08_redes_neuronales_convolucionales_(2).ipynb

{
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 "metadata": {
  "colab": {
   "name": "08_redes_neuronales_convolucionales (2).ipynb",
   "provenance": []
  },
  "kernelspec": {
   "name": "conda-env-py38-py",
   "language": "python",
   "display_name": "Python [conda env:py38] *"
  },
  "accelerator": "GPU"
 },
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FKNAOn5Ia7pG"
   },
   "source": [
    "\n",
    "# 08. Redes neuronales convolucionales\n",
    "[**Python Deep Learning** Introducción práctica con Keras y TensorFlow 2. Jordi Torres. Editorial Marcombo ISBN: 9788426728289 ](https://www.marcombo.com/python-deep-learning-9788426728289/)\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "VcFrOCQOYU1R",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "65572de6-295d-4eca-e26f-43ea485a4402"
   },
   "source": [
    "%tensorflow_version 2.x\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(tf.__version__)\n"
   ],
   "execution_count": 9,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UsageError: Line magic function `%tensorflow_version` not found.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Nx0Q9RkQ1hFX"
   },
   "source": [
    "## Datos digits MNIST"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "EjwjDIuXeS_s",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "b6e703d3-6ba5-44fe-eefd-d35cf6a02a22"
   },
   "source": [
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.layers import Conv2D\n",
    "from tensorflow.keras.layers import MaxPooling2D\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(32, (5, 5), activation='relu', input_shape=(28, 28, 1)))\n",
    "model.add(MaxPooling2D((2, 2)))\n",
    "model.summary()"
   ],
   "execution_count": 11,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_7 (Conv2D)            (None, 24, 24, 32)        832       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2 (None, 12, 12, 32)        0         \n",
      "=================================================================\n",
      "Total params: 832\n",
      "Trainable params: 832\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "Z7SShiqCGM8g",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "a9a64ee8-514a-4b55-b4ca-ed8698cdb4d6"
   },
   "source": [
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(32, (5, 5), activation='relu', input_shape=(28, 28, 1)))\n",
    "model.add(MaxPooling2D((2, 2)))\n",
    "model.add(Conv2D(64, (5, 5), activation='relu'))\n",
    "model.add(MaxPooling2D((2, 2)))\n",
    "model.summary()"
   ],
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_6\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_8 (Conv2D)            (None, 24, 24, 32)        832       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_8 (MaxPooling2 (None, 12, 12, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 8, 8, 64)          51264     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_9 (MaxPooling2 (None, 4, 4, 64)          0         \n",
      "=================================================================\n",
      "Total params: 52,096\n",
      "Trainable params: 52,096\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "T-fqZVN9F18n"
   },
   "source": [
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.layers import Flatten\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(10, activation='softmax'))"
   ],
   "execution_count": 13,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "vJLHWHNueTZH",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "a2be57ab-a470-4941-dd9c-83f7a88e3e5d"
   },
   "source": [
    "model.summary()"
   ],
   "execution_count": 14,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_6\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_8 (Conv2D)            (None, 24, 24, 32)        832       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_8 (MaxPooling2 (None, 12, 12, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 8, 8, 64)          51264     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_9 (MaxPooling2 (None, 4, 4, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                10250     \n",
      "=================================================================\n",
      "Total params: 62,346\n",
      "Trainable params: 62,346\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "putRyHXReTb5",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "8b665c54-f389-4b38-d063-53daf97fcdb9"
   },
   "source": [
    "from keras.utils import to_categorical\n",
    "\n",
    "mnist = tf.keras.datasets.mnist\n",
    "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\n",
    "\n",
    "print (train_images.shape)\n",
    "print (train_labels.shape)\n",
    "train_images = train_images.reshape((60000, 28, 28, 1))\n",
    "train_images = train_images.astype('float32') / 255\n",
    "\n",
    "test_images = test_images.reshape((10000, 28, 28, 1))\n",
    "test_images = test_images.astype('float32') / 255\n",
    "\n",
    "train_labels = to_categorical(train_labels)\n",
    "test_labels = to_categorical(test_labels)\n",
    "\n",
    "print (train_images.shape)\n",
    "print (train_labels.shape)\n"
   ],
   "execution_count": 15,
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'tf' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "\u001B[1;32m<ipython-input-15-d85b4bc403c1>\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m      1\u001B[0m \u001B[1;32mfrom\u001B[0m \u001B[0mkeras\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mutils\u001B[0m \u001B[1;32mimport\u001B[0m \u001B[0mto_categorical\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      2\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m----> 3\u001B[1;33m \u001B[0mmnist\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mtf\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mkeras\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mdatasets\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mmnist\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m      4\u001B[0m \u001B[1;33m(\u001B[0m\u001B[0mtrain_images\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mtrain_labels\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m(\u001B[0m\u001B[0mtest_images\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mtest_labels\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mmnist\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mload_data\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      5\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;31mNameError\u001B[0m: name 'tf' is not defined"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "8jt2EkwxuQf4",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "b657f279-cfcd-4f68-facc-d11c5272e8bf"
