keras
2016-05-22 08:27
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Multilayer Perceptron (MLP):
from keras.models import Sequential from keras.layers.core import Dense, Dropout, Activation from keras.optimizers import SGD model = Sequential() model.add(Dense(20, 64, init='uniform')) model.add(Activation('tanh')) model.add(Dropout(0.5)) model.add(Dense(64, 64, init='uniform')) model.add(Activation('tanh')) model.add(Dropout(0.5)) model.add(Dense(64, 2, init='uniform')) model.add(Activation('softmax')) sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True) model.compile(loss='mean_squared_error', optimizer=sgd) model.fit(X_train, y_train, nb_epoch=20, batch_size=16) score = model.evaluate(X_test, y_test, batch_size=16)
Alternative implementation of MLP:
model = Sequential() 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, 2, 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)
VGG-like convnet:
from keras.models import Sequential from keras.layers.core import Dense, Dropout, Activation, Flatten from keras.layers.convolutional import Convolution2D, MaxPooling2D from keras.optimizers import SGD model = Sequential() model.add(Convolution2D(32, 3, 3, 3, border_mode='full')) model.add(Activation('relu')) model.add(Convolution2D(32, 32, 3, 3)) model.add(Activation('relu')) model.add(MaxPooling2D(poolsize=(2, 2))) model.add(Dropout(0.25)) model.add(Convolution2D(64, 32, 3, 3, border_mode='full')) model.add(Activation('relu')) model.add(Convolution2D(64, 64, 3, 3)) model.add(Activation('relu')) model.add(MaxPooling2D(poolsize=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(64*8*8, 256)) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(256, 10)) model.add(Activation('softmax')) sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True) model.compile(loss='categorical_crossentropy', optimizer=sgd) model.fit(X_train, Y_train, batch_size=32, nb_epoch=1)
Sequence classification with LSTM:
from keras.models import Sequential from keras.layers.core import Dense, Dropout, Activation, Embedding 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(Dropout(0.5)) model.add(Dense(128, 1)) model.add(Activation('sigmoid')) model.compile(loss='binary_crossentropy', optimizer='rmsprop') model.fit(X_train, Y_train, batch_size=16, nb_epoch=10) score = model.evaluate(X_test, Y_test, batch_size=16)
max_caption_len = 16 model = Sequential() model.add(Convolution2D(32, 3, 3, 3, border_mode='full')) model.add(Activation('relu')) model.add(Convolution2D(32, 32, 3, 3)) model.add(Activation('relu')) model.add(MaxPooling2D(poolsize=(2, 2))) model.add(Convolution2D(64, 32, 3, 3, border_mode='full')) model.add(Activation('relu')) model.add(Convolution2D(64, 64, 3, 3)) model.add(Activation('relu')) model.add(MaxPooling2D(poolsize=(2, 2))) model.add(Convolution2D(128, 64, 3, 3, border_mode='full')) model.add(Activation('relu')) model.add(Convolution2D(128, 128, 3, 3)) model.add(Activation('relu')) model.add(MaxPooling2D(poolsize=(2, 2))) model.add(Flatten()) model.add(Dense(128*4*4, 256)) model.add(Activation('relu')) 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.compile(loss='mean_squared_error', optimizer='rmsprop') # "images" is a numpy array of shape (nb_samples, nb_channels=3, width, height) # "captions" is a numpy array of shape (nb_samples, max_caption_len=16, embedding_dim=256) # captions are supposed already embedded (dense vectors). model.fit(images, captions, batch_size=16, nb_epoch=100)
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