tensorflow-basic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tensorflow\n",
    "* Elementos Básicos da Linguagem\n",
    "Fonte: https://mlfromscratch.com/tensorflow-2/#/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.1.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf; \n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modo eager executado por padrão no Tensorflow 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eager execution is enabled (running operations immediately)\n",
      "\n",
      "Turn eager execution off by running: \n",
      "from tensorflow.python.framework.ops import disable_eager_execution\n",
      "disable_eager_execution()\n"
     ]
    }
   ],
   "source": [
    "if(tf.executing_eagerly()):\n",
    "    print('Eager execution is enabled (running operations immediately)\\n')\n",
    "    print(('Turn eager execution off by running: \\n{0}\\n{1}').format('' \\\n",
    "        'from tensorflow.python.framework.ops import disable_eager_execution', \\\n",
    "        'disable_eager_execution()'))\n",
    "else:\n",
    "    print('You are not running eager execution. TensorFlow version >= 2.0.0' \\\n",
    "          'has eager execution enabled by default.')\n",
    "    print(('Turn on eager execution by running: \\n\\n{0}\\n\\nOr upgrade '\\\n",
    "           'your tensorflow version by running:\\n\\n{1}').format(\n",
    "           'tf.compat.v1.enable_eager_execution()',\n",
    "           '!pip install --upgrade tensorflow\\n' \\\n",
    "           '!pip install --upgrade tensorflow-gpu'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exemplo de multiplicação de dois valores constante usando tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(15, shape=(), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "a = tf.constant(5)\n",
    "b = tf.constant(3)\n",
    "c = a * b\n",
    "print(c)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Como desligar modo eager\n",
    "* Para retornar ao modo eager, é necessário reiniciar o kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.python.framework.ops import disable_eager_execution\n",
    "disable_eager_execution()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Verificar se há GPU disponível para uso\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n",
      "WARNING:tensorflow:From <ipython-input-7-8db21c955b8a>:4: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.config.list_physical_devices('GPU')` instead.\n",
      "Is your GPU available for use?\n",
      "Yes, your GPU is available: True\n",
      "\n",
      "Your devices that are available:\n",
      "['/physical_device:CPU:0', '/physical_device:XLA_CPU:0', '/physical_device:GPU:0', '/physical_device:XLA_GPU:0']\n"
     ]
    }
   ],
   "source": [
    "print(tf.config.list_physical_devices('GPU'))\n",
    "\n",
    "print(('Is your GPU available for use?\\n{0}').format(\n",
    "    'Yes, your GPU is available: True' if tf.test.is_gpu_available() == True else 'No, your GPU is NOT available: False'\n",
    "))\n",
    "\n",
    "print(('\\nYour devices that are available:\\n{0}').format(\n",
    "    [device.name for device in tf.config.experimental.list_physical_devices()]\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Executando mesmo código em GPU e CPU para comparar tempo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(\n",
      "[[1. 0. 0. ... 0. 0. 0.]\n",
      " [0. 1. 0. ... 0. 0. 0.]\n",
      " [0. 0. 1. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 1. 0. 0.]\n",
      " [0. 0. 0. ... 0. 1. 0.]\n",
      " [0. 0. 0. ... 0. 0. 1.]], shape=(10000, 10000), dtype=float32)\n",
      "CPU:  0.3702702522277832\n",
      "tf.Tensor(\n",
      "[[1. 0. 0. ... 0. 0. 0.]\n",
      " [0. 1. 0. ... 0. 0. 0.]\n",
      " [0. 0. 1. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 1. 0. 0.]\n",
      " [0. 0. 0. ... 0. 1. 0.]\n",
