9MMpW0Ph88iyfSVZ5bE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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bLVQYO48MPoU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "651e83db-379b-4bd2-dc8c-ee9626ee518f"
      },
      "source": [
        "## Test\n",
        "test_loss_history1, outputMemory = testModel(inputTestSet, targetTestSet, gates, model, readMemory, writeMemory)\n",
        "plt.plot(test_loss_history1, label='LSTM V1')\n",
        "\n",
        "plt.legend(loc='upper right')\n",
        "plt.ylabel('loss value')\n",
        "plt.xlabel('batches')\n",
        "plt.title(\"Testing Loss\")\n",
        "# plt.ylim(0, 10)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bhv+knRhbQqGA+YgzFfAmpVEXC9ofDqWFpLagKWjRRzbzUYEZa1up69ajtNnE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\nh8PhCHBCweFwOBwBTig4HA6HI+D/ARgD0gRPPLC4AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D7OzuE6KL4bP",
        "colab_type": "text"
      },
      "source": [
        "### Test model larger array size"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uQ6hS8ZhL7vU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119017
        },
        "outputId": "2938a642-61fd-46f0-fada-dcc994fbfa42"
      },
      "source": [
        "## Generate dataset\n",
        "inputSet2, targetSet2 = dataset6(500, M)"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "index:\n",
            "[0 2 1]\n",
            "2\n",
            "15\n",
            "9\n",
            "\n",
            "index:\n",
            "[0 1 2]\n",
            "6\n",
            "10\n",
            "18\n",
            "[11 14 17  0  2 15  9  6 10 18  0  0  0  0  0  0  0  0  0  0]\n",
            "[11 14 17  0  2 15  9  6 10 18  0  2  6  0  9 10  0 15 18  0]\n",
            "\n",
            "index:\n",
            "[0 1 2]\n",
            "2\n",
            "10\n",
            "16\n",
            "\n",
            "index:\n",
            "[0 1 2]\n",
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            "19\n",
            "[11 14 17  0  2 10 16  1 12 19  0  0  0  0  0  0  0  0  0  0]\n",
            "[11 14 17  0  2 10 16  1 12 19  0  2  1  0 10 12  0 16 19  0]\n",
            "\n",
            "index:\n",
            "[0 2 1]\n",
            "4\n",
            "14\n",
            "12\n",
            "\n",
            "index:\n",
            "[2 0 1]\n",
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            "[11 14 17  0  4 14 12 16  1 11  0  0  0  0  0  0  0  0  0  0]\n",
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            "\n",
            "index:\n",
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            "19\n",
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            "\n",
            "index:\n",
            "[1 0 2]\n",
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            "[11 14 17  0  6 19 12 12  1 14  0  0  0  0  0  0  0  0  0  0]\n",
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            "\n",
            "index:\n",
            "[0 1 2]\n",
            "5\n",
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            "19\n",
            "\n",
            "index:\n",
            "[2 1 0]\n",
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            "[11 14 17  0  5  9 19 17  9  1  0  0  0  0  0  0  0  0  0  0]\n",
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            "\n",
            "index:\n",
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            "\n",
            "index:\n",
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            "[11 14 17  0  1 18 13  1 18 11  0  0  0  0  0  0  0  0  0  0]\n",
            "[11 14 17  0  1 18 13  1 18 11  0  1  1  0 13 11  0 18 18  0]\n",
            "\n",
            "index:\n",
            "[2 1 0]\n",
            "14\n",
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            "4\n",
            "\n",
            "index:\n",
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            "[11 14 17  0 14 11  4  4 15  7  0  0  0  0  0  0  0  0  0  0]\n",
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            "\n",
            "index:\n",
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            "\n",
            "index:\n",
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            "[11 14 17  0 13 15  4  1 10 14  0  0  0  0  0  0  0  0  0  0]\n",
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            "\n",
            "index:\n",
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            "14\n",
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            "\n",
            "index:\n",
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            "[11 14 17  0 14 13  1 14  6  9  0  0  0  0  0  0  0  0  0  0]\n",
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            "\n",
            "index:\n",
            "[2 1 0]\n",
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            "2\n",
            "\n",
            "index:\n",
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            "[11 14 17  0 14 13  2  4 18  8  0  0  0  0  0  0  0  0  0  0]\n",
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            "\n",
            "index:\n",
            "[0 1 2]\n",
            "2\n",
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            "15\n",
            "\n",
            "index:\n",
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            "[11 14 17  0  2  9 15  5 15  7  0  0  0  0  0  0  0  0  0  0]\n",
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            "\n",
            "index:\n",
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            "17\n",
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            "\n",
            "index:\n",
            "[0 1 2]\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B-fHCo_VMDOO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "5096837c-7422-409c-d10e-b98108b8d54e"
      },
      "source": [
        "## Test\n",
        "test_loss_history3, outputMemory = testModel(inputSet2, targetSet2, gates, model3, readMemory, writeMemory)\n",
        "# plt.plot(loss_history, label='LSTM V1')\n",
        "# plt.plot(loss_history2, label='LSTM V2')\n",
        "plt.plot(test_loss_history3, label='')\n",
        "\n",
        "plt.legend(loc='upper right')\n",
        "plt.ylabel('loss value')\n",
        "plt.xlabel('batches')\n",
        "plt.title(\"Testing Loss- MLP - Dimension-400, Learning rate = 0.001, Testset-Array size = 6\")\n",
        "# plt.ylim(0, 10)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TDaK/Hsm4mar7ZiaTqfex1/UMYEPfoPVeJYj8MMYZn0c5hdEXSEn042xQWhht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\nXXXaITjmzXsAcOeEACL69UNtbLnubRqugKPDaPQNaMJGH3C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VQOi5AfT7+M5dawLfTfP7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\ngQSkGwgmAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "24euZSeuMaF-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "db187786-5d72-4020-bcab-e7d66f922562"
      },
      "source": [
        "## Test\n",
        "test_loss_history2, outputMemory = testModel(inputSet2, targetSet2, gates, model2, readMemory, writeMemory)\n",
        "plt.plot(test_loss_history2, label='')\n",
        "\n",
        "plt.legend(loc='upper right')\n",
        "plt.ylabel('loss value')\n",
        "plt.xlabel('batches')\n",
        "plt.title(\"Testing Loss- LSTM V2, Testset-Array size = 6\")\n",
        "# plt.ylim(0, 10)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wqY5talo4nuDg4NAMqCdTeArAPkQ0jYg64BuSb1XuWQZ/b2EQ0RvhM4WkkszBwcHBYVhQ\nN6bAzEUAZwOYDWA+fC+juUR0IRGdEtz2NQCfI6LnAdwA4FPsNo12cHBwaBjqmhCPmWcBmKWcu0A6\nngfgqHq2wcHBwcHBHo02NG/zcGKRg4NDM8ExhQbDacscHByaCY4pNBiCJTjnIwcHh2aAYwoNhhMU\nHBwcmgmOKTQYLs2Fg4NDM8ExhUbD8QQHB4cmgmMKDYbjCQ4ODs0ExxQaDGdTcHBwaCY4ptBgCJuC\nS4jn4ODQDHBMocFwkoKDg0MzwTGFBsMLuIKTExwcHJoBjik0GE5ScHBwaCY4puDg4ODgEMIxhQbD\nSQoODg7NBMcUGozI+6jBDXFwcHCAYwoNh5MUHBwcmgmOKTQYjic4ODg0ExxTaDDcfgoODg7NBMcU\nGgzHEhwcHJoJjik0GE5QcHBwaCY4ptBwOK7g4ODQPHBMocGIJAXnk+rg4NB4OKbQYDg5wcHBoZng\nmEKD4WwKDg4OzYS6MgUiOpGIFhLRIiI6V3P9Z0T0XPD3EhFtrGd7mhFuj2YHB4dmQlu9CiaiPIAr\nAbwbQA+Ap4joVmaeJ+5h5v+R7v8SgDfXqz3NCs/z/7s0Fw4ODs2AekoKhwFYxMxLmHkIwI0ATk25\n/wwAN9SxPU0JJyk4ODg0E+rJFHYFsFz63ROcS4CI9gAwDcC9hutnEdEcIpqzZs2amje0kRA2BSco\nODg4NAOaxdB8OoCbmLmku8jMM5h5OjNPnzx58jA3zcHBwWHbQT2ZwgoAu0m/pwTndDgd26DqCHDe\nRw4jEys2bsUnr3kCG/uHGt0UhzJRT6bwFIB9iGgaEXXAJ/y3qjcR0X4AJgB4rI5taVo4m4LDSMQl\n/1qAh15ei7vnr250UxzKRN2YAjMXAZwNYDaA+QBmMvNcIrqQiE6Rbj0dwI28DaQLZWYUS55yrkGN\ncXCoI15a1QsA2HW77ga3xKFc1NWmwMyzmHlfZt6LmX8UnLuAmW+V7vlfZk7EMIxEfOfmF7H3d++I\nnXM8YdvGHS++jsGi1pTW0nh51RYAvqv1d29+Eb99YHGDW+Rgi2YxNG8TuOHJ5YlzQkBycQrbHh5d\nvBZfuO4ZXHzHwkY3peYoev64Zgaue2IZfnLHgga3yMEWjik0GE5S2Haxsb8AAHht49YGt6R+cDaz\n1oNjCg2Gsyk4jGQp0Y3v1oNjCg2HmzXbOkYy4RzJ7zZS4ZhCg+EmzbaLESwghHDqo9aDYwoNgOx9\n66aMw0iGW/S0HhxTaAA8aaJEuY+2hXWjg4yRSi9L0gD3HFdoOTim0ADIkoLnXFIdRhgKUoCmYwmt\nB8cUGgB5ojTrQurS2Qvw58dfbXQztgmMtAWBzBQcV2g91G2THQczZEbQrIa4K+/zI1A/ccQeDW6J\nQ6uhWJJtZs05vh3McJJCAxCbKG7OOIwwxNRHbny3HBxTaADY8QSHEYxCzNDcwIY4VATHFBoMt5La\ndjFSv32hKEsKI/QlRzAcU2gAdDaFEWZrbBl4HuPOuSsd8aohip7zPmplOKbQAMi+244WNRZ/mbMc\nZ/3pacyck8xgW2+MNK8jgaGiG9+tDMcUGgA2HDsMP/oGiwCA+a9vaXBLRg5ikoLjCi0HxxQagFia\nCzdpaoLHFq/D8vX9ZT83eWwnAGBt72CtmzQsOOcvz+G03zbXTrYueK214ZhCA+Akhdrjf/7yHGY8\nuKTs58Z0+qE663or32B+yZpe9GwonyHVYj3w92dX4Mml66svqIYolJz6qJXhmEIDwI4r1BxDJa+i\nbS3Ft6hGUjjusgfw9ovvq/j5kWZbiEsKzTvAZz61HFPPvR0b+ipfEIxEOKbQCOi8j0YaZRhmMHMs\nktYWwui/zhGGmqHYIpLCHx9fCgDo2TByd76rBI4pNADy6qmZJ00rweNoX+BynwOA9Y4p1AxDkqTQ\nCllSm1maaQQcU2gAYnEKbjzWBMwc83op48mat2VbRywhXhPDpavXwzGFBkAmQ62wkmoFMKNC9VEd\nGmOJRq9QN/UXYnsf1Aqtoj6qB5av78e81zY3uhlVwTGFBiAWvFZhGefd8iLe+qO7a9OgEQBGpeqj\nbYxqBdgyUMDBF96Ji2bNr3nZQy1iaBao5RA4+pL7cPIVD9WuwAagrkyBiE4kooVEtIiIzjXccxoR\nzSOiuUR0fT3bUy9sHSqVJTLXQn3058eXYc2W1vStrwc85qpsCo1AI9UXWwb8oL3bXni9qnLefvG9\n+NNjS2PntmVJYSSgbkyBiPIArgRwEoD9AZxBRPsr9+wD4NsAjmLmAwB8tV7tqSfeeMG/cMaMx63v\nZ02kgnM+qg6++qh8XbYLHqwOPRu24vx/zI2dK8QMzcPdIodqUU9J4TAAi5h5CTMPAbgRwKnKPZ8D\ncCUzbwAAZl5dx/bUFXNe3WB/szM01xyVSgqu/2uP+H4KzdvBw70Qe3TRWkw993as3jIwvBWXiXoy\nhV0ByFnGeoJzMvYFsC8RPUJEjxPRibqCiOgsIppDRHPWrFlTp+YOH1zsWu3BqExSaKRNoZH6dkEQ\n69GGWERzzUuvPYarjb97ZCkA4NllG4epxsrQaENzG4B9ABwD4AwA/0dE26k3MfMMZp7OzNMnT548\nzE2sPZxLau3BzBV50jj1RuUwSQFFt0ezFq2iIq4nU1gBYDfp95TgnIweALcyc4GZXwHwEnwmMaIR\nC15zs6Ym8Di+QrVFM6g3WtVf3tR1MqPdVr270tDsXVJPpvAUgH2IaBoRdQA4HcCtyj23wJcSQEST\n4KuTys9q1mJwkkLtUamkUG3/NwNTqQS1YESmN6+Fy/VwYLhZcVRfM/dKHZkCMxcBnA1gNoD5AGYy\n81wiupCITglumw1gHRHNA3AfgG8w87p6talZ4GwKtYfHQKGCiOZqV7LbsvrJ1Hfx1PDD1ZrmR2jH\nafI+aatn4cw8C8As5dwF0jEDOCf422bgefKkcS6p1UL0YSNsCpWl1oijVVWIJqYg92mrvtu2DCtJ\ngYj2IKLjg+NuIhpb32Y5ONhD0KZK0lxUS7TqkSZiOCDeu5pVq9mm0FqSwnCpAIXKrtm7JJMpENHn\nANwE4Krg1BT4tgCHCuFsCrWF6MJKVu3VSwrVf8BGGJprMe5sDM1NbXNpkHjezF0C2EkKXwRwFIDN\nAMDMLwPYoZ6NGumopfdRU0+6YYJYmVYkKVTZf5XU2QyohVeQaexyixiaHfSwYQqDQUQyAICI2uC+\ndVWopaTgeIKkPqrEplDlSr8WNoVGQPRZNW9v6rpWUx8NF1rFbmjDFB4gou8A6CaidwP4K4B/1rdZ\nIxsx76PgR6UqhJKbdZKkMPzqo1a1KdQCJimrZdRHAYarhfWMIq8lbJjCuQDWAHgRwH/D9yY6r56N\nGumQJ4ogaJWuIlxwUIRCJXEK8nEFfVmN+qiRn64W48ZGUmhmnjn8cQqtISpkuqQyswfg/4I/hxqg\nlnEKLaq9qCkEEaoseC16puQx2vLlTdxqJAUvEhOHHaH6qJoBaHg2ph6tovjhwnAz52Zfx2UyBSJ6\nBZpvy8x71qVF2wDYYtas3DSAHcd1gjJECCcpRKvRksdg5sw+iz9b3aq2Ft5HjUAtWm2OU2DkcxR+\nj2bHsLUxVB81N2zUR9MBvDX4OxrAFQD+XM9GjXTEeUJyiCxcuQVHXHQPfh9kVUyDYwrxSV0uka42\nT081kkKrq49MJXjMyLeKVRXDaFMYpnqqRSZTYOZ10t8KZv45gPcOQ9tGLHSGZhlL1/UBAB5bkp3x\nw6mP4oS9XB0/V8kUqvE+ChcEDWAOtWBIaRHN+ZxPAlvBEF+tB1q5aHbpyUZ9dKj0MwdfcqhreoyR\njiztUTljxoaQDRRKGCp5GNfVbl9wK0FmCp4HIG/9aLXqo1aVFGrBiUzt50B9BLSGd9zweR+1hqxg\nQ9wvk46LAJYCOK0urWlBVML1Y8FrVdoabSbdyVc8hCVr+rD0JyNTwJMJe/mSQtzQXC6qsSmEjzaA\nVtRicWx0SfWAXAskfxM0erhUsK3BEuy8j44djoa0KiqZXFk2BQGbQWQzoJes6bMoqTVRLHn4zs0v\nRr+rsClUwuCrkxQaRzGjqqtov+G8J0kKw62aqQjO+ygGI1MgotTMpcx8ee2b03qoSFJg/bF01rqs\nbd2m8PiS9bjj3yvD3+Xq+OX+r0hSqCZOoeInq0ctAqhsbAqtwBOGq43DLZlUijRJwWVCtUBFkkIs\n95HmulApWYgKzT7A6g31/csl0tW7pFbBlRvpfVSDxYSpv1rNpjDc6qNmZ5RGpsDM3x/OhrQqKllx\nxcagZkBGquZsrtAK3h0qiiUPR1x0L85/3xtx6iG7VlVWgilY9Mfd81Zhrx3GYNqk0cqGMMNrU2hk\nuoNqU2eXPDaqhmSX1Gb2tBGza7hb2OwqNRvvoy4AnwFwAIAucZ6ZP13HdrUMKhnzthGfNpJCPeZc\nvSdy72ARa3sHcf4t/66aKahNLVksgb9x0/M49ZBd8b+nHBBbtVWyqi1VoT4SdTfCAFntJ97v/DvQ\n1ab38vIYyIXqo+YmgMAwSgrUGtKTTfDanwDsBOA9AB6Av5/Clno2qpVQEVPQeB9VinoMsHqP2VwN\nfdjVCV2wINKFEmOw6DMP+VsMd0RzI2lDtXUXSowtg0XttZihubnpn49hXlg1O6O0YQp7M/P5APqY\n+Vr4gWuH17dZrYNKPnDc0Oz/kH2Yax2nUC7qPWjFm9aCoamMxYbReMxhRtVYRHMFFKwq76NmUB/V\no2zZ0NzEXEHMuXqM97Qim7lPADumUAj+bySiNwEYD7fJTohKPi8bjlVYGZrrMMDqPWRFk+th7CxY\npM8ueRyu8OOG5kpsClVENAfVNYJE1JPvyzaFZl8VA/XpC917h4uhJmcKNsFrM4hoAoDzAdwKYExw\n7IBKJYWkykKm/2IVZ2Norsf4qvc8Fu9fG9VX+ZICc8Q84mkuyq+9OkmhdqgmEWCt4TEjJ/TnLeAy\nXRdJQXcydEmteXU1hQ1T+D0zl+DbE1xmVAWV2RTk5zXeR5H7USYqZUo9G7Zit+1HGdpX31Ermlwb\nm0L8t41NwVcfCe+b6P5qIppzlViLa0iMPAbKyfpdTc1Z0qnH9ffJ3zJQwJjOtpqkjqhVC3X7pOjQ\n7NKTjfroFSKaQUTvolZJ3jGMqDZ4TXu9jLIqIWS/uncRjr7kPixdq490rveYreWkUMuy6Y8Sc6j2\nqVVEc74CrlBL76Ny217NJ9haKGW2JUcEImDzQCH1Xh1WbhpIvd43WMQRP74Hs+euKrtsGaFLqtQZ\nl9+5EFPPvb2i8uJjSVdfayQJtGEK+wG4G8AXASwlol8R0dvr26zhx0Wz5lc0GCqbXLXzPqrk+Tvn\n+ZNp49byJ2wtUMs5kZAUMnT8zByoj3Q2hfLrF5JCJeulWrr+DhQ9/OLulzGQQbBrUXc2UwByOf//\n359ZUVbZ9y9cjSMuugd3zTMT/M0DBfQNlbBmSzrzsIXcFVfcu6jicrL2pqYWUR/ZpM7uZ+aZzPwh\nAIcAGAdflZQJIjqRiBYS0SIiOldz/VNEtIaIngv+Plv2G9QIVz24BEB5httiybMybKrQ5j7S0BQb\nMlOJXn5TwAzGdOr9zOtuU6ihekr9XllxA+J2ISlUm+bCq0J9VMtu/tvTPfjZ3S/hV5ZETdRdCXPY\nOpTOFGSbQrl4fvkmAMCLPRuN9xRDhl5RFQnUqhx5/KSN8ZGgPgIRvZOIfg3gafgBbJlZUokoD+BK\nACcB2B/AGUS0v+bWvzDzIcHf1fZNrw+E/7oNjr7kPhz243vKriNLzCxnolYywARTMD06XDaFNNzx\n4uv44K8fyeyLZERz+vcT9+slhfS65r++GXt/ZxZWbNyaeKaS/XdrSRvGd/tp0Res3Fz3urMkBd+m\nUBlTEIuctOcjJ4HadGCtxnuW1Nkq3keZTIGIlgL4KoCHABzIzKcx898syj4MwCJmXsLMQwBuBHBq\nNY2tJ9oDK13WgJfxeobu04RYaoWU++SJUfIY/3huBTwlvUAlLqlCz2uSMnRxFLWEDSP7wnXP4Nll\nGzNXcWpRWcFkYkIWtd5H6c9e/8QyFD3GPfMj1UZoF2iwpNDd4Ut9r6ztQ8ljXPXA4tQVfTWrVTtJ\nobKyPQsbjfjGfUMl/HjWfGuVmYo0dU61GXN1z9sY37950/O4+F8Lyq67lrCRFA5i5g8y8w3MXE4O\n5l0BLJd+9wTnVHyYiF4gopuiyup4AAAgAElEQVSIaDddQUR0FhHNIaI5a9asKaMJ9ugMQvbLYQqV\nIu59pLmu0Sj9/pFX8JUbn8PfnumJEfNqUnebFtXV6tlt67dB1qqq3IR44nZ9nIJ9u8JnhE2h/Ee1\ngYuVQvTT0nX9uOXZFbjojgX4+d0vpdQd/K+grv4MpsCMitVHYmzLTOHFnk04/vIHsCVYzAhJ4bcP\nLMaMB5fgD48u1Za1evNAJgPz25vshYrGAuuPo3qCaymFz5zTg9/cv7j8ymsIG5uCnTxaGf4JYCoz\nHwTgLgDXGtowg5mnM/P0yZMn16UhXe1+V4hVxwk/ewDf+8e/61KX1qaggTyvVm32pZL1fUMxQlaN\nKGpasbDFPdWgnDLLZgqW9xc0eumsdum+lVcmYV+5aQAb+ob88mrYteK9Sx6HCxtTGgqgOpVJ1sq8\nKkkh7M/o3EurtmDR6t5wDgjGL1S9pjFy2I/vwZm/fzKzzmpVuAIxYq95XFweCbmPKsUKAPLKf0pw\nLkSw7/Ng8PNqAG+pY3tS0ZH3u0KsLF5a1YtrH3u1LnVVm/tIXuFnDd40oqojgiWP8Z/XRBOpHuO3\nLEkh8/3iv4sZhn