forward_model.py 文件源码

def decode(self, input):
        # returns a decoder
        hidden = tf.matmul(input, self.weights["decoder1_weights"]) + self.weights["decoder1_biases"]
        hidden_relu = tf.nn.relu(hidden)

        # output is encoding_size x 1 x small_encoding_size
        # multiheaded_hidden = tf.matmul(input, self.weights["multiheaded1_weights"]) + self.weights["multiheaded1_biases"]
        # multiheaded_hidden = tf.reshape(multiheaded_hidden, [-1, self.arch_params['output_dim'], 1, self.arch_params['small_encoding_dim']])
        # multiheaded_hidden = tf.nn.relu(multiheaded_hidden)
        #
        # h = tf.scan(lambda a,x: tf.batch_matmul(x, self.weights["multiheaded2_weights"]), multiheaded_hidden,
        #            initializer=tf.Variable(tf.constant(0.0, shape=[self.arch_params['output_dim'],1,1])))
        # multiheaded_output = h + self.weights["multiheaded2_biases"]
        # output1 = tf.reshape(multiheaded_output, [-1, self.arch_params['output_dim']])

        output1 = tf.matmul(hidden_relu, self.weights["decoder2_weights"]) + self.weights["decoder2_biases"]
        output = output1
        return output