   },
   "source": [
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='sgd',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(train_images, train_labels,\n",
    "          batch_size=100,\n",
    "          epochs=5,\n",
    "          verbose=1)\n",
    "\n",
    "test_loss, test_acc = model.evaluate(test_images, test_labels)\n",
    "\n",
    "print('Test accuracy:', test_acc)"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p_6zyoOJ1lHu"
   },
   "source": [
    "## Datos Fashion-MNIST"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "ybltm0ul14JF"
   },
   "source": [
    "fashion_mnist = keras.datasets.fashion_mnist\n",
    "\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "n6FGwIXd14WJ"
   },
   "source": [
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "v6I04A_S14ZE"
   },
   "source": [
    "train_images = train_images.reshape((60000, 28, 28, 1))\n",
    "train_images = train_images.astype('float32') / 255\n",
    "\n",
    "test_images = test_images.reshape((10000, 28, 28, 1))\n",
    "test_images = test_images.astype('float32') / 255"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q6ztRsZotjOJ"
   },
   "source": [
    "Modelo 1: modelo de base basado en el mismo modelo que el usado para el conjunto datos digits MNIST "
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "F0Nj5zs-2sQ1"
   },
   "source": [
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(32, (5, 5), activation='relu', input_shape=(28, 28, 1)))\n",
    "model.add(MaxPooling2D((2, 2)))\n",
    "model.add(Conv2D(64, (5, 5), activation='relu'))\n",
    "model.add(MaxPooling2D((2, 2)))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(10, activation='softmax'))"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "sRiC2Hnq2sTr"
   },
   "source": [
    "model.summary()"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "tBDazX0d2sYp"
   },
   "source": [
    "model.compile(optimizer='sgd',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(train_images, train_labels, epochs=5)\n",
    "test_loss, test_acc = model.evaluate(test_images,  test_labels)\n",
    "print('Test accuracy:', test_acc)"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "L5XbYrrHKO5E"
   },
   "source": [
    "predictions = model.predict(test_images)"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "TTPAeEl45iNx"
   },
   "source": [
    "def plot_image(i, predictions_array, true_label, img):\n",
    "  predictions_array, true_label, img = predictions_array, true_label[i], img[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "\n",
    "  plt.imshow(img, cmap=plt.cm.binary)    \n",
    "\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "  if predicted_label == true_label:\n",
    "    color = 'blue'\n",
    "  else:\n",
    "    color = 'red'\n",
    "\n",
    "  plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
    "                                100*np.max(predictions_array),\n",
    "                                class_names[true_label]),\n",
    "                                color=color)\n",
    "\n",
    "def plot_value_array(i, predictions_array, true_label):\n",
    "  predictions_array, true_label = predictions_array, true_label[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks(range(10))\n",
    "  plt.yticks([])\n",
    "  thisplot = plt.bar(range(10), predictions_array, color=\"#00FF00\")\n",
    "  plt.ylim([0, 1])\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "\n",
    "  thisplot[predicted_label].set_color('red')\n",
    "  thisplot[true_label].set_color('black')"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EQZcZ_RH0q45"
   },
   "source": [
    "Volvemos las imagenes a formato original para hacer el plot"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "U6O2NNvyKd6s"
   },
   "source": [
    "train_images = train_images.reshape((60000, 28, 28))\n",
    "test_images = test_images.reshape((10000, 28, 28))"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "STCV3LC-57tg"
   },
   "source": [
    "num_rows = 7\n",
    "num_cols = 2\n",
    "num_images = num_rows*num_cols\n",
    "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
    "for i in range(num_images):\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
    "  plot_image(i, predictions[i], test_labels, test_images)\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+2)     \n",
    "  plot_value_array(i, predictions[i], test_labels)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "Z0Y56EnBLQUm"
   },
   "source": [
    "train_images = train_images.reshape((60000, 28, 28, 1))\n",
    "test_images = test_images.reshape((10000, 28, 28, 1))\n"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6tV0gHRztcvI"
   },
   "source": [
    "Modelo 2: Duplicando las neuronas de modelo base y añadiendo una capa densa de 64 neuronas"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "5vu0mAps6wNt"
   },
   "source": [
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(64, (7, 7), activation=\"relu\", padding=\"same\", input_shape=(28, 28, 1)))\n",
    "model.add(MaxPooling2D(2, 2))\n",
    "model.add(Conv2D(128, (3, 3), activation=\"relu\", padding=\"same\"))\n",
    "model.add(MaxPooling2D(2, 2))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(64, activation=\"relu\"))\n",