9R3lCxhEtnL4jbBywZrEzvynUrPeKie/DmH9wFQEo1UYNOlhMB2pRWTZVZkoJX\nZiCdDNGuvPS8GKcDBf8dQ3filO8l+vTJV9Zn16ll9ub7j77kXvz58SRtiEvwyQLCoM0mD+irJ1N4\nCsA+RDSNiDoAnA4/IjoEEe0s/TwFwPw6ticVne2++qhS/WRZSDFCAYYQedm+IEsKGbNb9o5SiY9u\n4K/rG8RzyzdK9zS3pKAalrMkBQ5uX7ymD1fetxgPvhSpIytTGZTJFeS21LBryyU05dT9o9vn4Qt/\nfjr8bWNorlRS0MV9iLYOBS85VAyIa3Dh0tkLE0Q6i3EBkXOATo2aJkktX78V592S1CLITEr3tBgr\nzZxOHLAzNH+FiMaRj2uI6BkiOiHrOWYuAjgbwGz4xH4mM88loguJ6JTgti8T0Vwieh7AlwF8qvJX\nqQ6dbYGkMOw2BfMqxTSvZEKZRchkIqlWpSW4yqmGSwpZTEGxIdhKCpXUpUPkklqB95HmXP9QEZfO\nXoCVmwYw68XXrcuSJQWblpSjPvq/h16J7W5nG7xWCUqa/oyku7ikIH/Kn90Vt5/0pqjOEu3VnTN0\nT3r2U/lYI4UL9VGTex/ZpLn4NDP/gojeA2ACgE/CT6d9Z9aDzDwLwCzl3AXS8bcBfLusFtcJgikI\nEbWeiHv3JK/rxOJYCH0Z3kcykfSYkYO8AssenM0uKajXbW0KOpj6Y23vIKb/8O7Q7TNenv+/Ehqo\ns0dccc8i/PaBxbjyPt/Y+Oz578aE0R2ZZeneO62bxe2VqS+z+ljPJBev6cUv73kZl370YLTn9etR\n0SeypOGpTEHjTKCqq8phCroxkbbVqAnygkM7r4X6qNUlBUQLj5MB/ImZ56IyZ4umxvB6H9mJmSa9\nbJbuUsZQjCnEJ7SO4Kpn6jF8y1koZb2fSgyrYQqmR19e1Qsgiu+ARt9tg6dfjeu3dY8OFuPjLytC\nO2yH1Hi7zZkq/7JZ7+zbFJLnz5n5PG557jW80LPJ+KxOfSReTTAFXcCoqq7qC5hCh4H5xKATmA2v\nmDp+MlxSxbl/Pv9a2L5mhA1TeJqI7oTPFGYT0VgATW4qKR+dwvvIQhdZLUzBa0NFD32DxSiAR3qG\nDIQoU31UijOQcl0w6xN9WUtJwVN+ZxCslJFbyQou0hOn37d5oIAP/+axzPISQXCWTUpTE+pQzVfN\nGjcmScGmYaGkkEtKtIOh+ihZjlpf74BPdIUGwKbOrHNA+vjKUuuKsbe2dwjna2wSMhppd7BhCp8B\ncC6AtzJzP4B2AP9V11Y1AGJFMVAcBklBs9InInzimidwwPdmZwbTyIStHJ07c3lSBhAZZmuJciSF\nbENz/HpW2pFKslfapCzImsQ6B4a0AKeobjuUq6eW6/5/1z1dVt6vrHdlg0uq1SIk+Hwx76PgwaGS\n34c2koJQH4nFXmp7de0w2hTM5cRyH6W4LwPA8g39qW2yyfZbL9gwhbcBWMjMG4noEwDOA2CW/1oU\nYvDYBLtUCzYcC/e5cNLp8iGRqru0Vx8xOMZQbFZI9Uh5UVPvI2XylGuDiLXLSAnM5YW6+dRa04MU\nZVTrtQOUt7c3M2PWiyvTbzY8a4Ip95EuBkGFGNs5MquPbGwKfUMW6qO0CGPDO6ZJk1lSeFZwm4zh\nWJyaYMMUfgOgn4gOBvA1AIsB/LGurWoAwjiF4XZJ1aw00wgXq3aBTJ27YlMw1FPyGJu2FhKDteER\nzWXaFLJWWDbG18Qzym+t+3DGO+m+qc7LTCVutn2lt6Wkj6NKkWlT8PT2MJs6xdheuXkAl9+5ECyp\nPNNsCmp1ofqoXZ/0MatdlamP5DKT98XS22RJlsOwODXBhikU2X+DUwH8ipmvBDC2vs0afohBnOZ9\nVPIYi1ZvqbounaFZHiIRsTAYmstxSVVsCiYPib88tRzHXHpfwqWzWbyPNvQN4eNXP47VmweU66pN\nofbqo9TyNOkydNAb9TUrXot7ssq327Gv8u+aNuYGCiVsLZQM6qNAUoBPFF/R7Ochxufld72EK+5d\nhIWrtoTPCZtCwcamMOgTVRubgt4t3KBKtHRpzmI0Wb0/LItTA2yYwhYi+jZ8V9TbiSgH364woiA+\ndlrw2k/vXIjjL39QO5jLq0tzLIuWIp+Ohfooyz0wFrzmmb2PVm7aig39hcQqrB72rrIkhaCNf5mz\nHI8sWoerH34ldr1cSSFV/JfKenzJOjyzbENme23VR1qmoHtWtSlUICnYGL91ixFbpDGUt198L15Z\n25cZp3DjU8tx7E/vxxNL1illK+3k6FxoaNZICmoCvd5B31PMJl6iHBnLNkOAnikg9bqM4XCNN8GG\nKXwMwCD8eIWV8NNVXFrXVjUA4oOlGSrnLPV1/upqtVzEmIJIdRBrS7Si0j+fJAAmFBLeR8l6AGCw\n5CXuV+uqFSqRFExmFnWSRvczLr/rJby0Ki7Z6d4nF+qWo3Onz3gcH/r1o5nts/U+0qX0Dgmz5eoz\nDaWYmjD7Gd0tm/rtNl1Ka+/aXj+vU05DWeR2PRXYz257IR6gpy5yiGzjFOK/NwbvMu/1zQlX4PAZ\nQ51qW+PntacTz8jHt73wGu6Zv2rkSAoBI7gOwHgieh+AAWYecTaFMLAk5atXkjNfB7kG2eAXtUXz\njHSuVI73kUIsTKqnQpA6QGWKDbcpCCJv2IwoGafgt3/LYBFX3PMyPnZV3A1Ux/PbAgpWCgm8whiR\nJFThNdbfo0IrwYj6pFPqyta2r+T3ysoU65ebvOfgC+8Mk86lwWZM6GwKUaAfYXEgbb+wIu6zkiZR\nCacJXeyGWtuaLYPhsckVWDTxf/85D3NfU3xnDO+YxnDjm+xEOPv6Z/GZa+coGoL0ThwOhxcTbNJc\nnAbgSQAfhb+5zhNE9JF6N2y4Ib5RVvBTLZC1YihHP51FNOJSiXk1UzBJCpoWbuovxPIjlYuyJAVl\nJa4yZlWVoBJE9X10dQvVAzPjyvsW4aDvx4P105qrSjIm6Ag1Jw40kpBlX5UtKRjOZ+2PDNgxKp3a\nRiaES1b7AYFbh+JBXGrbCRS5pKZICmp9qyWmYIOrH3pFaYf+PlublCrdqNez1UeNYwo2aS6+Cz9G\nYTUAENFk+Hs231TPhg03xAerZNOacqGzKcTURxqbgnwc8yAqg4Ekg9eSTCFpaE6W+cnfPYEXejbh\nlYtOrigbZly3mp5RU6dOkGGKaDZ1i24l2hYwBY8Zz7y6AVsG4oRKV1TfYBFtebJWH+lWt+GzUg3q\n+9kyULkfbFROpmJtPqdNm3SGZlkFKPpDTVynd5P2/4tob51NQWUKa8pkCrb9nm5TiI7F43I8QlYc\ngww1sn04YcMUcoIhBFiH+mZXrQtWbR7Aio1bcfCU7bS7Og2npCCTmSh9cnS1XEKfeq+yOlHdUAWE\naK4Oeh2TFGkKPAbyUlde/8QyHL3PJOy2/ajUNsnvX/Q43PVOh4jIB4xSuW7KfWRi7rruygf1lzxo\n8xvpcMD3ZmOvyaNx2LSJfrkZk1wrKXD0/4kl6zBU8hKSkO0iReemnG4g16xGLGFjZ0qLUwCiIDV1\nRax+T9mxYijF+yiu0uPymYLS76Y3FO3WMU/dzmtL1kROKfGU9+ntafbgtX8R0Wwi+hQRfQrA7VCS\n3LUCbn52BT7060dj4pwMG5tCrRAbEOHclAl9+jPlpKpQjdKmgSkGoW7CmSD31UChhO/c/GJCh6+D\nXIWOWMZSd6uG5ixJQTA3AxPRMVFhU1BddgVUIigIyOI1fYn2maBb3YpHmIGPzXgcn7zmSc2KNb3c\nsHx5oWAjKdgVq4WdTcFcp9zPqqSgN70oTEEzh2UmtLG/EAvatEGi3w0vKUuGd7xoNpKLI5k5yUn6\nsvrwSzc8i6seaMwObDaG5m8AmAHgoOBvBjN/q94NqzUiDxPTx/b/lzyuyuPmH8+twGOL16Xeo+EJ\ncaJfhgokiwDIcyMteK1gmHC2hjVBlDZuzfZg8WLPJSfvB658JKpDMcaqK7qSmjo7Q1LQEf1QfeRx\n2YsCT2mfCTpmqzNSq6o026yuum+RBvGsbcI92zYJ6G0K4vmovVsLpcygrlA/L1ScujgFiZKtC3a2\n22lcV2Y7BRLxIaa5J134wnXPGK/pMqKuD9oFAIMWaU8uuqMxezXbqI/AzH8D8Lc6t6WuEIM0Kyil\n6LGVIe32F/S57r9y43MAgKU/ea/xWZ0Xgk4SkMe+PMdMxisdVAZiyn0U2hS8bJuCgH+vHzEqVsLl\n+oUPFr3USMiES2qZkoLafB3RyYc2BX1/pvVwGKeQ8h2Wru3TujHrVIdZ6rHY87LEZWFTOPkXD2Ft\n7yCe/O7x4blBg+ScBhu2mRa8Jv53tecwUPAwUPDQ3eGPo7TI7/QsqVGF4vrozvRoZnmBkcw5lU4n\ndIjPteCc1NZ1fZHUoHM51RW9essAJo/prMk+3rYwSgpEtIWINmv+thBRPfdtrgtEp2ZtVq8aY034\n4vXPZN6zrncQh//4bsx/Pd5dWdtx6lwj5fsGpYFW7h7Gsb0YpGtDFcQpeJ5PhNf1DobPlWuo3CCt\nnnRQXVLV4mUmls9RbK9ifXnJc235aMFQaWK5tMeO+en9+MZNL2ieDf5L5xLEKaVc+ZLJViRj3uub\nQ68cWwO5DtVKCoJoj+n07TeCQK7ZMohHFSl7Y38BS9b4nkqDlrmPxHXTng1+ufFxl7DlGKV0Y5GK\nS6qQFKLr8tzSeRfppNjDfnQPfvfIUnOldYCx15h5LDOP0/yNZeZxw9nIWiBvqT4qepw6wQF7fewD\nL63Bqs2DmPHgkvjzsqSgeU6oRHRtJVBMUsian6q3UTx4LTpWd7XS3aOi6Hm44t5FeMsP7w79260k\nBanMdbZMIdQfkfY6ALTnKSQIKvNnZqztHUx1SfWZgq7BKe2rQtWokxIT6rE0pmxQHyViO7R1l9XU\nsp/V5z4KpPHgG40JVvL9gVuqvOWnwGlXPYbZc1cBkNNcpMcp6K4vWdMbqm8eW7wOh1x4Fx6ToqnV\nYDsznTC/vC59uUmNqZMUTGU//PIa9A8V8dsHFifjKeqAlvMiqhS5XIb6KPhf8jzzh6+RBCeXrku9\nHOrFNc1gcEzkL89TybyiNMYppInL7LtwAn5aCMAuy6fcv+szmIKqs09IClJ7O/K58J3Ufvm/h5Zg\n+g/vxlJNihLZplBu5ljVvdYWnqSmlJ8rxyVVvqTzJEtDJUwhlIospKm01Nli4TG609dei0CtrAVC\nepxCdKy7ftxlD+DYn94PAGH6kjiSQYNPLV0fMiwB2/0U3vfLh/Hxqx832nd8tZnijpvy2XoHivjJ\nHQuqig+yxTbDFEL1kfSN/r1iE/46ZzmAaMBX4+NtC51aSC5STBoTQZAlhSVrerF8fTI3OzPjsjsX\n4tV1cT/peACNzBTiq7jwnuD/vQtW4YJ/xDcGKXmMN+7sWwQeX+KnEhCSwj3zV2HqubcndOnMXJak\nEE4q1qun5PfpaMuFq0T1O963YA0A4FVNX+XDiGZ9nyfcdGPSVvJb2qDoRawlpj5S7ktjNDJzul3y\nhBHfMK09FSX/4/j/NOgERlFnIZQUAqYQEEfx2wSTNAvEJVTTPt2bUpwg1PYuXduHj/72Mfzgtnmx\n82n9pto6Hlm0LvX+1ZvjbrOme4ko7PNK974uB9sMUxArCXmSve+XD4e6XtkltZKMlzqYitGd1636\nTM/LTGHmnB4cfcl9iXte2zSAX967CJdLG5qzwhR0kkJSfeTf8+k/zMEfH3s1dq3kRXnzl6z19b6C\n+Yp750r2lF/fvwjTvj0rJjqv77W1KfhI6n5l9VEkKZQj/os4CTbYFNRTJm+ZcshsyVJSKHnAaxu3\n4md3vZRYoZvGR9ZGQ2ltTUvlYqOWCsvXSrnxcsZ2+UxAuKVmGYZFQJfOhz9maM6Yn9rNjZTfiwM7\nxoqN8UVNWtG6sZNGK1aqGX8zpHKg8v02ysE2xBSSkoIMQQttbAqVuPHJiBuakxMtXOlpJh8zMGQR\n7agLCFPVRzGbgkF9lLp9pRepsvqDVMVpg3bmU75UtkySXtb3xVdL7//lw0qb4wwyzY+/LU9h+02T\nUTfvhE3BtCBQiYgp02w5q++ipKaMSQoal9Tzb/k3fnHPy5jzalztYVw0BN8ydVFZgbSbZpyeeu7t\nuGf+KuleTZWhpJBUH/39mZ5Q2jQhGqP6Qfl/Dy7BQKFklBQA4KezF2rb1j9UwrWPLg1/vx6k+9hp\nXGfsvjQir1MVqfe/a78dcNuX3h7UsTV2LW2XQzHmnKRQQwhiZeLGcpqLrMltk3AsDZmG5tDXXnON\n2S4wR1NwJWku0laFMlMQG5GnDVoR6bx0XaTXV9VHLyoJ0rL6Om5ozoU5gORsqab7w+ekhHja1Z7K\nFFh/XI5GxjZ/lecxugJ3zdc2KkTE8G2qUR+9/1cPx+JsdN5qpmeve2KZ9rno+Xj7hLqof6iEc2Y+\nb25sgDSbwpNL1+NHs+bjinteTo0GnvXi69r23/zsCnzv1rnhb0Gw1VgHnZQxWCzhE1c/gec1+n51\nPHW157H7RH8eqMkH0yQFcWk4mIJVnMJIQCgpmFaQwf+ix5n7EqetRGzAGkIin4sMzXpJYdAi17o+\nMtesDxdZUtXVThphKXkcTlSx/WGapDBlgj8Z5ND/rMRfan6gNJ17ey6yKZjjUZLnhEtqqcRaRqwO\nGZNdppytS/14GJ2kkKx7l/E+YXp9k2qf0ZddjfoIAK5/chnetpefviMe1yL+65+Wv32awV6oKCOm\nUEzcq0NanILAloFi6mp+sOhZMW/R16MUO4da9qOL1uI/rn4CAPDwomQ56jwkAsZ2tmF0Rz7xPY02\nBakcXUryWmMbkhSE3lh/XTY0Z0kKZYfQq3XFjpOEITI0J8vq2dCP3z6wGER6FZGA7tnU1NkG0TzL\nBU/0hShL54o4VPTwt6d7MC7QIYtNiuS4AhNEc0zqI3nStbdFGTVNhEGb5iIvSQo6YpbCKLNW/CZD\ncUk2NMs2BY1L6uSxvgojoW7QlhypAMX1tHTUOmS5upo/WdR2vfoo3j6xVaZtnp/I0JyyogaH8+d7\n7z8gcX2w6FmxbrGKT3MyAIC/Pt2TWo46dnJEICKM6WpLpMdO8+oS36Tl1UdEdCIRLSSiRUR0bsp9\nHyYiJqLp9WqL4LAmnbH4HlZMocwoULU0rfeRLCmE4n+yHTc8uRx9QyUwRzl7dNANMGazPty0CsuW\nFOIDW9ekX923CF/76/P45/OvAQA2bvVVRnJcgbkOvz3iroTOXWpuWy6XKmWZzrflyN/MxTMZmlXC\noGes+vr0520j5z3mkFHYrizlb7hsXT/2+s4sTD33dqtnAcQGrC7Jm9kjSu6XFEkhaJ/YKjNrC1WB\ntJ3XZAgmM2VCN84+du+YBDNYLFnp+cR8kOfLLc+uwL0LVsXu0yXXlKEyMHG7PFYFTGOFKOrzllYf\nEVEewJUA3g2gB8BTRHQrM89T7hsL4CsAnqhXW4D0NBcyIyjZGJqrtSloj6MjHWHT1diWJ8DgZWea\nlCa/+jCvTMb+A2q+HZVB6gatMCavCqJpxVaDHflcfBOgFEOdaR7HXFLlOAU1eC3oQd23IwB5osDJ\nwNyGqE59/bpnde6TQKCq0hhuVRWU7DGmulTaqI/mva5PPpA2guX30KXPMNWbaXTneJmCKdjOp6LH\n8DzOvF8wjbY8IZej2PcaKnpWLrVhZLz0Hl/9y3OJ+/IZRFodO2J+5HOUuJbqfeTFn68n6ikpHAZg\nETMvYeYhADcCOFVz3w8AXAyguj0uM0ApTEH2OJJ1vYkygv823j+650LECHP8v2iDcptxhSug7nVr\ncq0sxYh6dM0UvJbUp0t1MCdy5+gGbUden9emoy0XY0K6SSEuhzYFqfj//tMcLFgZbbfZls9Oc6GT\n8ojIn6QGQ7PaLFNsgm7YmNpR9CI1Rlrak5KXQiwsmIJxVZ9CgOIprpNSUVZWAMCgsgrbF2cKJsap\nw1DJy7xfuKTmc5Qg2kOllOBUCWFkfMatuQxJQa1L0KE2jerUTn2U3p5aoJ5MYVcAy6XfPcG5EER0\nKIDdmDku2yogorOIaA4RzVmzZk1FjdHtwysgZ0aVdb0m2NoUWPkvEB8oSQZQUmwKF/9rAX6vyX+S\nl3Q1H5vxeEod0TmdN0kpxhTVdzNLDjpJQbeQaW/Tj+T2fC7mU6738xZGhaB8icWK9AdyeWqcglqi\n7tvlKFi5lSy9jwxt1o0bk/5bVh/JXa4SBo/NaibTSI3vy61/No3Y6RYo/vl0QpkVyJdUHyVtCpPH\ndqa60g4WvUyVoyi/PZeDmv6I2c4QL+7Jit5OSa/kt8Xj2OJNHOZylFCbpTGrkCkMA1domKGZiHIA\nLgfwtax7mXkGM09n5umTJ0+uqL58hqQgTtvYFEziq22agxhL0Dyi2hR+c78+r3pbygAxGfp06g55\nkmRJCqrhUSWyOnG60zBzZBdStezoXND24HcawfBtFPqIZgFh15CRI39FWWK7NBdFDWMF9N9fTe0d\nllFiRAsCMxH3PImRp0htMsT3VL+3jNREhwaVWClkCvpns9RHoaE5uK9DSApSH7Xl0ndCHyyWMhdl\nory2PMUWTgIDFt57OvWRDml2PcD/fm15mSlIkoIyNsxeUzSsLqn1ZAorAOwm/Z4SnBMYC+BNAO4n\noqUAjgBwa72MzWlZUmVGYBO8Zko3nNQ9+7/TcrVH6qPk6jOLOaUZufRqENX7yD+WJ1liO06N540Y\nl2k2BfkpMflVqOoj3apazQ0lM28V7TGbgh2DBnzjeD5PQYxK8rraByaPo3IkhXhEs3Q+YdTWE6a/\nP9MT23ciVqcU/JiV60sHuclxBphsb/y5dKkvkkyDlXw+aWjOEaUSviELSaEgla9bj5Sz/3GWpJBF\npEsex7K1Ch6isymkVSXubXX10VMA9iGiaUTUAeB0ALeKi8y8iZknMfNUZp4K4HEApzDznHo0Jm2T\nHT+6NLoe20FJc79597b4b2NUbcy8LCZwBLGSylK1tqW6pOompTpxg/qk90nEKaSUW7RVH6VIClk7\nhqnGTdFfOr1ym1SeeM5mDhEoMjQbbDEy5Ij2mKRgWHDoUPQ86Z2k8wpTLknOAfK4OWfm81imyeME\nxFUfZgKuPw9Ei4eLZs3H61LAXLT5k/7huAFeV67/XxD1fM6X7mQVYj5nwRQyJkYoKRjKGijDezB7\nYZb+fMljdMhMIc2mMNLVR8xcBHA2gNkA5gOYycxziehCIjqlXvWakBan4HnRQC+W4sEtusGdtaWn\ngPzRL7pjfnislxSic6WMACwBVVKI2Qu0QVjxlfD9C1djsFiKqYyy4hRiwUye2dActozNTKFDUvcA\n+lW1anRXCYuM9ny0+lJX12ldSYFNwWN9nELCS8RgHJeJ9urNA+gfKpq9jzzZ+8gsLbG0SLGVfuTY\nkUo2i/EYePrVDbjqwSX4n5nPSec5vK59LmMxpSbEyxH5rpnSGMgH7sEmDJW8bO8j2dCsIaL9g3bB\nckDSi02FTj0Ve54VSUHyPkpzdVYhurbV1Udg5lnMvC8z78XMPwrOXcDMt2ruPaZeUgIQiW26SS/n\noVFX06+s7cWlsxeAmcPBOlTSi58JcVD6fdUDS8Jj+a5IJJcIgxJ8ZIJqU4i5DxompdzGBSu34JJ/\nLYwxgkSWVKUY9iKCX9QwhXmvb8ZJv3goVqfpPTrackb9fHhOkRTEby1TCHy/mRlbBuwnfi7wPioa\nDM3pNoXovHzbYT++B6dd9ViKpBD1i1yeuuCQU5PYasRCSYH1EdpZ8I3bfmUipxWQrdaMebalMAXB\nKHNEsXxV/rl0u5GvPsqSFDy05/0gMZ3drbcMpuAx4zs3v4jjLrtfez2LRpcSNgX/v88MOXGvqY6R\n4n3UVEhzSZX1uzKDAIBPXvMkrrxvcSyjoUlSUCeCMfJSs7qU7zTl7VGhrhpKHuOe+avwuT/OMRr6\n1POvruuPEXZTllTdb13wGoDYTnMlj43BSe35bJtCtK1mnDDqkhIKL6dHFq3D1//6fPBcNnLk92WJ\nTeoj9bvqXT7VJ/+9YrOVTUHug5uUCFnZJdXGlVIuT2YoKmylUNnelG1T0B8LiFOFUH1EgQoxLimk\nrYYHi15mFtSix2H7deqWPsu0GoD/na5/YlksNYuMrE/icVx9RJKkoDKBtLLEuMyKi6gFthmmkA/V\nR3oVhSl4TUSRyh/DqD5KGCTNtocbn1yGNVsGtVQrO52AD3XyFD0Pn7l2Du6at8rob68Sg/Y8pXof\nqd1VUplCyYsNehUes5EwqgRBVsuItMqqTSFya9RICkE7Hl281tgeHShYsZY8Q5oLtQ881h5rVSap\nkgKHxwLqKlZ2SbVlCjj+dHMAACAASURBVKH6yNMTmt7BIn48y7wpvMccEq9CjCmkM6cs9VHkJBBI\nCoF6R+5Dgo2hOSui2QuTHOqIaN9gGYZmyz43oVjSq4/8mBrFfmQYK3fNW4V7F6wGoE8jU2tsM0wh\nLXX21iBtBCA+TPKmoRTCKaASP9PY/efzr+Hcv7+IGQ8ujmqSHs1K6gYAP/nQgYlzurQVMnz1Ufxc\nPhdnComBmiYpsG9oHtOVDIzn8H6zW6ZqaJbrOvNtUzG6I5+MOwj10hpJIfRmyZ7I8dWbTzx8qSZ+\n34a+IVx258LYOVMcgK5aY5xCyYsifFOInJx6wzbllmCYJhfbK+/TZG6L1SlJCkV7piB/k9QU00JS\nIEJ7jhLzKY3sDRRKmYulYilS2egkhXLVR9XAY47ZNcSh3vvIXNdVwZa+Tn1UQ4TeR5oR9b5fPhx6\ncnisn8j/llI627qkmiQFkRd/wugObabMaI9m7ePYb6exOP2w3RPn5XYPatQ6qmcV4Nsl0iSFASVp\nF3O0WhkMJmjajlklzywpdLTFfbVjrok5CgJ8IgkOiBiH3iWVjNfUM52SmyyBQsOfOjG/d+vcRJ/I\nKh5dllSTJCGjUIq7QZsg27hstsH0yxZxCvq+L2R433jMIWGOMT0RM2JoxqCGgWjbFxSaywUeY3IE\nNjhVT68S9E8fNQ1H7T0xdq7oeWGSQ51Noa8sQ3N1TEHYFOSgNdGucryPBLJyLdUC20zqbN12nDLk\nD6RTTXz+z8+Ex6bgGXUAZWUA9dU54ji6V0wak01BrIKSu3RxkDxLn15bpz7K53IYKkp1K++2oT+e\nb0d+x7RtFCOiojfeAhr1kVR1m6JaUI2tWpfUnEibkD25OttzCFIxIZ+D0dDcP2SX2htAyHnikpd5\nVc1I3q9CtnNY2xQkRqqTQtRx09mWixF03TiR6zd1r+z/n/YJRJtCQ7Nyc5rbpfo9Otpy6G6P79hW\nKDHac5HuPquMNGRJZ1nJ/Ioeh7EXHjPSch/Z8B+nPqoh0uIUVGSFwZtWWiZvAlOdxRJjQ7+fMTQm\nKWQ8Z4qiLJS8cNDppBmdy6UqKagDVbRPLkNATC6d+kguzygpJAzNcYNjW5CPSG6X6tYYK6/NXn0k\nUiyIukJJocyVoS7NxVCsP/VjZUjK658WjBXL4FumKsNjfT+pxXR3xImqyUCtc6GVEWMKKf0oxls+\nR77HmDLf0mwKKkHP55KEsliKJIVqXTiznD2y3GOF+kiWEPz/ySypNuPWqY9qCLFisGMK6feYJIX4\nqpdDlZTJW6JQ8sLNu2NBYVKaAh3aUyQFYVjTRW16nJys+QxD80aFKchtEvngx2aoj0yEUbikhjl1\npNvE6kqo0tR9FXSEVEy4WOyARhITdQsIpqDPkpo+FnTRzTFGZxhLsqSQJtlc8I+54T4K5TIsj1nr\npaWWoq60Gfp2Zy1WbNVH4n2FK7BKDLNsCjJEipKw7ezPNyFNV6tuyWLEKi1Q1VViToqzuVyapOD/\n/s3HD8WBu47X1jcc6qNthilkqY9kZEkKRpdUqfBLZy/EP57z8+yYgmUKJQ+rt/jeTfLcjVJn6+s3\nSQpFL9LH6iQFedUZlaUyhWz1kRiWofooRVLI8j4SZfrtlyUFYJftuvF8z8ZY+mhBGHXETqwOdcRT\nbYNsU8gLBmTwPkpD3OgspJhsg2uh5EUBkxkqiLvnrw7KL6tpPlMoJh9SCXZXe1JSSPPCslEfpa2r\noohmCrzf4ow1TUUyqGEK6nQolaIkdFUzhYxOV7UGaqBmyYsbmtskiSHh/h38nDS2E/vvPE5bX8sH\nrzUTaqo+MlyXB/f9C1eHx30GHWb/UCkkuqWYpJAuppvSW8gDUG9oTg7yfI7icQrKu6mSQlx95DO7\nrrY4UQEU7yOjoTluA5DLzhHhw2+ZggUrt+Dl1b0xN93Hl6zDU68kN3lPMzTr0naHdQWqKhHstc8O\nY6Q70ydhLE215pyJIQ6V9HEKaSjX6Ol5eoajDqskU0jPXWRqhfxImtolVB8RBalJ4n2YRse3FlT1\nESXVR54XLpyq9evPohcqLVB3QxRzUpQix0+oXnnhlptERrvKMPCEbYkpmOMUVGSqjywkBfnYtAet\nUAuM726Pu5NKEak6mNJGFEuR+khvaE6qR9oUl0D5uCOfw/q+oVifyRNf6Hffse9kXPKRg/CeA3ZM\n1JlmUxATSFyXiWM+R9hz0mgAwMb+QiyA6/QZj+OiO5J+9qHkoek39ZvKLqn5YBIWS/4K+YQDdsT/\nvn9/bZtV6FJLy3WJ73rgruOx3ah2qT1eah4nHcp1jyyxfkOapKQQH09q4sTwuQwHCBnvP2gX4zXR\nZ0TJ8SfOm6BmOBWBhzIKJQ7HVt0lBXVcKckfix7HCLxN7qMcmTMgO/VRDRHGKVjMv6zgGKNLquLD\nL2AKllmx0Vcd7TiuU3vdrD7SD4yi54UDUG9o1nsfmeIUJoxux8b+QiIVhZiDQl0wqiOP06bvFqpv\nZPzz+dfQK6WckImxIOK6dNci2hXwCagNQQptClpJId4fqk1BSApCB2ybeKygeO2I9gqIvrvoQwdi\ndEdb7LlyJQUbpiA3m5m1Uq3aP6pNwWRotoms3nl8Fxb84ER89uhpxnuKsqE5X56hWZUUcjmCKjjL\nLqnVJpDLEs7UeaaqdoeKnj8uWVxPsSl4kVrNRPyd+qiGCNM9pwxoQbCywujFRPvkEXvEzsuDW9Zr\nm/yiVwepM7Yf3aG9XrakIKmP9IbmZHDWyk1b8e2/vxiVIRGoCaM6sGlrIXZO530kCKz8zmLoPvHK\netwq7WEge7qETEG4UCrqI6EmGyp5VlHeHW0RE1GhEt7RknFcRNaGidoyUi3IGCp5CSYdtylE7pfq\nPSFTsFQL2QSvycTEY/0CR60uoT7y9IxKtFcOblPxjn0mo6s9n2oXUBPWJfcxNj+rMzTrJIWQ+Far\nPrKkBQLqhlJDgUegmuVULyn4/4UBXgfnfVRDiE5OXWkGRCgruEeod05Q1CWxWAcLprA+0NdPGKVn\nCiblbZpNQQwak0uqymhueS6+6Yw8yMd2taF3sBhPFS0VqzIFG523vCrtVDZZKSqSQkc+up4VTQtE\nq7Stkg1Hl0oCAL578hvDVBoixbJQC+YlQpNFU2T1gM5dVtSrfrOhUpTmwhY2ahuZmPhpSHTPpEsK\nDH0/y95HJqK1g0HqlSG+d44o2BgproIrR32UJ41NoeSFC456ex8lbQpJSSGfi3o8khRyxn060pmC\nkxRqhrQ0FwKyuiIN/odOfrjYBjYyUzAYmsV4k3XNMrLiFNQ9qoqlKDhGZ2hW26WDTDzHdrVjsOjF\noprlNolVmyDeNuoNWW0TSQpeom2q+shm4yGhR1ZVDEByxTx10mi8YcexAEQK58gLy5ccMl8FQBCx\nGi44knWVpFWxjELJbgP5WF02TCHmnqkPXlNVqKqkYIqElg3NsnQ057zjsd9Ofl+OTfFEEyiE0pPw\n1483SEf3Iq+6pPpI7trrnliGZ5ZtDN+p/jYFRS2pYQptuVzI0ENJIZ+UFKJ7zMTfMYUawsb7qL0M\n9ZEum2NW6moTxnWZmIKpnTbeR3bBayrkVZuIVJbdUj3JrTVNUjDVIk/S9nyKpCCpjwolL3RxTItG\nFeXptlvUETlxRgQXyUFV5Uy+MNli8Fv2XRffQadiKtNuHE8cZ2ierEMvZRB3AdXQbIpCl2M+ZOY+\naUxnSITHGsayDNkltS2f3JZS1/cEfw5vHVLVR/r7xTvZMncTsr6RKompkkKh5Nv5wrGWEtEs5mbe\nkPIbcEyhprCJU2i3VB8VSozOfE6TutouIZiM0R15ozrIFGFrur/gSRHNGsLoeRY6Ui+uPgLibql+\nMJpfhpigQg1kYy+VuyxySfXrlFddsvqoUIr6oTdln4SIKciRtQjqiBr3/VMOABCtzIShWas+yn6l\nkBDrvI9EH+kkBbvE3hFkAmVql0xMPE8faKnW2tmmSgpZ6iPEjOZA1Oc2koKc5qI9n4uNOYae8DH8\nOJSBYtIlVWdMFiqxaolouXEKCTVh0UOeom+XGqcQ3EMpjg4Ze/rUBNsMUxB9nKaXFUQla2NwANh+\nTEfC6BM3yNq1a3x3e0INJOBHZ5pz/CQimkscDhp18vht8lf5aSK13D1i1bdxayQp6FxtBXG30XnL\nk1R2SV22rh9nX/9sdJ9BfbQlJZlZm0Z9JFZfshrlzCOnAogb9nI5Cld9wvBsi1B9FPyW6xKSTdIr\nxbxVpglxSSHbZdEzqY8yJIUSs9bQLNt1VOKflgdLRcLQbJElldnv56RLKiXmIRA5NJgCPW1RtU2h\n5MV2Z5Mjmj2OL9Ji3kcNVB9tMwnxRGemcX7hkWKTRXHSmE5N0IykPrLkCuO6242qAM/gZ25SH4nk\nW4A5IV6JfWO0TUownaQQWwUXzOoj09CVJ7BsSJ63dnPsvqT6KGAKqZJC0vMqipZOUx/FI7tzFDFc\nm6+oOjEUYkyhGLtH4G/PxDfTsYFMoEz9qzIFm9xHtpKCnBBvtEL8w5QnNuojifi1q7p1NqvG8jnC\notW9AHxGNlDwtN5HgCQpVLnstfE+GtfVhiP2nIg7561K2BQKJY6psMSxnJIlh7ijgjz2VTj1UQ0R\n5T7yfx9z6X2Je0QOH5utHCeN6UhM9IqZguEasz5QThcPAASrkhRDs1BH2Q6scQFTWNsrM4WoPWLV\n1pn3J2DM0G5YYcUlhcglVW1SjiTJrRjFKfQOxtNuyMhrvI8Af2Jr3T6lCNI8xW0K4Xix+I4RU/B/\ny3rmSFJI33vYBlbeRyQzBb3ThPptOjXBa3pbRHR9lJJET/T5OBtDczCmSRiaLeMUBCE98YCdMHWi\nH9iYz+ndNDtrZGjOjmhmdHfkQwlJNdqrbRBjVPzXBbySwU4COPVRTSH6WHzkpev6E/eIlbEdU+hM\nDEbZpmCKelYxvrvduDTymLXR0O2Ggf7lG57FkrX+toHa3EdIdydUIVZ9y6S+0hGZME5BmkCmAD9Z\nugptCiVPyxg7JKYhyk6zKQiioaoYCp5n2HQoei4XkxSi1WeW04G4H0hXH+UNK79yYGNoluvxPJMa\nKP67U4nCVdUaav3MSTXJ1oK9pCBUomJFbGNoBiJCutv23eE5k/5dSArVxilk2cnEQky0YYLGk1B+\nH9EeMVbjEfHBPTlnaB4W2KS5EEwhbTUq4DMFRVKQRpCNXQIQNgU9PAZeWZvcG1ZICmnDY+5rmxPn\nRPBajgjXnDk9cV0db6I/RLZXwD5ltYkpyrSkLZ+cGAIsXS8UI+8jHcP+xnvegFlfPlq7rzAQjyiX\nxftQXM8Jl9Tod8gULJi7aKcuollsCmOa5OVA7iaTHSouKZgimuPnVPWRmsRQXVB5zEl7WnC/naFZ\nVh/lMKi00UT3hHqwPZ+LCKhRfZQep2D7PbIkxaGiB5KkTJ17eVtMUqDYfzn/kU3uI8cUagibOAWR\n7XP23FWZ5U0a26lNXV0uxnebV1bMHOpQZZj0jVnwOJrQ73rjjthr8ujYdXXAhZLC+nRJIRzg0uub\nmIJcR7gpTokThFwYFkWdYnLqGMh2o9qx/y7jjBNdSBffOXk/PP+9E2J1ANEub+H7SMbLrJgVcT8Q\nBcrJjHN9n58aPV8D9REgEakUvbtAiVkbs6EydlVS6NmwFa9K0qHwNJJtCqo9TfxU1Uo6hKmzAzWd\nPFYYZiO6GDsdbbmwr/O5dEOziSnYMC/ALk4hn6PQDrmdJhA1p2EKYg5f9+Sr4bUo6hmJ1B3h844p\n1A5ynMI8zSoaAMZ0Zou+Att1t6fGKdhifIahedHq3oSeVjVm2UIkxFNXKwLqgBvd6U+sFRu3hucE\nkTxt+pRke6X3V4mp8HCRJ3wkKXja2AIi3y214Jl3bwMiYmEiAFsCyW/70Z2xNBuhoVnxC5e9j6yY\nQmh/QOKZ1zcNgAjhRvLVQqwmrQzNnj6QT5U+VZsCAPzh0aXhseizaDvOpKRw25fejgvet39ZO4Pl\niRLR1IA5lYOQ8mVJwaR/78pwSR2XshgT6GjLWWVJzVEkweqyE8jzSkxd8Z0u+Ve0/7dwDmnP5ZA3\nzHFyNoXaQQzW/sESTr7iIe09aobDNHS05ZKGZtud1SWkuaQK9dFesVTO6dJFGjyPsXLTQDhw8wqh\nShp7KcGAhOi/+/ajNO01q8+EikLuMkEoCyX9ihbwVQaFopc6OSM9rf77iQmrrmLlKNOcMnHF6k6f\nJkKpP3RJTXofLV3bh/Hd7VUnZhPIIlLyN/WYE0Z3ADHpc+rEUdh5fFdqmaLfZPWROmYP2GU8Pv32\naemNV5AjwuSxybQYJr4iGGJHPhdT/en6VjAFU54wETB6xmG74+Ap+g1tOvM2TMFPcyJsRzqbgmzn\nyaek9F6xcSu62/PYblR7Q11S68oUiOhEIlpIRIuI6FzN9c8T0YtE9BwRPUxEdvmKK4CYuGt7B433\nlDNvO/I5o161HGSpj1Zs3IopE+IEWCei2sBj39ZwQLCrkzxfHv/2uxJMjijp/iqI/cQxycl81N6T\novsU9ZFguHJpInlYoeQlNk8RBLYtn8MNTy7DyiB5oA5inpgW4/cu8Pe2SDIF/39eyTUjG5ptGL2c\nhPD2F15XNt7xpcpaQazWdcThoCnjY9/UM6iPZNz/jWMTNgUVahoT5tp4weRywA4KU/ClEPMiCVD2\nwjDFKQRMYVSn/t2E+qg9T0bG0dGWs9wiM1IfjdcxBY2hWSfVLl/fjykTukFkTrPS0uojIsoDuBLA\nSQD2B3CGhuhfz8wHMvMhAC4BcHm92iO+wZpUpmDf4W35ZCIudQAdvNt2maswfy8Ff6Yf/8YdEuW9\nvnEAu2wXL0O3GrHB+r4hrNi4FQfu6u/qJFYtnW057DS+K/H+BEJ7MAHFRBReRToV1jff8wacfeze\nADRMIdzrIDonB6fpsroCwKatBfQNlbCxP80V1Swp7LpdN65/YhkAYJQShSvrpVVjoJh85aiPvv/P\nefji9c/gyWADIEGYxgdM3CQRloNQfaQUtf3oDvztC0fGJIVCSS8pqMga9j0bgu1Ag2/np0+v/l3y\nRklBX3ZMfRScM8UpCElBjbwWEEyhIOUL++937okbPndEeI+vPrJ7DxFUqVUfpRiaZSzfsBW7BRK4\nKsULDANPqKukcBiARcy8hJmHANwI4FT5BmaWlfujUW7cfxkQA032uU/cU0Z57Zo0F7KkMHXiKNz8\nhSNx0pt2Ti1nXHd76PbYKelXc+Qn0hsqeZiyXXfsGeHhsOuE+PksLFjpd/d+OwVMIWi+IIjqOPUl\nBX+ICHH7j48tBaBXtbXlc5gabIyjMoVOTXptwSiGil5iRZvlkv+DD7wpPA4DE5WHPnzoFOw4rjP0\nAFIlBdEUVQUhG5p1rssqRP8Jg/zG/iG05ShMiS6Y+B4Tkyq3sM4c4dazj8qsyzPYFLrb82jP52IG\nyvV9Q0bXYBlZiyHxbWSXVJsF1EUfOjD1ej5H2GFsfMHDMEvsMUlB+nY6JiJOycT32yftF0omYjz7\nuYn86/vvPC5M7CfqsdrDQjI07zQuuQjUMQV1fjAzetb3Y7dgTpsCVFtdfbQrgOXS757gXAxE9EUi\nWgxfUvhyvRoTSgpb4pLCf79jT3wu2BCknP5uzydD0WVJ4Vsn7uenamhLL3R8d3uooujUbEADJIm/\nUB9d/OGD8LV372vdZmFgFIE/4ZaFhtVLjiLC3d3h/xdeKe35HM49aT98RtEjCwKpuhnq3Faj/Q84\nYWhOm4pE/l4WR+090W9nUOfO47pwxmG7h/dddtrBMdWIyaaQeG+DntoEEd0r3m3zQBGjO9tC5i3U\nR78641DM+ORbtGV0t+etJrxgqiohFIStkmAttd4fnHpA7PeuwaJk1ouv44e3zcOStX3IETDRsA+I\nwBmH7W7MAEzkv8PEMckyTG/ghZICSZKCnonoaPkHD901nOPC0FwsRfnC2nK5GA1oz+diXlgm5Mhn\ngLtt361VB8tzWRxu2hqXfAeLHrYMFrFDwFR0QXD+863NFKzAzFcy814AvgXgPN09RHQWEc0hojlr\n1qypqJ5cKCnEmcL4Ue3hByjHTtyezyWYiJAUbv/y23HSgb6EYPIUEt4447vbQx20vPqW3QTV1ZQY\neGO72vGld+2DJT8+2arNS9f1oy1HoTpKEJJwl6oEUaJwxaLuw9zRRvj8O/fC+e+LawRNKyFBOMd1\nR+K8jfpIB2GgFuoYMU9yOcJFHzoQx7xhMr5+gs8sZc+ahPpIuKQSYctANEm3DpXKWpEJFYUob8XG\nrdh1u+5QlSCY+PhR7TjhgJ3w9/93ZKKMrva81YQ/83dPAkgSTtUDa9qk0bCFWu1bp20fHj/wjWNw\n3WcPBwA8ungdrn74lbC+u855J+78n3eklm16I9FenT7f1PeC6Xbk46modXp27YqdKPxGIgJ5SGYK\nikpYzQllQj5HOPWQXfHQN4/TLibaNYbmzQNJpgBE8151ExYYBp5QV6awAsBu0u8pwTkTbgTwAd0F\nZp7BzNOZefrkyZMraoyJKchGxhIzbjzriMSzOrTnc4kBIFb8cipskxFr9+1HYcKodmw3KtqfWb5X\nJmDdHfnYqkwt03ZVW/IYu07oDpmAKimokNVH3coq2/RepliB3bcfhe+fcgB+ecahUhmR3j4hKaSI\n7WqchkoU/vBfh+Hs4/YBEJ9cqtExdEnNETZtjYLiXt80UFbKZVGuWMkOFT3sOqEbB+wyTnv/obtP\nwGES4QV8ScyGKTzfs0l7XjVgTilDtahKHfJCZo+JozFJp/eHb8fYd8exiWs2kL+ZXB+zWWJnSX0k\nuxOr4/+yjx6M3TWqurZc9JxYCA4VoxQr7fl4LInOXVaHLPtKTFII7t1FUQkPJZiCvu5a2HKyUE+m\n8BSAfYhoGhF1ADgdwK3yDUS0j/TzvQBerldjxKpYpTVigxXAj/Q8Ys+JVuW15ykhPus2VDERz4++\nZTc8cu5x/h61gaFZlhRkItyRz+Hp899t1S4AOOYNZsYpu5LKWwMCwLq+uL2FpPYnJAXDe+kI25F7\nTcSx++2AM4+cGjMsyllpVUnhgF30boJye8N2pkwUWQwfpdlMxm9zXJzfb+exsdXqHhNH4fg3xnfZ\nk6EGdwG+yuXY/XYwPZLov642O/UR4C8+VJYZ6dAFw7cnHuqtqr1I961tiZPpPtllWdWfm9YD8uJJ\nlvLUKky2m1wOuPI/DsW79tshlJYLkqSg2glNKhwVWbGkcfWRf/MZb90dR+41MWy76A/R97ZSSj1Q\nt5qZuQjgbACzAcwHMJOZ5xLRhUR0SnDb2UQ0l4ieA3AOgDPr1R6zmxuHE8nkUjr3++/B3kqsQHs+\nh672PO4+5504ep9JsedlwmiKfWjPUygNFDXqI3lAivO/+9R0/OiDb0IWVAIut0kO2pE3EdeBKPI+\nUgOc2g3vpYu2vv5zR+CUg3dJnA/VR0WOMYVFPzop0d+65wTSVtidkveUmkhQTp29KcgE+7tPTcex\nb9ghka8mbZKGkoIk7EyZ0I3Dp22PX3/8UPyPxu6j9lN3R1J9dNx+O2BHzfaWfUOlhKfb2/aaGLQ1\neidb/bM6N9RVqs7oWUs1hvxd9t1xrHEeCqYrRzTrdikzvXc+Rzhs2va45lNvDeeeiEgGfEkiFkdj\nKS5mMfO4+ihQeeYIh0+b6Gcu9jiUFDoyJIXhQF3ZETPPYuZ9mXkvZv5RcO4CZr41OP4KMx/AzIcw\n87HMPLdebTF9uKIXbaeoSx4G6BNUiQGz9w5jcNp0X0t2jaRvFegwLCPkgVvUqI+6JSIkVmrH7bcj\nPn74Htrynj3/3fjCMXsBiK8yLvnIQbj+s4eHRtbRHbKHU1xSOOfd++Ly0w4OvWZIar+6ajJLCvZD\nSmQj/dndL+GxJevwhh3H4r6vH2PMAisIvCCoYXxCypwUk0uXfiGMhZDaLIzw8jcspfjOA5GkIHs/\nTR7rp1Y/+cCdM42PQGBTUOoY19Wm3ZWvb7AYq+snHzoQ33u/bxwW/U9EVnsbAMm5oS5kRGR52jMm\nmO46XFKfib748KFT8PPTDzGmSBGvrEoKar+Zghhj8zJ0cvDC8dOep5jbsKrClD2TYuVmcEidoRmI\n4nR+/8grYVbjjiDjsC7KfLjQcEPzcMHo5iZtYWkKVBEbu8fOScQ+GQkcHZtWG3F/8rg+EYirj0yr\nchkTRneE6hG57NOm74Yj954UEkXZVhHuAhW08cvv2gcfOnRKOC1iNoX26mwKJogJ3T9UwuSxnakG\nUkFc1UmfNilFn+r81cNAsBzwq/84FF8/Yd+wfnmTopKUpVUHYUSX78la6amr7+72fCIgrMT6NOl9\ng8WYa++ek8dIG9X759pyZJ3fRx2/OiOn2l5b1bbpvu9LHk6i7KP2nogxnW3G2JBQUpAT4uWS6iNT\nbrCYBB+qLqOYC9V5RCYH3zzxDTj7uL215arDL22uyHNTOEz88Pb5WL3Zt3V2ZBiahwPbDFMw6TaL\nHoeDyMQUxF6yMuSVkxqUZGNTkE+H6qOYpBBNaNtcRyKcnjUOnYIZyCvmSGw2qI8QqSBU9YlJLVau\ny5y84jW5LwqMkaJQ4+00Q6y40lbNeSLstv0onH3cPuE4kYPlTCmoBUSfynwjSyesMrZujfeRJ0mx\nMnoVSUEe2qLcrvactaSgTo3/396Zx0lVXXn8d6qrqxcauoEGuukGmqWBBtkbAwHZbBZxmahgZOI2\n0TEzShRBGTsaP2acZNQYzcRMEp3Ej5/PjGZGTWZU1HFkyXzijiuigOKIQYPigjsi3XXnj3fvq/tu\n3bdUdVU13XW+fPj0q1fvvbr3vfvuuefcc8+xtTc1KFKaZjar93X0sql3yy/KLQCsnNHoMR/p5Uob\nsPmZj7TjVLs43JHSFOIl3mvpQr7EopG43xm/t3C8d07PYz7SrqH3KWotTWpOoZeaj440bG1FT3bv\n19DJYp/VR3DmiF8sGwAAF2BJREFUdfWRq98oXx8xqIlmvfF4NIWIUVHVyMM2qFXXsAkFW3gL/S8Q\nzc4MZK4p6IJ4QIDfe9+yOGplaA0/85INVe5yi/lIj59jMlcL2dHpE4JaYTNNhWsKhlCwzCnobVO/\nr58d6vDN16zaXlm8BF8fXQudEybbF1KanWqQ5qW0ouhxvuzX0oWCuhd+WQOfaF+E606d7I7cS0so\n5ZJK6aYW8z7+4ymTUF9d7qlXSlPwTjTrt0K/x7YYS355oG88bSrulG68ev0Abxwkff8BOaelysWa\nQoGwvfydQni8j3zPNR58aYD5SFcf/ecUUtupdQp2T5monh5q5GEzdShtpsJmPgpwSVWXMl1S/bSX\nrrjM2UIErGlrxvGT6vHSD5a6oydVXvVbQWNWNWK3JSbSQ2ebVCRKcPOqaQCc9StByXZspqkwTcEU\nqrY5Bb1t6qPKTw56c0p4NQUpFEpj+N7y8bj3wjk4dboT0dZcaKjIZE2G6syjJB8yy6ajp/NMGELB\n1BQqE3Fvh66FnyiJUZpjhamFrTp6OJ5oP9azTz2fIO8jfW7DNneh3gnz/pWXlmDKsBr3s80l1dmf\n2j4gPf+OhInmosnRDKhOxNuYndGY9D4KMBGYAsUbCjn13fPfX+xpBFHmFNxFOT5zClFxE8hbqqFM\nSp6JZl/vo1Snq84rN0YufvU68Ll/GJEwbJrCmraU505qQjCmlTIY9XLZyuvGPvLpudQ5nclk4MjY\npimEqf9p3kcW85HQwpyXxmL4Ek4ZzIVPuiBWHVSZ9LaaMqwGNzRW4/oVk/Hyn+1rHCIpd/IYZcKL\nqin4Xdr2jqhdfiFSFAmPlk5p9zpKvhE1oXu4I+lxzdbvhZ7SVk/RqkhpCunX1+unl0fvNnThdUCa\nK1Vdo1oH8kFRaQq229zRqXkfBYx+bKEQ3G3tKzNOu69Q0F5kZZrQG0I2NkVlVjFjAOnowialKZje\nJvKvtq/M9D7yUW9bfBZsRaF/SNiE1IjOeJIBg1bTY8lzmjbaDDq3U5tT+MnKKemeOJbzw9R/s6kN\n6VeWdp1Obb7LqykYQkHbVkXTR5rK/OmnEWSi3VWVed2ow4hy6ZQ3mTQfGfnFzfvtBMRT5qN0d+Eo\nJkzVfr/qFJ7Bhn4v9LhRtmdcGZDIR2+jevn1d003LbvmIxVNuACL1PwoKqFgm0juTKb8lIM606AJ\nVP0BpmkUERZ5KWHkWX2bhaaQSiDvbz7SvY/8NQV5jm4+iuh91FBTgW1XL7F+F0ZYiOlUOAL14oRf\nU9mbbeVNhggFdU5SpPIKVyRKIk10hwl1c6RdV12eprF0aFqsPo9iago66nnZhJLf/YqkKcjr9snQ\nfBQFpXWnItN6r212yIm41/vIvNdRnB3KPC6p9sGGvnamhCht0Fgp74U9IF9qX9zPfKSVUzk2ZJtA\nK5d0fwkKiE0T8MwpZGA+0glqggmfgHgeodBpWdGchaagGnVU85HNXg2k6kNIxYoxX7wg9bZvRK8X\nkzC1X/cnj4oyE9hGj34B8RTqd3RNIWGMJgF7pxCmKZgdX311Rbr3kdY29Q7EDKamf1Zt3CaU/OqZ\nzZxCdPNR+LWVv35UzzVv6Oz0+ZsojgilmlBQ1TfP0zWFklj6nKNq52Hh1T3eRyV2YaE0he5cn6Ao\nqjkFG1G8j4BglVRphLbVp6pTCrqe6iA8i9eymlNQI9to5iNz8ZpCvSRC/gPSX7wg9ZaIsHHtPOz9\n8KBrg45C8+DgODp6NMuopCJr2uYUvNc1cSO7at5HiXjKQ2Xj2vkAUi+0TpimYHYk9ZZ8FslkqqP0\nmBo+d4RAPOaMXnUN2NU6LZ2LXz2zEwo5NB9lmLHH430Uo7RJ2UjmI9keRg+q8tUUDhkTzaYgV+lq\nw3JWJPw0Be33jiRNgYWCZ/Gav8QPenFUY7F1an6hs2Me81F64ppsNIWgiWb3uhZNIS0tJ1LX8TMf\nhTFmcF+M8enk/7h+Ydpod/O6+daEKzqqmGmxcgImFWzuvu55oZqCFLJJ4Xa2iXjMHfvWViVQU5nA\ns29+mHZumKZgdqpD+pWnu6Tq3kfadyqoY/vyFlQmSrBwXCrGUodlIaTCrKbKdxAp7688Vwn5wwHv\nShDnzxuVFsFU3eewtQ9zx9Ti0d3ve821lonmKBpHIh7DHed9DS31/XDdQzs95VBcsngsvv9f2wE4\nAx3T/KxMaWERfj3u69pP6L/3oeF91J0UtVCoKotj7eJxbtjk1iZv5MprvnEU/iyT1geZNvbIPAW2\neD1RVv6OrO2DV9/9zJPKLxtNwU0gH/Bu6eq8UmX9NQWkRZXMBcMGVHrC5wLR6kuGphBlfHu4Q4ay\nCJpTCNEUOpLCY+I7Y9YI/OIPr2v3JP38MBOG0hTOnDUCHcmktJN7H5y+2l4fTavAhRWlJZ78Eaqs\ngN2l0dTu1LnZaApR0lTaWNPWnBbCXI+WG8Svz251O089zEV5abR5PBOVPtYNIW+8B2fOGoEX/vQR\nfvfcW0gKka4pyHp8EaIp6AMS/TnGLSZBFgrdzK/PbkVddTnqqsvxv5ctwDCZC1mp5StnNLovflBM\nn2NbhuD6h3fhzNnpcYn81EH9RfzJaVPxrTcPeHIxZ6MpuH77FlXhtNZh+IcHdnhMXDUVjreP6e3h\nIWQyNlfYgviZ6HHvdYI0o5Z6xxtKH02nzksFVbOhP7vD2pzCZUvH4ZLFY1OusVncGjVRu6hlsFu2\ntPSuPusUPpCagq1pBWkKfvfJ9mg3rZvvCaWhcNcpRDQftdT3w76PU/m1bc9ZCdAwoVBeWuKGnE5q\nz84csGTaVImc2GY2k6i6x8lkuj6qtKawPNheF9rUftvC1iPBfNT9JehGdEk9YmAf16SzXCbI8UbK\n9L/OmMFVeP1HyzF6UHRNQW8cVWVxzBs7yDPezGZkHtNG+Cbnzh2J1354nJvwBQDqqh0BYYbM1tHz\nGOeTKJpC2jqFCL3xpMZqbLt6CY63rOZV98nXfKS9tMoMVRZ3Jpr152rrPMNQnXdpwGAjmRRuh6l3\n6Mqn3TbCD5pT8Bvd264zelAVmi25EjJd0fyzVdM8eY+DktBEnacAvM/OfFcydeeMUVC8pJS22NYy\nBEP6laFWZourysJ8pJfNfPamE4Oasyo0RSkU1EP1U/FvWDkFT7Yf61HlMon+qePnKWNTOfWXMxuX\nVHW6dUWz0ZEBjh0bAD74LD2PAuCMpHU1PZ9EWdbvNzEelkbXFmlUPy/M+wiwhzdXmAmCoqB+M8jb\npFMIN6dwh8WGb4u+mgqumN5+/EbiUR6tOqQqoseNoqos7ob19kN1jlGvCRjmoy6aXGJEvsLZ1RSE\nwKC+ZXjqe23u3KHy5AubaPYNCWPGUzPqERQ+Pp8UpfmouqIU73/2la+XQiIeQ121dzIsW63OL/aR\nOdEKeF9OW+7aMNQoIymADd+di0+/7Ag8XgmF94xsdCkzVPhoOldEGd2ZroPThtVg8879ac8qKm7s\nI785Be2hm0lQdJT5oLQk3UPFj+tXTMbtj+/BjOH9/cuXhGtSfEczwShGWTRTWxY/hS48b9FyRUcR\n+OrUTM1HiraWIdi4413rd8oZIxOhoIgRZRQLy8Y3pjVglE903rgl2oHSnqvkYOOLEE3Bz1pg7u/O\nVcw6RSkUzHy2Uch2AkjvWNYvG4epjTW48M7n8PUx6aMn/eVUwd8yIabNKRzV4J+5TKG8QPzi1wPA\nlMYaPPvmgayEVK5Rnbd6eS5cOAZLJtZhnE+c+zDCNAX9mdsi2SqUUFg6sQ4btu2L9Nv11RVoP64l\n8JikEG5azU8sAl7PoqdQnbWtThOH9sN35o/CWbOb0KClg8ykK1JumJlONP/LWTN8Nbo1bWPx8cEO\nnCJjNNVUlnqi1NrQA+J1lenD+2O6j3COuQOtVOGVhtZfOoaEaar+QsFb+AMhdS4URSkUVN+biUlk\ngCVYWxT0BnHBAice+/NX2Vf86g08myiJKbNPtOOVfbjJSF948rQG/HzLblRXlqJ9+XicPK3BOl+S\nC+7+m9nYse+TSMfWS41AD2SXrUAAUiM+v6ig+rNzXVstz6VBpnac2TQgslCIQofMqa0zbEAF9n7o\neMTZBiopTSG9TrEYWQVRJoMjN1tghi6pROl5DxS1VWVu8EEA2LJuAT471IHTb33SfeYmYQI9V6gm\noAvB606djJlNb2He2Gj54ktihEuXjMWdT/3Js1/vf+IxwvQR/lpjISlKoaAI8m830ePytB83PvJ5\nmTRavZFkE/vE1RQyqNeDFx2Ttuhu7eKx+NsFo12hManRq3X8cf3CjMvmx8ymAZhpuAL7MXKQo+Ir\nN+Gu4nYsPvfatsDQpinMGDEAG9fOw+hBVbjxkVet3l/ZkBTCHdErj7jpw/tjxfRhvlFYlQkmk3ZH\nRNhz7fFouvyB0GNT4SFyF+bCpH+fBPr3SeCxyxf5HuMuPDTqOXqQf5KmbLCl6q2pTOC8Y0ZldJ3V\ni5qxelGzZ59qJsMHVGLLpQsy0tjySVEKBX1xVlRUBM9F4wfjO/NHZ/ybSyb4J353y9XFVqHej0wG\ncRMsAexiMfKENjYZZjFbFIJRtY62svfAFzm5XthoUxfMfcvi+PRQh6/LoFqo98yVbTkpG+B4H5WX\nluCxyxfhwOdf4YSbH8WSCXVWTyqFLbVrLvjRyZPwwwd2uAsMZ42KJsjzhRK85pPbtG5BTn9Hd0m1\ncVRDP5wyrTGraytT3JwxtaFCvE+iBJ+HTGjniqISCg+vmYe+5XF8+/atADITCgP7OC/DR5aQBmFs\nu3pJpHUHpnYwuG8ZTp9pLvMKuoDzJyjMRU+mSU4GRs0oFoYeadOP2qoE/mrOSCydWIfHX38/o3y8\n2dJQU4G3PzrotoeGmgo01FRg5zXLQl2VL1gwGmvvetEzZ5ALTpwyFCdOGQrAWcOQ6+tnimrh6h59\ne87INFNbtqxfNg5TGp18CGquwW+ObsN3j8n6dwZWlWHzuvme9Ul+bL2yLesFg5lSVEJB2Z/PnTsS\nl92zLaNG1L+PM6kUNgFmw88lMoynr8hs1NlY4zQuPWtYrjnPJ1FLIaiuKMWvzpiBacNrwg+OwOqF\nzbhp46uBsXKeuXKxu10IF8Gd1yxDSYzw44d34bRW74AgytqVU6Y3uhO2+SJf80uZoMY96slddeKE\nnF1bzf0BzsLUJ9uPzdrDLQybB5kNcxV4PikqoaBY2ToMK1szGIFD0xQsrqS5pKW+H862rIyOwvCB\nlXiifRGG9M1PA95z7fF5uW4mLDuqLmfXuritGRe3NYcfWEBUx/+95cGeScXOT0+fips3v5bVyv9M\nyVYgXLBgNLbseq9Lv/3gRcfg6Tc+6NI1MqUohUI2KE0hV5OIfjx0cfbqKOC4OjJMb2fpxDosnZi7\nAUI+WL9sPNYvi+6UYmPC0H7Web98wkIhImXxElyxvAXHjM2faYZhupP7V8/1jerLFA95DXNBRMuI\naBcR7Saiyy3fryWiV4hoGxFtIqLs7CYF4q/njcL4usJKbYYpFJMaq7l9M/kTCkRUAuCfARwHYAKA\nVURkzgY9D6BVCDEZwD0Ars9XeRiGYZhw8qkpHA1gtxDi/4QQXwH4dwB/oR8ghNgihFBO508CyK/b\nBMMwDBNIPoVCA4C92ue35D4/zgXwUB7LwzAMw4RwREw0E9EZAFoBWAOIE9H5AM4HgOHDh9sOYRiG\nYXJAPjWFtwFP1sVGuc8DEbUBuALASUKIQ+b3ACCEuFUI0SqEaB00KFoQKoZhGCZz8ikUtgJoJqKR\nRJQAcDqA+/QDiGgagFvgCIT9eSwLwzAME4G8CQUhRAeA1QAeBrADwF1CiJeJ6O+J6CR52I8BVAG4\nm4heIKL7fC7HMAzDFIC8zikIIR4E8KCx7yptO3chJRmGYZguQ/kO25BriOg9AG9meXotgPdzWJye\nANe5OOA6FwddqfMIIUTopGyPEwpdgYieEUK0dnc5CgnXuTjgOhcHhahzXsNcMAzDMD0LFgoMwzCM\nS7EJhVu7uwDdANe5OOA6Fwd5r3NRzSkwDMMwwRSbpsAwDMMEwEKBYRiGcSkaoRCW8KenQkS3EdF+\nItqu7RtARI8Q0Wvyb3+5n4joZ/IebCOi6d1X8uwhomFEtEUmaHqZiC6W+3ttvYmonIieJqIXZZ1/\nIPePJKKnZN3+Q4aUARGVyc+75fdN3Vn+bCGiEiJ6nog2yM+9ur4AQER7iOglGeXhGbmvYG27KIRC\nxIQ/PZXbASwz9l0OYJMQohnAJvkZcOrfLP+fD+CXBSpjrukAsE4IMQHALAAXyufZm+t9CMAiIcQU\nAFMBLCOiWQCuA3CTEGIMgANwQtBD/j0g998kj+uJXAwnTI6it9dXsVAIMVVbk1C4ti2E6PX/AcwG\n8LD2uR1Ae3eXK4f1awKwXfu8C0C93K4HsEtu3wJgle24nvwfwL0AFhdLvQFUAngOwNfgrG6Ny/1u\nO4cTc2y23I7L46i7y55hPRtlB7gIwAYA1Jvrq9V7D4BaY1/B2nZRaArIPOFPT2eIEGKf3H4HwBC5\n3evugzQTTAPwFHp5vaUp5QUA+wE8AuB1AB8JJ/gk4K2XW2f5/ccABha2xF3mpwDWA0jKzwPRu+ur\nEAD+h4ielblkgAK27SMiyQ6TP4QQgoh6pd8xEVUB+B2ANUKIT4jI/a431lsI0QlgKhHVAPhPAOO7\nuUh5g4hOALBfCPEsES3o7vIUmLlCiLeJaDCAR4hop/5lvtt2sWgKkRL+9CLeJaJ6AJB/Va6KXnMf\niKgUjkC4Qwjxe7m719cbAIQQHwHYAsd8UkNEanCn18uts/y+GsAHBS5qV5gD4CQi2gMnv/siAP+E\n3ltfFyHE2/LvfjjC/2gUsG0Xi1AITfjTy7gPwNly+2w4Nne1/yzpsTALwMeaStpjIEcl+A2AHUKI\nG7Wvem29iWiQ1BBARBVw5lB2wBEOK+RhZp3VvVgBYLOQRueegBCiXQjRKIRogvO+bhZCfAu9tL4K\nIupDRH3VNoAlALajkG27uydVCjh5sxzAq3DssFd0d3lyWK/fAtgH4DAce+K5cGypmwC8BmAjgAHy\nWILjhfU6gJcAtHZ3+bOs81w4dtdtAF6Q/5f35noDmAzgeVnn7QCukvtHAXgawG4AdwMok/vL5efd\n8vtR3V2HLtR9AYANxVBfWb8X5f+XVV9VyLbNYS4YhmEYl2IxHzEMwzARYKHAMAzDuLBQYBiGYVxY\nKDAMwzAuLBQYhmEYFxYKTNFDRE2kRZmNcPw5RDQ0wjE/73rpGKawsFBgmMw5B0CgUGCYngoLBYZx\niBPRHUS0g4juIaJKIrqKiLYS0XYiulWuGl0BoBXAHTLefQURzSSix2Wug6fVilQAQ4nov2UM/OvV\nDxHREiJ6goieI6K7ZQwnENG15OSI2EZEN3TDPWAYXrzGMDLS6htwApE9RkS3AXgFwG1CiA/lMf8K\n4C4hxP1E9AcAlwohnpFhU3YC+KYQYisR9QPwBYAzAFwFJ4LrITghjecCOAjg9wCOE0J8TkR/B6AM\nzqrUxwGMF0IIIqoRTowjhikoHCWVYRz2CiEek9v/BuAiAG8Q0Xo4+QsGwAk7cL9x3jgA+4QQWwFA\nCPEJAMiIrZuEEB/Lz68AGAGgBk6ip8fkMQkAT8AJ9fwlgN+Qk2VsQ36qyTDBsFBgGAdTZRYAfgEn\nlsxeIroaTnydTDikbXfCed8IwCNCiFXmwUR0NIBj4QR0Ww0nMijDFBSeU2AYh+FENFtu/yWAR+X2\n+9Lmv0I79lMAat5gF4B6IpoJAETUVwvtbONJAHOIaIw8vg8RjZW/US2EeBDAJQCm5KRWDJMhrCkw\njMMuOLme1XzCLwH0hxOR9B044dcVtwP4FREdhJPT4JsAbpYhrQ8CaPP7ESHEe0R0DoDfElGZ3H0l\nHEFzLxGVw9Em1uauagwTHZ5oZhiGYVzYfMQwDMO4sFBgGIZhXFgoMAzDMC4sFBiGYRgXFgoMwzCM\nCwsFhmEYxoWFAsMwDOPy/+ke4M6cfER/AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YWU2n7kzMeuB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "fe40a129-7ffa-4682-90bb-6db1debb65ee"
