CIFAR10-CNN-AlexNET.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy.random import seed\n",
    "seed(1)\n",
    "from tensorflow import set_random_seed\n",
    "set_random_seed(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Keras modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.utils import to_categorical\n",
    "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Flatten, Activation\n",
    "from keras.layers import Conv2D, AveragePooling2D, BatchNormalization, Dropout, MaxPooling2D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cargo datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.datasets import cifar10\n",
    "((x, y), (x_test,y_test)) = cifar10.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_valid, y_train, y_valid = train_test_split(x, y, test_size = 0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Paso a float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train.astype('float32')\n",
    "x_valid = x_valid.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "x_train /= 255\n",
    "x_valid /= 255\n",
    "x_test /= 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_categorical = to_categorical(y_train)\n",
    "y_val_categorical = to_categorical(y_valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Red neuronal: CNN tipo AlexNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 32, 32, 20)        7280      \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 32, 32, 20)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 20)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 16, 16, 20)        3620      \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 16, 16, 20)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 20)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 8, 8, 20)          10020     \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 8, 8, 20)          0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 20)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 4, 4, 20)          3620      \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 4, 4, 20)          0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 2, 2, 20)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 80)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 120)               9720      \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 120)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 84)                10164     \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 84)                0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 10)                850       \n",
      "=================================================================\n",
      "Total params: 45,274\n",
      "Trainable params: 45,274\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(filters=20, kernel_size=(11, 11), input_shape=(32,32,3), padding='same'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D())\n",
    "\n",
    "model.add(Conv2D(filters=20, kernel_size=(3, 3), padding='same'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D())\n",
    "\n",
    "model.add(Conv2D(filters=20, kernel_size=(5, 5), padding='same'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D())\n",
    "\n",
    "\n",
    "model.add(Conv2D(filters=20, kernel_size=(3, 3), padding='same'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D())\n",
    "\n",
    "model.add(Flatten())\n",
    "\n",
    "model.add(Dense(units=120, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(units=84, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(units=10, activation = 'softmax'))\n",
    "\n",
    "model.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Callbacks\n",
    "earlystop = EarlyStopping(monitor='val_acc', min_delta=0, patience=5, verbose=2)\n",
