Pytorch设立计算图并自动计算

本博文参考七月在线pytorch课程
1.numpy和pytorch实现梯度下降法

使用numpy实现简单神经网络

import numpy as np
N, D_in, H, D_out = 64, 1000, 100, 10

# 随机创建一些训练数据
x = np.random.randn(N, D_in)
y = np.random.randn(N, D_out)

w1 = np.random.randn(D_in, H)
w2 = np.random.randn(H, D_out)

learning_rate = 1e-6
for it in range(500):
    # Forward pass
    h = x.dot(w1) # N * H
    h_relu = np.maximum(h, 0) # N * H
    y_pred = h_relu.dot(w2) # N * D_out
    
    # compute loss
    loss = np.square(y_pred - y).sum()
    print(it, loss)
    
    # Backward pass
    # compute the gradient
    grad_y_pred = 2.0 * (y_pred - y)
    grad_w2 = h_relu.T.dot(grad_y_pred)
    grad_h_relu = grad_y_pred.dot(w2.T)
    grad_h = grad_h_relu.copy()
    grad_h[h<0] = 0
    grad_w1 = x.T.dot(grad_h)
    
    # update weights of w1 and w2
    w1 -= learning_rate * grad_w1
    w2 -= learning_rate * grad_w2

使用pytorch实现简单神经网络

N, D_in, H, D_out = 64, 1000, 100, 10

# 随机创建一些训练数据
x = torch.randn(N, D_in)
y = torch.randn(N, D_out)

w1 = torch.randn(D_in, H)
w2 = torch.randn(H, D_out)

learning_rate = 1e-6
for it in range(500):
    # Forward pass
    h = x.mm(w1) # N * H
    h_relu = h.clamp(min=0) # N * H
    y_pred = h_relu.mm(w2) # N * D_out
    
    # compute loss
    loss = (y_pred - y).pow(2).sum().item()
    print(it, loss)
    
    # Backward pass
    # compute the gradient
    grad_y_pred = 2.0 * (y_pred - y)
    grad_w2 = h_relu.t().mm(grad_y_pred)
    grad_h_relu = grad_y_pred.mm(w2.t())
    grad_h = grad_h_relu.clone()
    grad_h[h<0] = 0
    grad_w1 = x.t().mm(grad_h)
    
    # update weights of w1 and w2
    w1 -= learning_rate * grad_w1
    w2 -= learning_rate * grad_w2

设定初始值

#numpy
x = np.random.randn(N, D_in)
y = np.random.randn(N, D_out)

w1 = np.random.randn(D_in, H)
w2 = np.random.randn(H, D_out)

#pytorch
x = torch.randn(N, D_in)
y = torch.randn(N, D_out)

w1 = torch.randn(D_in, H)
w2 = torch.randn(H, D_out)