Tensorflow MINIST数据模型的训练，使用

淩亂°似流年 2022-04-18 02:50 311阅读 0赞

在查看本篇文章前，请提前阅读上一章Tensorflow开发环境的搭建

[TensorFlow环境搭建][TensorFlow]

在PyChrem新建项目，并创建python package ： com.test

在根目录下创建 train.py 文件

# coding=utf-8
    # 载入MINIST数据需要的库
    from tensorflow.examples.tutorials.mnist import input_data
    # 保存模型需要的库
    from tensorflow.python.framework.graph_util import convert_variables_to_constants
    from tensorflow.python.framework import graph_util
    # 导入其他库
    import tensorflow as tf
    import cv2
    import numpy as np
    
    # 获取MINIST数据
    mnist = input_data.read_data_sets("MNIST_data", one_hot=True)
    # 创建会话
    sess = tf.InteractiveSession()
    
    # 占位符
    x = tf.placeholder("float", shape=[None, 784], name="Mul")
    y_ = tf.placeholder("float", shape=[None, 10], name="y_")
    # 变量
    W = tf.Variable(tf.zeros([784, 10]), name='x')
    b = tf.Variable(tf.zeros([10]), 'y_')
    
    
    #用于将自定义输入图片反转
    def reversePic(src):
            # 图像反转
        for i in range(src.shape[0]):
            for j in range(src.shape[1]):
                src[i,j] = 255 - src[i,j]
        return src
    
    
    # 权重
    def weight_variable(shape):
        initial = tf.truncated_normal(shape, stddev=0.1)
        return tf.Variable(initial)
    
    
    # 偏差
    def bias_variable(shape):
        initial = tf.constant(0.1, shape=shape)
        return tf.Variable(initial)
    
    
    # 卷积
    def conv2d(x, W):
        return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
    
    
    # 最大池化
    def max_pool_2x2(x):
        return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                              strides=[1, 2, 2, 1], padding='SAME')
    
    
    # 相关变量的创建
    W_conv1 = weight_variable([5, 5, 1, 32])
    b_conv1 = bias_variable([32])
    x_image = tf.reshape(x, [-1, 28, 28, 1])
    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
    h_pool1 = max_pool_2x2(h_conv1)
    W_conv2 = weight_variable([5, 5, 32, 64])
    b_conv2 = bias_variable([64])
    # 激活函数
    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
    h_pool2 = max_pool_2x2(h_conv2)
    W_fc1 = weight_variable([7 * 7 * 64, 1024])
    b_fc1 = bias_variable([1024])
    W_fc2 = weight_variable([1024, 10])
    b_fc2 = bias_variable([10])
    
    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)
    keep_prob = tf.placeholder("float", name='rob')
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
    
    # 用于训练用的softmax函数
    y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2, name='res')
    # 用于训练作完后，作测试用的softmax函数
    y_conv2 = tf.nn.softmax(tf.matmul(h_fc1, W_fc2) + b_fc2, name="final_result")
    
    # 交叉熵的计算，返回包含了损失值的Tensor。
    
    cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))
    # 优化器，负责最小化交叉熵
    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
    
    correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
    # 计算准确率
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
    # 初始化所以变量
    sess.run(tf.global_variables_initializer())
    
    # 保存输入输出，可以为之后用
    tf.add_to_collection('res', y_conv)
    tf.add_to_collection('output', y_conv2)
    tf.add_to_collection('x', x)
    
    # 训练开始
    for i in range(20000):
        batch = mnist.train.next_batch(50)# 每一步迭代加载50个训练样本，然后执行一次train_step
        if i % 100 == 0:
            train_accuracy = accuracy.eval(feed_dict={
                x: batch[0], y_: batch[1], keep_prob: 1.0})
            print("step %d, training accuracy %g" % (i, train_accuracy))
        # run()可以看做输入相关值给到函数中的占位符，然后计算的出结果，这里将batch[0]，给xbatch[1]给y_
        train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
    
