08_redes_neuronales_convolucionales.ipynb

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    "colab": {
      "name": "08_redes_neuronales_convolucionales (2).ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FKNAOn5Ia7pG",
        "colab_type": "text"
      },
      "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_type": "code",
        "outputId": "a6bc45e9-8843-42e9-a424-5f6e97e6af1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "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": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n",
            "2.1.0-rc1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nx0Q9RkQ1hFX",
        "colab_type": "text"
      },
      "source": [
        "## Datos digits MNIST"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EjwjDIuXeS_s",
        "colab_type": "code",
        "outputId": "8e4b439d-468b-4da0-a54b-8d3d7b9e2e07",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        }
      },
      "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": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 24, 24, 32)        832       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 12, 12, 32)        0         \n",
            "=================================================================\n",
            "Total params: 832\n",
            "Trainable params: 832\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z7SShiqCGM8g",
        "colab_type": "code",
        "outputId": "ea689113-befb-4bec-efd3-3f7298b52b21",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "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": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_1 (Conv2D)            (None, 24, 24, 32)        832       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 12, 12, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 8, 8, 64)          51264     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 4, 4, 64)          0         \n",
            "=================================================================\n",
            "Total params: 52,096\n",
            "Trainable params: 52,096\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T-fqZVN9F18n",
        "colab_type": "code",
        "colab": {}
      },
      "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": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vJLHWHNueTZH",
        "colab_type": "code",
        "outputId": "5d3345be-da8c-4e1f-f587-2d32e246274a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_1 (Conv2D)            (None, 24, 24, 32)        832       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 12, 12, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 8, 8, 64)          51264     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 4, 4, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 10)                10250     \n",
            "=================================================================\n",
            "Total params: 62,346\n",
            "Trainable params: 62,346\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "putRyHXReTb5",
        "colab_type": "code",
        "outputId": "620a7f31-a1ff-40cd-90ce-1f0022fa1752",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "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": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "(60000, 28, 28)\n",
            "(60000,)\n",
            "(60000, 28, 28, 1)\n",
            "(60000, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8jt2EkwxuQf4",
        "colab_type": "code",
        "outputId": "bd06002e-fd3b-4856-f8da-53fb3ec700ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        }
      },
      "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": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples\n",
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.9310 - accuracy: 0.7577\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 2s 34us/sample - loss: 0.2706 - accuracy: 0.9194\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 2s 34us/sample - loss: 0.1943 - accuracy: 0.9421\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 2s 34us/sample - loss: 0.1529 - accuracy: 0.9553\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 2s 34us/sample - loss: 0.1284 - accuracy: 0.9626\n",
            "10000/10000 [==============================] - 1s 76us/sample - loss: 0.1070 - accuracy: 0.9700\n",
            "Test accuracy: 0.97\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p_6zyoOJ1lHu",
        "colab_type": "text"
      },
      "source": [
        "## Datos Fashion-MNIST"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ybltm0ul14JF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fashion_mnist = keras.datasets.fashion_mnist\n",
        "\n",
        "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n6FGwIXd14WJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v6I04A_S14ZE",
        "colab_type": "code",
        "colab": {}
      },
      "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": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q6ztRsZotjOJ",
