55 lines
1.6 KiB
Python
55 lines
1.6 KiB
Python
from __future__ import absolute_import
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from __future__ import print_function
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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
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from keras.regularizers import l2, l1
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from keras.constraints import maxnorm
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from keras.optimizers import SGD, Adam, RMSprop
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from keras.utils import np_utils
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import numpy as np
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'''
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Train a simple deep NN on the MNIST dataset.
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'''
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batch_size = 64
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nb_classes = 10
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nb_epoch = 20
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np.random.seed(1337) # for reproducibility
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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(60000,784)
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X_test=X_test.reshape(10000,784)
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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[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(Dense(784, 128))
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model.add(Activation('relu'))
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model.add(Dropout(0.2))
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model.add(Dense(128, 128))
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model.add(Activation('relu'))
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model.add(Dropout(0.2))
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model.add(Dense(128, 10))
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model.add(Activation('softmax'))
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rms = RMSprop()
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model.compile(loss='categorical_crossentropy', optimizer=rms)
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model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=True, verbose=2, 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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