-
-
Save ShakeDewan/7608edc4bd1df66b1ce29a30894a40a5 to your computer and use it in GitHub Desktop.
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| 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)) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment