64 lines
2.1 KiB
Python
64 lines
2.1 KiB
Python
import numpy as np
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from keras.datasets import reuters
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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.layers.normalization import BatchNormalization
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from keras.utils import np_utils
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from keras.preprocessing.text import Tokenizer
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'''
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Train and evaluate a simple MLP on the Reuters newswire topic classification task.
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GPU run command:
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THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python examples/reuters_mlp.py
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CPU run command:
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python examples/reuters_mlp.py
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'''
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max_words = 10000
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batch_size = 16
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print "Loading data..."
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(X_train, y_train), (X_test, y_test) = reuters.load_data(nb_words=max_words, test_split=0.2)
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print len(X_train), 'train sequences'
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print len(X_test), 'test sequences'
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nb_classes = np.max(y_train)+1
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print nb_classes, 'classes'
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print "Vectorizing sequence data..."
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tokenizer = Tokenizer(nb_words=max_words)
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X_train = tokenizer.sequences_to_matrix(X_train, mode="binary")
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X_test = tokenizer.sequences_to_matrix(X_test, mode="binary")
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print 'X_train shape:', X_train.shape
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print 'X_test shape:', X_test.shape
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print "Convert class vector to binary class matrix (for use with categorical_crossentropy)"
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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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print 'Y_train shape:', Y_train.shape
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print 'Y_test shape:', Y_test.shape
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print "Building model..."
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model = Sequential()
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model.add(Dense(max_words, 256, init='normal'))
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model.add(Activation('relu'))
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model.add(BatchNormalization(input_shape=(256,))) # try without batch normalization (doesn't work as well!)
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model.add(Dropout(0.5))
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model.add(Dense(256, nb_classes, init='normal'))
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model.add(Activation('softmax'))
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model.compile(loss='categorical_crossentropy', optimizer='adadelta')
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print "Training..."
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model.fit(X_train, Y_train, nb_epoch=5, batch_size=batch_size)
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score = model.evaluate(X_test, Y_test, batch_size=batch_size)
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print 'Test score:', score
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classes = model.predict_classes(X_test, batch_size=batch_size)
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acc = np_utils.accuracy(classes, y_test)
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print 'Test accuracy:', acc
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