CIFAR 10 Small VGG for testing purposes

CIFAR10VGGsml.ipynb

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    "'''\n",
    "Cifar-10 classification - VGG-net adaptation\n",
    "Original dataset and info: https://www.cs.toronto.edu/~kriz/cifar.html for more information\n",
    "Code base attributed to Giuseppe Bonaccorso and modified\n",
    "See: https://www.bonaccorso.eu/2016/08/06/cifar-10-image-classification-with-keras-convnet/ for further information\n",
    "'''\n",
    " \n",
    "from __future__ import print_function\n",
    "\n",
    "import numpy as np\n",
    "import timeit \n",
    "\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.datasets import cifar10\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Dropout, Flatten\n",
    "from keras.layers.convolutional import Conv2D\n",
    "from keras.optimizers import Adam\n",
    "from keras.layers.pooling import MaxPooling2D\n",
    "from keras.utils import to_categorical"
   ]
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    "# For reproducibility\n",
    "np.random.seed(1000)\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    # Load the dataset\n",
    "    (X_train, Y_train), (X_test, Y_test) = cifar10.load_data()\n",
    "\n",
    "    # Create the model\n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(32, 32, 3)))\n",
    "    model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(Dropout(0.5))  #we will adjust this dropout\n",
    "\n",
    "    model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(Dropout(0.5)) #we will adjust this dropout\n",
    "\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(1024, activation='relu'))\n",
    "    model.add(Dropout(0.5)) #this dropout will remain constant\n",
    "    model.add(Dense(10, activation='softmax'))\n"
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    "  # Compile the model\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "                  optimizer=Adam(lr=0.0001, decay=1e-6),\n",
    "                  metrics=['accuracy'])"
   ]
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    "# Train the model\n",
    "tic=timeit.default_timer()\n",
    "model.fit(X_train / 255.0, to_categorical(Y_train),\n",
    "              batch_size=256,  # we will adjust this batch size\n",
    "              shuffle=True,\n",
    "              epochs=250,\n",
    "              validation_data=(X_test / 255.0, to_categorical(Y_test)),\n",
    "              callbacks=[EarlyStopping(min_delta=0.001, patience=3)])\n",
    "toc=timeit.default_timer()\n"
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   "source": [
    "# Evaluate the model\n",
    "scores = model.evaluate(X_test / 255.0, to_categorical(Y_test))\n",
    "secs = (toc-tic)\n",
    "print('Loss: %.3f' % scores[0])\n",
    "print('Accuracy: %.3f' % scores[1])\n",
    "print('Time: %.1f' % secs)"
   ]
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