75 lines
2.4 KiB
Python
75 lines
2.4 KiB
Python
from __future__ import absolute_import
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from __future__ import print_function
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import numpy as np
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np.random.seed(1337) # for reproducibility
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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.core import Dense, Dropout, Activation, Flatten
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from keras.layers.convolutional import Convolution2D, MaxPooling2D
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from keras.utils import np_utils
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'''
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Train a simple convnet on the MNIST dataset.
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Run on GPU: THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python mnist_cnn.py
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Get to 99.25% test accuracy after 12 epochs (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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batch_size = 128
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nb_classes = 10
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nb_epoch = 12
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# input image dimensions
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img_rows, img_cols = 28, 28
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# number of convolutional filters to use
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nb_filters = 32
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# size of pooling area for max pooling
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nb_pool = 2
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# convolution kernel size
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nb_conv = 3
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# the data, shuffled and split between tran and test sets
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(X_train, y_train), (X_test, y_test) = mnist.load_data()
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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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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 = np_utils.to_categorical(y_train, nb_classes)
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Y_test = np_utils.to_categorical(y_test, nb_classes)
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model = Sequential()
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model.add(Convolution2D(nb_filters, nb_conv, nb_conv,
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border_mode='same',
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input_shape=(1, img_rows, img_cols)))
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model.add(Activation('relu'))
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model.add(Convolution2D(nb_filters, nb_conv, nb_conv))
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model.add(Activation('relu'))
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model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
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model.add(Dropout(0.25))
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model.add(Flatten())
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model.add(Dense(128))
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model.add(Activation('relu'))
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model.add(Dropout(0.5))
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model.add(Dense(nb_classes))
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model.add(Activation('softmax'))
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model.compile(loss='categorical_crossentropy', optimizer='adadelta')
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model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=True, verbose=1, validation_data=(X_test, Y_test))
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score = model.evaluate(X_test, Y_test, show_accuracy=True, verbose=0)
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print('Test score:', score[0])
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print('Test accuracy:', score[1])
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