自定义损失函数应该如何实现?使用以下代码会导致错误:
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import numpy as np
import matplotlib.pyplot as plt
import torch.utils.data as data_utils
import torch.nn as nn
import torch.nn.functional as F
num_epochs = 20
x1 = np.array([0,0])
x2 = np.array([0,1])
x3 = np.array([1,0])
x4 = np.array([1,1])
num_epochs = 200
class cus2(torch.nn.Module):
def __init__(self):
super(cus2,self).__init__()
def forward(self, outputs, labels):
# reshape labels to give a flat vector of length batch_size*seq_len
labels = labels.view(-1)
# mask out 'PAD' tokens
mask = (labels >= 0).float()
# the number of tokens is the sum of elements in mask
num_tokens = int(torch.sum(mask).data[0])
# pick the values corresponding to labels and multiply by mask
outputs = outputs[range(outputs.shape[0]), labels]*mask
# cross entropy loss for all non 'PAD' tokens
return -torch.sum(outputs)/num_tokens
x = torch.tensor([x1,x2,x3,x4]).float()
y = torch.tensor([0,1,1,0]).long()
train = data_utils.TensorDataset(x,y)
train_loader = data_utils.DataLoader(train , batch_size=2 , shuffle=True)
device = 'cpu'
input_size = 2
hidden_size = 100
num_classes = 2
learning_rate = .0001
class NeuralNet(nn.Module) :
def __init__(self, input_size, hidden_size, num_classes) :
super(NeuralNet, self).__init__()
self.fc1 = nn.Linear(input_size , hidden_size)
self.relu = nn.ReLU()
self.fc2 = nn.Linear(hidden_size , num_classes)
def forward(self, x) :
out = self.fc1(x)
out = self.relu(out)
out = self.fc2(out)
return out
for i in range(0 , 1) :
model = NeuralNet(input_size, hidden_size, num_classes).to(device)
criterion = nn.CrossEntropyLoss()
# criterion = Regress_Loss()
# criterion = cus2()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
total_step = len(train_loader)
for epoch in range(num_epochs) :
for i,(images , labels) in enumerate(train_loader) :
images = images.reshape(-1 , 2).to(device)
labels = labels.to(device)
outputs = model(images)
loss = criterion(outputs , labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# print(loss)
outputs = model(x)
print(outputs.data.max(1)[1])
对训练数据做出完美预测:
tensor([0, 1, 1, 0])
使用来自 here 的自定义损失函数:
在上面的代码中实现为cus2
取消注释代码 # criterion = cus2()
以使用此损失函数返回:
tensor([0, 0, 0, 0])
同时返回一个警告:
UserWarning: invalid index of a 0-dim tensor. This will be an error in PyTorch 0.5. Use tensor.item() to convert a 0-dim tensor to a Python number
我没有正确实现自定义损失函数?
最佳答案
除以下情况外,您的损失函数在编程上是正确的:
# the number of tokens is the sum of elements in mask
num_tokens = int(torch.sum(mask).data[0])
当您执行 torch.sum
时,它会返回一个 0 维张量,因此会出现无法索引的警告。要解决此问题,请按照建议执行 int(torch.sum(mask).item())
或 int(torch.sum(mask))
也可以。
现在,您是否尝试使用自定义损失来模拟 CE 损失?如果是,那么您缺少 log_softmax
要解决此问题,请在语句 4 之前添加 outputs = torch.nn.functional.log_softmax(outputs, dim=1)
。请注意,如果您已附加教程,log_softmax
已经在转发调用中完成。你也可以那样做。
另外,我注意到学习速度很慢,即使有 CE 损失,结果也不一致。在自定义和 CE 损失的情况下,将学习率提高到 1e-3 对我来说效果很好。
关于python - PyTorch 自定义损失函数,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/53980031/