前向传播

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import datasets

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
#x:[60k,28,28]
#y:[60k]
(x,y),_ = datasets.mnist.load_data()

# X:[0~255] => [0~1.]
x = tf.convert_to_tensor(x,dtype= tf.float32) / 255.
y = tf.convert_to_tensor(y,dtype= tf.int32)

print(x.shape,y.shape,x.dtype,y.dtype)
print(tf.reduce_max(x),tf.reduce_min(x))#查看最小值和最大值
print(tf.reduce_max(y),tf.reduce_min(y))


train_db = tf.data.Dataset.from_tensor_slices((x,y)).batch(128) #每次加载128个数据集
train_iter = iter(train_db)
sample = next(train_iter)
print('batch:',sample[0].shape,sample[1].shape)
#数据集加载完毕

#[b,784] => [b,512] => [b,128] => [b,10]
#[dim_in,din_out] , [dim_out]
w1 = tf.Variable(tf.random.truncated_normal([784,256],stddev=0.1))#不加方差范围 会梯度爆炸
b1 = tf.Variable(tf.zeros([256]))

w2 = tf.Variable(tf.random.truncated_normal([256,128],stddev=0.1))
b2 = tf.Variable(tf.zeros([128]))

w3 = tf.Variable(tf.random.truncated_normal([128,10],stddev=0.1))
b3 = tf.Variable(tf.zeros([10]))

lr = 1e-3
for epoch in range(10): #对每个数据集迭代10次
    for step, (x, y) in enumerate(train_db): #外层循环：对128张图片进行一次循环 循环6k张图片
        #x:[128,28,28]
        #y:[128]
        #[b,28,28] => [b,28*28]
        x = tf.reshape(x,[-1,28*28])

        with tf.GradientTape() as type:#记录梯度信息 默认跟踪tf.Variable类型
            #x:[b,28*28]
            # h1 = x@w1 + b1
            #[b,784]@[784,256] + [256] => [b,256] +[256]
            h1 = x@w1 + b1 #自动broadcast
            h1 = tf.nn.relu(h1) #转换为非线性
            h2 = h1@w2 + b2
            h2 = tf.nn.relu(h2)
            out = h2@w3 + b3


            #计算误差
            #out:[b,10]
            #y:[b] => [b,10]
            y_onehot = tf.one_hot(y,depth=10)

            #mse = mean(sum(y-out)^2)
            loss = tf.square(y_onehot - out)
            #mean:scalar
            loss = tf.reduce_mean(loss)

        #计算梯度
        grads = type.gradient(loss,[w1, b1, w2, b2, w3, b3]) #grads返回梯度数组
        #w1 = w1 - lr * w1 grad
        w1.assign_sub(lr * grads[0]) #原地更新 类型保持不变
        b1.assign_sub(lr * grads[1])
        w2.assign_sub(lr * grads[2])
        b2.assign_sub(lr * grads[3])
        w3.assign_sub(lr * grads[4])
        b3.assign_sub(lr * grads[5])

        if step % 100 == 0:
            print(step, 'loss', float(loss))

运算结果：