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| from keras.models import Sequential | |
| from keras.layers import Dense | |
| x, y = ... | |
| x_val, y_val = ... | |
| # 1-dimensional MSE linear regression in Keras | |
| model = Sequential() | |
| model.add(Dense(1, input_dim=x.shape[1])) | |
| model.compile(optimizer='rmsprop', loss='mse') | |
| model.fit(x, y, nb_epoch=10, validation_data=(x_val, y_val)) | |
| # 2-class logistic regression in Keras | |
| model = Sequential() | |
| model.add(Dense(1, activation='sigmoid', input_dim=x.shape[1])) | |
| model.compile(optimizer='rmsprop', loss='binary_crossentropy') | |
| model.fit(x, y, nb_epoch=10, validation_data=(x_val, y_val)) | |
| # logistic regression with L1 and L2 regularization | |
| from keras.regularizers import l1l2 | |
| reg = l1l2(l1=0.01, l2=0.01) | |
| model = Sequential() | |
| model.add(Dense(1, activation='sigmoid', W_regularizer=reg, input_dim=x.shape[1])) | |
| model.compile(optimizer='rmsprop', loss='binary_crossentropy') | |
| model.fit(x, y, nb_epoch=10, validation_data=(x_val, y_val)) |
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