学习十

卷积神经网络

卷积神经⽹网络 卷积层：定义过滤器器（观察窗⼝口）⼤大⼩小， 步⻓长(移动的像素数量量)1 奇数 1*1， 3*3， 5*5
28，28，1
卷积层：32个filter, 3*3，步⻓长1, p=1
H2 = (28-3+ 2P)/1+1= 28 w2=(28-3+ 2P)/1+1 = 28 [27, 27, 32] relu 池化：[2,2] 2
增加激活函数：增加⽹网络的⾮非线性分割能⼒力力
sigmoid= 1/1+e^-z relu = max(0, x)
卷积层，激活，池化，全连接

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


# 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])

        #对形状进行改变改变
        x_reshape=tf.reshape(x,[-1,28,28,1])
        x_relu1=tf.nn.relu(tf.nn.cov2d(x_reshape,w_conv1,strides=[1,1,1,1],padding="SAME")+b_conv1)
        #池化
        x_pool1=tf.nn.max_pool(x_relu1,ksize=[1,2,2,1],strides=[1,2,2,1],padding="SAME")
    with tf.variable_scope("conv2"):
        w_conv2=weight_variables([5,5,32,64])
        b_conv2=bias_variables([64])

        #卷积 激活 池化计算
        x_relu2=tf.nn.relu(tf.nn.conv2d(x_pool1,w_conv2,strides=[1,1,1,1],padding="SAME")+b_conv2)

        x_pool2 = tf.nn.max_pool(x_relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")

    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()