      " [0. 0. 0. ... 0. 0. 1.]], shape=(10000, 10000), dtype=float32)\n",
      "GPU:  0.26410627365112305\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "cpu_slot = 0\n",
    "gpu_slot = 0\n",
    "\n",
    "# Using CPU at slot 0\n",
    "with tf.device('/CPU:' + str(cpu_slot)):\n",
    "    # Starting a timer\n",
    "    start = time.time()\n",
    "\n",
    "    # Doing operations on CPU\n",
    "    print(tf.eye(10000,10000))\n",
    "\n",
    "    # Printing how long it took with CPU\n",
    "    end = time.time() - start\n",
    "    print(\"CPU: \", end)\n",
    "\n",
    "# Using the GPU at slot 0\n",
    "with tf.device('/GPU:' + str(gpu_slot)):\n",
    "    # Starting a timer\n",
    "    start = time.time()\n",
    "\n",
    "    # Doing operations on CPU\n",
    "\n",
    "    print(tf.eye(10000,10000))\n",
    "\n",
    "    # Printing how long it took with CPU\n",
    "    end = time.time() - start\n",
    "    print(\"GPU: \",end)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Operações Comuns com Tensorflow\n",
    "\n",
    "* Montando tensores com tf.constant e tf.Variable\n",
    "* Concatenação de dois tensores com tf.concat\n",
    "* Montando tensores com tf.zeros ou tf.ones\n",
    "* Remodelando  com tf.reshape\n",
    "* Cast de tensores para outros tipos de dados com tf.cast\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Montando tensores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making a constant tensor A, that does not change\n",
    "A = tf.constant([[3, 2],\n",
    "                 [5, 2]])\n",
    "\n",
    "# Making a Variable tensor VA, which can change. Notice it's .Variable\n",
    "VA = tf.Variable([[3, 2],\n",
    "                 [5, 2]])\n",
    "\n",
    "# Making another tensor B\n",
    "B = tf.constant([[9, 5],\n",
    "                 [1, 3]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Concatenção"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding B's columns to A:\n",
      "[[3 2 9 5]\n",
      " [5 2 1 3]]\n",
      "\n",
      "Adding B's rows to A:\n",
      "[[3 2]\n",
      " [5 2]\n",
      " [9 5]\n",
      " [1 3]]\n"
     ]
    }
   ],
   "source": [
    "# Making a constant tensor A, that does not change\n",
    "A = tf.constant([[3, 2],\n",
    "                 [5, 2]])\n",
    "\n",
    "# Making a Variable tensor VA, which can change. Notice it's .Variable\n",
    "VA = tf.Variable([[3, 2],\n",
    "                 [5, 2]])\n",
    "\n",
    "# Making another tensor B\n",
    "B = tf.constant([[9, 5],\n",
    "                 [1, 3]])\n",
    "\n",
    "# Concatenate columns\n",
    "AB_concatenated = tf.concat(values=[A, B], axis=1)\n",
    "print(('Adding B\\'s columns to A:\\n{0}').format(\n",
    "    AB_concatenated.numpy()\n",
    "))\n",
    "\n",
    "# Concatenate rows\n",
    "AB_concatenated = tf.concat(values=[A, B], axis=0)\n",
    "print(('\\nAdding B\\'s rows to A:\\n{0}').format(\n",
    "    AB_concatenated.numpy()\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Criando tensores com apenas zeros ou uns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor full of zeros as int32, 3 rows and 4 columns:\n",
      "[[0 0 0 0]\n",
      " [0 0 0 0]\n",
      " [0 0 0 0]]\n",
      "\n",
      "Tensor full of ones as float32, 5 rows and 3 columns:\n",
      "[[1. 1. 1.]\n",
      " [1. 1. 1.]\n",
      " [1. 1. 1.]\n",
      " [1. 1. 1.]\n",
      " [1. 1. 1.]]\n"
     ]
    }
   ],
   "source": [
    "# Making a tensor filled with zeros. shape=[rows, columns]\n",
    "tensor = tf.zeros(shape=[3, 4], dtype=tf.int32)\n",
    "print(('Tensor full of zeros as int32, 3 rows and 4 columns:\\n{0}').format(\n",
    "    tensor.numpy()\n",
    "))\n",
    "\n",
    "# Making a tensor filled with zeros with data type of float32\n",
    "tensor = tf.ones(shape=[5, 3], dtype=tf.float32)\n",