    "checkpointer = ModelCheckpoint(filepath='checkpoint.cifar10-cnn-tipoAlexNet.hdf5', verbose=1, save_best_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45000, 32, 32, 3)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45000, 10)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_categorical.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 45000 samples, validate on 5000 samples\n",
      "Epoch 1/100\n",
      " - 7s - loss: 1.9838 - acc: 0.2508 - val_loss: 1.7636 - val_acc: 0.3560\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 1.76358, saving model to checkpoint.cifar10-cnn-tipoAlexNet.hdf5\n",
      "Epoch 2/100\n",
      " - 6s - loss: 1.7054 - acc: 0.3666 - val_loss: 1.5685 - val_acc: 0.4286\n",
      "\n",
      "Epoch 00002: val_loss improved from 1.76358 to 1.56847, saving model to checkpoint.cifar10-cnn-tipoAlexNet.hdf5\n",
      "Epoch 3/100\n",
      " - 5s - loss: 1.5831 - acc: 0.4188 - val_loss: 1.4627 - val_acc: 0.4696\n",
      "\n",
      "Epoch 00003: val_loss improved from 1.56847 to 1.46268, saving model to checkpoint.cifar10-cnn-tipoAlexNet.hdf5\n",
      "Epoch 4/100\n",
      " - 5s - loss: 1.5119 - acc: 0.4500 - val_loss: 1.4514 - val_acc: 0.4712\n",
      "\n",
      "Epoch 00004: val_loss improved from 1.46268 to 1.45136, saving model to checkpoint.cifar10-cnn-tipoAlexNet.hdf5\n",
      "Epoch 5/100\n",
      " - 5s - loss: 1.4564 - acc: 0.4729 - val_loss: 1.3707 - val_acc: 0.5076\n",
      "\n",
      "Epoch 00005: val_loss improved from 1.45136 to 1.37070, saving model to checkpoint.cifar10-cnn-tipoAlexNet.hdf5\n",
      "Epoch 6/100\n",
      " - 5s - loss: 1.4089 - acc: 0.4949 - val_loss: 1.3462 - val_acc: 0.5154\n",
      "\n",
      "Epoch 00006: val_loss improved from 1.37070 to 1.34623, saving model to checkpoint.cifar10-cnn-tipoAlexNet.hdf5\n",
      "Epoch 7/100\n",
      " - 6s - loss: 1.3661 - acc: 0.5119 - val_loss: 1.2970 - val_acc: 0.5364\n",
      "\n",
      "Epoch 00007: val_loss improved from 1.34623 to 1.29699, saving model to checkpoint.cifar10-cnn-tipoAlexNet.hdf5\n",
      "Epoch 8/100\n",
      " - 6s - loss: 1.3271 - acc: 0.5276 - val_loss: 1.3117 - val_acc: 0.5256\n",
      "\n",
      "Epoch 00008: val_loss did not improve from 1.29699\n",
      "Epoch 9/100\n",
      " - 6s - loss: 1.3112 - acc: 0.5335 - val_loss: 1.2834 - val_acc: 0.5388\n",
      "\n",
      "Epoch 00009: val_loss improved from 1.29699 to 1.28340, saving model to checkpoint.cifar10-cnn-tipoAlexNet.hdf5\n",
      "Epoch 10/100\n",
      " - 6s - loss: 1.2778 - acc: 0.5477 - val_loss: 1.2750 - val_acc: 0.5412\n",
      "\n",
      "Epoch 00010: val_loss improved from 1.28340 to 1.27504, saving model to checkpoint.cifar10-cnn-tipoAlexNet.hdf5\n",
      "Epoch 11/100\n",
      " - 6s - loss: 1.2512 - acc: 0.5553 - val_loss: 1.2346 - val_acc: 0.5582\n",
      "\n",
      "Epoch 00011: val_loss improved from 1.27504 to 1.23459, saving model to checkpoint.cifar10-cnn-tipoAlexNet.hdf5\n",
      "Epoch 12/100\n",
      " - 6s - loss: 1.2363 - acc: 0.5662 - val_loss: 1.2114 - val_acc: 0.5616\n",
      "\n",
      "Epoch 00012: val_loss improved from 1.23459 to 1.21137, saving model to checkpoint.cifar10-cnn-tipoAlexNet.hdf5\n",
      "Epoch 13/100\n",
      " - 6s - loss: 1.2125 - acc: 0.5736 - val_loss: 1.2348 - val_acc: 0.5526\n",
      "\n",
      "Epoch 00013: val_loss did not improve from 1.21137\n",
      "Epoch 14/100\n",
      " - 7s - loss: 1.2002 - acc: 0.5807 - val_loss: 1.2030 - val_acc: 0.5754\n",
      "\n",
      "Epoch 00014: val_loss improved from 1.21137 to 1.20296, saving model to checkpoint.cifar10-cnn-tipoAlexNet.hdf5\n",
      "Epoch 15/100\n",
