model.py 文件源码

def initilizae_layer(self, name_scope, row_size, col_size, activation_function, last_hidden):
        # Bulid layer of the network with weights and biases
        weights = get_scope_variable(name_scope=name_scope, var="weights",
                                     shape=[row_size, col_size],
                                     initializer=tf.truncated_normal_initializer(mean=0.0, stddev=1.0 / np.sqrt(
                                         float(row_size))))
        biases = get_scope_variable(name_scope=name_scope, var='biases', shape=[col_size],
                                    initializer=tf.constant_initializer(0.0))

        self.weights_all.append(weights)
        self.biases_all.append(biases)
        variable_summaries(weights)
        variable_summaries(biases)
        with tf.variable_scope(name_scope) as scope:
            input = tf.matmul(last_hidden, weights) + biases
            if activation_function == None:
                output = input
            else:
                output = activation_function(input, name='output')
        self.inputs.append(input)
        self.hidden.append(output)
        return output