Implementing linear regression in keras
| """ | |
| 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) |
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@scottlawsonbc This will work if you reformat the Dense layer to the new format. |
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Hello, I'm knew to Keras. Can you show me how to write it with the new Dense format? Thanks! |
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@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