forward.py

import tensorflow as tf

INPUT_NODE = 256    # 输入层
OUTPUT_NODE = 10    # 输出层
LAYER1_NODE = 100   # 隐藏层
LAYER2_NODE = 100   # 隐藏层2

def get_weight(shape, regularizer):
    w = tf.Variable(tf.truncated_normal(shape,stddev=0.1))
    if regularizer != None: tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(regularizer)(w))
    return w


def get_bias(shape):  
    b = tf.Variable(tf.zeros(shape))  
    return b
	
def forward(x, regularizer):
    w1 = get_weight([INPUT_NODE, LAYER1_NODE], regularizer)
    b1 = get_bias([LAYER1_NODE])
    y1 = tf.nn.relu(tf.matmul(x, w1) + b1)

    w2 = get_weight([LAYER1_NODE, LAYER2_NODE], regularizer)
    b2 = get_bias([LAYER2_NODE])
    y2 = tf.nn.relu(tf.matmul(y1, w2) + b2)

    w3 = get_weight([LAYER2_NODE, OUTPUT_NODE], regularizer)
    b3 = get_bias([OUTPUT_NODE])
    y = tf.matmul(y2, w3) + b3
    return y