我在 Pytorch 中从头开始构建 Softmax 的问题是什么

Question

我读了这个 post 并尝试自己构建 softmax。这是代码

import torch
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import time
import sys
import numpy as np

#============================ get the dataset =========================

mnist_train = torchvision.datasets.FashionMNIST(root='~/Datasets/FashionMNIST', train=True, download=True, transform=transforms.ToTensor())
mnist_test = torchvision.datasets.FashionMNIST(root='~/Datasets/FashionMNIST', train=False, download=True, transform=transforms.ToTensor())

batch_size = 256
num_workers = 0  

train_iter = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True, num_workers=num_workers)
test_iter = torch.utils.data.DataLoader(mnist_test, batch_size=batch_size, shuffle=False, num_workers=num_workers)



#============================     train      =========================
num_inputs = 28 * 28
num_outputs = 10
epochs = 5
lr = 0.05

# Initi the Weight and bia
W = torch.tensor(np.random.normal(0, 0.01, (num_inputs, num_outputs)), dtype=torch.float)
b = torch.zeros(num_outputs, dtype=torch.float)
W.requires_grad_(requires_grad = True)
b.requires_grad_(requires_grad=True)

# softmax function
def softmax(X):
    X = X.exp()
    den = X.sum(dim=1, keepdim=True)
    return X / den  

# loss
def cross_entropy(y_hat, y):
    return - torch.log(y_hat.gather(1, y.view(-1, 1))).sum()

# accuracy function
def accuracy(y_hat, y):
    return (y_hat.argmax(dim=1) == y).float().mean().item()
    

for epoch in range(epochs):

    train_loss_sum = 0.0
    train_acc_sum = 0.0
    n_train = 0

    for X, y in train_iter:
        # X.shape: [256, 1, 28, 28]
        # y.shape: [256]
        
        # flatten the X into [256, 28*28]
        X = X.flatten(start_dim=1)  
        y_pred = softmax(torch.mm(X, W) + b)
        
        loss = cross_entropy(y_pred, y)
       
        loss.backward()

        W.data = W.data - lr * W.grad
        b.data = b.data - lr* b.grad

        W.grad.zero_()
        b.grad.zero_()

        train_loss_sum += loss.item() 

        train_acc_sum += accuracy(y_pred, y)
        n_train += y.shape[0]
    
    # evaluate the Test
   
    test_acc, n_test = 0.0, 0
    with torch.no_grad():

        for X_test, y_test in test_iter:
            X_test = X_test.flatten(start_dim=1) 
            y_test_pred = softmax(torch.mm(X_test, W) + b)
            test_acc += accuracy(y_test_pred, y_test)
            n_test += y_test.shape[0]

    print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f'
              % (epoch + 1, train_loss_sum/n_train , train_acc_sum / n_train, test_acc / n_test))

对比原版post，我转

def cross_entropy(y_hat, y):
    return - torch.log(y_hat.gather(1, y.view(-1, 1)))

进入

def cross_entropy(y_hat, y):
    return - torch.log(y_hat.gather(1, y.view(-1, 1))).sum()

因为 backward 需要一个标量。

但是，我的结果是

epoch 1, loss nan, train acc 0.000, test acc 0.000
epoch 2, loss nan, train acc 0.000, test acc 0.000
epoch 3, loss nan, train acc 0.000, test acc 0.000
epoch 4, loss nan, train acc 0.000, test acc 0.000
epoch 5, loss nan, train acc 0.000, test acc 0.000

有什么想法吗？

谢谢。

Answer 1

变化：

def cross_entropy(y_hat, y):
    return - torch.log(y_hat.gather(1, y.view(-1, 1))).sum()

收件人：

def cross_entropy(y_hat, y):
    return - torch.log(y_hat[range(len(y_hat)), y] + 1e-8).sum()

输出应该是这样的：

epoch 1, loss 9.2651, train acc 0.002, test acc 0.002
epoch 2, loss 7.8493, train acc 0.002, test acc 0.002
epoch 3, loss 6.6875, train acc 0.002, test acc 0.003
epoch 4, loss 6.0928, train acc 0.003, test acc 0.003
epoch 5, loss 5.1277, train acc 0.003, test acc 0.003

注意 nan 的问题也可能由 softmax(X) 中的 X = X.exp() 引起，当 X 太大时 exp() 将输出 inf，发生这种情况时，您可以尝试在使用 exp()

之前剪掉 X

我在 Pytorch 中从头开始构建 Softmax 的问题是什么

What is the Problem in my Building Softmax from Scratch in Pytorch

python-3.x

pytorch

loss-function

softmax