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nzw
60 lines
1.9 KiB
Python
60 lines
1.9 KiB
Python
'''
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Trains a WRN-28-8 model on the CIFAR-10 Dataset.
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Performance is slightly less than the paper, since
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they use WRN-28-10 model (95.83%).
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Gets a 95.54% accuracy score after 300 epochs.
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'''
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from __future__ import absolute_import
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from __future__ import print_function
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from __future__ import division
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from keras.datasets import cifar10
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import keras.callbacks as callbacks
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import keras.utils.np_utils as kutils
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from keras.preprocessing.image import ImageDataGenerator
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from keras_contrib.applications.wide_resnet import WideResidualNetwork
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batch_size = 64
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epochs = 300
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img_rows, img_cols = 32, 32
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(trainX, trainY), (testX, testY) = cifar10.load_data()
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trainX = trainX.astype('float32')
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trainX /= 255.0
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testX = testX.astype('float32')
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testX /= 255.0
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tempY = testY
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trainY = kutils.to_categorical(trainY)
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testY = kutils.to_categorical(testY)
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generator = ImageDataGenerator(rotation_range=10,
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width_shift_range=5. / 32,
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height_shift_range=5. / 32,
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horizontal_flip=True)
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generator.fit(trainX, seed=0, augment=True)
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# We will be training the model, therefore no need to load weights
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model = WideResidualNetwork(depth=28, width=8, dropout_rate=0.0, weights=None)
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model.summary()
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model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc'])
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print('Finished compiling')
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model.fit_generator(generator.flow(trainX, trainY, batch_size=batch_size), steps_per_epoch=len(trainX) // batch_size,
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epochs=epochs,
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callbacks=[
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callbacks.ModelCheckpoint('WRN-28-8 Weights.h5', monitor='val_acc', save_best_only=True,
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save_weights_only=True)],
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validation_data=(testX, testY))
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scores = model.evaluate(testX, testY, batch_size)
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print('Test loss : %0.5f' % (scores[0]))
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print('Test accuracy = %0.5f' % (scores[1]))
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