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Tensorflow 实现简单CNN 数字识别

Tensorflow 实现简单CNN 数字识别

作者: 想飞的大兔子 | 来源:发表于2018-03-23 14:08 被阅读0次

    卷积的好处是,不管图片尺寸如何,我们需要训练的权值数量只和卷积核大小、卷积核数量有关。
    每个卷积层包含三个部分:卷积、池化和非线性激活函数

    __author__ = 'ding'
    '''
    Tensorflow 实现简单CNN
    '''
    import tensorflow as tf
    from tensorflow.examples.tutorials.mnist import input_data
    
    mnist = input_data.read_data_sets('./MNIST_data', one_hot=True)
    sess = tf.InteractiveSession()
    
    
    # 初始化CNN的权重
    def weight_variable(shape):
        initial = tf.truncated_normal(shape=shape, stddev=0.1)
        return tf.Variable(initial)
    
    
    # 初始化CNN的偏置
    def bias_variable(shape):
        initial = tf.constant(0.1, shape=shape)
        return tf.Variable(initial)
    
    
    # 卷积层定义
    def conv2d(x, W):
        return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
    
    
    # 池化层定义
    def max_pool_2x2(x):
        return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
    
    
    # 定义输入的placeholder
    # x_image 使用reshape将1*784的形式转换成原始形式28*28  -1代表样本数量不固定,1代表颜色通道数量
    x = tf.placeholder(tf.float32, [None, 784])
    y_ = tf.placeholder(tf.float32, [None, 10])
    x_image = tf.reshape(x, [-1, 28, 28, 1])
    
    # 定义第一层卷积层
    W_conv1 = weight_variable([5, 5, 1, 32])
    b_conv1 = bias_variable([32])
    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
    h_pool1 = max_pool_2x2(h_conv1)
    
    # 第二层卷积层
    W_conv2 = weight_variable([5, 5, 32, 64])
    b_conv2 = bias_variable([64])
    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])
    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)
    
    # dropout层 用来减少过拟合
    keep_prob = tf.placeholder(tf.float32)
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
    
    # 将dropout层输出连接一个softmax层 得到概率输出
    W_fc2 = weight_variable([1024, 10])
    b_fc2 = bias_variable([10])
    y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
    
    # 定义损失函数
    cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv), reduction_indices=[1]))
    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
    
    correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    
    init = tf.global_variables_initializer()
    sess.run(init)
    
    for i in range(20000):
        batch = mnist.train.next_batch(50)
        if i % 100 == 0:
            train_accuracy = sess.run(accuracy, feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.1})
            print('step %d, training accuracy %g' % (i, train_accuracy))
    
        sess.run(train_step, feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
    print('test accuracy %g' % sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
    

    利用CNN网络来进行图像识别 准确率达到99.2% 比之前的简单的神经网络识别率要高出不少。

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