      },
      "source": [
        "## Test\n",
        "test_loss_history1, outputMemory = testModel(inputSet2, targetSet2, gates, model, readMemory, writeMemory)\n",
        "plt.plot(test_loss_history1, label='')\n",
        "\n",
        "plt.legend(loc='upper right')\n",
        "plt.ylabel('loss value')\n",
        "plt.xlabel('batches')\n",
        "plt.title(\"Testing Loss- LSTM V1, Testset-Array size = 6\")\n",
        "# plt.ylim(0, 10)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4QSkplDBi1XYujoc5FvqSJ7SOoUbYaRJK5ERhTIGIfkdEa4joMcv9DxDRo0S0iIjmENGh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\nCgEBAQEBCoEoBAQEBAQo/D/Ygeo6YQdEjgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OxNAhayeP7B-",
        "colab_type": "text"
      },
      "source": [
        "#### Debug"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OoKj0c8CV88j",
        "colab_type": "code",
        "outputId": "62053526-14a6-4870-e964-cf3c45545553",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "np.argmax(inputTestSet[0], axis=1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 9, 12, 15,  0,  2, 11, 16, 13,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
              "        0,  0,  0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GIjmJSBlV_Nk",
        "colab_type": "code",
        "outputId": "aee24deb-8b87-4fcf-e045-7c719445f8ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "np.argmax(targetTestSet[0], axis=1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 9, 12, 15,  0,  2, 11, 16, 13,  0,  2,  0,  0, 11, 13,  0, 16,  0,\n",
              "        0,  0,  0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cCh2vI9eo8DY",
        "colab_type": "code",
        "outputId": "0dad4576-bd6e-4887-c2e5-6330fca1bfae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 18666
        }
      },
      "source": [
        "## Test\n",
        "test_loss_history3, outputMemory = testModel([inputTestSet[0]], [targetTestSet[0]], gates, model3, readMemory, writeMemory, debug=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "outputs\n",
            "Timestep:  0\n",
            "\n",
            " Gate Controller vals:\n",
            "[[1.0000000e+00 2.0749768e-30 1.1916680e-15]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.0453767  0.04226048 0.02849487 0.883868  ]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[1.9230084e-02 2.0583003e-04 5.4097444e-02 3.5864084e-03 9.2288029e-01]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.512354   0.00083659 0.00189412 0.07207454 0.40687093 0.00596978]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[1.2186808e-23 1.2133012e-21 4.1034584e-24 1.0000000e+00 7.3779851e-23\n",
            "  2.1282702e-16 5.5865900e-08]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[3.5184866e-03 4.8063193e-02 2.3894047e-02 6.6559601e-01 2.5245256e-03\n",
            "  1.3693526e-01 1.1938834e-01 8.0025529e-05]]\n",
            "[[4.5159176e-01 3.1089552e-05 5.2530062e-03 3.2291140e-02 3.7055090e-03\n",
            "  2.8689563e-01 6.2116768e-02 1.5811506e-01]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[5.6772782e-03 8.1340671e-02 1.4191890e-02 4.7099214e-02 3.7019590e-01\n",
            "  4.6109533e-01 2.5586702e-04 1.4742390e-02 5.4015005e-03]]\n",
            "[[4.9728586e-04 1.6325013e-01 6.7094785e-01 2.8066393e-03 7.2426163e-04\n",
            "  3.7025618e-03 1.6551193e-02 1.2989542e-01 1.1624657e-02]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[7.4379193e-03 4.8484094e-02 9.1743648e-05 9.4398636e-01 1.6029055e-14\n",
            "  9.8656967e-21 7.4103524e-20 8.3416541e-16 7.6490275e-19 7.8414146e-09]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[1.3653632e-01 1.3125417e-02 2.1065094e-03 3.0703610e-02 3.2844748e-02\n",
            "  1.9387269e-02 8.6959660e-02 4.1804025e-01 4.1367733e-03 1.5088046e-05\n",
            "  2.5614432e-01]]\n",
            "[[8.1861824e-01 1.1866103e-03 4.1935939e-02 1.2788396e-04 1.1283267e-04\n",
            "  1.5929068e-02 2.1714901e-03 9.9785149e-02 9.9708140e-03 9.9505801e-03\n",
            "  2.1137048e-04]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.00781604 0.04827284 0.00674964 0.00453703 0.1424778  0.00268889\n",
            "  0.00199615 0.00799214 0.00176385 0.7352005  0.0318898  0.00861527]]\n",
            "[[2.1818192e-03 2.9864493e-03 4.4218684e-03 6.0452044e-04 5.5626389e-02\n",
            "  2.5704704e-02 6.0334738e-02 1.2443966e-03 6.9052068e-05 8.4447902e-01\n",
            "  5.3961028e-04 1.8074655e-03]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[3.6810230e-02 1.5053297e-03 3.9957955e-01 2.8727984e-03 5.8195617e-02\n",
            "  3.5830840e-01 1.8043151e-03 1.6469647e-03 4.8614820e-03 1.9043056e-05\n",
            "  5.2889921e-02 2.1723878e-02 5.9782371e-02]]\n",
            "[[6.8405222e-07 3.0259639e-02 1.4960403e-03 2.1183260e-02 5.7697713e-01\n",
            "  2.2831335e-04 9.7733527e-02 4.4258453e-05 3.7412941e-03 2.5964089e-02\n",
            "  2.0117259e-03 4.1708662e-04 2.3994286e-01]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[1.5550413e-03 1.2430949e-03 7.8114673e-02 5.3044711e-03 1.5401042e-01\n",
            "  3.1111389e-01 5.7170969e-03 3.5578959e-02 2.3066084e-04 6.6627451e-04\n",
            "  4.0161628e-03 3.7658876e-01 1.8907957e-02 6.9526681e-03]]\n",
            "[[3.8272094e-02 1.9639877e-03 2.1917576e-03 3.1029666e-03 1.2531076e-03\n",
            "  4.6492435e-04 3.7088394e-04 8.8878855e-02 8.1431192e-01 4.7762599e-02\n",
            "  2.3740045e-06 9.1869710e-04 2.5475328e-04 2.5115770e-04]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[1.3818569e-02 1.4386839e-03 1.2363028e-01 2.3599780e-04 4.1851485e-01\n",
            "  7.5875809e-03 1.2377842e-04 1.6459118e-03 2.2313809e-02 3.1122419e-03\n",
            "  3.7259415e-01 1.9871132e-03 3.2326382e-02 2.6622610e-04 4.0436658e-04]]\n",
            "[[2.5247655e-06 4.1988315e-03 4.5429420e-07 9.8991245e-07 2.5548105e-05\n",
            "  2.3343831e-03 9.9195862e-01 5.6967925e-04 2.4808390e-04 2.1217998e-06\n",
            "  1.6255719e-04 5.9183702e-07 3.0141935e-04 4.1198688e-05 1.5305563e-04]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[3.7066128e-30 3.7137304e-31 1.9721000e-30 8.0971861e-01 2.3629589e-32\n",
            "  6.1188754e-28 2.3863946e-31 1.9028133e-01 7.5347585e-29 8.7095104e-31\n",
            "  1.2564325e-18 1.8353885e-31 3.9587424e-29 2.9392148e-31 1.9857788e-30\n",
            "  3.8376306e-30]]\n",
            "[[2.0305785e-25 4.3938325e-25 1.6529171e-23 4.1408792e-02 1.4455858e-26\n",
            "  3.9896306e-23 3.0989181e-06 4.4749994e-03 2.4898822e-25 1.2788677e-26\n",
            "  9.5411319e-01 3.5625392e-25 2.6038668e-24 1.2204910e-25 1.0970296e-24\n",
            "  1.8355019e-27]]\n",
            "\n",
            "Register controller  0\n",
            "[[3.1552466e-27 3.1788458e-24 9.8842195e-26 2.2599125e-20 2.8718260e-24\n",
            "  2.0481198e-01 6.8514164e-06 6.3038482e-14 2.5063392e-19 8.3460892e-24\n",
            "  4.7619590e-08 2.4799871e-25 7.8864104e-01 6.5401648e-03 1.2912038e-23\n",
            "  2.9621995e-22 1.4232832e-26]]\n",
            "\n",
            "Register controller  1\n",
            "[[1.3834112e-27 1.0671792e-28 1.0478688e-27 1.2679284e-16 1.7715035e-27\n",
            "  1.7907765e-15 1.4170737e-09 4.2862471e-12 6.8089277e-25 1.4088726e-28\n",
            "  1.0000000e+00 3.9121061e-28 2.0289024e-11 3.3972417e-18 1.2649496e-28\n",
            "  2.2088881e-28 1.5536732e-27]]\n",
            "\n",
            "Register controller  2\n",
            "[[1.74249533e-24 2.23958797e-22 5.93375178e-23 9.47114655e-17\n",
            "  1.75265507e-24 1.13814545e-04 7.23106461e-03 9.22801904e-11\n",
            "  5.07707748e-21 2.88034296e-22 1.92546402e-03 2.31247549e-25\n",
            "  9.90729630e-01 4.65497418e-09 8.19594421e-23 4.56895968e-22\n",
            "  1.77347086e-23]]\n",
            "\n",
            "Output registers\n",
            "[[[2.7659265e-23 9.9999321e-01 6.8514164e-06 3.5217017e-21 0.0000000e+00\n",
            "   0.0000000e+00 0.0000000e+00 0.0000000e+00 4.4952245e-08 2.7327276e-19\n",
            "   6.3038482e-14 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
            "   0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 2.6673523e-09]\n",
            "  [9.8657016e-21 2.0290818e-11 1.4170737e-09 2.3945506e-19 0.0000000e+00\n",
            "   0.0000000e+00 0.0000000e+00 0.0000000e+00 9.4398636e-01 9.6095824e-16\n",
            "   4.2862471e-12 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
            "   0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 5.6013763e-02]\n",
            "  [6.7601245e-22 9.9084347e-01 7.2310646e-03 5.1553159e-18 0.0000000e+00\n",
            "   0.0000000e+00 0.0000000e+00 0.0000000e+00 1.8176117e-03 9.6322714e-17\n",
            "   9.2280190e-11 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
            "   0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 1.0785249e-04]]]\n",
            "\n",
            "Output memory\n",
            "[[0.00000000e+00 0.00000000e+00 2.82434903e-35 2.27849449e-39\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  8.20869033e-30 1.00000000e+00 4.07850725e-32 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 4.87082917e-31]\n",
            " [2.33876714e-42 1.75470594e-41 7.68556067e-28 6.20019374e-32\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  2.23373198e-22 1.02697068e-23 1.10983510e-24 0.00000000e+00\n",
            "  1.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 1.32544015e-23]\n",
            " [3.81953335e-37 2.86522856e-36 1.25496915e-22 1.01242478e-26\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  3.64744332e-17 1.67693219e-18 1.81224092e-19 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 2.16430070e-18]\n",
            " [1.00260615e-28 7.52106410e-28 3.29422362e-14 2.65755822e-18\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  9.57433421e-09 4.40185305e-10 4.75703088e-11 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 5.68116776e-10]\n",
            " [0.00000000e+00 0.00000000e+00 1.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  1.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 1.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [1.11864493e-38 8.39153124e-38 3.67548795e-24 2.96513671e-28\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  1.06824410e-18 4.91131122e-20 5.30759632e-21 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 6.33869020e-20]\n",
            " [7.63697732e-21 5.72888943e-20 2.50925177e-06 2.02429559e-10\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  7.29289114e-01 3.35294679e-02 3.62349022e-03 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 4.32741717e-02]\n",
            " [1.79466568e-21 1.34627102e-20 5.89666286e-07 4.75703088e-11\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  1.71380654e-01 7.87932053e-03 8.51508870e-04 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 1.01692947e-02]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [6.63775464e-40 4.97932212e-39 2.18094156e-25 1.75943706e-29\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  6.33869020e-20 2.91424797e-21 3.14939350e-22 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 3.76121852e-21]]\n",
            "\n",
            "\n",
            "outputs\n",
            "Timestep:  1\n",
            "\n",
            " Gate Controller vals:\n",
            "[[9.999008e-01 1.097665e-08 9.923162e-05]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.1986769  0.1933457  0.17136309 0.4366143 ]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.16743852 0.04890985 0.21649697 0.10642657 0.46072808]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.31445208 0.05503779 0.06913551 0.17834932 0.2901515  0.09287381]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[7.1419521e-07 2.3469743e-06 5.2862475e-07 9.8951972e-01 1.1287947e-06\n",
            "  5.9333335e-05 1.0416191e-02]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.06551555 0.13149747 0.10805314 0.26242223 0.06106579 0.17719722\n",
            "  0.17068543 0.02356319]]\n",
            "[[0.2282148  0.01777004 0.07066131 0.11180884 0.06471136 0.201285\n",
            "  0.13340282 0.17214581]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.06724702 0.13653415 0.08471987 0.11639838 0.20313042 0.21227106\n",
            "  0.02943029 0.08491115 0.06535766]]\n",
            "[[0.03788985 0.17685471 0.26276863 0.06034724 0.04162672 0.06638804\n",
            "  0.09835182 0.16873133 0.08704166]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[1.4791527e-01 2.5109467e-01 4.5234706e-02 5.5193281e-01 1.0856500e-04\n",
            "  2.3555517e-06 4.0890459e-06 4.8777529e-05 7.4175214e-06 3.6514048e-03]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.12596978 0.06896417 0.04324698 0.08739482 0.08952457 0.0770945\n",
            "  0.11532221 0.17478298 0.05168686 0.01146564 0.15454753]]\n",
            "[[0.27226752 0.04808705 0.12282611 0.0261806  0.02512159 0.09496673\n",
            "  0.05601878 0.15375836 0.08307416 0.08713944 0.03055958]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.06411361 0.10334995 0.06131031 0.05544006 0.13790889 0.04887345\n",
            "  0.04517798 0.0627634  0.04314912 0.21612434 0.0956004  0.06618849]]\n",
            "[[0.05368692 0.05895179 0.06577272 0.03825153 0.12502734 0.10561653\n",
            "  0.13040143 0.04619008 0.02178271 0.2650461  0.03733946 0.05193335]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.08602609 0.03635734 0.16201018 0.04483114 0.10085835 0.16119763\n",
