71 lines
2.2 KiB
Python
71 lines
2.2 KiB
Python
'''Trains a simple convnet on the MNIST dataset.
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Gets to 99.25% test accuracy after 12 epochs
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(there is still a lot of margin for parameter tuning).
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16 seconds per epoch on a GRID K520 GPU.
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'''
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from __future__ import print_function
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import keras
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from keras.datasets import mnist
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from keras.models import Sequential
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from keras.layers import Dense, Dropout, Flatten
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from keras.layers import Conv2D, MaxPooling2D
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from keras import backend as K
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batch_size = 128
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num_classes = 10
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epochs = 12
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# input image dimensions
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img_rows, img_cols = 28, 28
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# the data, shuffled and split between train and test sets
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(x_train, y_train), (x_test, y_test) = mnist.load_data()
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if K.image_data_format() == 'channels_first':
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x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
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x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
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input_shape = (1, img_rows, img_cols)
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else:
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x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
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x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
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input_shape = (img_rows, img_cols, 1)
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x_train = x_train.astype('float32')
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x_test = x_test.astype('float32')
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x_train /= 255
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x_test /= 255
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print('x_train shape:', x_train.shape)
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print(x_train.shape[0], 'train samples')
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print(x_test.shape[0], 'test samples')
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# convert class vectors to binary class matrices
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y_train = keras.utils.to_categorical(y_train, num_classes)
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y_test = keras.utils.to_categorical(y_test, num_classes)
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model = Sequential()
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model.add(Conv2D(32, kernel_size=(3, 3),
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activation='relu',
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input_shape=input_shape))
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model.add(Conv2D(64, (3, 3), activation='relu'))
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Dropout(0.25))
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model.add(Flatten())
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model.add(Dense(128, activation='relu'))
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model.add(Dropout(0.5))
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model.add(Dense(num_classes, activation='softmax'))
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model.compile(loss=keras.losses.categorical_crossentropy,
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optimizer=keras.optimizers.Adadelta(),
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metrics=['accuracy'])
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model.fit(x_train, y_train,
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batch_size=batch_size,
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epochs=epochs,
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verbose=1,
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validation_data=(x_test, y_test))
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score = model.evaluate(x_test, y_test, verbose=0)
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print('Test loss:', score[0])
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print('Test accuracy:', score[1])
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