        "colab_type": "text"
      },
      "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",
        "colab_type": "code",
        "colab": {}
      },
      "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": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sRiC2Hnq2sTr",
        "colab_type": "code",
        "outputId": "d843407d-982d-4b9d-c188-2fe2df30c344",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_3 (Conv2D)            (None, 24, 24, 32)        832       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 12, 12, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 8, 8, 64)          51264     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tBDazX0d2sYp",
        "colab_type": "code",
        "outputId": "7646d9e2-587b-475b-9de8-90f4d33d6d99",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        }
      },
      "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": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples\n",
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 5s 79us/sample - loss: 0.8425 - accuracy: 0.6980\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 5s 76us/sample - loss: 0.5186 - accuracy: 0.8138\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 4s 75us/sample - loss: 0.4547 - accuracy: 0.8384\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 4s 74us/sample - loss: 0.4177 - accuracy: 0.8527\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 5s 76us/sample - loss: 0.3941 - accuracy: 0.8610\n",
            "10000/10000 [==============================] - 1s 67us/sample - loss: 0.3996 - accuracy: 0.8593\n",
            "Test accuracy: 0.8593\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "L5XbYrrHKO5E",
        "colab": {}
      },
      "source": [
        "predictions = model.predict(test_images)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TTPAeEl45iNx",
        "colab_type": "code",
        "colab": {}
      },
      "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": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EQZcZ_RH0q45",
        "colab_type": "text"
      },
      "source": [
        "Volvemos las imagenes a formato original para hacer el plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U6O2NNvyKd6s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_images = train_images.reshape((60000, 28, 28))\n",
        "test_images = test_images.reshape((10000, 28, 28))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "STCV3LC-57tg",
        "colab_type": "code",
        "outputId": "3fe307b3-e7fc-443e-e3a9-e7bda50cca43",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "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": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QxX7VjEvhJUcVWzR9k8BiapI+p5n7GyB3n1Ow/gbIu88pSH/DAQ2RQ0Q6I8on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\nGhEREck9NWhEREQk9/4/Zn/SJs9Q/38AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x1008 with 28 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z0Y56EnBLQUm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_images = train_images.reshape((60000, 28, 28, 1))\n",
        "test_images = test_images.reshape((10000, 28, 28, 1))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6tV0gHRztcvI",
        "colab_type": "text"
      },
      "source": [
        "Modelo 2: Duplicando las neuronas de modelo base y añadiendo una capa densa de 64 neuronas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5vu0mAps6wNt",
        "colab_type": "code",
        "colab": {}
      },
      "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": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G1qOIHnyL_Te",
        "colab_type": "code",
        "outputId": "1b90ca23-7cf2-4998-a50e-f3cb5e80983d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        }
      },
      "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": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples\n",
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 6s 95us/sample - loss: 0.7661 - accuracy: 0.7264\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 5s 91us/sample - loss: 0.4844 - accuracy: 0.8240\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 5s 91us/sample - loss: 0.4181 - accuracy: 0.8500\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 5s 91us/sample - loss: 0.3796 - accuracy: 0.8641\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 5s 90us/sample - loss: 0.3527 - accuracy: 0.8738\n",
            "10000/10000 [==============================] - 1s 72us/sample - loss: 0.3737 - accuracy: 0.8628\n",
            "\n",
            "Test accuracy: 0.8628\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZEyd3lnut9In",
        "colab_type": "text"
      },
      "source": [
        "Modelo 3: modelo 3 cambiando al optimizador 'adam'"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UR6POPWWMAoU",
        "colab_type": "code",
        "outputId": "42e458be-f3e9-4059-e2b5-09927fd1d959",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        }
      },
