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nzw
54 lines
1.6 KiB
Python
54 lines
1.6 KiB
Python
'''
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Trains a Residual-of-Residual Network (WRN-40-2) model on the CIFAR-10 Dataset.
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Gets a 94.53% accuracy score after 150 epochs.
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'''
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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.datasets import cifar10
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from keras.preprocessing.image import ImageDataGenerator
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from keras.optimizers import Adam
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from keras_contrib.applications import ResidualOfResidual
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batch_size = 64
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epochs = 150
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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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testX = testX.astype('float32')
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trainX /= 255
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testX /= 255
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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=15,
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width_shift_range=5. / 32,
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height_shift_range=5. / 32)
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generator.fit(trainX, seed=0)
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model = ResidualOfResidual(depth=40, width=2, dropout_rate=0.0, weights=None)
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optimizer = Adam(lr=1e-3)
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model.compile(loss='categorical_crossentropy', optimizer=optimizer, 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=[callbacks.ModelCheckpoint('weights/RoR-WRN-40-2-Weights.h5', monitor='val_acc',
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save_best_only=True, save_weights_only=True)],
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validation_data=(testX, testY),
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verbose=2)
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scores = model.evaluate(testX, testY, batch_size)
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print('Test loss : ', scores[0])
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print('Test accuracy : ', scores[1])
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