      " - 6s - loss: 1.1734 - acc: 0.5893 - val_loss: 1.2199 - val_acc: 0.5774\n",
      "\n",
      "Epoch 00015: val_loss did not improve from 1.20296\n",
      "Epoch 16/100\n",
      " - 6s - loss: 1.1681 - acc: 0.5917 - val_loss: 1.2135 - val_acc: 0.5512\n",
      "\n",
      "Epoch 00016: val_loss did not improve from 1.20296\n",
      "Epoch 17/100\n",
      " - 5s - loss: 1.1477 - acc: 0.6015 - val_loss: 1.2314 - val_acc: 0.5710\n",
      "\n",
      "Epoch 00017: val_loss did not improve from 1.20296\n",
      "Epoch 18/100\n",
      " - 6s - loss: 1.1432 - acc: 0.6023 - val_loss: 1.1909 - val_acc: 0.5792\n",
      "\n",
      "Epoch 00018: val_loss improved from 1.20296 to 1.19095, saving model to checkpoint.cifar10-cnn-tipoAlexNet.hdf5\n",
      "Epoch 19/100\n",
      " - 7s - loss: 1.1257 - acc: 0.6083 - val_loss: 1.1974 - val_acc: 0.5802\n",
      "\n",
      "Epoch 00019: val_loss did not improve from 1.19095\n",
      "Epoch 20/100\n",
      " - 7s - loss: 1.1126 - acc: 0.6116 - val_loss: 1.1804 - val_acc: 0.5810\n",
      "\n",
      "Epoch 00020: val_loss improved from 1.19095 to 1.18041, saving model to checkpoint.cifar10-cnn-tipoAlexNet.hdf5\n",
      "Epoch 21/100\n",
      " - 5s - loss: 1.1075 - acc: 0.6181 - val_loss: 1.1799 - val_acc: 0.5874\n",
      "\n",
      "Epoch 00021: val_loss improved from 1.18041 to 1.17990, saving model to checkpoint.cifar10-cnn-tipoAlexNet.hdf5\n",
      "Epoch 22/100\n",
      " - 6s - loss: 1.0895 - acc: 0.6216 - val_loss: 1.1738 - val_acc: 0.5944\n",
      "\n",
      "Epoch 00022: val_loss improved from 1.17990 to 1.17382, saving model to checkpoint.cifar10-cnn-tipoAlexNet.hdf5\n",
      "Epoch 23/100\n",
      " - 6s - loss: 1.0789 - acc: 0.6261 - val_loss: 1.2126 - val_acc: 0.5750\n",
      "\n",
      "Epoch 00023: val_loss did not improve from 1.17382\n",
      "Epoch 24/100\n",
      " - 5s - loss: 1.0792 - acc: 0.6273 - val_loss: 1.1837 - val_acc: 0.5856\n",
      "\n",
      "Epoch 00024: val_loss did not improve from 1.17382\n",
      "Epoch 25/100\n",
      " - 6s - loss: 1.0716 - acc: 0.6286 - val_loss: 1.2047 - val_acc: 0.5818\n",
      "\n",
      "Epoch 00025: val_loss did not improve from 1.17382\n",
      "Epoch 26/100\n",
      " - 6s - loss: 1.0588 - acc: 0.6319 - val_loss: 1.1667 - val_acc: 0.5910\n",
      "\n",
      "Epoch 00026: val_loss improved from 1.17382 to 1.16667, saving model to checkpoint.cifar10-cnn-tipoAlexNet.hdf5\n",
      "Epoch 27/100\n",
      " - 5s - loss: 1.0533 - acc: 0.6370 - val_loss: 1.2069 - val_acc: 0.5844\n",
      "\n",
      "Epoch 00027: val_loss did not improve from 1.16667\n",
      "Epoch 00027: early stopping\n"
     ]
    }
   ],
   "source": [
    "batch_size = 64\n",
    "epocs = 100\n",
    "\n",
    "history = model.fit(x_train, \n",
    "        y_train_categorical,\n",
    "        epochs=epocs, batch_size=batch_size, \n",
    "        verbose=2, \n",
    "        validation_data = (x_valid, y_val_categorical),\n",
    "        callbacks=[checkpointer, earlystop],\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 302,
       "width": 1048
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(1, 2, sharex=True, figsize=(18,5))\n",
    "ax1.plot(history.history['loss'], label=\"loss\")\n",
    "ax1.plot(history.history['val_loss'], label=\"val_loss\")\n",
    "ax1.legend()\n",
    "\n",
    "ax2.plot(history.history['acc'], label=\"acc\")\n",
    "ax2.plot(history.history['val_acc'], label=\"val_acc\")\n",
    "ax2.legend()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights('checkpoint.mnist-cnn.hdf5')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (tensorflow-gpu)",
   "language": "python",
   "name": "tensorflow-gpu"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}