Colab 4 - Construir una Red Neuronal Convolucional con TensorFlow 2.0.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/joanby/tensorflow2/blob/master/Colab%204%20-%20Construir%20una%20Red%20Neuronal%20Convolucional%20con%20TensorFlow%202.0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zJMS8p3wfPXO"
   },
   "source": [
    "## Paso 1: Instalar las dependencias y la configuración del notebook en GPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PXNUanZvcwwZ"
   },
   "outputs": [],
   "source": [
    "#!pip install tensorflow-gpu==2.3.0\n",
    "%tensorflow_version 2.x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lRHFdNYAfWKJ"
   },
   "source": [
    "## Paso 2: Importar las librerías necesarias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 37
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2846,
     "status": "ok",
     "timestamp": 1599237193516,
     "user": {
      "displayName": "Juan Gabriel Gomila Salas",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjdBM44CAiZlDiF-IkE0NJQH-LHKt1b0A22wmma_w=s64",
      "userId": "03106941341701838274"
     },
     "user_tz": -120
    },
    "id": "tS5xFeQwe9Xu",
    "outputId": "5099a4ec-28a1-48bc-c052-eda0857acacf"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'2.3.0'"
      ]
     },
     "execution_count": 1,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from tensorflow.keras.datasets import cifar10\n",
    "\n",
    "%matplotlib inline\n",
    "tf.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "e8zYubaPfy-S"
   },
   "source": [
    "## Paso 3: Pre procesado de datos\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AOAGjiPogF0w"
   },
   "source": [
    "### Cargar el dataset  Cifar10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1192,
     "status": "ok",
     "timestamp": 1599237247076,
     "user": {
      "displayName": "Juan Gabriel Gomila Salas",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjdBM44CAiZlDiF-IkE0NJQH-LHKt1b0A22wmma_w=s64",
      "userId": "03106941341701838274"
     },
     "user_tz": -120
    },
    "id": "v1N6LbS-e9a1"
   },
   "outputs": [],
   "source": [
    "# Configurar el nombre de las clases del dataset\n",
    "class_names = ['avión', 'coche', 'pájaro', 'gato', 'ciervo', 'perro', 'rana', 'caballo', 'barco', 'camión']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 9632,
     "status": "ok",
     "timestamp": 1599237257133,
     "user": {
      "displayName": "Juan Gabriel Gomila Salas",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjdBM44CAiZlDiF-IkE0NJQH-LHKt1b0A22wmma_w=s64",
      "userId": "03106941341701838274"
     },
     "user_tz": -120
    },
    "id": "f13-8-m_fqKP",
    "outputId": "01b760c5-81a5-4b3b-cfcc-d64c3317a4e2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
      "170500096/170498071 [==============================] - 6s 0us/step\n"
     ]
    }
   ],
   "source": [
    "# Cargar el dataset\n",
    "(X_train, y_train), (X_test, y_test) = cifar10.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XqDIpsJWgUkz"
   },
   "source": [
    "### Normalización de las imágenes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 985,
     "status": "ok",
     "timestamp": 1599237258816,
     "user": {
      "displayName": "Juan Gabriel Gomila Salas",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjdBM44CAiZlDiF-IkE0NJQH-LHKt1b0A22wmma_w=s64",
      "userId": "03106941341701838274"
     },
     "user_tz": -120
    },
    "id": "-QFncPlpgNmU"
   },
   "outputs": [],
   "source": [
    "X_train = X_train / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 664,
     "status": "ok",
     "timestamp": 1599237259536,
     "user": {
      "displayName": "Juan Gabriel Gomila Salas",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjdBM44CAiZlDiF-IkE0NJQH-LHKt1b0A22wmma_w=s64",
      "userId": "03106941341701838274"
     },
     "user_tz": -120
    },
    "id": "L1rFHX67gc6G",
    "outputId": "137a6b27-4cca-437b-b260-c4fe41e8285b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 32, 32, 3)"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 694,
     "status": "ok",
     "timestamp": 1599237260732,
     "user": {
      "displayName": "Juan Gabriel Gomila Salas",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjdBM44CAiZlDiF-IkE0NJQH-LHKt1b0A22wmma_w=s64",
      "userId": "03106941341701838274"
     },
     "user_tz": -120
    },
    "id": "Ve2WbRGWgc3I"
   },
   "outputs": [],
   "source": [
    "X_test = X_test / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 284