      "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": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples\n",
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 6s 98us/sample - loss: 0.4139 - accuracy: 0.8510\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 6s 94us/sample - loss: 0.2719 - accuracy: 0.9001\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 6s 93us/sample - loss: 0.2276 - accuracy: 0.9152\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 6s 94us/sample - loss: 0.1990 - accuracy: 0.9260\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 6s 94us/sample - loss: 0.1719 - accuracy: 0.9353\n",
            "10000/10000 [==============================] - 1s 72us/sample - loss: 0.2440 - accuracy: 0.9099\n",
            "\n",
            "Test accuracy: 0.9099\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iBLBezVui5X1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kogvBWoruYhp",
        "colab_type": "text"
      },
      "source": [
        "Modelo 4: Buscando un modelo más complejo que usa capas BathNormalization y Dropout"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K_bynmW9liwR",
        "colab_type": "code",
        "colab": {}
      },
      "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": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UMVtk4yqmk5k",
        "colab_type": "code",
        "outputId": "6590908a-fee8-448b-a246-7051f228e6bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        }
      },
      "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": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples\n",
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 14s 227us/sample - loss: 0.5052 - accuracy: 0.8259\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 13s 210us/sample - loss: 0.3161 - accuracy: 0.8894\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 13s 210us/sample - loss: 0.2763 - accuracy: 0.9017\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 13s 209us/sample - loss: 0.2481 - accuracy: 0.9124\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 13s 209us/sample - loss: 0.2231 - accuracy: 0.9216\n",
            "10000/10000 [==============================] - 1s 111us/sample - loss: 0.2030 - accuracy: 0.9256\n",
            "\n",
            "Test accuracy: 0.9256\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JYQg0a86yVOi",
        "colab_type": "text"
      },
      "source": [
        "Modelo 5: Modelo anterior añadiendo más epochs (10 epochs)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yTh4OaK6yOEx",
        "colab_type": "code",
        "outputId": "6f9a4d03-4550-42b4-98c1-ba579a765057",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        }
      },
      "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": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples\n",
            "Epoch 1/10\n",
            "60000/60000 [==============================] - 14s 229us/sample - loss: 0.5098 - accuracy: 0.8217\n",
            "Epoch 2/10\n",
            "60000/60000 [==============================] - 13s 214us/sample - loss: 0.3239 - accuracy: 0.8857\n",
            "Epoch 3/10\n",
            "60000/60000 [==============================] - 13s 213us/sample - loss: 0.2839 - accuracy: 0.8990\n",
            "Epoch 4/10\n",
            "60000/60000 [==============================] - 13s 214us/sample - loss: 0.2501 - accuracy: 0.9107\n",
            "Epoch 5/10\n",
            "60000/60000 [==============================] - 13s 212us/sample - loss: 0.2228 - accuracy: 0.9201\n",
            "Epoch 6/10\n",
            "60000/60000 [==============================] - 13s 214us/sample - loss: 0.2050 - accuracy: 0.9273\n",
            "Epoch 7/10\n",
            "60000/60000 [==============================] - 13s 214us/sample - loss: 0.1893 - accuracy: 0.9324\n",
            "Epoch 8/10\n",
            "60000/60000 [==============================] - 13s 213us/sample - loss: 0.1733 - accuracy: 0.9385\n",
            "Epoch 9/10\n",
            "60000/60000 [==============================] - 13s 213us/sample - loss: 0.1661 - accuracy: 0.9394\n",
            "Epoch 10/10\n",
            "60000/60000 [==============================] - 13s 212us/sample - loss: 0.1504 - accuracy: 0.9460\n",
            "10000/10000 [==============================] - 1s 106us/sample - loss: 0.2015 - accuracy: 0.9322\n",
            "\n",
            "Test accuracy: 0.9322\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YpJwKcAgGGhq",
        "colab_type": "text"
      },
      "source": [
        "Modelo 6: Modelo anterior añadiendo más epochs (30 epochs)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ZDDas7oF97f",
        "colab_type": "code",
        "outputId": "19bbd615-097a-4084-80fb-6c76c10aaf4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "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": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples\n",
            "Epoch 1/30\n",
            "60000/60000 [==============================] - 14s 226us/sample - loss: 0.5218 - accuracy: 0.8189\n",
            "Epoch 2/30\n",
