minist优化模型inference

# -*- coding: utf-8 -*-

import tensorflow as tf

INPUT_NODE = 784
OUTPUT_NODE = 10

LAYER1_NODE = 500


def get_weight_variable(shape,regularizer):
    weights = tf.get_variable('weights',shape,initializer=tf.truncated_normal_initializer(stddev=0.1))
    if regularizer != None:
        tf.add_to_collection('losses',regularizer(weights))
        return weights



#辅助函数
def inference(input_tensor, regularizer):
    with tf.variable_scope('layer1'):
        weights = get_weight_variable([INPUT_NODE,LAYER1_NODE],regularizer)
        biases = tf.get_variable('biases',[LAYER1_NODE],initializer=tf.constant_initializer(0.0))
        layer1 = tf.nn.relu(tf.matmul(input_tensor,weights)+biases)

    with tf.variable_scope('layer2'):
        weights = get_weight_variable([LAYER1_NODE,OUTPUT_NODE],regularizer)
        biases = tf.get_variable('biases',[OUTPUT_NODE],initializer=tf.constant_initializer(0.0))

        layer2 = tf.matmul(layer1,weights)+biases

    return layer2