def call(self, _inputs, _states, training=None):
# print(_inputs.shape, _states[0].shape)
h_tm1_c = _states[0][:, :self.units] # the center of the last state
h_tm1_r = _states[0][:, self.units:self.units *
2] # the radius of the last state
h_tm1_e = _states[0][:,
self.units * 2:] # the errors of the last state
inputs_c = _inputs[:, :self.input_dim] # the center of the inputs
inputs_r = _inputs[:, self.input_dim:self.input_dim *
2] # the radius of the inputs
inputs_e = _inputs[:,
self.input_dim * 2:] # the errors of the last state
h_tm1 = AI(h_tm1_c, h_tm1_r, h_tm1_e, False)
inputs = AI(inputs_c, inputs_r, inputs_e, False)
matrix_inner = AI(h_tm1_c, h_tm1_r, h_tm1_e, False)
# print(inputs_c, inputs_r, inputs_e)
if self.implementation == 1:
raise NotImplementedError()
else:
# inputs projected by all gate matrices at once
inputs.matmul(self.kernel)
if self.use_bias:
# biases: bias_z_i, bias_r_i, bias_h_i
inputs.bias_add(self.input_bias)
x_z = inputs[:, :self.units]
x_r = inputs[:, self.units:2 * self.units]
x_h = inputs[:, 2 * self.units:]
if self.reset_after:
# hidden state projected by all gate matrices at once
matrix_inner.matmul(self.recurrent_kernel)
if self.use_bias:
matrix_inner.bias_add(self.recurrent_bias)
else:
# hidden state projected separately for update/reset and new
matrix_inner.matmul(self.recurrent_kernel[:, :2 * self.units])
recurrent_z = matrix_inner[:, :self.units]
recurrent_r = matrix_inner[:, self.units:2 * self.units]
z = x_z + recurrent_z
# z.activation(self.recurrent_activation)
r = x_r + recurrent_r
r.activation(self.recurrent_activation)
if self.reset_after:
recurrent_h = r * matrix_inner[:, 2 * self.units:]
else:
recurrent_h = r * h_tm1
recurrent_h.matmul(self.recurrent_kernel[:, 2 * self.units:])
hh = x_h + recurrent_h
# hh.activation(self.activation)
# previous and candidate state mixed by update gate
h = z.GRU_merge1(self.recurrent_activation, h_tm1, hh, self.activation)
h_state = h.to_state()
return h_state, [h_state]
def cell(h_tm1_c, h_tm1_r, h_tm1_e, inputs_c, inputs_r, inputs_e):
h_tm1 = AI(h_tm1_c, h_tm1_r, h_tm1_e, False)
inputs = AI(inputs_c, inputs_r, inputs_e, False)
matrix_inner = AI(h_tm1_c, h_tm1_r, h_tm1_e, False)
# print(inputs_c, inputs_r, inputs_e)
if self.implementation == 1:
raise NotImplementedError()
else:
# inputs projected by all gate matrices at once
inputs.matmul(self.kernel)
if self.use_bias:
# biases: bias_z_i, bias_r_i, bias_h_i
inputs.bias_add(self.input_bias)
x_z = inputs[:, :self.units]
x_r = inputs[:, self.units:2 * self.units]
x_h = inputs[:, 2 * self.units:]
if self.reset_after:
# hidden state projected by all gate matrices at once
matrix_inner.matmul(self.recurrent_kernel)
if self.use_bias:
matrix_inner.bias_add(self.recurrent_bias)
else:
# hidden state projected separately for update/reset and new
matrix_inner.matmul(self.recurrent_kernel[:, :2 *
self.units])
recurrent_z = matrix_inner[:, :self.units]
recurrent_r = matrix_inner[:, self.units:2 * self.units]
z = x_z + recurrent_z
# z.activation(self.recurrent_activation)
r = x_r + recurrent_r
r.activation(self.recurrent_activation)
if self.reset_after:
recurrent_h = r * matrix_inner[:, 2 * self.units:]
else:
recurrent_h = r * h_tm1
recurrent_h.matmul(self.recurrent_kernel[:,
2 * self.units:])
hh = x_h + recurrent_h
# hh.activation(self.activation)
# previous and candidate state mixed by update gate
h = z.GRU_merge1(self.recurrent_activation, h_tm1, hh,
self.activation)
return h
