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- '''Trains a simple deep NN on the MNIST dataset.
- Gets to 98.40% test accuracy after 20 epochs
- (there is *a lot* of margin for parameter tuning).
- 2 seconds per epoch on a K520 GPU.
- '''
- from __future__ import print_function
- import keras
- from keras.datasets import mnist
- from keras.models import Sequential
- from keras.layers import Dense, Dropout
- from keras.optimizers import RMSprop
- batch_size = 128
- num_classes = 10
- epochs = 20
- # the data, split between train and test sets
- (x_train, y_train), (x_test, y_test) = mnist.load_data()
- x_train = x_train.reshape(60000, 784)
- x_test = x_test.reshape(10000, 784)
- x_train = x_train.astype('float32')
- x_test = x_test.astype('float32')
- x_train /= 255
- x_test /= 255
- print(x_train.shape[0], 'train samples')
- print(x_test.shape[0], 'test samples')
- # convert class vectors to binary class matrices
- y_train = keras.utils.to_categorical(y_train, num_classes)
- y_test = keras.utils.to_categorical(y_test, num_classes)
- model = Sequential()
- model.add(Dense(512, activation='relu', input_shape=(784,)))
- model.add(Dropout(0.2))
- model.add(Dense(512, activation='relu'))
- model.add(Dropout(0.2))
- model.add(Dense(num_classes, activation='softmax'))
- model.summary()
- model.compile(loss='categorical_crossentropy',
- optimizer=RMSprop(),
- metrics=['accuracy'])
- history = model.fit(x_train, y_train,
- batch_size=batch_size,
- epochs=epochs,
- verbose=1,
- validation_data=(x_test, y_test))
- score = model.evaluate(x_test, y_test, verbose=0)
- print('Test loss:', score[0])
- print('Test accuracy:', score[1])
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