            "  0.03837624 0.03811925 0.05195605 0.01156119 0.09521502 0.07420353\n",
            "  0.09928801]]\n",
            "[[0.00548211 0.09520961 0.04222476 0.08603661 0.20993088 0.02580793\n",
            "  0.13065264 0.01626446 0.05470991 0.09231102 0.04552832 0.03034118\n",
            "  0.16550055]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.03659561 0.03362715 0.10043906 0.04919374 0.12358762 0.1465042\n",
            "  0.05091982 0.08105351 0.02147404 0.02859622 0.04676756 0.15621522\n",
            "  0.07074432 0.05428193]]\n",
            "[[0.11348955 0.04988101 0.05136261 0.058375   0.04487409 0.0343614\n",
            "  0.03191933 0.13536188 0.25250188 0.12017215 0.00846574 0.04135358\n",
            "  0.02884315 0.02903869]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.0684534  0.03764581 0.12282325 0.02273635 0.16931003 0.05755111\n",
            "  0.0191995  0.03848549 0.07755649 0.04592786 0.16515845 0.04066997\n",
            "  0.08367002 0.02453055 0.02628169]]\n",
            "[[0.01456152 0.10429367 0.00897175 0.01110167 0.02658311 0.08836243\n",
            "  0.4470541  0.05982672 0.04837032 0.01361354 0.04375466 0.0096696\n",
            "  0.05156709 0.02998722 0.04228254]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[7.9954692e-09 4.3585020e-09 6.7832602e-09 5.9816873e-01 2.0443869e-09\n",
            "  3.0337439e-08 3.8300390e-09 4.0182135e-01 1.7955903e-08 5.3311617e-09\n",
            "  9.8116961e-06 3.6125809e-09 1.4441629e-08 4.0852446e-09 6.8344015e-09\n",
            "  8.0339158e-09]]\n",
            "[[1.3908327e-07 1.7486023e-07 4.5889144e-07 2.5137866e-01 6.9221549e-08\n",
            "  5.8346814e-07 1.9306824e-02 1.3570584e-01 1.4378865e-07 6.8255432e-08\n",
            "  5.9360623e-01 1.6076406e-07 2.8327969e-07 1.1938519e-07 2.2047415e-07\n",
            "  4.0009972e-08]]\n",
            "\n",
            "Register controller  0\n",
            "[[4.2299551e-08 2.7042927e-07 1.0523053e-07 3.0008721e-06 2.7336426e-07\n",
            "  3.4677431e-01 2.2162417e-02 1.5821133e-04 5.5786923e-06 3.4955187e-07\n",
            "  5.7922541e-03 1.3700807e-07 4.8989114e-01 1.3521047e-01 3.9245029e-07\n",
            "  8.9809470e-07 6.1911933e-08]]\n",
            "\n",
            "Register controller  1\n",
            "[[6.64372450e-08 3.39413653e-08 6.01824510e-08 5.93187033e-05\n",
            "  7.00148988e-08 1.13323156e-04 4.37352853e-03 9.57707874e-04\n",
            "  3.53209344e-07 3.41834401e-08 9.93107796e-01 4.55013733e-08\n",
            "  1.36568793e-03 2.16410172e-05 3.37269874e-08 3.99946138e-08\n",
            "  6.67597604e-08]]\n",
            "\n",
            "Register controller  2\n",
            "[[2.8391966e-07 1.0370206e-06 7.3691137e-07 3.4658049e-05 2.9006722e-07\n",
            "  5.7783462e-02 1.7634095e-01 1.3835127e-03 2.4541048e-06 1.1084094e-06\n",
            "  1.2289485e-01 1.6817448e-07 6.3762212e-01 3.9318493e-03 8.0719053e-07\n",
            "  1.2827497e-06 5.2695430e-07]]\n",
            "\n",
            "Output registers\n",
            "[[[1.36327997e-01 8.36667538e-01 2.21689064e-02 1.64821915e-06\n",
            "   1.05826413e-27 0.00000000e+00 0.00000000e+00 1.37341395e-03\n",
            "   2.55473452e-07 3.50756729e-10 1.09617718e-17 3.19691049e-03\n",
            "   3.31745127e-06 1.56576658e-04 2.41953231e-08 2.51076174e-11\n",
            "   1.18502619e-09 0.00000000e+00 8.14949090e-05 2.17938596e-05]\n",
            "  [1.91430598e-01 1.81641593e-03 4.37448500e-03 9.97595089e-06\n",
            "   6.40521082e-27 0.00000000e+00 0.00000000e+00 2.35477939e-01\n",
            "   3.22569491e-08 2.12801643e-09 6.25182983e-18 5.48124552e-01\n",
            "   1.07291475e-04 9.47690103e-04 4.14839587e-06 8.12018952e-10\n",
            "   7.17244575e-09 0.00000000e+00 1.39726652e-02 3.73405707e-03]\n",
            "  [2.76206266e-02 6.95448339e-01 1.76344857e-01 1.44131436e-05\n",
            "   9.25417735e-27 0.00000000e+00 0.00000000e+00 2.91398652e-02\n",
            "   9.80286131e-07 3.06763392e-09 7.32468432e-17 6.78291842e-02\n",
            "   4.09046261e-05 1.36921217e-03 5.13354678e-07 3.09580334e-10\n",
            "   1.03626698e-08 0.00000000e+00 1.72908593e-03 4.62139113e-04]]]\n",
            "\n",
            "Output memory\n",
            "[[2.24912270e-07 3.99483530e-10 3.79711231e-08 2.77878165e-09\n",
            "  1.78415893e-30 0.00000000e+00 0.00000000e+00 2.76693555e-07\n",
            "  3.26257494e-13 9.99998033e-01 2.65276176e-22 6.44062652e-07\n",
            "  4.94221183e-07 2.63977199e-07 4.87448840e-12 3.74043635e-12\n",
            "  1.99787062e-12 0.00000000e+00 1.64182978e-08 4.38764491e-09]\n",
            " [5.90818381e-08 1.04939682e-10 9.97457228e-09 7.29953598e-10\n",
            "  4.68677826e-31 0.00000000e+00 0.00000000e+00 7.26841805e-08\n",
            "  8.57040540e-14 1.55329186e-13 7.07948010e-23 1.69187771e-07\n",
            "  9.99999583e-01 6.93437414e-08 1.28047139e-12 9.82569169e-13\n",
            "  5.24817386e-13 0.00000000e+00 4.31289582e-09 1.15258336e-09]\n",
            " [2.78804077e-06 4.95204810e-09 4.70694800e-07 3.44461242e-08\n",
            "  2.21166588e-29 0.00000000e+00 0.00000000e+00 3.42992803e-06\n",
            "  4.04436554e-12 7.32990404e-12 1.84508064e-19 7.98388191e-06\n",
            "  6.12642771e-06 3.27229463e-06 6.04247694e-11 9.99975622e-01\n",
            "  2.47658560e-11 0.00000000e+00 2.03523271e-07 5.43897976e-08]\n",
            " [4.78859904e-04 8.50538925e-07 8.08441764e-05 5.91629350e-06\n",
            "  3.79864644e-27 0.00000000e+00 0.00000000e+00 5.89107280e-04\n",
            "  1.02288951e-08 1.69729009e-09 4.73712077e-11 1.37127144e-03\n",
            "  1.05224457e-03 5.62032918e-04 1.03782556e-08 7.96374966e-09\n",
            "  4.25365920e-09 0.00000000e+00 3.49561378e-05 9.34228501e-06]\n",
            " [3.07459277e-25 5.46101432e-28 1.00000000e+00 3.79864644e-27\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 3.78245270e-25\n",
            "  4.46000095e-31 8.08326245e-31 3.62637826e-40 8.80445684e-25\n",
            "  6.75609568e-25 3.60861783e-25 6.66351674e-30 5.11324658e-30\n",
            "  2.73112646e-30 0.00000000e+00 2.24441189e-26 5.99799296e-27]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  1.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [2.66173174e-07 4.72770101e-10 4.49370532e-08 3.28855831e-09\n",
            "  2.11146884e-30 0.00000000e+00 0.00000000e+00 3.27453932e-07\n",
            "  3.86110523e-13 6.99782951e-13 3.13941977e-22 7.62218122e-07\n",
            "  5.84887687e-07 9.99997973e-01 5.76872924e-12 4.42663180e-12\n",
            "  2.36438668e-12 0.00000000e+00 1.94302885e-08 5.19257348e-09]\n",
            " [4.72138884e-10 8.38601270e-13 7.97094959e-11 5.83325575e-12\n",
            "  3.74533071e-33 0.00000000e+00 0.00000000e+00 5.80838877e-10\n",
            "  6.85952427e-16 1.24132647e-15 5.30815330e-21 1.35202505e-09\n",
            "  1.03747577e-09 5.54144564e-10 1.02325917e-14 7.85197509e-15\n",
            "  4.19395701e-15 0.00000000e+00 3.44655138e-11 9.21060450e-12]\n",
            " [1.01897086e-07 1.80987045e-10 2.52645236e-06 1.46136359e-09\n",
            "  8.08317876e-31 0.00000000e+00 0.00000000e+00 1.25356735e-07\n",
            "  7.29288459e-01 3.35294381e-02 3.62348696e-03 2.91794265e-07\n",
            "  2.23908188e-07 1.19595555e-07 2.20839948e-12 1.69461439e-12\n",
            "  9.05140492e-13 0.00000000e+00 7.43835216e-09 4.32741381e-02]\n",
            " [3.01583566e-17 6.70260444e-20 5.89666286e-07 4.75703088e-11\n",
            "  2.39222667e-40 0.00000000e+00 0.00000000e+00 3.70994740e-17\n",
            "  1.71380654e-01 7.87932053e-03 8.51508870e-04 8.63568552e-17\n",
            "  6.62658955e-17 3.53944463e-17 6.53578454e-22 5.01523129e-22\n",
            "  2.67877398e-22 0.00000000e+00 2.20138921e-18 1.01692947e-02]\n",
            " [6.19574280e-07 1.10047227e-09 1.04600474e-07 7.65481367e-09\n",
            "  4.91488933e-30 0.00000000e+00 0.00000000e+00 7.62218122e-07\n",
            "  8.98753782e-13 1.62889244e-12 7.30766209e-22 1.77422351e-06\n",
            "  1.36144956e-06 7.27187910e-07 1.34279350e-11 1.03039200e-11\n",
            "  5.50360929e-12 0.00000000e+00 4.52280986e-08 1.20868107e-08]\n",
            " [6.84363395e-02 1.21554905e-04 1.15538584e-02 8.45528033e-04\n",
            "  5.42884116e-25 0.00000000e+00 0.00000000e+00 8.41923580e-02\n",
            "  9.92736773e-08 1.79922637e-07 8.07182694e-17 1.95975468e-01\n",
            "  1.50381684e-01 8.03230181e-02 1.48320999e-06 1.13814053e-06\n",
            "  6.07912341e-07 0.00000000e+00 4.99576190e-03 1.33507326e-03]\n",
            " [4.54904772e-02 8.07990436e-05 7.67999236e-03 5.62032918e-04\n",
            "  3.60861783e-25 0.00000000e+00 0.00000000e+00 5.59636988e-02\n",
            "  6.59884378e-08 1.19596791e-07 5.36544318e-17 1.30267322e-01\n",
            "  9.99605656e-02 5.33917025e-02 9.85907946e-07 7.56536053e-07\n",
            "  4.04086848e-07 0.00000000e+00 3.32074449e-03 8.87439644e-04]\n",
            " [4.68915141e-12 8.32875300e-15 7.91652442e-13 5.79342685e-14\n",
            "  3.71975774e-35 0.00000000e+00 0.00000000e+00 5.76872924e-12\n",
            "  6.80207823e-18 1.23280194e-17 5.53069071e-27 1.34279350e-11\n",
            "  1.03039200e-11 5.50360842e-12 1.01627245e-16 7.79836169e-17\n",
            "  4.16532091e-17 0.00000000e+00 3.42301843e-13 9.14771460e-14]\n",
            " [5.17949843e-07 9.19969434e-10 8.74435884e-08 6.39924780e-09\n",
            "  4.10873456e-30 0.00000000e+00 0.00000000e+00 6.37196820e-07\n",
            "  7.51337469e-13 1.36171662e-12 6.10903730e-22 1.48320999e-06\n",
            "  1.13814053e-06 6.07912341e-07 1.12254451e-11 8.61383968e-12\n",
            "  4.60089042e-12 0.00000000e+00 3.78096487e-08 1.01042952e-08]\n",
            " [3.44287656e-07 6.11515116e-10 5.81248365e-08 4.25365920e-09\n",
            "  2.73112665e-30 0.00000000e+00 0.00000000e+00 4.23552592e-07\n",
            "  4.99423311e-13 9.05149870e-13 4.06075277e-22 9.85908059e-07\n",
            "  7.56536110e-07 4.04086848e-07 7.46169185e-12 5.72572632e-12\n",
            "  3.05826892e-12 0.00000000e+00 2.51325414e-08 6.71645006e-09]\n",
            " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
            " [1.57940452e-08 2.80529853e-11 2.66645106e-09 1.95134742e-10\n",
            "  1.25289231e-31 0.00000000e+00 0.00000000e+00 1.94302885e-08\n",
            "  2.29108233e-14 4.15233508e-14 1.86285243e-23 4.52280986e-08\n",
            "  3.47057600e-08 1.85373068e-08 3.42301843e-13 2.62665161e-13\n",
            "  1.40296737e-13 0.00000000e+00 1.15294430e-09 3.08114173e-10]\n",
            " [4.24881597e-09 7.54664040e-12 7.17312154e-10 5.24939398e-11\n",
            "  3.37045322e-32 0.00000000e+00 0.00000000e+00 5.22701571e-09\n",
            "  6.16339119e-15 1.11703571e-14 3.19950649e-22 1.21669821e-08\n",
            "  9.33632816e-09 4.98679142e-09 9.20839197e-14 7.06605523e-14\n",
            "  3.77417586e-14 0.00000000e+00 3.10157927e-10 8.28869681e-11]]\n",
            "\n",
            "\n",
            "outputs\n",
            "Timestep:  2\n",
            "\n",
            " Gate Controller vals:\n",
            "[[9.9999511e-01 2.7912839e-11 4.9367445e-06]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.17891    0.170981   0.14832814 0.5017808 ]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.1427479  0.02797209 0.20145945 0.07804927 0.54977125]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.35450146 0.03553012 0.04787088 0.16997078 0.32087395 0.07125273]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[7.1801440e-09 3.5499525e-08 4.8201070e-09 9.9753976e-01 1.3480218e-08\n",
            "  2.5596698e-06 2.4575833e-03]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.04907092 0.12462167 0.09535237 0.31600058 0.04481688 0.183376\n",
            "  0.17396244 0.01279906]]\n",
            "[[0.25856784 0.00877433 0.05472902 0.10171915 0.04802128 0.22054727\n",
            "  0.12774849 0.17989258]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.05348245 0.13714018 0.07358359 0.11093295 0.23334493 0.24885224\n",
            "  0.01785419 0.0734949  0.05131458]]\n",
            "[[0.0246499  0.19047548 0.32095063 0.04542949 0.02793782 0.05127838\n",
            "  0.08598952 0.17835048 0.07493833]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[1.1226962e-01 2.2274394e-01 2.3569651e-02 6.4057422e-01 7.8985349e-06\n",
            "  4.8746273e-08 1.0110823e-07 2.7278054e-06 2.2368097e-07 8.3153672e-04]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.13296637 0.05983536 0.03145743 0.0810915  0.08366182 0.06885776\n",
            "  0.11799415 0.2054705  0.03995976 0.00544533 0.17325996]]\n",
            "[[0.35118106 0.03467896 0.12228986 0.01561566 0.01473571 0.08662789\n",
            "  0.04311707 0.16416948 0.07261338 0.07604473 0.0189262 ]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.0554551  0.1053957  0.05236462 0.04558373 0.15336686 0.03849258\n",
            "  0.03478869 0.05455854 0.03291734 0.27680388 0.0930336  0.05723939]]\n",
            "[[0.04215087 0.04749806 0.05484891 0.02663836 0.12997219 0.10264797\n",
            "  0.13632973 0.03420641 0.01262516 0.34725586 0.0259197  0.0399068 ]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.08387069 0.02668193 0.19297849 0.03480548 0.10158837 0.19066381\n",
            "  0.02877617 0.02799812 0.04268628 0.00576362 0.09518982 0.06884268\n",
            "  0.10015453]]\n",
            "[[0.00203745 0.08999916 0.03097346 0.07971525 0.2595168  0.01608122\n",
            "  0.1381428  0.00883239 0.04371898 0.08750496 0.03409184 0.01986878\n",
            "  0.18951692]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.02717979 0.02456604 0.10480164 0.0405177  0.13775197 0.17322129\n",
            "  0.04254751 0.07989448 0.01345895 0.01974809 0.03779388 0.18651332\n",
            "  0.06568529 0.04632012]]\n",
            "[[0.11465235 0.03900979 0.04033323 0.04714604 0.03361497 0.02362064\n",
            "  0.02147453 0.14821047 0.33606097 0.1236468  0.00366659 0.03035164\n",
            "  0.01903469 0.01917729]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.06211658 0.02804596 0.13613024 0.01460318 0.20876057 0.04995823\n",
            "  0.01159482 0.02920615 0.07393977 0.0368626  0.20077239 0.03149329\n",