            "60000/60000 [==============================] - 13s 213us/sample - loss: 0.3229 - accuracy: 0.8865\n",
            "Epoch 3/30\n",
            "60000/60000 [==============================] - 13s 212us/sample - loss: 0.2865 - accuracy: 0.8982\n",
            "Epoch 4/30\n",
            "60000/60000 [==============================] - 13s 213us/sample - loss: 0.2493 - accuracy: 0.9110\n",
            "Epoch 5/30\n",
            "60000/60000 [==============================] - 13s 216us/sample - loss: 0.2243 - accuracy: 0.9214\n",
            "Epoch 6/30\n",
            "60000/60000 [==============================] - 13s 213us/sample - loss: 0.2036 - accuracy: 0.9283\n",
            "Epoch 7/30\n",
            "60000/60000 [==============================] - 13s 211us/sample - loss: 0.1881 - accuracy: 0.9330\n",
            "Epoch 8/30\n",
            "60000/60000 [==============================] - 13s 212us/sample - loss: 0.1695 - accuracy: 0.9391\n",
            "Epoch 9/30\n",
            "60000/60000 [==============================] - 13s 212us/sample - loss: 0.1597 - accuracy: 0.9418\n",
            "Epoch 10/30\n",
            "60000/60000 [==============================] - 13s 213us/sample - loss: 0.1473 - accuracy: 0.9470\n",
            "Epoch 11/30\n",
            "60000/60000 [==============================] - 13s 214us/sample - loss: 0.1343 - accuracy: 0.9514\n",
            "Epoch 12/30\n",
            "60000/60000 [==============================] - 13s 212us/sample - loss: 0.1299 - accuracy: 0.9540\n",
            "Epoch 13/30\n",
            "60000/60000 [==============================] - 13s 212us/sample - loss: 0.1206 - accuracy: 0.9570\n",
            "Epoch 14/30\n",
            "60000/60000 [==============================] - 13s 213us/sample - loss: 0.1120 - accuracy: 0.9597\n",
            "Epoch 15/30\n",
            "60000/60000 [==============================] - 13s 213us/sample - loss: 0.1075 - accuracy: 0.9616\n",
            "Epoch 16/30\n",
            "60000/60000 [==============================] - 13s 213us/sample - loss: 0.0953 - accuracy: 0.9656\n",
            "Epoch 17/30\n",
            "60000/60000 [==============================] - 13s 212us/sample - loss: 0.0941 - accuracy: 0.9671\n",
            "Epoch 18/30\n",
            "60000/60000 [==============================] - 13s 213us/sample - loss: 0.0882 - accuracy: 0.9689\n",
            "Epoch 19/30\n",
            "60000/60000 [==============================] - 13s 212us/sample - loss: 0.0884 - accuracy: 0.9689\n",
            "Epoch 20/30\n",
            "60000/60000 [==============================] - 13s 213us/sample - loss: 0.0784 - accuracy: 0.9722\n",
            "Epoch 21/30\n",
            "60000/60000 [==============================] - 13s 213us/sample - loss: 0.0821 - accuracy: 0.9706\n",
            "Epoch 22/30\n",
            "60000/60000 [==============================] - 13s 212us/sample - loss: 0.0744 - accuracy: 0.9742\n",
            "Epoch 23/30\n",
            "60000/60000 [==============================] - 13s 214us/sample - loss: 0.0765 - accuracy: 0.9731\n",
            "Epoch 24/30\n",
            "60000/60000 [==============================] - 13s 216us/sample - loss: 0.0715 - accuracy: 0.9748\n",
            "Epoch 25/30\n",
            "60000/60000 [==============================] - 13s 213us/sample - loss: 0.0656 - accuracy: 0.9770\n",
            "Epoch 26/30\n",
            "60000/60000 [==============================] - 13s 212us/sample - loss: 0.0679 - accuracy: 0.9758\n",
            "Epoch 27/30\n",
            "60000/60000 [==============================] - 13s 210us/sample - loss: 0.0597 - accuracy: 0.9794\n",
            "Epoch 28/30\n",
            "60000/60000 [==============================] - 13s 212us/sample - loss: 0.0562 - accuracy: 0.9804\n",
            "Epoch 29/30\n",
            "60000/60000 [==============================] - 13s 213us/sample - loss: 0.0556 - accuracy: 0.9804\n",
            "Epoch 30/30\n",
            "60000/60000 [==============================] - 13s 213us/sample - loss: 0.0548 - accuracy: 0.9800\n",
            "10000/10000 [==============================] - 1s 108us/sample - loss: 0.2281 - accuracy: 0.9396\n",
            "\n",
            "Test accuracy: 0.9396\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-NUPmba5zFcV",
        "colab_type": "text"
      },
      "source": [
        "Modelo 7: Modelo anterior añadiendo más epochs (30 epochs) y diferentes hiperparámetros al optimizador Adam"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oWms_Gu7nQxv",
        "colab_type": "code",
        "outputId": "921a8ce4-4ccf-4c7c-df4b-62764e4cc175",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "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": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples\n",
            "Epoch 1/30\n",
            "60000/60000 [==============================] - 14s 228us/sample - loss: 0.5048 - accuracy: 0.8254\n",
            "Epoch 2/30\n",
            "60000/60000 [==============================] - 13s 210us/sample - loss: 0.3187 - accuracy: 0.8865\n",
