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Francois 2015-03-28 01:46:10 -07:00
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@ -53,7 +53,7 @@ model.add(Dense(20, 64, init='uniform', activation='tanh'))
model.add(Dropout(0.5))
model.add(Dense(64, 64, init='uniform', activation='tanh'))
model.add(Dropout(0.5))
model.add(Dense(64, 1, init='uniform', activation='sigmoid')
model.add(Dense(64, 1, init='uniform', activation='softmax')
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='mean_squared_error', optimizer=sgd)
@ -106,7 +106,7 @@ from keras.layers.recurrent import LSTM
model = Sequential()
model.add(Embedding(max_features, 256))
model.add(LSTM(256, 128), activation='sigmoid', inner_activation='hard_sigmoid')
model.add(LSTM(256, 128, activation='sigmoid', inner_activation='hard_sigmoid'))
model.add(Dropout(0.5))
model.add(Dense(128, 1))
model.add(Activation('sigmoid'))
@ -152,7 +152,7 @@ model.add(Dropout(0.5))
model.add(Repeat(max_caption_len))
# the GRU below returns sequences of max_caption_len vectors of size 256 (our word embedding size)
model.add(GRU(256, 256), return_sequences=True)
model.add(GRU(256, 256, return_sequences=True))
model.compile(loss='mean_squared_error', optimizer='rmsprop')
@ -164,9 +164,9 @@ model.fit(images, captions, batch_size=16, nb_epoch=100)
```
In the examples folder, you will find example models for real datasets:
- CIFAR10 small images classification: convnet with realtime data augmentation
- IMDB movie reviews: sentiment classification
- Reuters newswires: topic classification
- CIFAR10 small images classification: convnet with realtime data augmentation
- IMDB movie reviews: sentiment classification
- Reuters newswires: topic classification
## Warning