%pylab inline
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.optimizers import SGD
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.layers.embeddings import Embedding
from keras.layers.recurrent import LSTM
Populating the interactive namespace from numpy and matplotlib
hr="-"*120
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)
score
--------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-3-c6952c64fd1e> in <module>() 13 model.compile(loss='mean_squared_error', optimizer=sgd) 14 ---> 15 model.fit(X_train, y_train, nb_epoch=20, batch_size=16) 16 score = model.evaluate(X_test, y_test, batch_size=16) 17 NameError: name 'X_train' is not defined
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)
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)
--------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-6-2e6a879537ab> in <module>() 25 model.compile(loss='categorical_crossentropy', optimizer=sgd) 26 ---> 27 model.fit(X_train, Y_train, batch_size=32, nb_epoch=1) NameError: name 'X_train' is not defined
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)
--------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-7-54c18582a8b8> in <module>() 1 model = Sequential() ----> 2 model.add(Embedding(max_features, 256)) 3 model.add(LSTM(256, 128, activation='sigmoid', inner_activation='hard_sigmoid')) 4 model.add(Dropout(0.5)) 5 model.add(Dense(128, 1)) NameError: name 'Embedding' is not defined
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)