            "  0.08287686 0.01591701 0.01772225]]\n",
            "[[0.00651811 0.08888029 0.00346653 0.00456445 0.01446146 0.07202975\n",
            "  0.61821383 0.04262089 0.03213359 0.00598815 0.02812162 0.00382183\n",
            "  0.03499519 0.01721189 0.02697243]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[2.2692072e-11 1.0239753e-11 1.8283743e-11 6.2713963e-01 3.7469017e-12\n",
            "  1.3523108e-10 8.5334361e-12 3.7286004e-01 6.6288357e-11 1.3356378e-11\n",
            "  2.8442568e-07 7.8974691e-12 5.0463102e-11 9.3302675e-12 1.8338785e-11\n",
            "  2.3008572e-11]]\n",
            "[[1.1083181e-09 1.4741786e-09 5.3401528e-09 2.1831512e-01 4.3630446e-10\n",
            "  7.3777007e-09 7.2795623e-03 9.7576521e-02 1.1669322e-09 4.2815781e-10\n",
            "  6.7682880e-01 1.3512272e-09 2.8074736e-09 9.0621127e-10 2.0146289e-09\n",
            "  2.0935194e-10]]\n",
            "\n",
            "Register controller  0\n",
            "[[2.2969221e-10 2.7033877e-09 7.8142914e-10 6.4685025e-08 2.7047753e-09\n",
            "  3.4398437e-01 8.8681523e-03 1.2634989e-05 1.5084767e-07 3.8133114e-09\n",
            "  1.5003927e-03 1.0991745e-09 5.4610050e-01 9.9533841e-02 4.4343822e-09\n",
            "  1.3318195e-08 3.9091932e-10]]\n",
            "\n",
            "Register controller  1\n",
            "[[2.9412806e-10 1.2089817e-10 2.6470023e-10 2.4050948e-06 3.1990999e-10\n",
            "  5.8208543e-06 7.3812465e-04 9.7194359e-05 2.7095532e-09 1.2605851e-10\n",
            "  9.9899751e-01 1.8247366e-10 1.5825266e-04 6.4478280e-07 1.2257947e-10\n",
            "  1.5329700e-10 3.0225966e-10]]\n",
            "\n",
            "Register controller  2\n",
            "[[2.7723490e-09 1.5626823e-08 9.8833297e-09 1.6206070e-06 2.8291705e-09\n",
            "  3.0666560e-02 1.3504685e-01 2.1518156e-04 4.8261601e-08 1.7109496e-08\n",
            "  8.3825924e-02 1.3896020e-09 7.4938011e-01 8.6357811e-04 1.1179837e-08\n",
            "  2.0549457e-08 6.3154206e-09]]\n",
            "\n",
            "Output registers\n",
            "[[[9.96999890e-02 8.90096307e-01 8.86832085e-03 3.10679056e-08\n",
            "   1.16716218e-12 7.49394317e-34 7.99682384e-05 7.49048212e-10\n",
            "   1.31025925e-04 9.40833988e-09 1.87861195e-04 8.04185984e-04\n",
            "   1.77811910e-06 1.05470990e-05 2.13244566e-05 1.53298485e-09\n",
            "   2.79420760e-07 4.74511216e-06 1.26812836e-06 8.91844757e-05]\n",
            "  [1.10618114e-01 7.77724152e-03 7.40937889e-04 2.38986786e-07\n",
            "   8.97827559e-12 5.76463862e-33 5.32447733e-02 5.91673484e-08\n",
            "   8.72401372e-02 6.98493807e-07 1.23951644e-01 5.35445333e-01\n",
            "   1.14957686e-03 8.20659698e-05 1.41868694e-02 1.15602411e-07\n",
            "   2.14941542e-06 3.15940985e-03 8.44324240e-04 5.93811572e-02]\n",
            "  [1.01455301e-02 7.80685544e-01 1.35047153e-01 5.29104739e-07\n",
            "   1.98774712e-11 1.27626346e-32 4.46777139e-03 8.94636276e-09\n",
            "   7.32032536e-03 2.52568896e-07 1.04289418e-02 4.49292511e-02\n",
            "   9.76558658e-05 1.79679031e-04 1.19070627e-03 4.12411474e-08\n",
            "   4.75870320e-06 2.65106210e-04 7.08475200e-05 4.98267543e-03]]]\n",
            "\n",
            "Output memory\n",
            "[[5.83637473e-07 2.50915946e-08 7.28260119e-08 3.92726252e-09\n",
            "  4.31431759e-14 2.77007339e-35 1.72666006e-07 2.76702082e-07\n",
            "  2.82913248e-07 9.99993384e-01 4.65431071e-07 2.38046277e-06\n",
            "  1.37224947e-06 6.55232498e-07 4.66546766e-08 2.31697754e-08\n",
            "  1.03305524e-08 1.02455635e-08 1.91568272e-08 1.96953550e-07]\n",
            " [2.69414215e-07 1.45827048e-08 3.04111722e-08 1.40334999e-09\n",
            "  2.52962070e-14 1.62418149e-35 1.01239536e-07 7.26897369e-08\n",
            "  1.65880877e-07 8.35275102e-08 2.72896926e-07 1.18729542e-06\n",
            "  9.99997318e-01 2.98749626e-07 2.73535345e-08 1.35839597e-08\n",
            "  6.05648331e-09 6.00729733e-09 5.91861582e-09 1.14060065e-07]\n",
            " [2.91968081e-06 1.40135104e-08 4.83485053e-07 3.48675364e-08\n",
            "  1.58326111e-14 1.01655688e-35 6.33646806e-08 3.42992575e-06\n",
            "  1.03826956e-07 5.22861576e-08 1.70803105e-07 8.62109027e-06\n",
            "  6.44863530e-06 3.41587156e-06 1.71798913e-08 9.99973893e-01\n",
            "  3.81512200e-09 3.75989950e-09 2.04527922e-07 1.25057227e-07]\n",
            " [5.47136005e-04 5.57961221e-06 8.74466568e-05 6.13086695e-06\n",
            "  8.26421310e-12 5.30616387e-33 3.30747243e-05 5.88569033e-04\n",
            "  5.42030939e-05 2.72898906e-05 8.91548552e-05 1.70262775e-03\n",
            "  1.21946936e-03 6.36464043e-04 8.94628738e-06 4.44548414e-06\n",
            "  1.98271755e-06 1.96257042e-06 3.54486765e-05 4.62203352e-05]\n",
            " [2.58133848e-09 1.77680620e-10 9.99999940e-01 8.26438570e-12\n",
            "  3.10451604e-16 1.99330178e-37 1.24247779e-09 7.07304495e-14\n",
            "  2.03579753e-09 1.02510234e-09 3.34916961e-09 1.24948887e-08\n",
            "  6.31817265e-09 2.81542145e-09 3.35684758e-10 1.66699182e-10\n",
            "  7.43226800e-11 7.37254841e-11 1.97065940e-11 1.38567458e-09]\n",
            " [1.65738760e-30 1.14082550e-31 1.61037436e-31 5.30627480e-33\n",
            "  1.99330178e-37 0.00000000e+00 7.97751780e-31 4.54135626e-35\n",
            "  1.30711483e-30 6.58182562e-31 2.15038542e-30 1.00000000e+00\n",
            "  4.05667878e-30 1.80768424e-30 2.15531497e-31 1.07031758e-31\n",
            "  4.77200101e-32 4.73365698e-32 1.26529198e-32 8.89693400e-31]\n",
            " [1.13613285e-09 7.82031384e-11 1.10390537e-10 3.63743085e-12\n",
            "  1.36640060e-16 8.77318384e-38 5.46855727e-10 3.11308223e-14\n",
            "  8.96022145e-10 4.51181592e-10 1.47408086e-09 5.49941559e-09\n",
            "  2.78083756e-09 1.23916055e-09 1.47746010e-10 7.33698519e-11\n",
            "  1.00000000e+00 3.24490330e-11 8.67352717e-12 6.09881423e-10]\n",
            " [6.99558711e-07 3.03039869e-08 8.70461605e-08 4.67606665e-09\n",
            "  5.21224570e-14 3.34660200e-35 2.08602543e-07 3.27463908e-07\n",
            "  3.41795300e-07 1.72107562e-07 5.62300102e-07 2.86001023e-06\n",
            "  1.64565733e-06 9.99992669e-01 5.63646800e-08 2.79919501e-08\n",
            "  1.24805766e-08 1.23779467e-08 2.27387638e-08 2.37836744e-07]\n",
            " [2.59616229e-07 1.78384383e-08 2.52590322e-08 8.35506220e-10\n",
            "  3.11666586e-14 2.00110292e-35 1.24734044e-07 5.87937588e-10\n",
            "  2.04376491e-07 1.02911422e-07 3.36227714e-07 1.25573092e-06\n",
            "  6.35327410e-07 2.83198148e-07 3.36998589e-08 1.67351661e-08\n",
            "  7.46135953e-09 7.40140171e-09 2.01283723e-09 1.39118967e-07]\n",
            " [6.40767952e-03 4.41051961e-04 6.24893408e-04 2.05157976e-05\n",
            "  7.70625619e-10 4.94791903e-31 3.08416877e-03 2.90211489e-07\n",
            "  6.71990454e-01 3.32073830e-02 1.16272457e-02 3.10159903e-02\n",
            "  1.56836323e-02 6.98875356e-03 8.33261118e-04 4.13792877e-04\n",
            "  1.84489181e-04 1.83006763e-04 4.89239501e-05 4.30139899e-02]\n",
            " [3.80020100e-03 2.61578272e-04 3.69800255e-04 1.21667290e-05\n",
            "  4.57041377e-10 2.93450386e-31 1.82915386e-03 1.04128119e-07\n",
            "  1.65687710e-01 8.98892991e-03 5.73892053e-03 1.83947552e-02\n",
            "  9.30150226e-03 4.14481387e-03 4.94189153e-04 2.45411589e-04\n",
            "  1.09416542e-04 1.08537359e-04 2.90117005e-05 1.16936173e-02]\n",
            " [1.63979109e-06 7.13253598e-08 2.03727609e-07 1.09210516e-08\n",
            "  1.22700122e-13 7.87814822e-35 4.91065805e-07 7.62235743e-07\n",
            "  8.04611375e-07 4.05153912e-07 1.32369598e-06 6.71256794e-06\n",
            "  3.85856947e-06 1.83992006e-06 1.32686466e-07 6.58950015e-08\n",
            "  2.93801374e-08 2.91386026e-08 5.30161444e-08 5.59748287e-07]\n",
            " [7.18520582e-02 2.76468671e-03 9.31230560e-03 5.27763972e-04\n",
            "  4.72964334e-09 3.03673976e-30 1.89288016e-02 4.00156043e-02\n",
            "  3.10148541e-02 1.56172337e-02 5.10236658e-02 2.83498377e-01\n",
            "  1.67728245e-01 8.10676813e-02 5.11476817e-03 2.54015648e-03\n",
            "  1.13257428e-03 1.12318713e-03 2.67458544e-03 2.17448920e-02]\n",
            " [5.46824783e-02 1.66544365e-03 7.55579211e-03 4.61520860e-04\n",
            "  2.81281887e-09 1.80601316e-30 1.12573579e-02 3.85000259e-02\n",
            "  1.84452049e-02 9.28792916e-03 3.03448495e-02 2.02824265e-01\n",
            "  1.26011610e-01 6.22388944e-02 3.04211956e-03 1.51088345e-03\n",
            "  6.73671952e-04 6.67983084e-04 2.46300618e-03 1.31652802e-02]\n",
            " [3.85339699e-05 2.65239873e-06 3.74409251e-06 1.23370000e-07\n",
            "  4.63439096e-12 2.97558138e-33 1.85475856e-05 1.06162301e-09\n",
            "  3.03901870e-05 1.53026267e-05 4.99960770e-05 1.86522491e-04\n",
            "  9.43170671e-05 4.20283432e-05 5.01106888e-06 2.48846914e-06\n",
            "  1.10948167e-06 1.10056681e-06 2.94178449e-07 2.06852128e-05]\n",
            " [1.04249362e-03 7.17234579e-05 1.01328842e-04 3.34231186e-06\n",
            "  1.25316868e-10 8.04616068e-32 5.01538510e-04 6.56888801e-07\n",
            "  8.21769936e-04 4.13792732e-04 1.35192566e-03 5.04514854e-03\n",
            "  2.55151535e-03 1.13707222e-03 1.35502502e-04 6.72897877e-05\n",
            "  3.00010925e-05 2.97600236e-05 7.99204645e-06 5.59351232e-04]\n",
            " [6.18318503e-04 4.25376675e-05 6.01024039e-05 1.98272755e-06\n",
            "  7.43226730e-11 4.77200043e-32 2.97451421e-04 4.36993133e-07\n",
            "  4.87373618e-04 2.45411356e-04 8.01797258e-04 2.99227680e-03\n",
            "  1.51333213e-03 6.74417708e-04 8.03635412e-05 3.99080927e-05\n",
            "  1.77929869e-05 1.76500143e-05 4.74271928e-06 3.31739633e-04]\n",
            " [6.92711583e-11 4.76812426e-12 6.73062214e-12 2.21777810e-13\n",
            "  8.33108189e-18 5.34909816e-39 3.33423428e-11 1.89807738e-15\n",
            "  5.46313689e-11 2.75089951e-11 8.98761898e-11 3.35304895e-10\n",
            "  1.69550457e-10 7.55528626e-11 9.00822212e-12 4.47343334e-12\n",
            "  1.99447620e-12 1.97845017e-12 5.28833650e-13 3.71850814e-11]\n",
            " [4.15281534e-08 1.79940263e-09 5.16686427e-09 2.77524725e-10\n",
            "  3.09498216e-15 1.98718041e-36 1.23866215e-08 1.94309866e-08\n",
            "  2.02954791e-08 1.02195843e-08 3.33888437e-08 1.69793253e-07\n",
            "  9.76934444e-08 4.66050523e-08 3.34688099e-09 1.66213521e-09\n",
            "  7.41084638e-10 7.34990735e-10 1.34940459e-09 1.41223060e-08]\n",
            " [2.76547514e-08 1.61864155e-09 2.99151259e-09 1.27430122e-10\n",
            "  2.81497784e-15 1.80739933e-36 1.12659988e-08 5.22765520e-09\n",
            "  1.84593230e-08 9.29498789e-09 3.03681418e-08 1.25462691e-07\n",
            "  6.66254962e-08 3.05152419e-08 3.04386805e-09 1.51159285e-09\n",
            "  6.73948564e-10 6.68495814e-10 4.88844742e-10 1.26473028e-08]]\n",
            "\n",
            "\n",
            "outputs\n",
            "Timestep:  3\n",
            "\n",
            " Gate Controller vals:\n",
            "[[9.9998462e-01 2.6749919e-10 1.5365826e-05]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.18660912 0.17925091 0.15677527 0.47736466]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.15223035 0.03474909 0.20813133 0.0883114  0.5165779 ]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.33999923 0.04209727 0.05512339 0.17377853 0.3099101  0.0790915 ]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[4.0973379e-08 1.7326741e-07 2.8575236e-08 9.9574262e-01 7.2086294e-08\n",
            "  8.4205121e-06 4.2485762e-03]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.05497917 0.12758614 0.10029987 0.29540163 0.05051996 0.18169165\n",
            "  0.17337163 0.01614992]]\n",
            "[[0.24753132 0.01147218 0.06039432 0.10569479 0.05384512 0.21362627\n",
            "  0.13015753 0.1772785 ]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.0585114  0.13731232 0.07784469 0.11348688 0.22185299 0.23502705\n",
            "  0.02163459 0.07776719 0.05656291]]\n",
            "[[0.02909721 0.18611534 0.29865104 0.05087763 0.03262827 0.0568168\n",
            "  0.09085096 0.1754761  0.07948665]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[1.2544705e-01 2.3426962e-01 3.0307425e-02 6.0848176e-01 2.1403659e-05\n",
            "  2.1240659e-07 4.1227523e-07 8.1586422e-06 8.4439114e-07 1.4631840e-03]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.1305851  0.06324415 0.03557697 0.08370568 0.08614126 0.07200631\n",
            "  0.11728316 0.19386116 0.04413696 0.00722842 0.1662309 ]]\n",
            "[[0.3207533  0.03949417 0.12314998 0.01909906 0.01811746 0.09029119\n",
            "  0.04790856 0.1610003  0.07686372 0.08048975 0.02283251]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.05881459 0.10506863 0.05586038 0.04925898 0.1479038  0.04230338\n",
            "  0.03851008 0.05776668 0.03661722 0.25294557 0.09430639 0.06064432]]\n",
            "[[0.04644409 0.05175842 0.05911596 0.03070838 0.12893325 0.10429571\n",
            "  0.13485038 0.03859276 0.01559889 0.31535798 0.02992896 0.04441526]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.08497184 0.03011178 0.18109436 0.03845458 0.10172243 0.17923722\n",
            "  0.03216612 0.03157355 0.04622725 0.00751411 0.09544355 0.07107317\n",
            "  0.10041003]]\n",
            "[[0.00297923 0.09250781 0.03501932 0.08255807 0.2404792  0.01931957\n",
            "  0.13586935 0.01117755 0.04782894 0.08972867 0.03823175 0.02338991\n",
            "  0.18091062]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.03053417 0.02776006 0.10362723 0.04376104 0.13265684 0.16302758\n",
            "  0.04568523 0.08059699 0.01608702 0.02279487 0.04104959 0.17517552\n",
            "  0.06782272 0.04942116]]\n",
            "[[0.115008   0.04316806 0.04453377 0.05148577 0.03777285 0.02741024\n",
            "  0.02511799 0.14425088 0.30354995 0.12323426 0.00506355 0.03435156\n",
            "  0.02245963 0.02259345]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.06469906 0.03146612 0.13168225 0.01736378 0.1937173  0.05300202\n",
            "  0.01411073 0.0325643  0.07563877 0.0401855  0.18753202 0.03482534\n",
            "  0.08372322 0.01883583 0.02065374]]\n",
