def step(self, cell_previous, hid_previous, train):
ingate = T.dot(hid_previous, self.lstmpar.W_hid_to_ingate) + K.reshape(self.lstmpar.b_ingate,[1,self.num_lstm])
forgetgate = T.dot(hid_previous, self.lstmpar.W_hid_to_forgetgate) + K.reshape(self.lstmpar.b_forgetgate,[1,self.num_lstm])
cell_input = T.dot(hid_previous, self.lstmpar.W_hid_to_cell) + K.reshape(self.lstmpar.b_cell,[1,self.num_lstm])
outgate = T.dot(hid_previous, self.lstmpar.W_hid_to_outgate) + K.reshape(self.lstmpar.b_outgate,[1,self.num_lstm])
# Compute peephole connections
ingate += cell_previous * K.reshape(self.lstmpar.W_cell_to_ingate,[1,self.num_lstm])
forgetgate += cell_previous * K.reshape(self.lstmpar.W_cell_to_forgetgate,[1,self.num_lstm])
# Apply nonlinearities
ingate = K.sigmoid(ingate)
forgetgate = K.sigmoid(forgetgate)
cell_input = K.tanh(cell_input)
# Compute new cell value
cell = forgetgate * cell_previous + ingate * cell_input
cell_bn = self.bn.set_output(cell,train=train)
outgate += cell_bn * K.reshape(self.lstmpar.W_cell_to_outgate,[1,self.num_lstm])
outgate = K.sigmoid(outgate)
# Compute new hidden unit activation
if self.use_th:
hid = outgate * K.tanh(cell_bn)
else:
hid = outgate * cell_bn
return [cell_bn, hid]
def step(self, cell_previous, hid_previous, train):
ingate = T.dot(hid_previous, self.lstmpar.W_hid_to_ingate) + K.reshape(
self.lstmpar.b_ingate, [1, self.num_lstm])
forgetgate = T.dot(hid_previous,
self.lstmpar.W_hid_to_forgetgate) + K.reshape(
self.lstmpar.b_forgetgate, [1, self.num_lstm])
cell_input = T.dot(hid_previous,
self.lstmpar.W_hid_to_cell) + K.reshape(
self.lstmpar.b_cell, [1, self.num_lstm])
outgate = T.dot(hid_previous,
self.lstmpar.W_hid_to_outgate) + K.reshape(
self.lstmpar.b_outgate, [1, self.num_lstm])
# Compute peephole connections
ingate += cell_previous * K.reshape(self.lstmpar.W_cell_to_ingate,
[1, self.num_lstm])
forgetgate += cell_previous * K.reshape(
self.lstmpar.W_cell_to_forgetgate, [1, self.num_lstm])
# Apply nonlinearities
ingate = K.sigmoid(ingate)
forgetgate = K.sigmoid(forgetgate)
cell_input = K.tanh(cell_input)
# Compute new cell value
cell = forgetgate * cell_previous + ingate * cell_input
cell_bn = self.bn.set_output(cell, train=train)
outgate += cell_bn * K.reshape(self.lstmpar.W_cell_to_outgate,
[1, self.num_lstm])
outgate = K.sigmoid(outgate)
# Compute new hidden unit activation
if self.use_th:
hid = outgate * K.tanh(cell_bn)
else:
hid = outgate * cell_bn
return [cell_bn, hid]
def tanh(x):
return K.tanh(x)
def tanh( x ):
return K.tanh( x )