TensoFlow自动求导机制

『TensorFlow』第二弹_线性拟合&神经网络拟合_恰是故人归

下面做了三个简单尝试，

• 利用包含gradients、assign等tf函数直接构建图进行自动梯度下降
• 利用优化器计算出导数，再将导数应用到变量上
• 直接使用优化器不显式得到导数

更新参数必须使用assign，这也可能会涉及到控制依赖问题。

# Author : Hellcat
# Time   : 2/20/2018

import tensorflow as tf

tf.set_random_seed(1000)

def get_fake_data(batch_size=8):
    x = 20 * tf.random_uniform([batch_size,1],dtype=tf.float32)
    y = tf.multiply(x,3) + 1 + tf.multiply(
        tf.random_normal([batch_size,1],mean=0,stddev=0.01,dtype=tf.float32),1)
    return x, y

x, y = get_fake_data()

w = tf.Variable(tf.random_uniform([1,1], dtype=tf.float32), name='w')
b = tf.Variable(tf.random_uniform([1,1], dtype=tf.float32), name='b')

lr = 0.001

y_pred = tf.add(tf.multiply(w,x),b)
loss = tf.reduce_mean(tf.pow(tf.multiply(0.5,(y_pred - y)),2),axis=0)

# 梯度尝试
grad_w, grad_b = tf.gradients(loss,[w,b])
train_w = tf.assign(w,tf.subtract(w,lr*grad_w))
train_b = tf.assign(b,tf.subtract(b,lr*grad_b))
train = [train_w, train_b]

# 使用优化器
# optimizer = tf.train.GradientDescentOptimizer(lr)      # 优化器&学习率选择
# ## 优化器+梯度操作
# grads_and_vars = optimizer.compute_gradients(loss, [w,b])
# train = optimizer.apply_gradients(grads_and_vars)
## 优化器径直优化
# train = optimizer.minimize(loss)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for ii in range(80000):
        sess.run([train])
        if ii % 1000 == 0:
            print(sess.run(w),sess.run(b))

PyTorch自动求导机制

由于梯度是会累加的，所以清空梯度一定不要忘记。

import torch as t
from torch.autograd import Variable as V
import matplotlib.pyplot as plt
from IPython import display

# 指定随机数种子
t.manual_seed(1000)

def get_fake_data(batch_size=8):
    x = t.rand(batch_size,1)*20
    y = x * 2 + 3 + 3*t.randn(batch_size,1)
    return x, y

x, y = get_fake_data()
plt.scatter(x.squeeze(), y.squeeze())

w = V(t.rand(1,1),requires_grad=True)
b = V(t.rand(1,1),requires_grad=True)

lr = 0.001

for ii in range(8000):
    x, y = get_fake_data()
    x, y = V(x), V(y)
    # print(x, y)
    y_pred = x.mm(w) + b.expand_as(x)

    loss = 0.5*(y_pred - y)**2
    loss = loss.sum()  # 集结loss向量

    loss.backward()

    w.data.sub_(lr * w.grad.data)
    b.data.sub_(lr * b.grad.data)

    w.grad.data.zero_()
    b.grad.data.zero_()

    if ii % 1000 == 0:
        display.clear_output(wait=True)
        x = t.arange(0,20).view(-1,1)
        y = x.mm(w.data) + b.data.expand_as(x)
        plt.plot(x.numpy(), y.numpy())
        x2, y2 = get_fake_data(batch_size=20)
        plt.scatter(x2, y2)

        plt.xlim(0,20)
        plt.ylim(0,40)
        plt.show()
        
print(w.data.squeeze(), b.data.squeeze())