61 lines
1.9 KiB
Python
61 lines
1.9 KiB
Python
'''Trains and evaluate a simple MLP
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on the Reuters newswire topic classification task.
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'''
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from __future__ import print_function
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import numpy as np
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import keras
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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 import Dense, Dropout, Activation
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from keras.preprocessing.text import Tokenizer
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max_words = 1000
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batch_size = 32
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epochs = 5
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print('Loading data...')
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(x_train, y_train), (x_test, y_test) = reuters.load_data(num_words=max_words,
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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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num_classes = np.max(y_train) + 1
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print(num_classes, 'classes')
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print('Vectorizing sequence data...')
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tokenizer = Tokenizer(num_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 '
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'(for use with categorical_crossentropy)')
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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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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(512, input_shape=(max_words,)))
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model.add(Activation('relu'))
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model.add(Dropout(0.5))
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model.add(Dense(num_classes))
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model.add(Activation('softmax'))
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model.compile(loss='categorical_crossentropy',
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optimizer='adam',
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metrics=['accuracy'])
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history = 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_split=0.1)
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score = model.evaluate(x_test, y_test,
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batch_size=batch_size, verbose=1)
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print('Test score:', score[0])
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print('Test accuracy:', score[1])
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