            "[[0.00899276 0.09606681 0.00504786 0.00648712 0.01851209 0.07904295\n",
            "  0.5556971  0.04927849 0.03812093 0.00830124 0.03373891 0.00552165\n",
            "  0.04114738 0.02157938 0.03246529]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[2.0828778e-10 1.0109457e-10 1.7136453e-10 6.1637264e-01 4.0648960e-11\n",
            "  1.0486685e-09 8.5887242e-11 3.8362622e-01 5.5173949e-10 1.2859777e-10\n",
            "  1.0875965e-06 8.0145703e-11 4.2863921e-10 9.3081182e-11 1.7203425e-10\n",
            "  2.1042368e-10]]\n",
            "[[6.90998414e-09 9.00467256e-09 2.88725488e-08 2.31174573e-01\n",
            "  2.97771763e-09 3.86542460e-08 1.05745755e-02 1.10911749e-01\n",
            "  7.23741733e-09 2.92807645e-09 6.47339046e-01 8.26767632e-09\n",
            "  1.61328604e-08 5.75904524e-09 1.19425021e-08 1.53114388e-09]]\n",
            "\n",
            "Register controller  0\n",
            "[[1.66064906e-09 1.54859876e-08 5.01762942e-09 2.77394378e-07\n",
            "  1.55389497e-08 3.46283376e-01 1.25768371e-02 3.30348048e-05\n",
            "  5.92647780e-07 2.11842437e-08 2.51431554e-03 6.85801194e-09\n",
            "  5.26337862e-01 1.12253495e-01 2.42295055e-08 6.57652137e-08\n",
            "  2.66440070e-09]]\n",
            "\n",
            "Register controller  1\n",
            "[[2.2847677e-09 1.0187773e-09 2.0616209e-09 8.0869713e-06 2.4584081e-09\n",
            "  1.7900558e-05 1.4469809e-03 2.3098147e-04 1.7081311e-08 1.0493020e-09\n",
            "  9.9793613e-01 1.4730199e-09 3.5743596e-04 2.4379642e-06 1.0258417e-09\n",
            "  1.2578613e-09 2.3292259e-09]]\n",
            "\n",
            "Register controller  2\n",
            "[[1.6083886e-08 7.6793569e-08 5.0782042e-08 5.1885418e-06 1.6411532e-08\n",
            "  3.9260086e-02 1.5050739e-01 4.3757830e-04 2.1488664e-07 8.3481623e-08\n",
            "  9.7529203e-02 8.5776310e-09 7.1071881e-01 1.5411036e-03 5.6633205e-08\n",
            "  9.8736258e-08 3.3889226e-08]]\n",
            "\n",
            "Output registers\n",
            "[[[1.12598382e-01 8.72634768e-01 1.25774965e-02 1.40362133e-07\n",
            "   3.30865598e-13 3.17292506e-05 1.80212867e-09 5.20343638e-05\n",
            "   1.52534136e-04 7.38985473e-05 3.22362583e-04 1.36246462e-03\n",
            "   3.35151782e-07 3.77340330e-05 1.35708769e-05 1.26881716e-07\n",
            "   2.17454112e-06 5.05882554e-07 3.53888099e-05 1.01113306e-04]\n",
            "  [1.36862159e-01 5.74463233e-03 1.44711800e-03 9.81397420e-07\n",
            "   2.31337128e-12 1.25933941e-02 3.79054711e-07 2.06525195e-02\n",
            "   6.05404973e-02 2.93198172e-02 1.26666725e-01 5.40761113e-01\n",
            "   4.15391041e-05 3.56019568e-03 5.38618956e-03 4.94468222e-05\n",
            "   7.49301515e-04 1.99738715e-04 1.40458727e-02 4.01316658e-02]\n",
            "  [1.49167050e-02 7.50503659e-01 1.50507718e-01 1.85923147e-06\n",
            "   4.38263184e-12 1.23076374e-03 4.13588417e-08 2.01838976e-03\n",
            "   5.91668533e-03 2.86590611e-03 1.24204643e-02 5.28491177e-02\n",
            "   7.99616100e-06 7.15773436e-04 5.26399119e-04 4.87174793e-06\n",
            "   7.68958416e-05 1.95539924e-05 1.37271604e-03 3.92210903e-03]]]\n",
            "\n",
            "Output memory\n",
            "[[7.77579487e-07 3.27009495e-08 9.59291597e-08 4.95671726e-09\n",
            "  4.55698237e-14 1.78459008e-08 1.72666461e-07 3.05968285e-07\n",
            "  3.68719725e-07 9.99991298e-01 6.68949838e-07 3.14676595e-06\n",
            "  1.82181111e-06 8.74734496e-07 5.42878098e-08 2.77192971e-08\n",
            "  1.35699310e-08 1.05478986e-08 3.90610069e-08 2.53824339e-07]\n",
            " [3.37330590e-07 1.72474159e-08 3.85016463e-08 1.76385451e-09\n",
            "  2.61460030e-14 6.24941654e-09 1.01239756e-07 8.29385627e-08\n",
            "  1.95929346e-07 1.15711572e-07 3.44166864e-07 1.45564638e-06\n",
            "  9.99996722e-01 3.75616821e-07 3.00265803e-08 1.51771538e-08\n",
            "  7.19087723e-09 6.11317486e-09 1.28888260e-08 1.33975590e-07]\n",
            " [3.22274605e-06 2.59046899e-08 5.19586933e-07 3.64761625e-08\n",
            "  1.96248301e-14 2.78877916e-08 6.33657891e-08 3.47564946e-06\n",
            "  2.37917362e-07 1.95906452e-07 4.88842886e-07 9.81857102e-06\n",
            "  7.15114811e-06 3.75887794e-06 2.91082927e-08 9.99970496e-01\n",
            "  8.87732199e-09 4.23238022e-09 2.35631546e-07 2.13929482e-07]\n",
            " [6.90948975e-04 1.12480420e-05 1.04541934e-04 6.88897626e-06\n",
            "  1.00613823e-11 1.33151616e-05 3.30214425e-05 6.09446259e-04\n",
            "  1.18137032e-04 9.58176970e-05 2.40859445e-04 2.27160961e-03\n",
            "  1.55291031e-03 7.99202302e-04 1.46270067e-05 7.83275937e-06\n",
            "  4.39646465e-06 2.18496575e-06 5.02418116e-05 8.85776972e-05]\n",
            " [2.92244740e-09 1.91064109e-10 9.99999940e-01 1.00750137e-11\n",
            "  3.14719777e-16 3.13874968e-11 1.24247923e-09 5.15454138e-11\n",
            "  2.18671570e-09 1.18674603e-09 3.70712172e-09 1.38426772e-08\n",
            "  7.10887038e-09 3.20148574e-09 3.49110157e-10 1.74701004e-10\n",
            "  8.00201641e-11 7.42572739e-11 5.47142782e-11 1.48570012e-09]\n",
            " [1.21847843e-09 4.78072824e-11 1.45150059e-10 6.46776252e-12\n",
            "  1.52463879e-17 1.12119591e-10 5.30261810e-15 1.83873208e-10\n",
            "  5.39096157e-10 5.77409232e-10 1.27864386e-09 1.00000000e+00\n",
            "  2.82445867e-09 1.37906364e-09 4.79569752e-11 2.85833822e-11\n",
            "  2.03520343e-11 1.89960461e-12 1.25051289e-10 3.57302160e-10]\n",
            " [3.42522561e-08 1.37752187e-09 4.05531608e-09 1.79419951e-10\n",
            "  5.51010285e-16 3.04721515e-09 5.46999612e-10 4.99738473e-09\n",
            "  1.55477160e-08 1.61441598e-08 3.62253942e-08 1.36347751e-07\n",
            "  7.95447050e-08 3.87197083e-08 1.45113299e-09 8.50216342e-10\n",
            "  9.99999642e-01 8.40769676e-11 3.40734974e-09 1.03207318e-08]\n",
            " [7.55271174e-07 3.24898721e-08 9.36828499e-08 4.97179098e-09\n",
            "  5.28195380e-14 5.12648102e-09 2.08602643e-07 3.35870965e-07\n",
            "  3.66444340e-07 1.98508516e-07 6.20763615e-07 3.08014114e-06\n",
            "  1.77480001e-06 9.99992073e-01 5.85573972e-08 2.92988602e-08\n",
            "  1.34111318e-08 1.24647945e-08 2.84565118e-08 2.54173642e-07]\n",
            " [2.01428907e-06 8.66832792e-08 2.34282822e-07 1.01494342e-08\n",
            "  5.31217052e-14 1.61458559e-07 1.24739202e-07 2.65375775e-07\n",
            "  9.80701316e-07 9.34411332e-07 2.17754064e-06 8.18878470e-06\n",
            "  4.70269515e-06 2.26912221e-06 1.02759941e-07 5.78965214e-08\n",
            "  3.67692934e-08 1.01367936e-08 1.82093729e-07 6.53651568e-07]\n",
            " [1.14705535e-02 6.28038601e-04 1.23651093e-03 4.82194228e-05\n",
            "  7.91537613e-10 5.02105744e-04 2.89462600e-03 8.23712675e-04\n",
            "  6.33101225e-01 3.37521248e-02 1.66387409e-02 5.06701842e-02\n",
            "  2.73684338e-02 1.27350613e-02 9.96811199e-04 5.16364467e-04\n",
            "  2.64292292e-04 1.80265371e-04 6.05934707e-04 4.19702716e-02]\n",
            " [7.03667663e-03 3.84278363e-04 7.58499373e-04 2.96510298e-05\n",
            "  4.81942430e-10 3.11125594e-04 1.75950362e-03 5.10338054e-04\n",
            "  1.60873309e-01 1.02488566e-02 9.06851422e-03 3.10540013e-02\n",
            "  1.67849623e-02 7.81377964e-03 6.08445436e-04 3.15382174e-04\n",
            "  1.61725082e-04 1.09674918e-04 3.74917086e-04 1.22397495e-02]\n",
            " [2.16286480e-06 9.18482357e-08 2.66038199e-07 1.36975471e-08\n",
            "  1.29244556e-13 4.81320690e-08 4.91065180e-07 8.41166582e-07\n",
            "  1.03603634e-06 6.53028792e-07 1.87260002e-06 8.77933417e-06\n",
            "  5.07106506e-06 2.43193040e-06 1.53273248e-07 7.81652361e-08\n",
            "  3.81169336e-08 2.99539167e-08 1.06699375e-07 7.13132067e-07]\n",
            " [8.11385140e-02 3.15891346e-03 1.00054257e-02 4.96754365e-04\n",
            "  2.74425438e-09 4.48181806e-03 8.54403805e-03 2.54117642e-02\n",
            "  3.55486497e-02 3.01301908e-02 7.41423070e-02 3.20412278e-01\n",
            "  1.88610539e-01 9.17172953e-02 4.22564521e-03 2.28912057e-03\n",
            "  1.32474909e-03 5.82902867e-04 6.20596157e-03 2.40975507e-02]\n",
            " [6.62755445e-02 2.28351820e-03 8.58258270e-03 4.64826328e-04\n",
            "  2.23413155e-09 2.77757272e-03 7.42985820e-03 2.99647227e-02\n",
            "  2.55288165e-02 2.04342604e-02 5.17034829e-02 2.53131509e-01\n",
            "  1.53137386e-01 7.52409175e-02 3.19581665e-03 1.70527049e-03\n",
            "  9.48802917e-04 4.87920654e-04 4.72348928e-03 1.75404884e-02]\n",
            " [3.86093707e-05 2.65535618e-06 3.75307536e-06 1.23770320e-07\n",
            "  4.63533075e-12 6.94124269e-09 1.85475692e-05 1.24450796e-08\n",
            "  3.04235364e-05 1.53383608e-05 5.00751958e-05 1.86820384e-04\n",
            "  9.44918502e-05 4.21136829e-05 5.01403383e-06 2.49023674e-06\n",
            "  1.11074075e-06 1.10068345e-06 3.01920039e-07 2.07073172e-05]\n",
            " [1.52217806e-03 9.03754772e-05 1.58595765e-04 5.90048876e-06\n",
            "  1.30705155e-10 4.46631384e-05 4.98798501e-04 7.38996823e-05\n",
            "  1.03202742e-03 6.41542836e-04 1.85388536e-03 6.93541393e-03\n",
            "  3.66269611e-03 1.68020907e-03 1.53865447e-04 7.83081632e-05\n",
            "  3.79443518e-05 3.03540273e-05 5.77628634e-05 6.98625343e-04]\n",
            " [9.21384781e-04 5.43657079e-05 9.62514750e-05 3.59588967e-06\n",
            "  7.78859338e-11 2.80825534e-05 2.96430226e-04 4.64901495e-05\n",
            "  6.20725390e-04 3.89191177e-04 1.11930235e-03 4.18786379e-03\n",
            "  2.21557077e-03 1.01751275e-03 9.20990424e-05 4.69301704e-05\n",
            "  2.28293866e-05 1.80651314e-05 3.60479644e-05 4.20092547e-04]\n",
            " [2.71751446e-06 1.06624356e-07 3.23719746e-07 1.44245966e-08\n",
            "  3.40107547e-14 2.50048600e-07 4.51671964e-11 4.10073199e-07\n",
            "  1.20234449e-06 1.28776287e-06 2.85171564e-06 1.07374981e-05\n",
            "  6.29926444e-06 3.07565688e-06 1.06962439e-07 6.37510027e-08\n",
            "  4.53910083e-08 4.23846824e-09 2.78889360e-07 7.96890845e-07]\n",
            " [4.65995811e-08 1.99838146e-09 5.77099346e-09 3.04444192e-10\n",
            "  3.15843890e-15 4.66653272e-10 1.23866428e-08 2.01962838e-08\n",
            "  2.25392505e-08 1.26228201e-08 3.87106880e-08 1.89831468e-07\n",
            "  1.09449125e-07 5.23448520e-08 3.54648266e-09 1.78110204e-09\n",
            "  8.25791879e-10 7.42897022e-10 1.86988069e-09 1.56094337e-08]\n",
            " [1.69873033e-07 7.19860793e-09 1.99331271e-08 8.82333984e-10\n",
            "  4.59450068e-15 1.30863702e-08 1.12666001e-08 2.66889550e-08\n",
            "  8.13815006e-08 7.66889983e-08 1.79608804e-07 6.87395186e-07\n",
            "  3.96290432e-07 1.91476687e-07 8.64130367e-09 4.84778440e-09\n",
            "  3.04939540e-09 8.90212737e-10 1.50845754e-08 5.43508634e-08]]\n",
            "\n",
            "\n",
            "outputs\n",
            "Timestep:  4\n",
            "\n",
            " Gate Controller vals:\n",
            "[[9.9998939e-01 1.2807619e-10 1.0613715e-05]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.18412852 0.17654492 0.15400723 0.4853194 ]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.14916253 0.0323989  0.20606703 0.08488629 0.52748525]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.3448127  0.03985281 0.05266562 0.17261    0.31357417 0.07648475]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[2.3232996e-08 1.0336815e-07 1.6003492e-08 9.9643928e-01 4.1752084e-08\n",
            "  5.7131992e-06 3.5548233e-03]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.05300331 0.12666497 0.09869628 0.3020952  0.04860584 0.1823193\n",
            "  0.17363866 0.01497642]]\n",
            "[[0.2511615  0.01051519 0.05850443 0.10441776 0.05188528 0.21592657\n",
            "  0.12940687 0.1781824 ]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.05684615 0.1373089  0.0764631  0.11270054 0.2256132  0.23953949\n",
            "  0.02033016 0.07637392 0.05482452]]\n",
            "[[0.02757961 0.18763311 0.305902   0.04906491 0.03103648 0.05498127\n",
            "  0.08928213 0.17651002 0.07801054]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[1.2107923e-01 2.3058552e-01 2.7941301e-02 6.1915398e-01 1.5477181e-05\n",
            "  1.3155959e-07 2.6095671e-07 5.7124953e-06 5.4796737e-07 1.2178605e-03]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.131391   0.06213145 0.03418772 0.08287504 0.08535808 0.07098517\n",
            "  0.11755573 0.19764498 0.04274036 0.00659269 0.16853781]]\n",
            "[[0.33061635 0.03788407 0.12296039 0.01789927 0.01694865 0.08915225\n",
            "  0.04632821 0.1621379  0.07550795 0.07907084 0.02149408]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.05772882 0.10523382 0.05472801 0.04805291 0.14973682 0.04104419\n",
            "  0.03727437 0.05673234 0.03538787 0.26060784 0.09393442 0.05953855]]\n",
            "[[0.04503444 0.05036486 0.05773803 0.02933953 0.12937619 0.10383361\n",
            "  0.1354345  0.03713464 0.01457104 0.325662   0.02858102 0.04293014]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.08465113 0.02896202 0.18495132 0.03724352 0.10172637 0.18294589\n",
            "  0.03103235 0.03037346 0.04507075 0.00689427 0.09539722 0.07036909\n",
            "  0.10038257]]\n",
            "[[0.00263391 0.09174157 0.03366741 0.08167705 0.24666503 0.0182092\n",
            "  0.1366865  0.01035823 0.04647955 0.08905608 0.03685188 0.02218959\n",
            "  0.18378402]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.02941252 0.02668831 0.10406038 0.04269574 0.13435996 0.16635004\n",
            "  0.04465893 0.08040626 0.01518341 0.02176359 0.03997408 0.17887695\n",
            "  0.06715319 0.04841654]]\n",
            "[[0.11499467 0.04180792 0.04315858 0.0500741  0.03639801 0.02613634\n",
            "  0.02388838 0.14566249 0.3140688  0.12348371 0.00456195 0.03302331\n",
            "  0.02130248 0.02143928]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[0.06388076 0.03032407 0.13320677 0.01642273 0.19861606 0.05202582\n",
            "  0.01324489 0.03144879 0.07512831 0.03909213 0.19187704 0.03372266\n",
            "  0.0835074  0.01784198 0.01966054]]\n",