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 904,
     "status": "ok",
     "timestamp": 1599237262151,
     "user": {
      "displayName": "Juan Gabriel Gomila Salas",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjdBM44CAiZlDiF-IkE0NJQH-LHKt1b0A22wmma_w=s64",
      "userId": "03106941341701838274"
     },
     "user_tz": -120
    },
    "id": "7jwvNZ82gh9d",
    "outputId": "517e7cd6-9d51-4934-bfb6-7318776513e0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f90801173c8>"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_test[10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 645,
     "status": "ok",
     "timestamp": 1599237265342,
     "user": {
      "displayName": "Juan Gabriel Gomila Salas",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjdBM44CAiZlDiF-IkE0NJQH-LHKt1b0A22wmma_w=s64",
      "userId": "03106941341701838274"
     },
     "user_tz": -120
    },
    "id": "KVa5IU6o-tJL",
    "outputId": "75bea9bd-ee54-4fa8-eaf8-3e5bd7fc6121"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=uint8)"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HXiuZulIguyf"
   },
   "source": [
    "## Paso 4: Construir una red neuronal convolucional"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZFTetk8ngy0f"
   },
   "source": [
    "### Definir el modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 6071,
     "status": "ok",
     "timestamp": 1599237273169,
     "user": {
      "displayName": "Juan Gabriel Gomila Salas",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjdBM44CAiZlDiF-IkE0NJQH-LHKt1b0A22wmma_w=s64",
      "userId": "03106941341701838274"
     },
     "user_tz": -120
    },
    "id": "1TR0JGP5gq2i"
   },
   "outputs": [],
   "source": [
    "model = tf.keras.models.Sequential()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "j92d4FE0hTZV"
   },
   "source": [
    "### Añadir la primera capa de convolución\n",
    "\n",
    "Hyper parámetros de la capa de la RNC:\n",
    "- Filtros: 32\n",
    "- Tamaño del kernel: 3\n",
    "- padding: same\n",
    "- Función de Activación: relu\n",
    "- input_shape: (32, 32, 3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 701,
     "status": "ok",
     "timestamp": 1599237273872,
     "user": {
      "displayName": "Juan Gabriel Gomila Salas",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjdBM44CAiZlDiF-IkE0NJQH-LHKt1b0A22wmma_w=s64",
      "userId": "03106941341701838274"
     },
     "user_tz": -120
    },
    "id": "LSkL1iOvg_dE"
   },
   "outputs": [],
   "source": [
    "model.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, padding=\"same\", activation=\"relu\", input_shape=[32, 32, 3]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gfRAaRmWiSlZ"
   },
   "source": [
    "### Añadir una segunda capa convolucional y la capa de max-pooling \n",
    "\n",
    "Hyper parámetros de la capa de la RNC:\n",
    "- Filtros: 32\n",
    "- Tamaño del kernel: 3\n",
    "- padding: same\n",
    "- Función de Activación: relu\n",
    "\n",
    "Hyper parámetros de la capa de MaxPool:\n",
    "- pool_size: 2\n",
    "- strides: 2\n",
    "- padding: valid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 687,
     "status": "ok",
     "timestamp": 1599237277486,
     "user": {
      "displayName": "Juan Gabriel Gomila Salas",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjdBM44CAiZlDiF-IkE0NJQH-LHKt1b0A22wmma_w=s64",
      "userId": "03106941341701838274"
     },
     "user_tz": -120
    },
    "id": "sSl7Es5yidMp"
   },
   "outputs": [],
   "source": [
    "model.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, padding=\"same\", activation=\"relu\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 635,
     "status": "ok",
     "timestamp": 1599237278220,
     "user": {
      "displayName": "Juan Gabriel Gomila Salas",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjdBM44CAiZlDiF-IkE0NJQH-LHKt1b0A22wmma_w=s64",
      "userId": "03106941341701838274"
     },
     "user_tz": -120
    },
    "id": "wmP9h5wliAR6"
   },
   "outputs": [],
   "source": [
    "model.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2, padding='valid'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wd8ERDyvin-0"
   },
   "source": [
    "### Añadir la tercera capa convolucional\n",
    "\n",
    "Hyper parámetros de la capa de la RNC:\n",
    "- Filtros: 64\n",
    "- Tamaño del kernel: 3\n",
    "- padding: same\n",
    "- Función de Activación: relu\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 891,
     "status": "ok",
     "timestamp": 1599237281179,
     "user": {
      "displayName": "Juan Gabriel Gomila Salas",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjdBM44CAiZlDiF-IkE0NJQH-LHKt1b0A22wmma_w=s64",
      "userId": "03106941341701838274"
     },
     "user_tz": -120
    },
    "id": "i9HWy6aFixEw"
   },
   "outputs": [],
   "source": [
    "model.add(tf.keras.layers.Conv2D(filters=64, kernel_size=3, padding=\"same\", activation=\"relu\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "O55kyOQGi44V"
   },
   "source": [
    "###  Añadir la cuarta capa convolucional y la capa de max-pooling\n",
    "\n",
    "Hyper parámetros de la capa de la RNC:\n",
    "- Filtros: 64\n",
    "- Tamaño del kernel: 3\n",
    "- padding: same\n",
    "- Función de Activación: relu\n",
    "\n",
    "Hyper parámetros de la capa de la MaxPool:\n",
    "- pool_size: 2\n",
    "- strides: 2\n",
    "- padding: valid\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 690,
     "status": "ok",
     "timestamp": 1599237284146,
     "user": {
      "displayName": "Juan Gabriel Gomila Salas",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjdBM44CAiZlDiF-IkE0NJQH-LHKt1b0A22wmma_w=s64",
      "userId": "03106941341701838274"
     },
     "user_tz": -120
    },
    "id": "5b7vAuhjjCF2"
   },
   "outputs": [],
   "source": [
    "model.add(tf.keras.layers.Conv2D(filters=64, kernel_size=3, padding=\"same\", activation=\"relu\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 731,
     "status": "ok",
     "timestamp": 1599237285722,
     "user": {
      "displayName": "Juan Gabriel Gomila Salas",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjdBM44CAiZlDiF-IkE0NJQH-LHKt1b0A22wmma_w=s64",
      "userId": "03106941341701838274"
     },
     "user_tz": -120
    },
    "id": "oc493G2BjFhg"
   },
   "outputs": [],
   "source": [
    "model.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2, padding='valid'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-hGnR3aXjKbZ"
   },
   "source": [
    "### Añadir la capa de flattening"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 609,
     "status": "ok",
     "timestamp": 1599237287827,
     "user": {
      "displayName": "Juan Gabriel Gomila Salas",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjdBM44CAiZlDiF-IkE0NJQH-LHKt1b0A22wmma_w=s64",
      "userId": "03106941341701838274"
     },
     "user_tz": -120
    },
    "id": "QLzu2cCVjI5Z"
   },
   "outputs": [],
   "source": [
    "model.add(tf.keras.layers.Flatten())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GpeRUvVWjR1W"
   },
   "source": [
    "### Añadir la primera capa fully-connected\n",
    "\n",
    "Hyper parámetros de la capa totalmente conectada:\n",
    "- units/neurons: 128\n",
    "- activation: relu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 667,
     "status": "ok",
     "timestamp": 1599237289775,
     "user": {
      "displayName": "Juan Gabriel Gomila Salas",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjdBM44CAiZlDiF-IkE0NJQH-LHKt1b0A22wmma_w=s64",
      "userId": "03106941341701838274"
     },
     "user_tz": -120
    },
    "id": "FWzYY8kKjhnZ"
   },
   "outputs": [],
   "source": [
    "model.add(tf.keras.layers.Dense(units=128, activation='relu'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UaakKTqRjrkF"
   },
   "source": [
    "### Añadir la capa de salida\n",
    "\n",
    "Hyper parámetros de la capa totalmente conectada:\n",
    "\n",
    " - units/neurons: 10 (number of classes)\n",
    " - activation: softmax\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 736,
     "status": "ok",
     "timestamp": 1599237291532,
     "user": {
      "displayName": "Juan Gabriel Gomila Salas",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjdBM44CAiZlDiF-IkE0NJQH-LHKt1b0A22wmma_w=s64",
      "userId": "03106941341701838274"
     },
     "user_tz": -120
    },
    "id": "4t-JmzRvjnBj"
   },
   "outputs": [],
   "source": [
    "model.add(tf.keras.layers.Dense(units=10, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 475
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 673,
     "status": "ok",
     "timestamp": 1599237292879,
     "user": {
      "displayName": "Juan Gabriel Gomila Salas",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjdBM44CAiZlDiF-IkE0NJQH-LHKt1b0A22wmma_w=s64",
      "userId": "03106941341701838274"
     },
     "user_tz": -120
    },
    "id": "aRr3bCU-ti06",
