PreTrainedRxTransferLearning.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "pretrained_rx_transferlearning.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/FraGoTe/redes-neuronales/blob/master/PreTrainedRxTransferLearning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eyUhcF61WImH",
        "colab_type": "text"
      },
      "source": [
        "##MODELOS DE RNA PRE-ENTRENADOS - Transfer Learning - Imágenes Médicas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FkKcHo3Pjxkp",
        "colab_type": "code",
        "outputId": "f4ed9bf5-612e-405f-fd10-fb7dc50dff48",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%tensorflow_version 1.x\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.15.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fHNy9zESXwk5",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "##Carga de datos\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rBrY9jd3WV8E",
        "colab_type": "text"
      },
      "source": [
        "Montando archivos:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NA2OlsMHWY40",
        "colab_type": "code",
        "outputId": "0b28ee1f-ac34-4439-c3d1-752e0eea7e35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MEj9ojRcWfki",
        "colab_type": "text"
      },
      "source": [
        "Mostrando lista de archivos:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-8NN__fMWgXh",
        "colab_type": "code",
        "outputId": "ffdfc95e-0419-48f5-efd8-f4fd3d3f2f14",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "%%bash\n",
        "ls -l /content/drive/My\\ Drive/RN_2019II/Sesion12-2"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 219\n",
            "drwx------ 2 root root   4096 Nov 10 16:43 dataset\n",
            "drwx------ 2 root root   4096 Nov 10 16:43 model\n",
            "-rw------- 1 root root 215808 Nov 19 05:04 pretrained_rx_transferlearning.ipynb\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hg3qcb3lsEG_",
        "colab_type": "text"
      },
      "source": [
        "## VISUALIZACION DE DATA\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3BOct0C3sPHJ",
        "colab_type": "code",
        "outputId": "1a35ea8d-cc76-402f-86e9-ffacfaf8fc85",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        }
      },
      "source": [
        "# Cargando dataset, redimensionando y guardando archivos\n",
        "from keras.preprocessing.image import load_img\n",
        "\n",
        "main_path = \"/content/drive/My Drive/RN_2019II/Sesion12-2/\"\n",
        "# Folders\n",
        "path_torax = \"\".join([main_path,\"dataset/train/torax/42_IM-2063-1001.png\"])\n",
        "path_abdomen = \"\".join([main_path,\"dataset/train/abdomen/openI_1.png\"])\n",
        "\n",
        "# Mostrar las imágenes de nuestro dataset\n",
        "import matplotlib.pyplot as plt\n",
        "img_rx_torax = load_img(path_torax)\n",
        "img_rx_abdomen = load_img(path_abdomen)\n",
        "\n",
        "plt.imshow(img_rx_torax)\n",
        "plt.show()\n",
        "plt.imshow(img_rx_abdomen)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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ZHmZ5+AUX4Eu3QUsu+MwnSMhXNSLkhCcoMeNGcQ59g8CTNsrV7/fDgBESrVar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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2oSL6b9m+kUA+y/tr2p6vNpC6O1tsrvu7swGIKl2Mnvt8wXt7e8Xc5tZa0yTd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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yzNaCkq8XIEm",
        "colab_type": "text"
      },
      "source": [
        "\n",
        " ## PARTE I - PREPROCESAMIENTO DE DATOS\n",
        "---\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q5HGp2W-XSkX",
        "colab_type": "code",
        "outputId": "6b60a3a9-8d4c-4e92-8cd6-df20df811cfb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "# Dataset Folder\n",
        "folder_main = \"\".join([main_path,\"dataset\"])\n",
        "\n",
        "# Preprocesamiento de imágenes: Definiremos una dimensión de 300x300\n",
        "img_width = 224\n",
        "img_height = 224\n",
        "\n",
        "print(\"\\nDescripción de la data de training:\")\n",
        "# Generador de data\n",
        "datagen = ImageDataGenerator( rescale = 1.0/255.0,\n",
        "                              horizontal_flip = True)\n",
        "# rescale: Normalizar valores [0-1] en lugar de [0-255]\n",
        "# horizontal_flip: valor booleno. Voltea aleatoriamente las entradas horizontalmente.\n",
        "\n",
        "training_set = datagen.flow_from_directory(folder_main + '/train',\n",
