cap12/CH12EJ3-Redes-neuronales-keras_FMNIST.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; clear: both;\">\n",
    "<div style=\"float: left; width: 50%;\">\n",
    "<img src=\"http://www.uoc.edu/portal/_resources/common/imatges/marca_UOC/UOC_Masterbrand.jpg\" align=\"left\">\n",
    "</div>\n",
    "<div style=\"float: right; width: 50%;\">\n",
    "<p style=\"margin: 0; padding-top: 22px; text-align:right;\"><a href=\"http://www.editorialuoc.cat/mineria-de-datos\" target=\"_blank\">Minería de datos: Modelos y algoritmos</a></p>\n",
    "<p style=\"margin: 0; text-align:right;\">Estudios de Informática, Multimedia y Telecomunicación</p>\n",
    "<p style=\"margin: 0; text-align:right; padding-button: 100px;\">Universitat Oberta de Catalunya (<b>UOC</b>)</p>\n",
    "</div>\n",
    "</div>\n",
    "<div style=\"width:100%;\">&nbsp;</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creación de una red neuronal para clasificación del conjunto de datos FASHION-MNIST\n",
    "\n",
    "En este ejemplo veremos la creación de una red neuronal, mediante el uso de Python y la librería `keras`, para realizar una clasificación de objetos a partir del conjunto de datos [Fashion-MNITS](http://yann.lecun.com/exdb/mnist/). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Carga de librerías y datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: keras in /home/xavier/.local/lib/python3.8/site-packages (2.4.3)\n",
      "Requirement already satisfied: scipy>=0.14 in /home/xavier/.local/lib/python3.8/site-packages (from keras) (1.5.4)\n",
      "Requirement already satisfied: numpy>=1.9.1 in /home/xavier/.local/lib/python3.8/site-packages (from keras) (1.18.5)\n",
      "Requirement already satisfied: pyyaml in /home/xavier/anaconda3/lib/python3.8/site-packages (from keras) (5.3.1)\n",
      "Requirement already satisfied: h5py in /home/xavier/.local/lib/python3.8/site-packages (from keras) (2.10.0)\n",
      "Requirement already satisfied: six in /home/xavier/anaconda3/lib/python3.8/site-packages (from h5py->keras) (1.15.0)\n",
      "\u001b[33mWARNING: You are using pip version 20.2.4; however, version 20.3.3 is available.\n",
      "You should consider upgrading via the '/home/xavier/anaconda3/bin/python -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import keras\n",
    "from keras.datasets import fashion_mnist\n",
    "from keras.utils import to_categorical\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Conv2D, Flatten, Activation, Dropout, MaxPooling2D, BatchNormalization\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Descarga el conjunto de datos MNIST y realiza la partición entrenamiento / test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train_orig, y_train_orig), (x_test_orig, y_test_orig) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualización de uno de los objetos para verificar, de forma visual, el tamaño, canal de color, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "first_image = x_train_orig[0]\n",
    "first_image = np.array(first_image, dtype='float')\n",
    "pixels = first_image.reshape((28, 28))\n",
    "plt.imshow(pixels, cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Realizamos una conversión de formato para que las imágenes estén correctamente almacenadas en la estructura de 4 dimensiones:\n",
    "- número de imagen \n",
    "- alto \n",
    "- ancho \n",
    "- canal de color (en este caso concreto, un sólo canal que corresponde a la escala de gris).\n",
    "\n",
    "A continuación, realizamos la conversión a variable categórica de las etiquetas de las imágenes mediante la función `to_categoricaly`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train_orig.reshape(60000,28,28,1)\n",
    "x_test = x_test_orig.reshape(10000,28,28,1)\n",
    "\n",
    "y_train = to_categorical(y_train_orig, 10)\n",
    "y_test = to_categorical(y_test_orig, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Creación del modelo "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A cotninuación construimos la red neuronal, que en este caso estará formada por la siguientes capas:\n",
    "- Capa convolucional de 32 *kernels* de tamaño *3x3* y activación `RELU`\n",
    "- Capa convolucional de 64 *kernels* de tamaño *3x3* y activación `RELU`\n",
    "- Capa *Max pooling*\n",
    "- Capa *Dropout* con probabilidad igual al 25%\n",
    "- Capa densamente conectada con 128 neuronas y activación `RELU`\n",
    "- Capa *Dropout*\n",
    "- Capa de salida, formada por 10 neuronas (tenemos 10 clases en el conjunto de datos) y *softmax*\n",
    "\n",
    "Finalmente, vemos los detalles del modelo construido (para cada capa, vemos las dimensiones de salida, número de parámetros entrenables, etc.)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv2D(32, kernel_size=(3, 3), activation=\"relu\", input_shape=(28,28,1)))\n",
    "model.add(Conv2D(64, (3, 3), activation=\"relu\"))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128, activation=\"relu\"))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(10, activation=\"softmax\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resumen de la información del modelo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 24, 24, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 12, 12, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 12, 12, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 9216)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 128)               1179776   \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 1,199,882\n",
      "Trainable params: 1,199,882\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Realizamos el entrenamiento sobre el conjunto de *train*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/12\n",
