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# from: https://github.com/fchollet/keras/blob/master/examples/reuters_mlp.py
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# Trains and evaluate a simple MLP on the Reuters newswire topic classification task.
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from __future__ import print_function
import numpy as np
np.random.seed(42) # for reproducibility
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from keras.datasets import reuters
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.utils import np_utils
from keras.preprocessing.text import Tokenizer
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max_words = 1000
batch_size = 32
nb_epoch = 5
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print('Loading data...')
(X_train, y_train), (X_test, y_test) = reuters.load_data(nb_words=max_words, test_split=0.2)
print(len(X_train), 'train sequences')
print(len(X_test), 'test sequences')
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nb_classes = np.max(y_train)+1
print(nb_classes, 'classes')
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print('Vectorizing sequence data...')
tokenizer = Tokenizer(nb_words=max_words)
X_train = tokenizer.sequences_to_matrix(X_train, mode='binary')
X_test = tokenizer.sequences_to_matrix(X_test, mode='binary')
print('X_train shape:', X_train.shape)
print('X_test shape:', X_test.shape)
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print('Convert class vector to binary class matrix (for use with categorical_crossentropy)')
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
print('Y_train shape:', Y_train.shape)
print('Y_test shape:', Y_test.shape)
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print('Building model...')
model = Sequential()
model.add(Dense(512, input_shape=(max_words,)))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
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model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
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history = model.fit(X_train, Y_train,
nb_epoch=nb_epoch, batch_size=batch_size,
verbose=1, validation_split=0.1)
score = model.evaluate(X_test, Y_test,
batch_size=batch_size, verbose=1)
print('Test score:', score[0])
print('Test accuracy:', score[1])
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