2015-04-06 00:01:04 +00:00
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## Examples
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2015-04-06 19:48:26 +00:00
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Here are a few examples to get you started!
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2015-04-06 00:01:04 +00:00
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### Multilayer Perceptron (MLP):
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```python
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from keras.models import Sequential
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from keras.layers.core import Dense, Dropout, Activation
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from keras.optimizers import SGD
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model = Sequential()
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model.add(Dense(20, 64, init='uniform'))
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model.add(Activation('tanh'))
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model.add(Dropout(0.5))
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model.add(Dense(64, 64, init='uniform'))
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model.add(Activation('tanh'))
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model.add(Dropout(0.5))
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model.add(Dense(64, 1, init='uniform'))
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model.add(Activation('softmax'))
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sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
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model.compile(loss='mean_squared_error', optimizer=sgd)
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model.fit(X_train, y_train, nb_epoch=20, batch_size=16)
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score = model.evaluate(X_test, y_test, batch_size=16)
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```
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### Alternative implementation of MLP:
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```python
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model = Sequential()
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model.add(Dense(20, 64, init='uniform', activation='tanh'))
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model.add(Dropout(0.5))
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model.add(Dense(64, 64, init='uniform', activation='tanh'))
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model.add(Dropout(0.5))
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model.add(Dense(64, 1, init='uniform', activation='softmax')
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sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
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model.compile(loss='mean_squared_error', optimizer=sgd)
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```
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### VGG-like convnet:
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```python
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from keras.models import Sequential
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from keras.layers.core import Dense, Dropout, Activation, Flatten
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from keras.layers.convolutional import Convolution2D, MaxPooling2D
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from keras.optimizers import SGD
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model = Sequential()
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model.add(Convolution2D(32, 3, 3, 3, border_mode='full'))
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model.add(Activation('relu'))
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model.add(Convolution2D(32, 32, 3, 3))
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model.add(Activation('relu'))
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model.add(MaxPooling2D(poolsize=(2, 2)))
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model.add(Dropout(0.25))
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model.add(Convolution2D(64, 32, 3, 3, border_mode='full'))
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model.add(Activation('relu'))
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model.add(Convolution2D(64, 64, 3, 3))
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model.add(Activation('relu'))
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model.add(MaxPooling2D(poolsize=(2, 2)))
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model.add(Dropout(0.25))
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2015-04-10 00:41:48 +00:00
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model.add(Flatten())
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2015-04-06 00:01:04 +00:00
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model.add(Dense(64*8*8, 256))
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model.add(Activation('relu'))
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model.add(Dropout(0.5))
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model.add(Dense(256, 10))
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model.add(Activation('softmax'))
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sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
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model.compile(loss='categorical_crossentropy', optimizer=sgd)
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model.fit(X_train, Y_train, batch_size=32, nb_epoch=1)
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```
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### Sequence classification with LSTM:
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```python
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from keras.models import Sequential
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from keras.layers.core import Dense, Dropout, Activation, Embedding
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from keras.layers.recurrent import LSTM
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model = Sequential()
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model.add(Embedding(max_features, 256))
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model.add(LSTM(256, 128, activation='sigmoid', inner_activation='hard_sigmoid'))
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model.add(Dropout(0.5))
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model.add(Dense(128, 1))
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model.add(Activation('sigmoid'))
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model.compile(loss='binary_crossentropy', optimizer='rmsprop')
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model.fit(X_train, Y_train, batch_size=16, nb_epoch=10)
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score = model.evaluate(X_test, Y_test, batch_size=16)
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```
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### Architecture for learning image captions with a convnet and a Gated Recurrent Unit:
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(word-level embedding, caption of maximum length 16 words).
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Note that getting this to actually "work" will require using a bigger convnet, initialized with pre-trained weights.
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Displaying readable results will also require an embedding decoder.
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```python
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max_caption_len = 16
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model = Sequential()
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model.add(Convolution2D(32, 3, 3, 3, border_mode='full'))
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model.add(Activation('relu'))
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model.add(Convolution2D(32, 32, 3, 3))
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model.add(Activation('relu'))
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model.add(MaxPooling2D(poolsize=(2, 2)))
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model.add(Convolution2D(64, 32, 3, 3, border_mode='full'))
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model.add(Activation('relu'))
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model.add(Convolution2D(64, 64, 3, 3))
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model.add(Activation('relu'))
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model.add(MaxPooling2D(poolsize=(2, 2)))
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model.add(Convolution2D(128, 64, 3, 3, border_mode='full'))
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model.add(Activation('relu'))
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model.add(Convolution2D(128, 128, 3, 3))
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model.add(Activation('relu'))
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model.add(MaxPooling2D(poolsize=(2, 2)))
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2015-04-10 00:41:48 +00:00
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model.add(Flatten())
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2015-04-06 00:01:04 +00:00
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model.add(Dense(128*4*4, 256))
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model.add(Activation('relu'))
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model.add(Dropout(0.5))
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model.add(Repeat(max_caption_len))
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# the GRU below returns sequences of max_caption_len vectors of size 256 (our word embedding size)
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model.add(GRU(256, 256, return_sequences=True))
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model.compile(loss='mean_squared_error', optimizer='rmsprop')
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# "images" is a numpy array of shape (nb_samples, nb_channels=3, width, height)
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# "captions" is a numpy array of shape (nb_samples, max_caption_len=16, embedding_dim=256)
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# captions are supposed already embedded (dense vectors).
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model.fit(images, captions, batch_size=16, nb_epoch=100)
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```
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In the [examples folder](https://github.com/fchollet/keras/tree/master/examples), you will find example models for real datasets:
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- CIFAR10 small images classification: Convnet with realtime data augmentation
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- IMDB movie review sentiment classification: LSTM over sequences of words
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- Reuters newswires topic classification: Multilayer Perceptron
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