    "outputId": "9c1ee1c5-3c4b-4702-f7de-323c56787648"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 32, 32, 32)        896       \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 16, 16, 64)        18496     \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 16, 16, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 4096)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 128)               524416    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 591,274\n",
      "Trainable params: 591,274\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DYgvbNihtprw"
   },
   "source": [
    "### Compilar el modelo\n",
    "\n",
    "#### sparse_categorical_accuracy\n",
    "sparse_categorical_accuracy comprueba si el valor verdadero maximal coincide con el índice maximal del valor de la predicción.\n",
    "\n",
    "https://stackoverflow.com/questions/44477489/keras-difference-between-categorical-accuracy-and-sparse-categorical-accuracy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 684,
     "status": "ok",
     "timestamp": 1599237296070,
     "user": {
      "displayName": "Juan Gabriel Gomila Salas",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjdBM44CAiZlDiF-IkE0NJQH-LHKt1b0A22wmma_w=s64",
      "userId": "03106941341701838274"
     },
     "user_tz": -120
    },
    "id": "oYHELxz4tsa-"
   },
   "outputs": [],
   "source": [
    "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"Adam\", metrics=[\"sparse_categorical_accuracy\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Gox3SmwUtwgX"
   },
   "source": [
    "### Entrenar el modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 211
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 35952,
     "status": "ok",
     "timestamp": 1599237333716,
     "user": {
      "displayName": "Juan Gabriel Gomila Salas",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjdBM44CAiZlDiF-IkE0NJQH-LHKt1b0A22wmma_w=s64",
      "userId": "03106941341701838274"
     },
     "user_tz": -120
    },
    "id": "D3MHvRYKe9fN",
    "outputId": "992911ee-62d1-48f6-9189-f25027a5977e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1563/1563 [==============================] - 5s 3ms/step - loss: 1.3594 - sparse_categorical_accuracy: 0.5103\n",
      "Epoch 2/5\n",
      "1563/1563 [==============================] - 6s 4ms/step - loss: 0.9141 - sparse_categorical_accuracy: 0.6783\n",
      "Epoch 3/5\n",
      "1563/1563 [==============================] - 5s 3ms/step - loss: 0.7495 - sparse_categorical_accuracy: 0.7367\n",
      "Epoch 4/5\n",
      "1563/1563 [==============================] - 5s 3ms/step - loss: 0.6330 - sparse_categorical_accuracy: 0.7792\n",
      "Epoch 5/5\n",
      "1563/1563 [==============================] - 5s 3ms/step - loss: 0.5427 - sparse_categorical_accuracy: 0.8093\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f90702866a0>"
      ]
     },
     "execution_count": 21,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, epochs=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "D8C7Pm0NuOrJ"
   },
   "source": [
    "### Evaluar el modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2006,
     "status": "ok",
     "timestamp": 1564609763373,
     "user": {
      "displayName": "Juan Gabriel Gomila Salas",
      "photoUrl": "https://lh4.googleusercontent.com/-RJ38mzk8Fog/AAAAAAAAAAI/AAAAAAAAA_g/VV8PcNMAUrk/s64/photo.jpg",
      "userId": "03106941341701838274"
     },
     "user_tz": -120
    },
    "id": "Z9r8TtNet3D0",
    "outputId": "072c4a8a-b049-4282-ca01-839ea0ed9be8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 1s 110us/sample - loss: 0.7642 - sparse_categorical_accuracy: 0.7530\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_accuracy = model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1416,
     "status": "ok",
     "timestamp": 1564609770437,
     "user": {
      "displayName": "Juan Gabriel Gomila Salas",
      "photoUrl": "https://lh4.googleusercontent.com/-RJ38mzk8Fog/AAAAAAAAAAI/AAAAAAAAA_g/VV8PcNMAUrk/s64/photo.jpg",
      "userId": "03106941341701838274"
     },
     "user_tz": -120
    },
    "id": "0rpAPpfzuV0p",
    "outputId": "20f0d11b-4aed-4856-e8fe-782f51d2f4ca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 0.753000020980835\n"
     ]
    }
   ],
   "source": [
    "print(\"Test accuracy: {}\".format(test_accuracy))"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Colab 4 - Construir una Red Neuronal Convolucional con TensorFlow 2.0.ipynb",
   "provenance": [
    {
     "file_id": "1M90124HfEO_zORhbVDdFeDMHV743eJmc",
     "timestamp": 1564596643353
    },
    {
     "file_id": "11eF0dRN2zWmXeBI3vQa2OTE-gzRp3Q7o",
     "timestamp": 1558700838561
    }
   ]
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}