class sae(object):
def __init__(self, n_sample=80, n_class=2, hidden=[200,200]):
self.n_in = n_sample
self.n_out = n_class
self.hidden = hidden
def train(self, train_x, train_y, batch_size=10, pre_epoch=10,
fine_epoch=50, dropout=0.5, regularizer_l2=1e-3, split=0.0):
self.batch_size = batch_size
self.p = dropout
self.l2 = regularizer_l2
struct = [self.n_in] + self.hidden
# nb_epoch = pre_epoch
## Pre-training autoencoders
encoder = Sequential()
encoder.add(Dense(self.hidden[0], input_dim=self.n_in, activation='tanh', W_regularizer=l2(self.l2)))
encoder.add(Dropout(self.p))
for n_out in self.hidden[1:]:
encoder.add(Dense(n_out, activation='tanh', W_regularizer=l2(self.l2)))
encoder.add(Dropout(self.p))
decoder = Sequential()
decoder.add(Dense(self.hidden[-2], input_dim=self.hidden[-1], activation='tanh', W_regularizer=l2(self.l2)))
decoder.add(Dropout(self.p))
# for n_in in struct[-(len(self.hidden)+1)::-1]:
for n_in in struct[-3::-1]:
decoder.add(Dense(n_in, activation='tanh', W_regularizer=l2(self.l2)))
decoder.add(Dropout(self.p))
autoencoder = AutoEncoder(encoder=encoder, decoder=decoder, output_reconstruction=True)
ae = Sequential()
ae.add(autoencoder)
pre_optimizer = Adagrad(lr=0.01, epsilon=1e-6)
ae.compile(loss='mse', optimizer=pre_optimizer)
ae.fit(train_x, train_x, nb_epoch=pre_epoch, batch_size=self.batch_size)
## fine-tuning
print 'Fine-tuning the network:'
self.model = Sequential()
self.model.add(ae.layers[0].encoder)
self.model.add(Dense(self.n_out, activation='softmax'))
fine_optimizer = Adagrad(lr=0.01, epsilon=1e-6)
self.model.compile(loss='categorical_crossentropy', optimizer=fine_optimizer)
# self.early_stopping = EarlyStopping(patience=20, monitor='val_loss')
# self.check_point = ModelCheckpoint(filepath='./tmp/weights.hdfs', save_best_only=True)
self.history = History()
# nb_epoch = fine_epoch
self.model.fit(train_x, train_y, nb_epoch=fine_epoch, batch_size=self.batch_size,
validation_split=split,
callbacks=[self.history]) ##self.check_point, ,self.early_stopping
return self.model
def predict(self, x):
return self.model.predict_probs(x)
