模型 windows训练 linux部署,Tensorflow模型保存与加载

一.保存模型

所谓的模型保存，也就是冻结(freeze)模型,将该模型的图结构和该模型的权重固化到一起。

二.加载模型

在恢复模型的时候，通过get_tensor_by_name获得模型中的变量，然后对变量进行赋值。

三.代码实例

Demo1:单纯地训练，不生成模型

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

import tensorflow as tf

import numpy as np

with tf.variable_scope('Placeholder'):

inputs_placeholder = tf.placeholder(tf.float32,name = 'inputs_placeholder',shape = [None,10])

labels_placeholder = tf.placeholder(tf.float32,name = 'labels_placeholder',shape = [None,1])

with tf.variable_scope('NN'):

W1 = tf.get_variable('W1',shape = [10,1],initializer = tf.random_normal_initializer(stddev = 1e-1))

b1 = tf.get_variable('b1',shape = [1],initializer = tf.constant_initializer(0.1))

W2 = tf.get_variable('W2',shape = [10,1],initializer = tf.random_normal_initializer(stddev = 1e-1))

b2 = tf.get_variable('b2',shape = [1],initializer = tf.constant_initializer(0.1))

a1 = tf.nn.relu(tf.matmul(inputs_placeholder,W1) + b1)

a2 = tf.nn.relu(tf.matmul(inputs_placeholder,W2) + b2)

y = tf.div(tf.add(a1,a2),2)

with tf.variable_scope('Loss'):

loss = tf.red