lenet5-forward

#coding:utf-8
import tensorflow as tf
IMAGE_SIZE = 28
NUM_CHANNELS = 1
CONV1_SIZE = 5
CONV1_KERNEL_NUM = 32
CONV2_SIZE = 5
CONV2_KERNEL_NUM = 64
FC_SIZE = 512
OUTPUT_NODE = 10

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 conv2d(x,w):  
    return tf.nn.conv2d(x, w, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):  
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') 

def forward(x, train, regularizer):
    conv1_w = get_weight([CONV1_SIZE, CONV1_SIZE, NUM_CHANNELS, CONV1_KERNEL_NUM], regularizer) 
    conv1_b = get_bias([CONV1_KERNEL_NUM]) 
    conv1 = conv2d(x, conv1_w) 
    relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_b)) 
    pool1 = max_pool_2x2(relu1) 

    conv2_w = get_weight([CONV2_SIZE, CONV2_SIZE, CONV1_KERNEL_NUM, CONV2_KERNEL_NUM],regularizer) 
    conv2_b = get_bias([CONV2_KERNEL_NUM])
    conv2 = conv2d(pool1, conv2_w) 
    relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_b))
    pool2 = max_pool_2x2(relu2)

    pool_shape = pool2.get_shape().as_list() 
    nodes = pool_shape[1] * pool_shape[2] * pool_shape[3] 
    reshaped = tf.reshape(pool2, [pool_shape[0], nodes]) 

    fc1_w = get_weight([nodes, FC_SIZE], regularizer) 
    fc1_b = get_bias([FC_SIZE]) 
    fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_w) + fc1_b) 
    if train: fc1 = tf.nn.dropout(fc1, 0.5)

    fc2_w = get_weight([FC_SIZE, OUTPUT_NODE], regularizer)
    fc2_b = get_bias([OUTPUT_NODE])
    y = tf.matmul(fc1, fc2_w) + fc2_b
    return y 

标签：lenet5,get,regularizer,tf,shape,NUM,forward,SIZE
来源： https://www.cnblogs.com/sqm724/p/13549900.html