tensorflow张量限幅 | 易学教程

本篇内容有clip_by_value、clip_by_norm、gradient clipping

1.tf.clip_by_value

a = tf.range(10)
print(a)
# if x<a res=a,else x=x
print(tf.maximum(a,2))
# if x>a,res=a,else x=x
print(tf.minimum(a,8))
# 综合maximum和minimum两个函数的功能，指定上下限
print(tf.clip_by_value(a,2,8))

2.tf.clip_by_norm

# 随机生成一个2行2列的tensor
a = tf.random.normal([2,2],mean=10)
# 打印二范数
print(tf.norm(a))
# 根据新的norm进行放缩
print(tf.clip_by_norm(a,15))
print(tf.norm(tf.clip_by_norm(a,15)))

3.tf.clip_by_global_norm

# gradient clipping为解决梯度下降和梯度消失问题
# 可保证整体向量同时缩放（等倍数）
for g in grads:
    grads,_ = tf.clip_by_global_norm(grads,15)

实测：

import  tensorflow as tf
from    tensorflow import keras
from    tensorflow.keras import datasets, layers, optimizers
import  os

os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
print(tf.__version__)

(x, y), _ = datasets.mnist.load_data()
x = tf.convert_to_tensor(x, dtype=tf.float32) / 50.
y = tf.convert_to_tensor(y)
y = tf.one_hot(y, depth=10)
print('x:', x.shape, 'y:', y.shape)
train_db = tf.data.Dataset.from_tensor_slices((x,y)).batch(128).repeat(30)
x,y = next(iter(train_db))
print('sample:', x.shape, y.shape)
# print(x[0], y[0])



def main():

    # 784 => 512
    w1, b1 = tf.Variable(tf.random.truncated_normal([784, 512], stddev=0.1)), tf.Variable(tf.zeros([512]))
    # 512 => 256
    w2, b2 = tf.Variable(tf.random.truncated_normal([512, 256], stddev=0.1)), tf.Variable(tf.zeros([256]))
    # 256 => 10
    w3, b3 = tf.Variable(tf.random.truncated_normal([256, 10], stddev=0.1)), tf.Variable(tf.zeros([10]))



    optimizer = optimizers.SGD(lr=0.01)


    for step, (x,y) in enumerate(train_db):

        # [b, 28, 28] => [b, 784]
        x = tf.reshape(x, (-1, 784))

        with tf.GradientTape() as tape:

            # layer1.
            h1 = x @ w1 + b1
            h1 = tf.nn.relu(h1)
            # layer2
            h2 = h1 @ w2 + b2
            h2 = tf.nn.relu(h2)
            # output
            out = h2 @ w3 + b3
            # out = tf.nn.relu(out)

            # compute loss
            # [b, 10] - [b, 10]
            loss = tf.square(y-out)
            # [b, 10] => [b]
            loss = tf.reduce_mean(loss, axis=1)
            # [b] => scalar
            loss = tf.reduce_mean(loss)



        # compute gradient
        grads = tape.gradient(loss, [w1, b1, w2, b2, w3, b3])
        # print('==before==')
        # for g in grads:
        #     print(tf.norm(g))
        
        grads,  _ = tf.clip_by_global_norm(grads, 15)

        # print('==after==')
        # for g in grads:
        #     print(tf.norm(g))
        # update w' = w - lr*grad
        optimizer.apply_gradients(zip(grads, [w1, b1, w2, b2, w3, b3]))



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




if __name__ == '__main__':
    main()

来源：https://www.cnblogs.com/zdm-code/p/12232644.html

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tensorflow

张量