8_最简单的神经网络_mnist 模糊数字图片识别 0-9

冷不防 2023-07-07 04:56 12阅读 0赞

### 文章目录 ###

*  一、数据集mnist下载
 *  二、0-9分类训练

说明:

*  本文主要是将TensorFlow例程拿过来解释了一下
 *  首先是数据的下载,注意下载的时候可能会出错,但是网上有很多其他的下载网址可用
 *  1 加载mnist数据集  
    `mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)`
 *  2 清除图形堆栈 `tf.reset_default_graph()`
 *  3 参数和输入输出占位 `tf.Variable和tf.placeholder`
 *  4 配置前向输出 `tf.nn.softmax(tf.matmul(x, W) + b)`
 *  5 配置交叉熵(或loss)与梯度下降`tf.reduce_mean, tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)`
 *  6 设置训练的参数设置
 *  7 启动训练
 *  8 测试
 *  9 保存训练模型
 *  10 加载训练模型
 *  11 重新测试

# 一、数据集mnist下载 #

[官网有下载但是有出错的情况, 这里给出直接下载][Link 1]

import tensorflow as tf #导入tensorflow库
    from tensorflow.examples.tutorials.mnist import input_data
    import pylab 
    mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)  # 下载并加载数据
     
    # print('训练集:', mnist.train.images) # 训练集
    print('训练集shape:', mnist.train.images.shape)  # 输入数据打印shape: (55000, 784)
    
    im = mnist.train.images[1]
    im = im.reshape(-1, 28)
    pylab.imshow(im)
    pylab.show()
    
    print('测试集shape:', mnist.test.images.shape) # 输入数据打印shape: (10000, 784) 测试集
    print('输入数据打印shape:', mnist.validation.images.shape)
    print("训练数据总数:" , mnist.train.num_examples) # 550 00
    print("测试数据总数:" , mnist.test.num_examples)   # 1w

Extracting MNIST_data/train-images-idx3-ubyte.gz
    Extracting MNIST_data/train-labels-idx1-ubyte.gz
    Extracting MNIST_data/t10k-images-idx3-ubyte.gz
    Extracting MNIST_data/t10k-labels-idx1-ubyte.gz
    训练集shape: (55000, 784)
    测试集shape: (10000, 784)
    输入数据打印shape: (5000, 784)
    训练数据总数: 55000
    测试数据总数: 10000

# 二、0-9分类训练 #

import tensorflow as tf #导入tensorflow库
    from tensorflow.examples.tutorials.mnist import input_data
    import pylab 
    
    # 1 加载数据
    mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
    print("训练数据总数:" , mnist.train.num_examples)
    print(mnist.test.nu)
    
    # 2 函数用于清除默认图形堆栈并重置全局默认图形
    tf.reset_default_graph() 
    
    # 3 tf Graph Input 输入输出占位
    x = tf.placeholder(tf.float32, [None, 784]) # mnist data维度 28*28=784
    y = tf.placeholder(tf.float32, [None, 10]) # 0-9 数字=> 10 classes
    
    # 4 Set model weights 权重占位,
    W = tf.Variable(tf.random_normal([784, 10]))
    b = tf.Variable(tf.zeros([10]))
    
    # 5 前向输出
    pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax分类 # 激活函数,输出为总输出的概率, 总和为1,
    
    # 6 生成的pred与样本标签y进行一次交叉熵运算, 然后在取平均值, 注意这里的交叉熵运行在上一节已经提到过
    # 将这个结果作为一次正向传播的误差, 通过梯度下降的优化方法找到能够使这个误差最小化的b,w偏移量
    # 更细b,w参数, 使其调整为合适的参数
    cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1))
    
    # 7 使用梯度下降优化器
    learning_rate = 0.01 # 学习率, 对于梯度下降法的输入参数
    optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
    
    # 8 训练参数设置
    training_epochs = 25
    batch_size      = 100
    display_step    = 1
    saver           = tf.train.Saver()
    model_path      = "log/521model.ckpt"
    
    # 9 启动session
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())# Initializing OP
    
        # 启动循环开始训练
        for epoch in range(training_epochs):  # 循环25次 25*550次循环,每次循环喂100 
            avg_cost = 0.
            total_batch = int(mnist.train.num_examples/batch_size) # total_batch=55000/100=550 一次训练
    
            # 注意下面的for是训练一遍数据的
            # 遍历全部数据集
            for _1 in range(total_batch):  # 1 - 550
                batch_xs, batch_ys = mnist.train.next_batch(batch_size) # 拿出来100个喂给训练兽
                # Run optimization op (backprop) and cost op (to get loss value)
                _2, c = sess.run([optimizer, cost], feed_dict={ x: batch_xs,
                                                              y: batch_ys})
                # Compute average loss 计算平均loss值,
                avg_cost += (c / total_batch)
            # 显示训练中的详细信息
            if (epoch+1) % display_step == 0:
                print ("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost))
    
        print( " Finished!")
    
        # 测试 model
        correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
        # 计算准确率
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
        print ("Accuracy:", accuracy.eval({ x: mnist.test.images, y: mnist.test.labels}))
    
        # Save model weights to disk
        save_path = saver.save(sess, model_path)
        print("Model saved in file: %s" % save_path)

Epoch: 0001 cost= 8.740313732
    ...
    Epoch: 0025 cost= 0.849729373
     Finished!
    Accuracy: 0.8294
    Model saved in file: log/521model.ckpt

''' #读取模型 print("Starting 2nd session...") with tf.Session() as sess: sess.run(tf.global_variables_initializer()) saver.restore(sess, model_path) # 测试 model correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) # 计算准确率 accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) print ("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})) output = tf.argmax(pred, 1) batch_xs, batch_ys = mnist.train.next_batch(2) outputval,predv = sess.run([output,pred], feed_dict={x: batch_xs}) print(outputval,predv,batch_ys) im = batch_xs[0] im = im.reshape(-1,28) pylab.imshow(im) pylab.show() im = batch_xs[1] im = im.reshape(-1,28) pylab.imshow(im) pylab.show() '''

[Link 1]: https://download.csdn.net/download/qq_32460819/12175929