如何实现自定义丢失功能?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])
对培训数据进行完美预测:
tensor([0, 1, 1, 0])
使用来自https://cs230-stanford.github.io/pytorch-nlp.html#writing-a-custom-loss-function的自定义丢失函数:
在上面的代码中实现为cus2
取消注释代码# criterion = cus2()
以使用此loss函数返回:
tensor([0, 0, 0, 0])
同时返回警告:
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丢失功能?
您的loss函数在编程上是正确的,但以下情况除外:
当您执行
torch.sum
时,它将返回一个0维张量,并因此发出无法索引的警告。若要修复此问题,请按建议执行int(torch.sum(mask).item())
,否则int(torch.sum(mask))
也将起作用。现在,你是想用定制损耗来模拟CE损耗吗?如果是,那么您就缺少
log_softmax
要修复此问题,请在语句4之前添加
outputs = torch.nn.functional.log_softmax(outputs, dim=1)
。注意,在附加教程的情况下,log_softmax
已经在forward调用中完成。你也可以这么做。另外,我注意到学习速度慢,甚至与CE丢失,结果不一致。把学习率提高到1e-3对我来说很好,以防客户和CE丢失。
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