    "print(('\\nTensor full of ones as float32, 5 rows and 3 columns:\\n{0}').format(\n",
    "    tensor.numpy()\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reshape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor BEFORE reshape:\n",
      "[[3 2]\n",
      " [5 2]\n",
      " [9 5]\n",
      " [1 3]]\n",
      "\n",
      "Tensor AFTER reshape:\n",
      "[[3 2 5 2 9 5 1 3]]\n"
     ]
    }
   ],
   "source": [
    "# Making a tensor for reshaping\n",
    "tensor = tf.constant([[3, 2],\n",
    "                      [5, 2],\n",
    "                      [9, 5],\n",
    "                      [1, 3]])\n",
    "\n",
    "# Reshaping the tensor into a shape of: shape = [rows, columns]\n",
    "reshaped_tensor = tf.reshape(tensor = tensor,\n",
    "                             shape = [1, 8])\n",
    "\n",
    "print(('Tensor BEFORE reshape:\\n{0}').format(\n",
    "    tensor.numpy()\n",
    "))\n",
    "print(('\\nTensor AFTER reshape:\\n{0}').format(\n",
    "    reshaped_tensor.numpy()\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor with floats:\n",
      "[[3.1 2.8]\n",
      " [5.2 2.3]\n",
      " [9.7 5.5]\n",
      " [1.1 3.4]]\n",
      "\n",
      "Tensor cast from float to int (just remove the decimal, no rounding):\n",
      "[[3 2]\n",
      " [5 2]\n",
      " [9 5]\n",
      " [1 3]]\n"
     ]
    }
   ],
   "source": [
    "# Making a tensor\n",
    "tensor = tf.constant([[3.1, 2.8],\n",
    "                      [5.2, 2.3],\n",
    "                      [9.7, 5.5],\n",
    "                      [1.1, 3.4]], \n",
    "                      dtype=tf.float32)\n",
    "\n",
    "tensor_as_int = tf.cast(tensor, tf.int32)\n",
    "\n",
    "print(('Tensor with floats:\\n{0}').format(\n",
    "    tensor.numpy()\n",
    "))\n",
    "print(('\\nTensor cast from float to int (just remove the decimal, no rounding):\\n{0}').format(\n",
    "    tensor_as_int.numpy()\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Algebra Linear\n",
    "\n",
    "* Transpor o tensor com tf.transpose\n",
    "* Multiplicação de matrizes com tf.matmul\n",
    "* Multiplicação por elementos com tf.multiply\n",
    "* Matriz identidade com tf.eye\n",
    "* Determinante com tf.linalg.det\n",
    "* Produto de ponto com tf.tensordot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The transposed matrix A:\n",
      "[[3 1]\n",
      " [7 9]]\n"
     ]
    }
   ],
   "source": [
    "# Some Matrix A\n",
    "A = tf.constant([[3, 7],\n",
    "                 [1, 9]])\n",
    "\n",
    "A = tf.transpose(A)\n",
    "\n",
    "print(('The transposed matrix A:\\n{0}').format(\n",
    "    A\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiplicação de Matrizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix Multiplication of A and v results in a new Tensor:\n",
      "[[29]\n",
      " [23]]\n"
     ]
    }
   ],
   "source": [
    "# Some Matrix A\n",
    "A = tf.constant([[3, 7],\n",
    "                 [1, 9]])\n",
    "\n",
    "# Some vector v\n",
    "v = tf.constant([[5],\n",
    "                 [2]])\n",
    "\n",
    "# Matrix multiplication of A.v^T\n",
    "Av = tf.matmul(A, v)\n",
    "\n",
    "print(('Matrix Multiplication of A and v results in a new Tensor:\\n{0}').format(\n",
    "    Av\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiplicação de matrix por um elemento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Element-wise multiplication of A and v results in a new Tensor:\n",
      "[[ 96 249  15]\n",
      " [ 51  69  30]\n",
      " [225 117 156]]\n"
     ]
    }
   ],
   "source": [
    "# Element-wise multiplication\n",
    "v = tf.constant([3])\n",
    "Av = tf.multiply(A, v)\n",
    "\n",
    "print(('Element-wise multiplication of A and v results in a new Tensor:\\n{0}').format(\n",
    "    Av\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculando matriz Identidade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Get rows and columns in tensor A:\n",
