使用Tensorflow实现CNN进行MNIST数字识别

迷南。 2022-06-05 05:29 212阅读 0赞

from __future__ import print_function
    import tensorflow as tf
    from tensorflow.examples.tutorials.mnist import input_data
    # number 1 to 10 data
    mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
    
    def compute_accuracy(v_xs, v_ys):#返回准确度
        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):#定义weight
        initial = tf.truncated_normal(shape, stddev=0.1)
        return tf.Variable(initial)
    
    def bias_variable(shape):#定义bias
        initial = tf.constant(0.1, shape=shape)
        return tf.Variable(initial)
    
    def conv2d(x, W):#定义卷积层
        # stride [1, x_movement, y_movement, 1]
        # Must have strides[0] = strides[3] = 1
        return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
    
    def max_pool_2x2(x):#定义池化层
        # stride [1, x_movement, y_movement, 1]
        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])/255.   # 28x28
    ys = tf.placeholder(tf.float32, [None, 10])
    keep_prob = tf.placeholder(tf.float32)
    x_image = tf.reshape(xs, [-1, 28, 28, 1])#-1指将所有图片的例子维度不管他，之后再加上所有的维度，然后将xs图片信息转变成28x28的矩阵，1指只有一个通道，即黑白的图片
    # print(x_image.shape) # [n_samples, 28,28,1]
    
    ## conv1 layer ##
    W_conv1 = weight_variable([5,5, 1,32]) # 指筛选器 5x5, 步数 1, 深度 32
    b_conv1 = bias_variable([32])
    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) # output size 28x28x32
    h_pool1 = max_pool_2x2(h_conv1)                                         # output size 14x14x32
    
    ## conv2 layer ##
    W_conv2 = weight_variable([5,5, 32, 64]) # patch 5x5, in size 32, out size 64
    b_conv2 = bias_variable([64])
    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) # output size 14x14x64，激活去线性化
    h_pool2 = max_pool_2x2(h_conv2)                                         # output size 7x7x64
    
    ## fc1 layer ##
    W_fc1 = weight_variable([7*7*64, 1024])
    b_fc1 = bias_variable([1024])
    # [n_samples, 7, 7, 64] ->> [n_samples, 7*7*64]
    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_variable([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]))       # loss
    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
    
    sess = tf.Session()
    # important step
    # tf.initialize_all_variables() no long valid from
    # 2017-03-02 if using tensorflow >= 0.12
    if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:
        init = tf.initialize_all_variables()
    else:
        init = tf.global_variables_initializer()
    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]))