Tensorflow 实现--卷积神经网络（两层）去识别mnist数据集

梦里梦外; 2022-04-17 06:05 257阅读 0赞

import tensorflow as tf
    from tensorflow.examples.tutorials.mnist import input_data
    
    # 定义一个初始化权重的函数
    def weight_variables(shape):
        w = tf.Variable(tf.random_normal(shape=shape, mean=0.0, stddev=1.0))
        return w
    
    
    def bias_variables(shape):
        b = tf.Variable(tf.constant(0.0, shape=shape))
        return b
    
    # 定义一个初始化偏置的函数
    
    def model():
        """
        自定义的卷积模型
        :return:
        """
        # 1.建立数据的占位符 x[None, 784] 表示样本数不确定 y_true[None,10] 相应的目标值也不确定
        with tf.variable_scope("data"):
            x = tf.placeholder(tf.float32, [None, 784])
            y_true = tf.placeholder(tf.int32, [None, 10])
    
        # 2.第一个卷积层  卷积:5*5*1, 32个，strips=[1,1,1,1] 激活:tf.nn.relu 池化
        with tf.variable_scope("conv1"):
            # 初始化权重
            w_conv1 = weight_variables([5, 5, 1, 32])
            b_conv1 = bias_variables([32])
    
            # 对x进行形状的改变[None, 784]   [None, 28, 28, 1]
            x_reshape = tf.reshape(x, [-1, 28, 28, 1])
    
            # [None, 28, 28, 1] --> [None, 28, 28, 32]
            x_relu1 = tf.nn.relu(tf.nn.conv2d(x_reshape, w_conv1, strides=[1, 1, 1, 1], padding='SAME')+b_conv1)
            # 池化 2*2 ， stripes2 [None, 28, 28, 32]->[None, 14, 14, 32]
            x_pool1 = tf.nn.max_pool(x_relu1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') # padding即是往外填充
    
        # 3.第二个卷积层  卷积:5*5*32, 64个filter，strips=[1,1,1,1] 激活:tf.nn.relu 池化
        with tf.variable_scope("conv2"):
            w_conv2 = weight_variables([5, 5, 32, 64])
            b_conv2 = bias_variables([64])  # 生成64个偏置
    
            # 卷积，激活，池化计算
            # [None, 14, 14, 32] -> [None, 14, 14, 64]
            x_relu2 = tf.nn.relu(tf.nn.conv2d(x_pool1, w_conv2, strides=[1, 1, 1, 1], padding='SAME')+b_conv2)
    
            # 池化 2*2 ， stripes2 [None, 14, 14, 64]->[None, 7, 7, 64]
            x_pool2 = tf.nn.max_pool(x_relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') # padding即是往外填充
        # 4.全连接层 [Non, 7, 7, 64] -> [None, 7*7*64]*[7*7*64,10] + [10] = [None, 10]
        with tf.variable_scope('quanlianjie'):
    
            # 随机初始化权重和偏置
            w_fc = weight_variables([7*7*64, 10])
    
            b_fc = bias_variables([10])
    
            # 修改形状[None,7,7,64] -> [None, 7*7*64]
            x_fc_reshape = tf.reshape(x_pool2, [-1, 7*7*64])
    
            # 进行矩阵运算得出每个样本的10个结果
            y_predict = tf.matmul(x_fc_reshape, w_fc) + b_fc
    
        return x, y_true, y_predict
    
    
    def conv_fc():
        # 获取真实数据
        mnist = input_data.read_data_sets("./data/mnist/input_data/", one_hot=True)
    
        # 定义模型，得出输出
        x, y_true, y_predict = model()
        # 3.求出所有样本的损失，然后求平均值
        with tf.variable_scope("soft_cross"):
            # 求平均交叉熵损失
            loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_true, logits=y_predict))
    
        # 4.梯度下降求出损失
        with tf.variable_scope("optimizer"):
            train_op = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
    
        # 5.计算准确率
        with tf.variable_scope("acc"):
            equal_list = tf.equal(tf.arg_max(y_true, 1), tf.arg_max(y_predict, 1))
    
            # equal_list None个样本 [1, 0, 1, 0 ......]
            accuracy = tf.reduce_mean(tf.cast(equal_list, tf.float32))
    
        #定义一个初始化变量op
        init_op = tf.global_variables_initializer()
    
        # 开启一个会话
        with tf.Session() as sess:
            sess.run(init_op)
    
            # 循环去训练
            for i in range(1000):
                mnist_x, mnist_y = mnist.train.next_batch(50)
                # 运行train_op训练
                sess.run(train_op, feed_dict={x: mnist_x, y_true: mnist_y})
    
                print("训练第%d步，准确率为:%f" % (i, sess.run(accuracy, feed_dict={x: mnist_x, y_true: mnist_y})))
    
        return None
    
    
    if __name__ == '__main__':
        conv_fc()