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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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