      "469/469 [==============================] - 132s 282ms/step - loss: 17.9746 - accuracy: 0.1978 - val_loss: 2.4848 - val_accuracy: 0.5379\n",
      "Epoch 2/12\n",
      "469/469 [==============================] - 117s 249ms/step - loss: 6.5689 - accuracy: 0.3167 - val_loss: 1.2629 - val_accuracy: 0.5806\n",
      "Epoch 3/12\n",
      "469/469 [==============================] - 123s 262ms/step - loss: 3.3983 - accuracy: 0.3430 - val_loss: 1.4255 - val_accuracy: 0.5395\n",
      "Epoch 4/12\n",
      "469/469 [==============================] - 134s 285ms/step - loss: 2.3783 - accuracy: 0.3502 - val_loss: 1.5371 - val_accuracy: 0.5418\n",
      "Epoch 5/12\n",
      "469/469 [==============================] - 118s 251ms/step - loss: 2.0596 - accuracy: 0.3734 - val_loss: 1.5313 - val_accuracy: 0.5527\n",
      "Epoch 6/12\n",
      "469/469 [==============================] - 116s 247ms/step - loss: 1.9155 - accuracy: 0.3983 - val_loss: 1.4697 - val_accuracy: 0.5769\n",
      "Epoch 7/12\n",
      "469/469 [==============================] - 113s 240ms/step - loss: 1.8349 - accuracy: 0.4184 - val_loss: 1.3985 - val_accuracy: 0.6030\n",
      "Epoch 8/12\n",
      "469/469 [==============================] - 110s 235ms/step - loss: 1.7426 - accuracy: 0.4402 - val_loss: 1.3081 - val_accuracy: 0.6293\n",
      "Epoch 9/12\n",
      "469/469 [==============================] - 124s 265ms/step - loss: 1.6664 - accuracy: 0.4616 - val_loss: 1.2316 - val_accuracy: 0.6512\n",
      "Epoch 10/12\n",
      "469/469 [==============================] - 119s 253ms/step - loss: 1.6142 - accuracy: 0.4767 - val_loss: 1.1583 - val_accuracy: 0.6650\n",
      "Epoch 11/12\n",
      "469/469 [==============================] - 122s 260ms/step - loss: 1.5683 - accuracy: 0.4931 - val_loss: 1.1157 - val_accuracy: 0.6755\n",
      "Epoch 12/12\n",
      "469/469 [==============================] - 119s 253ms/step - loss: 1.5266 - accuracy: 0.5057 - val_loss: 1.0743 - val_accuracy: 0.6844\n"
     ]
    }
   ],
   "source": [
    "batch_size = 128\n",
    "n_epochs = 12\n",
    "\n",
    "model.compile(loss=keras.losses.categorical_crossentropy,\n",
    "              optimizer=keras.optimizers.Adadelta(),\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "mfit = model.fit(x_train, y_train,\n",
    "          validation_data=(x_test, y_test),\n",
    "          batch_size=batch_size,\n",
    "          epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Evaluación del modelo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Revisamos la información disponible del entrenamiento para poder ver su evolución."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
     ]
    }
   ],
   "source": [
    "print(mfit.history.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creamos dos gráficas:\n",
    "1. La primera, donde presentamos la evolución de la *accuracy* del model durante las 12 épocas del entrenamiento, teniendo en cuenta los datos de entrenamiento y test.\n",
    "2. La segunda, donde presentamos la evolución de la función de *loss* durante el entrenamiento."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Accuracy\n",
    "plt.plot(mfit.history['accuracy'])\n",
    "plt.plot(mfit.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'validation'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "# Loss\n",
    "plt.plot(mfit.history['loss'])\n",
    "plt.plot(mfit.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'validation'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Realizamos la evaluación del modelo sobre conjunto de datos de *test*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss    : 1.0742838382720947\n",
      "Test accuracy: 0.6844000220298767\n"
     ]
    }
   ],
   "source": [
    "# Evaluación del modelo sobre el conjunto de test\n",
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "print('Test loss    :', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mostramos, en primer lugar, el vector de predicciones para cada uno de los 4 primeros registros de test. \n",
    "\n",
    "Cada vector está formado por 10 números, que indican la probabilidad de este registro de pertenecer a cada una de las 10 categorías que existen en este conjunto de datos.\n",
    "\n",
    "En segundo lugar, mostramos los valores correctos (etiquetados en el conjunto original) para estos 4 primeros registros correspondientes a las cuatro primeras imágenes del conjunto de test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.14480217e-05 1.19660235e-05 9.83990412e-06 3.15022844e-05\n",
      "  5.31880301e-04 2.08382029e-02 7.96285349e-06 4.54229087e-01\n",
      "  4.60096933e-02 4.78318363e-01]\n",
      " [9.99590456e-02 9.96513292e-02 1.00593142e-01 1.00256324e-01\n",
      "  1.00447178e-01 9.95220765e-02 1.00438789e-01 9.97426659e-02\n",
      "  9.98565778e-02 9.95327979e-02]\n",
      " [7.41981125e-07 9.98941123e-01 1.68126659e-04 1.63347431e-04\n",
      "  2.81428976e-04 9.52346600e-05 3.80696360e-06 3.42579879e-04\n",
      "  4.57100356e-07 3.12267753e-06]\n",
      " [1.96520105e-07 9.99319315e-01 2.53070786e-04 2.12107581e-04\n",
      "  1.44716410e-04 1.91246490e-05 1.00141324e-06 4.89802587e-05\n",
      "  4.08160616e-07 9.75764124e-07]]\n"
     ]
    }
   ],
   "source": [
    "# Predicción de las cuatro primeras imágenes del conjunto de test\n",
    "print(model.predict(x_test[:4]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
       "       [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En segundo lugar, mostramos los valores correctos (etiquetados en el conjunto original) para estos 4 primeros registros."
   ]
  }
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