            "Epoch 3/30\n",
            "60000/60000 [==============================] - 13s 212us/sample - loss: 0.2693 - accuracy: 0.9048\n",
            "Epoch 4/30\n",
            "60000/60000 [==============================] - 13s 211us/sample - loss: 0.2380 - accuracy: 0.9158\n",
            "Epoch 5/30\n",
            "60000/60000 [==============================] - 13s 211us/sample - loss: 0.2110 - accuracy: 0.9270\n",
            "Epoch 6/30\n",
            "60000/60000 [==============================] - 13s 213us/sample - loss: 0.1849 - accuracy: 0.9345\n",
            "Epoch 7/30\n",
            "60000/60000 [==============================] - 13s 210us/sample - loss: 0.1672 - accuracy: 0.9411\n",
            "Epoch 8/30\n",
            "60000/60000 [==============================] - 13s 211us/sample - loss: 0.1503 - accuracy: 0.9476\n",
            "Epoch 9/30\n",
            "60000/60000 [==============================] - 13s 210us/sample - loss: 0.1367 - accuracy: 0.9514\n",
            "Epoch 10/30\n",
            "60000/60000 [==============================] - 13s 210us/sample - loss: 0.1217 - accuracy: 0.9566\n",
            "Epoch 11/30\n",
            "60000/60000 [==============================] - 13s 215us/sample - loss: 0.1076 - accuracy: 0.9617\n",
            "Epoch 12/30\n",
            "60000/60000 [==============================] - 13s 214us/sample - loss: 0.1019 - accuracy: 0.9639\n",
            "Epoch 13/30\n",
            "60000/60000 [==============================] - 13s 212us/sample - loss: 0.0865 - accuracy: 0.9694\n",
            "Epoch 14/30\n",
            "60000/60000 [==============================] - 13s 210us/sample - loss: 0.0820 - accuracy: 0.9709\n",
            "Epoch 15/30\n",
            "60000/60000 [==============================] - 13s 211us/sample - loss: 0.0748 - accuracy: 0.9736\n",
            "Epoch 16/30\n",
            "60000/60000 [==============================] - 13s 211us/sample - loss: 0.0673 - accuracy: 0.9762\n",
            "Epoch 17/30\n",
            "60000/60000 [==============================] - 13s 212us/sample - loss: 0.0629 - accuracy: 0.9777\n",
            "Epoch 18/30\n",
            "60000/60000 [==============================] - 13s 213us/sample - loss: 0.0566 - accuracy: 0.9797\n",
            "Epoch 19/30\n",
            "60000/60000 [==============================] - 13s 212us/sample - loss: 0.0519 - accuracy: 0.9819\n",
            "Epoch 20/30\n",
            "60000/60000 [==============================] - 13s 210us/sample - loss: 0.0485 - accuracy: 0.9829\n",
            "Epoch 21/30\n",
            "60000/60000 [==============================] - 13s 211us/sample - loss: 0.0478 - accuracy: 0.9835\n",
            "Epoch 22/30\n",
            "60000/60000 [==============================] - 13s 211us/sample - loss: 0.0440 - accuracy: 0.9845\n",
            "Epoch 23/30\n",
            "60000/60000 [==============================] - 13s 211us/sample - loss: 0.0435 - accuracy: 0.9851\n",
            "Epoch 24/30\n",
            "60000/60000 [==============================] - 13s 210us/sample - loss: 0.0408 - accuracy: 0.9859\n",
            "Epoch 25/30\n",
            "60000/60000 [==============================] - 13s 210us/sample - loss: 0.0407 - accuracy: 0.9859\n",
            "Epoch 26/30\n",
            "60000/60000 [==============================] - 13s 210us/sample - loss: 0.0361 - accuracy: 0.9876\n",
            "Epoch 27/30\n",
            "60000/60000 [==============================] - 13s 210us/sample - loss: 0.0335 - accuracy: 0.9887\n",
            "Epoch 28/30\n",
            "60000/60000 [==============================] - 13s 211us/sample - loss: 0.0345 - accuracy: 0.9878\n",
            "Epoch 29/30\n",
            "60000/60000 [==============================] - 13s 210us/sample - loss: 0.0317 - accuracy: 0.9891\n",
            "Epoch 30/30\n",
            "60000/60000 [==============================] - 13s 211us/sample - loss: 0.0322 - accuracy: 0.9886\n",
            "10000/10000 [==============================] - 1s 108us/sample - loss: 0.2333 - accuracy: 0.9454\n",
            "\n",
            "Test accuracy: 0.9454\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-HtzExwn2u9U",
        "colab_type": "text"
      },
      "source": [
        "Visualización de las predicciones del modelo 7"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XR218aBirMdU",
        "colab_type": "code",
        "outputId": "eb242ad8-ad42-4807-fb3b-8e83213cfd64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "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": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7j8mf5wF336NlxMx6m1nf3WmyJef3mEXh7kuBF83sqPzQWWTrnlSye9+xSl4A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88+Ne/71ltvRfUw1yedm06f8eqrry7K3/jGN6K2NLcn/dlFpG0Kc2pSac7e+PHja97rtttuK8rH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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 576x1008 with 28 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}