Tensorflow 实现--卷积神经网络（两层）去识别mnist数据集

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()