            "[[0.00811478 0.0938574  0.00447521 0.00579645 0.01711538 0.07684038\n",
            "  0.57651204 0.04709666 0.03612849 0.00747806 0.03185717 0.00490795\n",
            "  0.03911003 0.02008778 0.03062222]]\n",
            "\n",
            " Gate Controller vals:\n",
            "[[1.0112888e-10 4.7939128e-11 8.2645168e-11 6.1989182e-01 1.8692090e-11\n",
            "  5.3798371e-10 4.0467935e-11 3.8010746e-01 2.7659136e-10 6.1475276e-11\n",
            "  7.0256203e-07 3.7664039e-11 2.1344766e-10 4.3987050e-11 8.2940467e-11\n",
            "  1.0228935e-10]]\n",
            "[[3.8075991e-09 4.9950093e-09 1.6666126e-08 2.2701886e-01 1.5932734e-09\n",
            "  2.2542903e-08 9.3679139e-03 1.0642880e-01 3.9951655e-09 1.5657861e-09\n",
            "  6.5718442e-01 4.5842206e-09 9.1294963e-09 3.1537220e-09 6.6899410e-09\n",
            "  8.0092821e-10]]\n",
            "\n",
            "Register controller  0\n",
            "[[8.72014239e-10 8.77192630e-09 2.73874989e-09 1.72683528e-07\n",
            "  8.79330120e-09 3.45686227e-01 1.12276440e-02 2.41650523e-05\n",
            "  3.79605524e-07 1.21219506e-08 2.12604529e-03 3.77857035e-09\n",
            "  5.32942176e-01 1.07993022e-01 1.39368277e-08 3.91002182e-08\n",
            "  1.42619350e-09]]\n",
            "\n",
            "Register controller  1\n",
            "[[1.1713872e-09 5.0864279e-10 1.0561652e-09 5.4472757e-06 1.2649903e-09\n",
            "  1.2413588e-05 1.1620289e-03 1.7422150e-04 9.3741148e-09 5.2601346e-10\n",
            "  9.9837023e-01 7.4586115e-10 2.7407528e-04 1.5806673e-06 5.1334448e-10\n",
            "  6.3355010e-10 1.1973854e-09]]\n",
            "\n",
            "Register controller  2\n",
            "[[9.0770786e-09 4.5745633e-08 2.9815833e-08 3.5539931e-06 9.2622008e-09\n",
            "  3.6256123e-02 1.4542060e-01 3.4750722e-04 1.3220284e-07 4.9851906e-08\n",
            "  9.2915662e-02 4.7441060e-09 7.2377908e-01 1.2771129e-03 3.3402277e-08\n",
            "  5.9252343e-08 1.9617842e-08]]\n",
            "\n",
            "Output registers\n",
            "[[[1.0826510e-01 8.7864125e-01 1.1228064e-02 8.5921727e-08 6.2553381e-06\n",
            "   1.8492974e-09 1.0259892e-05 3.0201518e-05 1.6278845e-04 6.2949468e-05\n",
            "   2.7137791e-04 1.1487306e-03 2.0384525e-06 2.3688746e-05 1.6584137e-05\n",
            "   3.7496298e-07 4.0220704e-07 6.9769221e-06 1.9943467e-05 9.9596342e-05]\n",
            "  [1.2774599e-01 6.3398010e-03 1.1623758e-03 6.1945411e-07 2.9374456e-03\n",
            "   8.1492999e-07 4.8179426e-03 1.4182239e-02 7.6443590e-02 2.9550659e-02\n",
            "   1.2618218e-01 5.3943038e-01 8.9023152e-04 1.4080338e-03 7.7870721e-03\n",
            "   1.7511733e-04 4.8817852e-05 3.2762927e-03 9.3651982e-03 4.6769302e-02]\n",
            "  [1.3166052e-02 7.6059860e-01 1.4542082e-01 1.2356020e-06 2.7338025e-04\n",
            "   7.6456494e-08 4.4839320e-04 1.3199047e-03 7.1144057e-03 2.7505800e-03\n",
            "   1.1780619e-02 5.0203379e-02 8.5539825e-05 4.1912022e-04 7.2474172e-04\n",
            "   1.6336326e-05 8.6959089e-06 3.0491591e-04 8.7159476e-04 4.3526962e-03]]]\n",
            "\n",
            "Output memory\n",
            "[[1.35260916e-06 5.99538765e-08 1.59995722e-07 7.54441043e-09\n",
            "  1.32224809e-08 1.78498372e-08 1.94354442e-07 3.69808333e-07\n",
            "  7.12971200e-07 9.99984741e-01 1.31851425e-06 5.57499152e-06\n",
            "  3.18052980e-06 1.51331392e-06 8.93829224e-08 4.80348703e-08\n",
            "  2.29020358e-08 2.52957015e-08 8.12172090e-08 4.64359147e-07]\n",
            " [5.47546676e-07 2.72103726e-08 6.19227620e-08 2.70985190e-09\n",
            "  4.83378404e-09 6.25088514e-09 1.09168461e-07 1.06277284e-07\n",
            "  3.21778600e-07 2.28268561e-07 5.81630502e-07 2.34334311e-06\n",
            "  9.99994695e-01 6.09065069e-07 4.28564455e-08 2.26040004e-08\n",
            "  1.06024531e-08 1.15045768e-08 2.83000343e-08 2.10941607e-07]\n",
            " [3.45252738e-06 3.67952744e-08 5.45187504e-07 3.75101479e-08\n",
            "  5.28384936e-09 2.78893371e-08 7.20328401e-08 3.50115192e-06\n",
            "  3.75484376e-07 3.18943421e-07 7.48416369e-07 1.07888982e-05\n",
            "  7.69409235e-06 4.01405350e-06 4.31327507e-08 9.99967754e-01\n",
            "  1.26065469e-08 1.01257811e-08 2.52477093e-07 2.98061593e-07]\n",
            " [8.03664676e-04 1.66191312e-05 1.17062838e-04 7.39110237e-06\n",
            "  2.61328250e-06 1.32979676e-05 3.72634422e-05 6.21240295e-04\n",
            "  1.86015473e-04 1.56540002e-04 3.68914276e-04 2.74845655e-03\n",
            "  1.81935006e-03 9.24331718e-04 2.15434848e-05 1.18373664e-05\n",
            "  6.23493133e-06 5.09677193e-06 5.85056841e-05 1.30068263e-04]\n",
            " [4.16085832e-09 2.49757215e-10 9.99999940e-01 1.56480124e-11\n",
            "  2.84765076e-11 3.13962398e-11 1.28918953e-09 1.89037591e-10\n",
            "  2.92811020e-09 1.84983318e-09 5.10605114e-09 1.90722158e-08\n",
            "  1.00350697e-08 4.57676030e-09 4.24692725e-10 2.18453589e-10\n",
            "  1.00118240e-10 1.06018694e-10 1.45503581e-10 1.93911731e-09]\n",
            " [1.43421985e-08 6.69791445e-10 1.60732239e-09 6.55260915e-11\n",
            "  3.01768693e-10 1.12212219e-10 4.95005481e-10 1.64090874e-09\n",
            "  8.39582270e-09 7.60429408e-09 1.61034137e-08 9.99999881e-01\n",
            "  3.38340591e-08 1.59531606e-08 8.48922654e-10 4.92239360e-10\n",
            "  2.33335878e-10 3.38482575e-10 1.08716602e-09 5.16226661e-09]\n",
            " [1.03844755e-07 4.67578154e-09 1.18089272e-08 4.92594576e-10\n",
            "  1.60022084e-09 3.04770409e-09 3.17188831e-09 1.27237563e-08\n",
            "  5.72103929e-08 5.34063460e-08 1.14838230e-07 4.30221689e-07\n",
            "  2.43982498e-07 1.16003264e-07 5.69849634e-09 3.30889294e-09\n",
            "  9.99998808e-01 1.86891080e-09 8.50925375e-09 3.58005110e-08]\n",
            " [8.72600424e-07 3.80505654e-08 1.06755017e-07 5.49978463e-09\n",
            "  2.69796230e-09 5.12730214e-09 2.13027818e-07 3.48896890e-07\n",
            "  4.36685497e-07 2.61330854e-07 7.53301038e-07 3.57559702e-06\n",
            "  2.05203355e-06 9.99990821e-01 6.57182042e-08 3.34440529e-08\n",
            "  1.53152584e-08 1.54739386e-08 3.70580899e-08 2.97131237e-07]\n",
            " [7.27346651e-06 3.35936733e-07 8.20230753e-07 3.38163026e-08\n",
            "  1.20933237e-07 1.61485602e-07 3.23101688e-07 8.49263188e-07\n",
            "  4.12920690e-06 3.75036279e-06 8.11840073e-06 3.03971647e-05\n",
            "  1.71294341e-05 8.10951951e-06 4.23738868e-07 2.43702090e-07\n",
            "  1.22119829e-07 1.45021090e-07 5.67647817e-07 2.57919010e-06]\n",
            " [1.65423267e-02 8.62514949e-04 1.80447649e-03 7.12804322e-05\n",
            "  1.35042414e-04 4.67080361e-04 2.91397958e-03 1.41820929e-03\n",
            "  5.92401564e-01 3.45394276e-02 2.21107919e-02 7.19312653e-02\n",
            "  3.93338501e-02 1.83675680e-02 1.28562702e-03 6.87788124e-04\n",
            "  3.41144623e-04 3.18296807e-04 9.94163449e-04 4.11893129e-02]\n",
            " [1.03232702e-02 5.37876796e-04 1.12576061e-03 4.45270416e-05\n",
            "  8.24742092e-05 2.97880499e-04 1.81973970e-03 8.86780210e-04\n",
            "  1.56158850e-01 1.17321685e-02 1.27333440e-02 4.48754281e-02\n",
            "  2.45439969e-02 1.14636179e-02 8.01398361e-04 4.28647938e-04\n",
            "  2.13035761e-04 1.96985493e-04 6.21872896e-04 1.30308913e-02]\n",
            " [5.37077085e-06 2.43883790e-07 6.23444407e-07 2.81333605e-08\n",
            "  7.37650652e-08 4.81528808e-08 6.12045255e-07 1.19729521e-06\n",
            "  2.95651080e-06 2.37066979e-06 5.49632568e-06 2.23256357e-05\n",
            "  1.26509203e-05 5.99435862e-06 3.49057075e-07 1.91499254e-07\n",
            "  9.01776787e-08 1.12227788e-07 3.41876557e-07 1.88763988e-06]\n",
            " [8.27356577e-02 3.60601279e-03 9.66111105e-03 4.32739034e-04\n",
            "  1.04601344e-03 2.05762521e-03 5.63780358e-03 1.67153291e-02\n",
            "  4.35515642e-02 3.81878540e-02 8.54204744e-02 3.39175880e-01\n",
            "  1.94066226e-01 9.26190764e-02 4.71607689e-03 2.65794038e-03\n",
            "  1.34636392e-03 1.43425993e-03 6.18369458e-03 2.77168900e-02]\n",
            " [7.21644834e-02 2.84608034e-03 8.84251762e-03 4.36264119e-04\n",
            "  6.39160105e-04 1.85963116e-03 6.02232013e-03 2.31458265e-02\n",
            "  3.37310359e-02 2.85628699e-02 6.60121739e-02 2.86836803e-01\n",
            "  1.68196827e-01 8.12382773e-02 3.83590534e-03 2.12362921e-03\n",
            "  1.08628091e-03 1.03953329e-03 5.19992039e-03 2.19195243e-02]\n",
            " [4.05060309e-05 2.74522768e-06 3.96440328e-06 1.32306653e-07\n",
            "  4.36367884e-08 6.95448232e-09 1.86187226e-05 2.23114782e-07\n",
            "  3.15588368e-05 1.63540190e-05 5.22175505e-05 1.94829030e-04\n",
            "  9.89733380e-05 4.42199707e-05 5.12973065e-06 2.55721966e-06\n",
            "  1.14151055e-06 1.14932448e-06 4.41023531e-07 2.14015909e-05]\n",
            " [2.16602115e-03 1.20747529e-04 2.30414968e-04 8.80526477e-06\n",
            "  1.50776787e-05 4.43194986e-05 5.19641209e-04 1.46122664e-04\n",
            "  1.41653290e-03 9.87631036e-04 2.58013327e-03 9.65026580e-03\n",
            "  5.18349744e-03 2.39528646e-03 1.92685053e-04 1.00863617e-04\n",
            "  4.82899850e-05 4.69343431e-05 1.05383486e-04 9.33252566e-04]\n",
            " [1.31763332e-03 7.30945030e-05 1.40429809e-04 5.38167887e-06\n",
            "  9.21241190e-06 2.79515880e-05 3.10129195e-04 9.07487556e-05\n",
            "  8.57616658e-04 6.01852313e-04 1.56653731e-03 5.85972006e-03\n",
            "  3.15167010e-03 1.45758013e-03 1.16111973e-04 6.08609807e-05\n",
            "  2.92225486e-05 2.82542023e-05 6.52474700e-05 5.64776128e-04]\n",
            " [2.72531884e-06 1.06994229e-07 3.24589251e-07 1.44597161e-08\n",
            "  1.79493906e-10 2.50048629e-07 3.39540618e-10 4.10939634e-07\n",
            "  1.20701668e-06 1.29194154e-06 2.86053159e-06 1.07704545e-05\n",
            "  6.31770490e-06 3.08432345e-06 1.07438751e-07 6.40267288e-08\n",
            "  4.55176625e-08 4.43863168e-09 2.79461489e-07 7.99748193e-07]\n",
            " [6.85339856e-08 3.03793835e-09 8.21480306e-09 4.03151623e-10\n",
            "  5.04366160e-10 4.66808037e-10 1.32139615e-08 2.26315038e-08\n",
            "  3.56706344e-08 2.43672460e-08 6.34881516e-08 2.82455687e-07\n",
            "  1.61277200e-07 7.67033441e-08 4.88518115e-09 2.55603605e-09\n",
            "  1.18176358e-09 1.30544697e-09 3.47791684e-09 2.36402382e-08]\n",
            " [5.94585345e-07 2.73273848e-08 6.72522233e-08 2.79359047e-09\n",
            "  9.76591963e-09 1.30893021e-08 2.72858660e-08 7.38417825e-08\n",
            "  3.35642483e-07 3.04094470e-07 6.59370926e-07 2.48086303e-06\n",
            "  1.39982910e-06 6.63126343e-07 3.45623192e-08 1.98527133e-08\n",
            "  9.94201699e-09 1.17827925e-08 4.62207055e-08 2.09850072e-07]]\n",
            "final cost: 1.562606e-06\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N8KIX7K1522T",
        "colab_type": "code",
        "outputId": "e0cbee56-ff72-465d-f749-9f955d412acb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "np.argmax(outputMemory, axis=1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 9, 12, 15, 11,  2, 11, 16, 13, 11,  8,  8, 11, 11, 11, 11, 11, 11,\n",
              "       11, 11, 11])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sSPwr6AjL9_H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## Test\n",
        "test_loss_history1, outputMemory = testModel(inputTestSet, targetTestSet, gates, model, readMemory, writeMemory)\n",
        "plt.plot(test_loss_history1, label='LSTM V1')\n",
        "\n",
        "plt.legend(loc='upper right')\n",
        "plt.ylabel('loss value')\n",
        "plt.xlabel('batches')\n",
        "plt.title(\"Testing Loss\")\n",
        "# plt.ylim(0, 10)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "edz6c1hoZJ43",
        "colab_type": "text"
      },
      "source": [
        "## Bucket Sort"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "89QSMdkFZMxS",
        "colab_type": "text"
      },
      "source": [
        "https://www.geeksforgeeks.org/bucket-sort-2/"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "46mVnczkaSMN",
        "colab_type": "code",
        "outputId": "5aef80ae-4ead-4984-c404-f92fa8abbd96",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "source": [
        "# Python3 program to sort an array \n",
        "# using bucket sort \n",
        "def insertionSort(b): \n",
        "\tfor i in range(1, len(b)): \n",
        "\t\tup = b[i] \n",
        "\t\tj = i - 1\n",
        "\t\twhile j >=0 and b[j] > up: \n",
        "\t\t\tb[j + 1] = b[j] \n",
        "\t\t\tj -= 1\n",
        "\t\tb[j + 1] = up\t \n",
        "\treturn b\t \n",
        "\t\t\t\n",
        "def bucketSort(x, M): \n",
        "\tarr = [] \n",
        "\tslot_num = 3 # 10 means 10 slots, each \n",
        "\t\t\t\t# slot's size is 0.1 \n",
        "\tfor i in range(slot_num): \n",
        "\t\tarr.append([]) \n",
        "\t\t\n",
        "\t# Put array elements in different buckets \n",
        "\tfor j in x: \n",
        "\t\tindex_b = int((slot_num * j)/M) \n",
        "\t\tarr[index_b].append(j) \n",
        "\t\n",
        "\t# Sort individual buckets \n",
        "\tfor i in range(slot_num): \n",
        "\t\tarr[i] = insertionSort(arr[i]) \n",
        "\t\n",
        "\t# concatenate the result \n",
        "\tk = 0\n",
        "\tfor i in range(slot_num): \n",
        "\t\tfor j in range(len(arr[i])): \n",
        "\t\t\tx[k] = arr[i][j] \n",
        "\t\t\tk += 1\n",
        "\treturn x, arr\n",
        "\n",
        "# Driver Code \n",
        "x = [1, 4, 3, 6, \n",
        "\t2, 5, 7, 9, 8] \n",
        "print(\"Sorted Array is\") \n",
        "print(bucketSort(x, 10))\n",
        "\n",
        "\n",
        "# Driver Code \n",
        "x = [1, 14, 13, 6, \n",
        "\t12, 15, 7, 19, 8] \n",
        "print(\"Sorted Array is\") \n",
        "print(bucketSort(x, 20))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sorted Array is\n",
            "([1, 2, 3, 4, 5, 6, 7, 8, 9], [[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n",
            "Sorted Array is\n",
            "([1, 6, 7, 8, 12, 13, 14, 15, 19], [[1, 6], [7, 8, 12, 13], [14, 15, 19]])\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}