        "                                                 target_size = (img_width, img_height),\n",
        "                                                 batch_size = 1,\n",
        "                                                 class_mode = 'binary')\n",
        "\n",
        "print(\"\\nDescripción de la data de testing:\")\n",
        "testing_set = datagen.flow_from_directory(folder_main + '/test',\n",
        "                                                 target_size = (img_width, img_height),\n",
        "                                                 batch_size = 1,\n",
        "                                                 class_mode = 'binary')\n",
        "\n",
        "num_imgs_training = len(training_set.filenames)\n",
        "num_imgs_testing = len(testing_set.filenames)\n",
        "\n",
        "print(\"\\n\")\n",
        "print(training_set.class_indices)\n",
        "print(\"Número de imágenes de entenamiento: \",num_imgs_training)\n",
        "print(\"Número de imágenes de testing: \",num_imgs_testing)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Descripción de la data de training:\n",
            "Found 65 images belonging to 2 classes.\n",
            "\n",
            "Descripción de la data de testing:\n",
            "Found 10 images belonging to 2 classes.\n",
            "\n",
            "\n",
            "{'abdomen': 0, 'torax': 1}\n",
            "Número de imágenes de entenamiento:  65\n",
            "Número de imágenes de testing:  10\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Wi1j84IZldb",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "## PARTE II - CONSTRUYENDO LA RED NEURONAL CONVOLUCIONAL\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HwlyGxY_Ztc4",
        "colab_type": "code",
        "outputId": "736f7f24-d3e9-4dcc-aced-3f6959d8fc44",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# VGG16\n",
        "from keras.applications import vgg16\n",
        "\n",
        "# Cargando modelo VGG\n",
        "# include_top=False (Sin clasificador)\n",
        "vgg_conv = vgg16.VGG16(weights='imagenet', include_top=False, input_shape=(img_width, img_height, 3))\n",
        "vgg_conv.summary();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4267: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
            "\n",
            "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "58892288/58889256 [==============================] - 2s 0us/step\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:203: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
            "\n",
            "Model: \"vgg16\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 224, 224, 3)       0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
            "=================================================================\n",
            "Total params: 14,714,688\n",
            "Trainable params: 14,714,688\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iWJgKL8PmAbo",
        "colab_type": "code",
        "outputId": "cc852fea-3d6d-47b1-a45b-0ec5f108c133",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 799
        }
      },
      "source": [
        "# Freeze todas las capas de Convolución\n",
        "for layer in vgg_conv.layers[:]:\n",
        "    layer.trainable = False\n",
        "\n",
        "vgg_conv.summary();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"vgg16\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 224, 224, 3)       0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
            "=================================================================\n",
            "Total params: 14,714,688\n",
            "Trainable params: 0\n",
            "Non-trainable params: 14,714,688\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YBZ0DJZGmRiI",
        "colab_type": "code",
        "outputId": "1ebee430-2060-4860-f699-2ab7d9afc2d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "source": [
        "# Verificando que las capas convolucionales no sean \"entrenables\"\n",
        "for layer in vgg_conv.layers:\n",
        "    print(layer, layer.trainable)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<keras.engine.input_layer.InputLayer object at 0x7f5770fe2860> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7f5771040fd0> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7f57b0468780> False\n",
            "<keras.layers.pooling.MaxPooling2D object at 0x7f577086c1d0> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7f576ffcf9b0> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7f576ffd43c8> False\n",