    # 将当前图设置为默认图
    graph_def = tf.get_default_graph().as_graph_def()
    # 将上面的变量转化成常量，保存模型为pb模型时需要,注意这里的final_result和前面的y_con2是同名，只有这样才会保存它，否则会报错，
    # 如果需要保存其他tensor只需要让tensor的名字和这里保持一直即可
    output_graph_def = tf.graph_util.convert_variables_to_constants(sess,
                                                                    graph_def, ['final_result'])
    # 保存前面训练后的模型为pb文件
    with tf.gfile.GFile("grf.pb", 'wb') as f:
        f.write(output_graph_def.SerializeToString())
    
    # 用saver 保存模型
    saver = tf.train.Saver()
    saver.save(sess, "model_data/model")
    
    print("test accracy %g" % accuracy.eval(feed_dict={
        x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))

然后鼠标右键，选择run运行即可，训练时间较长，大概需要1-2小时左右，和实际的电脑配置有关

在运行时，会自动下载训练模型，下载的文件保存在MNIST\_data文件夹中

接下来我们使用一张图片进行测试，编写模型恢复脚本 test.py

from tensorflow.examples.tutorials.mnist import input_data
    import tensorflow as tf
    import numpy as np
    import os
    from tensorflow.python.tools.inspect_checkpoint import print_tensors_in_checkpoint_file
    import cv2
    
    mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
    
    #用于将自定义输入图片反转
    def reversePic(src):
            # 图像反转
        for i in range(src.shape[0]):
            for j in range(src.shape[1]):
                src[i,j] = 255 - src[i,j]
        return src
    
    
    
    # pb模型的恢复
    def restore_model_pb():
        sess = tf.Session()
        tf.saved_model.loader.load(sess, ['mytag'], os.getcwd() + '\model2')
        input_x = sess.graph.get_tensor_by_name('input_x:0')
        op = sess.graph.get_tensor_by_name('predict:0')
        print(sess.run(op, feed_dict={input_x: np.expand_dims(mnist.test.images[15], axis=0)}))
        sess.close()
    
    
    # ckpt模型的恢复
    def restore_model_ckpt():
        sess = tf.Session()
        # 加载模型结构
        saver = tf.train.import_meta_graph('model_data/model.meta')
        # 只需要指定目录就可以恢复所有变量信息
        saver.restore(sess, tf.train.latest_checkpoint('model_data'))
        # 直接获取保存的变量
        print(sess.run('x:0'))
    
        input_x = sess.graph.get_tensor_by_name('Mul:0')
        # # 获取需要进行计算的operator
        op = sess.graph.get_tensor_by_name('final_result:0')
    
        # 导入图片，同时灰度化
        im = cv2.imread('pic/e2.jpg', cv2.IMREAD_GRAYSCALE)
        # 反转图像，因为e2.jpg为白底黑字
        im = reversePic(im)
        #cv2.namedWindow("camera", cv2.WINDOW_NORMAL);
        #cv2.imshow('camera', im)
        #cv2.waitKey(0)
    
        # 调整大小
        im = cv2.resize(im, (28, 28), interpolation=cv2.INTER_CUBIC)
        x_img = np.reshape(im, [-1, 784])
    
        # 用上面导入的图片对模型进行测试
        output = sess.run(op, feed_dict={input_x: x_img})
        # print 'the y_con :   ', '\n',output
        print('the predict is : ', np.argmax(output))
    
    
        #print(sess.run(op, feed_dict={input_x: np.expand_dims(mnist.test.images[15], axis=0)}))
        sess.close()
    
    
    #restore_model_pb()
    restore_model_ckpt()

直接运行即可

其中有使用到e2.jpg图片，和训练好的模型，我都已经打包上传，项目源代码下载：

[Tensorflow MINIST数据模型源码][Tensorflow MINIST]

[TensorFlow]: https://blog.csdn.net/cc_want/article/details/83822776
[Tensorflow MINIST]: https://download.csdn.net/download/cc_want/10770408