      "3 rows\n",
      "2 columns\n",
      "\n",
      "The identity matrix of A:\n",
      "[[1 0]\n",
      " [0 1]\n",
      " [0 0]]\n"
     ]
    }
   ],
   "source": [
    "# Some Matrix A\n",
    "A = tf.constant([[3, 7],\n",
    "                 [1, 9],\n",
    "                 [2, 5]])\n",
    "\n",
    "# Get number of dimensions\n",
    "rows, columns = A.shape\n",
    "print(('Get rows and columns in tensor A:\\n{0} rows\\n{1} columns').format(\n",
    "    rows, columns\n",
    "))\n",
    "\n",
    "# Making identity matrix\n",
    "A_identity = tf.eye(num_rows = rows,\n",
    "                    num_columns = columns,\n",
    "                    dtype = tf.int32)\n",
    "print(('\\nThe identity matrix of A:\\n{0}').format(\n",
    "    A_identity.numpy()\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Determinante"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The determinant of A:\n",
      "20.000001907348633\n"
     ]
    }
   ],
   "source": [
    "# Reusing Matrix A\n",
    "A = tf.constant([[3, 7],\n",
    "                 [1, 9]])\n",
    "\n",
    "# Determinant must be: half, float32, float64, complex64, complex128\n",
    "# Thus, we cast A to the data type float32\n",
    "A = tf.dtypes.cast(A, tf.float32)\n",
    "\n",
    "# Finding the determinant of A\n",
    "det_A = tf.linalg.det(A)\n",
    "\n",
    "print(('The determinant of A:\\n{0}').format(\n",
    "    det_A\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dot Product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dot product of A.B^T results in a new Tensor:\n",
      "[[8634 2719 8750]\n",
      " [2939 1329 2975]\n",
      " [7573 5341 7545]]\n",
      "\n",
      "Matrix Multiplication of A.B^T results in a new Tensor:\n",
      "[[8634 2719 8750]\n",
      " [2939 1329 2975]\n",
      " [7573 5341 7545]]\n"
     ]
    }
   ],
   "source": [
    "# Defining a 3x3 matrix\n",
    "A = tf.constant([[32, 83, 5],\n",
    "                 [17, 23, 10],\n",
    "                 [75, 39, 52]])\n",
    "\n",
    "# Defining another 3x3 matrix\n",
    "B = tf.constant([[28, 57, 20],\n",
    "                 [91, 10, 95],\n",
    "                 [37, 13, 45]])\n",
    "\n",
    "# Finding the dot product\n",
    "dot_AB = tf.tensordot(a=A, b=B, axes=1).numpy()\n",
    "\n",
    "print(('Dot product of A.B^T results in a new Tensor:\\n{0}').format(\n",
    "    dot_AB\n",
    "))\n",
    "\n",
    "# Which is the same as matrix multiplication in this instance (axes=1)\n",
    "# Matrix multiplication of A and B\n",
    "AB = tf.matmul(A, B)\n",
    "\n",
    "print(('\\nMatrix Multiplication of A.B^T results in a new Tensor:\\n{0}').format(\n",
    "    AB\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "ename": "InvalidArgumentError",
     "evalue": "Matrix size-incompatible: In[0]: [2,2], In[1]: [3,3] [Op:MatMul] name: Tensordot/MatMul/",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-33-c88bf17f9a07>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# Finding the dot product\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mdot_AB\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensordot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mB\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m print(('Dot product of A.B^T results in a new Tensor:\\n{0}').format(\n",