            "<keras.layers.pooling.MaxPooling2D object at 0x7f576ffdd128> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7f576ffe3710> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7f576ffe9898> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7f576ffef5f8> False\n",
            "<keras.layers.pooling.MaxPooling2D object at 0x7f576fffb5f8> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7f576ff81630> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7f576ff887b8> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7f576ff8f518> False\n",
            "<keras.layers.pooling.MaxPooling2D object at 0x7f576ff9b550> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7f576ffa0c50> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7f576ffa6780> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7f576ffae4e0> False\n",
            "<keras.layers.pooling.MaxPooling2D object at 0x7f576ffb6518> False\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_vtufIOTZwDy",
        "colab_type": "text"
      },
      "source": [
        "Arquitectura de la Red Neuronal Artificial final:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wUCDwvb3mgjy",
        "colab_type": "code",
        "outputId": "090a51e6-7dba-4cdd-c3a5-de0f68333582",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Flatten\n",
        "from keras.optimizers import SGD\n",
        "\n",
        "# Definiendo la ARQUITECTURA de la Red Neuronal Convolucional final\n",
        "model = Sequential()\n",
        "\n",
        "# Agregando la parte convolucional (base)\n",
        "model.add(vgg_conv)\n",
        "\n",
        "# Clasificador\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dense(64, activation='relu'))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "vgg16 (Model)                (None, 7, 7, 512)         14714688  \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 25088)             0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 128)               3211392   \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 64)                8256      \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 17,934,401\n",
            "Trainable params: 3,219,713\n",
            "Non-trainable params: 14,714,688\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4LZkSTA6Z4zI",
        "colab_type": "text"
      },
      "source": [
        "Entrenamiento:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j1xPCglyZ61i",
        "colab_type": "code",
        "outputId": "730cfb5c-5b38-48b3-cc03-f17f3acbb609",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        }
      },
      "source": [
        "# COMPILANDO la Red Neuronal Convolucional\n",
        "#opt = Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False)\n",
        "opt = SGD(lr=0.001, momentum=0.9)\n",
        "model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# ENTRENANDO la Red\n",
        "import datetime\n",
        "print('Iniciando a las: ', datetime.datetime.now())\n",
        "print(\"...\")\n",
        "\n",
        "epochs=10\n",
        "batch_size=2\n",
        "history = model.fit_generator(training_set, steps_per_epoch=num_imgs_training/batch_size,\n",
        "                              validation_data=testing_set,\n",
        "                              validation_steps=num_imgs_testing/batch_size,\n",
        "                              epochs=epochs)\n",
        "\n",
        "print(\"...\")\n",
        "print('Terminando a las: ', datetime.datetime.now())\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3657: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "Iniciando a las:  2019-11-10 16:40:45.287452\n",
            "...\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "Epoch 1/10\n",
            "33/32 [==============================] - 15s 448ms/step - loss: 1.4007 - acc: 0.3939 - val_loss: 0.7608 - val_acc: 0.4000\n",
            "Epoch 2/10\n",
            "10/32 [========>.....................] - ETA: 8s - loss: 0.6551 - acc: 0.4000"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G4CrkQWhtwCT",
        "colab_type": "text"
      },
      "source": [
        "Graficando resultados:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W70NlTOetyVq",
        "colab_type": "code",
        "outputId": "52702977-ccdb-4cfa-c1ca-3028314b1d3b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "source": [
        "# GRAFICANDO resultados\n",
        "import matplotlib.pyplot as plt\n",
        "# plot loss\n",
        "plt.subplot(211)\n",
        "plt.title('Cross Entropy Loss')\n",
        "plt.plot(history.history['loss'], color='blue', label='train')\n",
