def step(self, x, states):
prev_output = states[0]
B_U = states[1]
B_W = states[2]
h = ternarize_dot(x * B_W, self.W) + self.b
output = self.activation(h + ternarize_dot(prev_output * B_U, self.U))
return output, [output]
def step(self, inputs, states):
if 0 < self.dropout < 1:
h = ternarize_dot(inputs * states[1], self.kernel)
else:
h = ternarize_dot(inputs, self.kernel)
if self.bias is not None:
h = K.bias_add(h, self.bias)
prev_output = states[0]
if 0 < self.recurrent_dropout < 1:
prev_output *= states[2]
output = h + ternarize_dot(prev_output, self.recurrent_kernel)
if self.activation is not None:
output = self.activation(output)
# Properly set learning phase on output tensor.
if 0 < self.dropout + self.recurrent_dropout:
output._uses_learning_phase = True
return output, [output]