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July 31, 2015 16:22
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Implementing linear regression in keras
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| """ | |
| Author: Shubhanshu Mishra | |
| Posted this on the keras issue tracker at: https://github.com/fchollet/keras/issues/108 | |
| Implementing a linear regression using Keras. | |
| """ | |
| from keras.models import Sequential | |
| from keras.layers.core import Dense, Activation | |
| model = Sequential() | |
| model.add(Dense(2,1,init='uniform', activation='linear')) | |
| model.compile(loss='mse', optimizer='rmsprop') | |
| model.fit(X_train, y_train, nb_epoch=1000, batch_size=16,verbose=0) | |
| model.fit(X_train, y_train, nb_epoch=1, batch_size=16,verbose=1) | |
| score = model.evaluate(X_test, y_test, batch_size=16) |
@scottlawsonbc This will work if you reformat the Dense layer to the new format.
Hello, I'm knew to Keras. Can you show me how to write it with the new Dense format? Thanks!
@qks1lver it should be model.add(Dense(1, input_dim=2)) for the new api I think assuming 1 output from two input
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No longer works on the latest version of Keras