      "\u001b[0;32m~/.conda/envs/DL/lib/python3.7/site-packages/tensorflow_core/python/ops/math_ops.py\u001b[0m in \u001b[0;36mtensordot\u001b[0;34m(a, b, axes, name)\u001b[0m\n\u001b[1;32m   4104\u001b[0m     b_reshape, b_free_dims, b_free_dims_static = _tensordot_reshape(\n\u001b[1;32m   4105\u001b[0m         b, b_axes, True)\n\u001b[0;32m-> 4106\u001b[0;31m     \u001b[0mab_matmul\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmatmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma_reshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb_reshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4107\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma_free_dims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb_free_dims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4108\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0marray_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mab_matmul\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma_free_dims\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mb_free_dims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/DL/lib/python3.7/site-packages/tensorflow_core/python/util/dispatch.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    178\u001b[0m     \u001b[0;34m\"\"\"Call target, and fall back on dispatchers if there is a TypeError.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m       \u001b[0;31m# Note: convert_to_eager_tensor currently raises a ValueError, not a\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/DL/lib/python3.7/site-packages/tensorflow_core/python/ops/math_ops.py\u001b[0m in \u001b[0;36mmatmul\u001b[0;34m(a, b, transpose_a, transpose_b, adjoint_a, adjoint_b, a_is_sparse, b_is_sparse, name)\u001b[0m\n\u001b[1;32m   2796\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2797\u001b[0m       return gen_math_ops.mat_mul(\n\u001b[0;32m-> 2798\u001b[0;31m           a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name)\n\u001b[0m\u001b[1;32m   2799\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2800\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/DL/lib/python3.7/site-packages/tensorflow_core/python/ops/gen_math_ops.py\u001b[0m in \u001b[0;36mmat_mul\u001b[0;34m(a, b, transpose_a, transpose_b, name)\u001b[0m\n\u001b[1;32m   5614\u001b[0m         \u001b[0;32mpass\u001b[0m  \u001b[0;31m# Add nodes to the TensorFlow graph.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5615\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0m_core\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5616\u001b[0;31m       \u001b[0m_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from_not_ok_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5617\u001b[0m   \u001b[0;31m# Add nodes to the TensorFlow graph.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5618\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mtranspose_a\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/DL/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py\u001b[0m in \u001b[0;36mraise_from_not_ok_status\u001b[0;34m(e, name)\u001b[0m\n\u001b[1;32m   6604\u001b[0m   \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\" name: \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6605\u001b[0m   \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6606\u001b[0;31m   \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6607\u001b[0m   \u001b[0;31m# pylint: enable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6608\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/miniconda3/lib/python3.7/site-packages/six.py\u001b[0m in \u001b[0;36mraise_from\u001b[0;34m(value, from_value)\u001b[0m\n",
      "\u001b[0;31mInvalidArgumentError\u001b[0m: Matrix size-incompatible: In[0]: [2,2], In[1]: [3,3] [Op:MatMul] name: Tensordot/MatMul/"
     ]
    }
   ],
   "source": [
    "A = tf.constant([[0, 1],\n",
    "                 [1, 0]])\n",
    "\n",
    "# Defining another 3x3 matrix\n",
    "B = tf.constant([[28, 57, 20],\n",
    "                 [91, 10, 95],\n",
    "                 [37, 13, 45]])\n",
    "\n",
    "# Finding the dot product\n",
    "dot_AB = tf.tensordot(a=A, b=B, axes=1).numpy()\n",
    "\n",
    "print(('Dot product of A.B^T results in a new Tensor:\\n{0}').format(\n",
    "    dot_AB\n",
    "))\n"
   ]
  }
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