• Tensorflow 搭建自己的神经网络(三)


    CNN实现

    #!/usr/bin/env python2
    # -*- coding: utf-8 -*-
    """
    Created on Mon Apr  8 02:46:09 2019
    
    @author: xiexj
    """
    
    import tensorflow as tf
    from tensorflow.examples.tutorials.mnist import input_data 
    
    mnist=input_data.read_data_sets('MNIST_data', one_hot=True)
    
    def compute_accuracy(v_xs, v_ys):
    #    global prediction
        y_pre = sess.run(prediction, feed_dict={xs:v_xs,keep_prob:1})
        correct_prediction = tf.equal(tf.argmax(y_pre,1),tf.argmax(v_ys,1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
        result = sess.run(accuracy, feed_dict={xs:v_xs,ys:v_ys,keep_prob:1})
        return result
    
    def weight_variable(shape):
        initial = tf.truncated_normal(shape, stddev=0.1)
        return tf.Variable(initial)
    
    def bias_vatiable(shape):
        initial = tf.constant(0.1, shape=shape)
        return tf.Variable(initial)
    
    def conv2d(x, W):
        return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME')
    
    def max_pooling_2x2(x):
        return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')
        
    # define placeholder for inputs to network
    xs = tf.placeholder(tf.float32, [None, 784])
    ys = tf.placeholder(tf.float32, [None, 10])
    keep_prob = tf.placeholder(tf.float32)
    x_image = tf.reshape(xs, [-1, 28, 28, 1])# [n_samples, 28,28,1]
    
    ## conv1 layer ##
    W_conv1 = weight_variable([5,5,1,32])
    b_conv1 = bias_vatiable([32])
    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
    h_pool1 = max_pooling_2x2(h_conv1)
    
    ## conv2 layer ##
    W_conv2 = weight_variable([5,5,32,64])
    b_conv2 = bias_vatiable([64])
    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
    h_pool2 = max_pooling_2x2(h_conv2)
    
    ## fc1 layer ##
    W_fc1 = weight_variable([7*7*64, 1024])
    b_fc1 = bias_vatiable([1024])
    h_pool2_flat = tf.reshape(h_pool2, [-1,7*7*64])
    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
    
    ## fc2 layer ##
    W_fc2 = weight_variable([1024, 10])
    b_fc2 = bias_vatiable([10])
    prediction = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
    
    # the error between prediction and real data
    cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),
                                                  reduction_indices=[1]))
    train_step = tf.train.AdamOptimizer(0.0001).minimize(cross_entropy)
    init = tf.global_variables_initializer()
    
    with tf.Session() as sess:
        sess.run(init)
        for i in range(1000):
            batch_xs, batch_ys = mnist.train.next_batch(100)
            sess.run(train_step, feed_dict={xs:batch_xs,ys:batch_ys,keep_prob:0.5})
            if i%50 == 0:
                print(compute_accuracy(mnist.test.images[:1000], mnist.test.labels[:1000]))

    Saver 保存读取

    Tensorflow目前只能保存Varibales,而不能保存框架,所以需要重新定义一下框架,再把Varibales放进来重新学习。

    import tensorflow as tf
    import numpy as np
    
    W = tf.Variable([[1,2,3],[3,4,5]], dtype = tf.float32, name = 'weight')
    b = tf.Variable([[1,2,3]], dtype = tf.float32, name = 'biases')
    
    init = tf.global_variables_initializer()
    
    saver = tf.train.Saver()
    
    with tf.Session() as sess:
        sess.run(init)
        save_path = saver.save(sess, "my_net/save_net.ckpt")
        print("Save to path:",save_path)
    import tensorflow as tf
    import numpy as np
    
    # restore variables
    tf.reset_default_graph()
    
    W = tf.Variable(np.arange(6).reshape((2,3)), dtype=tf.float32, name='weight')
    b = tf.Variable(np.arange(3).reshape((1,3)), dtype=tf.float32, name='biases')
    
    saver = tf.train.Saver()
    
    with tf.Session() as sess:
        saver.restore(sess, "my_net/save_net.ckpt")
        print("weight:", sess.run(W))
        print("biases:", sess.run(b))

    根据教程编码会出现以下错误:NotFoundError: Tensor name “weight_1” not found in checkpoint files mynet/save_net.ckpt 

    添加一行: tf.reset_default_graph() : # 清除默认图的堆栈,并设置全局图为默认图

    原因:
    真正的原因是,我写的代码 保存和加载 在前后进行,在前后两次定义了

    W = tf.Variable(xxx,name="weight")
    

    相当于 在TensorFlow 图的堆栈创建了两次 name = “weight” 的变量,第二个(第n个)的实际 name 会变成 “weight_1” (“weight_n-1”),之后我们在保存 checkpoint 中实际搜索的是 “weight_n-1” 这个变量 而不是 “weight” ,因此就会出错。

    正常场景下,不会保存模型之后,马上加载(或在同一程序中加载),就不会出现这个情况,或者保存完之后 restart kernel (Spyder 中),再进行参数加载。

    参考博客:Tensorflow Saver & restore 以及报错问题 NotFoundError: "x_x" not found in checkpoint

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  • 原文地址:https://www.cnblogs.com/exciting/p/10673336.html
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