    "model.add(Dense(10, activation=\"softmax\"))\n",
    "\n"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "G1qOIHnyL_Te"
   },
   "source": [
    "\n",
    "model.compile(optimizer='sgd',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(train_images, train_labels, epochs=5)\n",
    "\n",
    "test_loss, test_acc = model.evaluate(test_images,  test_labels)\n",
    "print('\\nTest accuracy:', test_acc)"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZEyd3lnut9In"
   },
   "source": [
    "Modelo 3: modelo 3 cambiando al optimizador 'adam'"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "UR6POPWWMAoU"
   },
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(64, (7, 7), activation=\"relu\", padding=\"same\", input_shape=(28, 28, 1)))\n",
    "model.add(MaxPooling2D(2, 2))\n",
    "model.add(Conv2D(128, (3, 3), activation=\"relu\", padding=\"same\"))\n",
    "model.add(MaxPooling2D(2, 2))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(64, activation=\"relu\"))\n",
    "model.add(Dense(10, activation=\"softmax\"))\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(train_images, train_labels, epochs=5)\n",
    "\n",
    "test_loss, test_acc = model.evaluate(test_images,  test_labels)\n",
    "print('\\nTest accuracy:', test_acc)"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "iBLBezVui5X1"
   },
   "source": [
    ""
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kogvBWoruYhp"
   },
   "source": [
    "Modelo 4: Buscando un modelo más complejo que usa capas BathNormalization y Dropout"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "K_bynmW9liwR"
   },
   "source": [
    "from tensorflow.keras.layers import Dropout, BatchNormalization\n",
    "\n",
    "\n",
    "def make_model():\n",
    "  model = Sequential()\n",
    "  model.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu', strides=1, padding='same', input_shape=(28,28,1)))\n",
    "  model.add(BatchNormalization())\n",
    "\n",
    "  model.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu', strides=1, padding='same'))\n",
    "  model.add(BatchNormalization())\n",
    "  model.add(Dropout(0.25))\n",
    "\n",
    "  model.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu', strides=1, padding='same'))\n",
    "  model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "  model.add(Dropout(0.25))\n",
    "    \n",
    "  model.add(Conv2D(filters=128, kernel_size=(3, 3), activation='relu', strides=1, padding='same'))\n",
    "  model.add(BatchNormalization())\n",
    "  model.add(Dropout(0.25))\n",
    "\n",
    "  model.add(Flatten())\n",
    "  model.add(Dense(512, activation='relu'))\n",
    "  model.add(BatchNormalization())\n",
    "  model.add(Dropout(0.5))\n",
    "  model.add(Dense(128, activation='relu'))\n",
    "  model.add(BatchNormalization())\n",
    "  model.add(Dropout(0.5))\n",
    "  model.add(Dense(10, activation='softmax'))\n",
    "  return model\n",
    "\n"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "UMVtk4yqmk5k"
   },
   "source": [
    "model = make_model()\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(train_images, train_labels, epochs=5)\n",
    "\n",
    "test_loss, test_acc = model.evaluate(test_images,  test_labels)\n",
    "print('\\nTest accuracy:', test_acc)"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JYQg0a86yVOi"
   },
   "source": [
    "Modelo 5: Modelo anterior añadiendo más epochs (10 epochs)"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "yTh4OaK6yOEx"
   },
   "source": [
    "\n",
    "model = make_model()\n",
    "\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(train_images, train_labels, epochs=10)\n",
    "test_loss, test_acc = model.evaluate(test_images,  test_labels)\n",
    "\n",
    "print('\\nTest accuracy:', test_acc)"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YpJwKcAgGGhq"
   },
   "source": [
    "Modelo 6: Modelo anterior añadiendo más epochs (30 epochs)"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "1ZDDas7oF97f"
   },
   "source": [
    "\n",
    "model = make_model()\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(train_images, train_labels, epochs=30)\n",
    "test_loss, test_acc = model.evaluate(test_images,  test_labels)\n",
    "\n",
    "print('\\nTest accuracy:', test_acc)"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-NUPmba5zFcV"
   },
   "source": [
    "Modelo 7: Modelo anterior añadiendo más epochs (30 epochs) y diferentes hiperparámetros al optimizador Adam"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "oWms_Gu7nQxv"
   },
   "source": [
    "model = make_model()\n",
    "\n",
    "optimizer = tf.keras.optimizers.Adam (lr=0.001)\n",
    "\n",
    "model.compile(optimizer=optimizer,\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "reduce_lr = tf.keras.callbacks.LearningRateScheduler(lambda x: 1e-3 * 0.9 ** x)\n",
    "\n",
    "model.fit(train_images, train_labels, epochs=30, callbacks=[reduce_lr])\n",
    "\n",
    "test_loss, test_acc = model.evaluate(test_images,  test_labels)\n",
    "print('\\nTest accuracy:', test_acc)"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-HtzExwn2u9U"
   },
   "source": [
    "Visualización de las predicciones del modelo 7"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "XR218aBirMdU"
   },
   "source": [
    "\n",
    "predictions = model.predict(test_images)\n",
    "\n",
    "train_images = train_images.reshape((60000, 28, 28))\n",
    "test_images = test_images.reshape((10000, 28, 28))\n",
    "\n",
    "\n",
    "num_rows = 7\n",
    "num_cols = 2\n",
    "num_images = num_rows*num_cols\n",
    "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
    "for i in range(num_images):\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
    "  plot_image(i, predictions[i], test_labels, test_images)\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+2)     \n",
    "  plot_value_array(i, predictions[i], test_labels)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "execution_count": null,
   "outputs": []
  }
 ]
}