77 lines
2.6 KiB
Python
77 lines
2.6 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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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='adam')
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# import cPickle
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# model = cPickle.load(open('testsave.m.pkl'))
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for v in range(3):
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for sa in [True, False]:
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for vs in [0, 0.1]:
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print('='*40)
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print('v:%d, sa:%r, vs:%f' % (v, sa, vs))
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print("Training...")
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model.fit(X_train, Y_train, nb_epoch=2, batch_size=batch_size, verbose=v, show_accuracy=sa, validation_split=vs)
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score = model.evaluate(X_test, Y_test, batch_size=batch_size, verbose=v, show_accuracy=sa)
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print('Test score:', score)
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classes = model.predict_classes(X_test, batch_size=batch_size, verbose=v)
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acc = np_utils.accuracy(classes, y_test)
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print('Test accuracy:', acc)
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# model.save('testsave.m')
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