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39 lines
873 B
Python
39 lines
873 B
Python
import numpy as np
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import keras
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from keras.layers import Input, Dense
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from keras.models import Model
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from keras.optimizers import SGD
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import time
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inputs = Input(shape=(2,))
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x = Dense(5000, activation='sigmoid')(inputs)
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x = Dense(5000, activation='sigmoid')(x)
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x = Dense(5000, activation='sigmoid')(x)
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predictions = Dense(1, activation='sigmoid')(x)
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X = np.array([
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0, 0,
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0, 1,
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1, 0,
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1, 1]).reshape((4,2))
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Y = np.array([0, 1, 1, 0]).reshape((4,1))
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#sgd = SGD(lr=0.01, momentum=0.0, decay=0.0, nesterov=False)
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model = Model(input=inputs, output=predictions)
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model.compile(optimizer='adadelta',
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loss='binary_crossentropy',
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metrics=['accuracy'])
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start = time.time()
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for i in range(10):
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model.fit(X, Y, nb_epoch=200, verbose=0)
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print model.predict(X)
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print model.evaluate(X, Y, verbose=0)
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end = time.time()
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print(end - start)
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