动手学深度学习学习笔记tf2.0版（3.8: 多层感知机）

注意这里的bh维度为 1 * h,计算时使用广播机制，进行计算

所以引入激活函数

%matplotlib inline
import tensorflow as tf
from matplotlib import pyplot as plt
import numpy as np
import random

def use_svg_display():
    # 用矢量图显示
    %config InlineBackend.figure_format = 'svg'

def set_figsize(figsize=(3.5, 2.5)):
    use_svg_display()
    # 设置图的尺寸
    plt.rcParams['figure.figsize'] = figsize

def xyplot(x_vals, y_vals, name):
    set_figsize(figsize=(5, 2.5))
    plt.plot(x_vals.numpy(), y_vals.numpy())
    plt.xlabel('x')
    plt.ylabel(name + '(x)')

with tf.GradientTape() as t:
    t.watch(x)
    y=y = tf.nn.relu(x)
dy_dx = t.gradient(y, x)
xyplot(x, dy_dx, 'grad of relu')

部分参考自：
https://trickygo.github.io/Dive-into-DL-TensorFlow2.0/#/chapter03_DL-basics/3.8_mlp