        "plt.plot(history.history['val_loss'], color='orange', label='test')\n",
        "# plot accuracy\n",
        "plt.subplot(212)\n",
        "plt.title('Classification Accuracy')\n",
        "plt.plot(history.history['acc'], color='blue', label='train')\n",
        "plt.plot(history.history['val_acc'], color='orange', label='test')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f0910064710>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pwZlZs58IF3J9Qagm1AqYBgyIO64Y98e2wO7R8w7A7NruD2Ao4SK3rGraXAc8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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wRk23KN0Z_L8",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "## PARTE III - PREDICCIONES Y EVALUACIONES DE LA RED\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6-bkah_jaKo2",
        "colab_type": "code",
        "outputId": "2e3fa965-94f7-451e-9e24-16e4042e6963",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "# EVALUANDO la Red\n",
        "acc = model.evaluate_generator(testing_set, num_imgs_testing/batch_size)\n",
        "print('\\nValidación:')\n",
        "print('val_loss %.3f' % (acc[0] * 100.0))\n",
        "print('val_acc %.3f' % (acc[1] * 100.0))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validación:\n",
            "val_loss 2.903\n",
            "val_acc 100.000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pegPAcktuP_h",
        "colab_type": "text"
      },
      "source": [
        "Guardango el modelo en Disco:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MpuQC-zHuSWl",
        "colab_type": "code",
        "outputId": "b2997f06-571c-4e63-9aea-c1f4c250c597",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Guardar el modelo en disco\n",
        "cnn_model_json = model.to_json()\n",
        "with open(\"/content/drive/My Drive/MCD/RNA/pretrained/transfer-learning/RayosX-ToraxAbdomen/model/rx_model-vgg.json\",\"w\") as json_file:\n",
        "  json_file.write(cnn_model_json)\n",
        "\n",
        "model.save_weights(\"/content/drive/My Drive/MCD/RNA/pretrained/transfer-learning/RayosX-ToraxAbdomen/model/rx_model-vgg.h5\")\n",
        "print(\"Modelo guardado en disco ...\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Modelo guardado en disco ...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VE49E-iHaMM9",
        "colab_type": "text"
      },
      "source": [
        "Predicción:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_spodbINaRRr",
        "colab_type": "code",
        "outputId": "c63923fe-8cf8-4cde-8834-ec1781bddccb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        }
      },
      "source": [
        "# Cargando modelo de disco\n",
        "from keras.models import model_from_json\n",
        "import numpy as np\n",
        "\n",
        "json_file = open('/content/drive/My Drive/MCD/RNA/pretrained/transfer-learning/RayosX-ToraxAbdomen/model/rx_model-vgg.json', 'r')\n",
        "loaded_model_json = json_file.read()\n",
        "json_file.close()\n",
        "loaded_model = model_from_json(loaded_model_json)\n",
        "loaded_model.load_weights(\"/content/drive/My Drive/MCD/RNA/pretrained/transfer-learning/RayosX-ToraxAbdomen/model/rx_model-vgg.h5\")\n",
        "print(\"Modelo cargado de disco!\")\n",
        "\n",
        "\n",
        "# Testing con una imagen\n",
        "from keras.preprocessing import image\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "test_image_path = '/content/drive/My Drive/MCD/RNA/pretrained/transfer-learning/RayosX-ToraxAbdomen/dataset/samples/img2.png'\n",
        "test_image = image.load_img(test_image_path)\n",
        "plt.imshow(test_image)\n",
        "plt.show()\n",
        "\n",
        "test_image = image.load_img(test_image_path,target_size = (img_width, img_height))\n",
        "test_image = image.img_to_array(test_image)\n",
        "test_image = np.expand_dims(test_image, axis = 0)\n",
        "\n",
        "\n",
        "result = loaded_model.predict(test_image)\n",
        "\n",
        "if result[0][0] == 1:\n",
        "    print(result[0][0], ' --> RX de Tórax')\n",
        "else:\n",
        "    print(result[0][0], ' --> RX de Abdomen')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Modelo cargado de disco!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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krnySN5fLWd0hFpn5yGQyNlfkj/DSeAo84Orqqnq9nll44oV0Oq2NjQ0dHx8r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\nIaEipgoVhoSKmCpUGBL+H6u1g5wZBPaAAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "1.0  --> RX de Tórax\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}