【深度学习】tensorflow 卷积神经网络 实现手写数字识别

激活函数：

tf.nn.relu(features, name=None)

features:卷积后加上偏置的结果
return:结果

卷积层：

tf.nn.conv2d(input, filter, strides=, padding=, name=None)
计算给定4-D
input和filter张量的2维卷积
input：给定的输入张量，具有[batch,heigth,width,channel]，类型为float32,64
**filter：**指定过滤器的大小，[filter_height, filter_width,
in_channels, out_channels]
strides： strides = [1, stride, stride, 1],步长
padding：“SAME”, “VALID”，使用的填充算法的类型，
使用“SAME”。其中”VALID”表示滑动超出部分舍弃，
**“SAME”**表示填充，使得变化后height,width一样大

池化：

tf.nn.max_pool(value, ksize=, strides=, padding=,name=None)
输入上执行最大池数
value: 4-D Tensor形状[batch, height, width, channels]
**ksize:**池化窗口大小，[1, ksize, ksize, 1]
**strides:**步长大小，[1,strides,strides,1]
padding:“SAME”, “VALID”，使用的填充算法的类型，
使用“SAME”

def weight_variables(shape):
    w = tf.Variable(tf.random_normal(shape=shape, mean=0.0, stddev=1.0))
    return w


# 定义一个初始化偏置的函数
def bias_variables(shape):
    b = tf.Variable(tf.constant(0.0, shape=shape))
    return b


def model():
    """
    自定义的卷积模型
    :return:
    """
    # 1、准备数据的占位符 x [None, 784]  y_true [None, 10]
    with tf.variable_scope("data"):
        x = tf.placeholder(tf.float32, [None, 784])

        y_true = tf.placeholder(tf.int32, [None, 10])

    # 2、一卷积层 卷积: 5*5*1，32个，strides=1 激活: tf.nn.relu 池化
    with tf.variable_scope("conv1"):
        # 随机初始化权重, 偏置[32]
        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 ,strides2 [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")

    # 3、二卷积层卷积: 5*5*32，64个filter，strides=1 激活: tf.nn.relu 池化：
    with tf.variable_scope("conv2"):
        # 随机初始化权重,  权重：[5, 5, 32, 64]  偏置[64]
        w_conv2 = weight_variables([5, 5, 32, 64])

        b_conv2 = bias_variables([64])

        # 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, strides 2, [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")

    # 4、全连接层 [None, 7, 7, 64]--->[None, 7*7*64]*[7*7*64, 10]+ [10] =[None, 10]
    with tf.variable_scope("conv2"):

        # 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():
    # 获取真实的数据
    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()

    # 进行交叉熵损失计算
    # 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(0.0001).minimize(loss)

    # 5、计算准确率
    with tf.variable_scope("acc"):
        equal_list = tf.equal(tf.argmax(y_true, 1), tf.argmax(y_predict, 1))

        # equal_list  None个样本   [1, 0, 1, 0, 1, 1,..........]
        accuracy = tf.reduce_mean(tf.cast(equal_list, tf.float32))

    # 定义一个初始化变量的op
    init_op = tf.global_variables_initializer()

    # 开启回话运行
    with tf.Session() as sess:
        sess.run(init_op)

        # 循环去训练
        for i in range(1000):

            # 取出真实存在的特征值和目标值
            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()