unitary_rnn_cell_modern.py 文件源码

def __call__(self, inputs, state, scope=None ):
        zero_initer = tf.constant_initializer(0.)
        with tf.variable_scope(scope or type(self).__name__):

            #nick there are these two matrix multiplications and they are used to convert regular input sizes to complex outputs -- makes sense -- we can further modify this for lstm configurations
            mat_in = tf.get_variable('W_in', [self.input_size, self.state_size*2])
            mat_out = tf.get_variable('W_out', [self.state_size*2, self.output_size])

            in_proj = tf.matmul(inputs, mat_in)
            in_proj_c = tf.complex( in_proj[:, :self.state_size], in_proj[:, self.state_size:] )
            out_state = modrelu_c( in_proj_c + 
                ulinear_c(state,transform=self.transform),
                tf.get_variable(name='B', dtype=tf.float32, shape=[self.state_size], initializer=zero_initer)
                )
            out_bias = tf.get_variable(name='B_out', dtype=tf.float32, shape=[self.output_size], initializer = zero_initer)
            out = tf.matmul( tf.concat(1,[tf.real(out_state), tf.imag(out_state)] ), mat_out ) + out_bias
        return out, out_state