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Simple XOR learning with keras
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from keras.models import Sequential | |
from keras.layers.core import Dense, Dropout, Activation | |
from keras.optimizers import SGD | |
import numpy as np | |
X = np.array([[0,0],[0,1],[1,0],[1,1]]) | |
y = np.array([[0],[1],[1],[0]]) | |
model = Sequential() | |
model.add(Dense(8, input_dim=2)) | |
model.add(Activation('tanh')) | |
model.add(Dense(1)) | |
model.add(Activation('sigmoid')) | |
sgd = SGD(lr=0.1) | |
model.compile(loss='binary_crossentropy', optimizer=sgd) | |
model.fit(X, y, show_accuracy=True, batch_size=1, nb_epoch=1000) | |
print(model.predict_proba(X)) | |
""" | |
[[ 0.0033028 ] | |
[ 0.99581173] | |
[ 0.99530098] | |
[ 0.00564186]] | |
""" |
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