我在训练 MNIST 数据时收到此错误,csv 文件来自 Kaggle。有人可以告诉我哪里错了吗?这是我的代码。 PyTorch的版本是0.4.0。
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.utils.data as data
import torchvision
import matplotlib.pyplot as plt
torch.manual_seed(1)
# Training Parameters
EPOCH = 20
BATCH_size = 15
LR = 0.001
img_row, img_col = 28, 28
# Networks structure
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(
in_channels=1, out_channels=32,
kernel_size=5, stride=1, padding=2
),
nn.ReLU(),
nn.Conv2d(32, 32, 5, 1, 2),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2),
nn.Dropout(0.25)
)
self.conv2 = nn.Sequential(
nn.Conv2d(32, 64, 3, 1, 1),
nn.ReLU(),
nn.Conv2d(64, 64, 3, 1, 1),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Dropout(0.25)
)
self.out = nn.Sequential(
nn.Linear(64*7*7, 512),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(512, 10)
)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = x.view(x.size(0), -1)
output = self.out(x)
return output
# Torch Dataset
class Torch_Dataset(data.Dataset):
def __init__(self, root_dir, csvfile, img_rows, img_cols, train=True, transform=None):
self.root_dir = root_dir
self.transform = transform
self.train = train
if self.train:
y_data0 = pd.read_csv(csvfile, header=0, usecols=['label'])
y_data1 = np.array(y_data0)
self.y_data = torch.from_numpy(y_data1)
x_data0 = pd.read_csv(csvfile, header=0, usecols=[i for i in range(1, 785)])
x_data1 = np.array(x_data0)
x_data1 = x_data1.reshape(x_data1.shape[0], 1, img_rows, img_cols)
x_data1 = x_data1.astype('float32')
x_data1 /= 255
self.x_data = torch.from_numpy(x_data1)
else:
x_data0 = pd.read_csv(csvfile, header=0)
x_data1 = np.array(x_data0)
x_data1 = x_data1.reshape(x_data1.shape[0], 1, img_rows, img_cols)
x_data1 = x_data1.astype('float32')
x_data1 /= 255
self.x_data = torch.from_numpy(x_data1)
def __len__(self):
return len(self.x_data)
def __getitem__(self, idx):
if self.train:
img, target = self.x_data[idx], self.y_data[idx]
else:
img = self.x_data[idx]
target = None
# sample = {'img': img, 'target': target}
return img, target
train = Torch_Dataset(
root_dir='./', # root
csvfile='train.csv', # filename
img_rows=img_row, # image rows
img_cols=img_col, # image cols
train=True # train or test
)
# DataLoader
loader = data.DataLoader(
dataset=train, # torch dataset format
batch_size=BATCH_size, # mini batch size
shuffle=True, # shuffle the data
)
# train the data
cnn = CNN()
optimizer = torch.optim.Adam(cnn.parameters(), lr=LR)
loss_f = nn.CrossEntropyLoss()
for epoch in range(EPOCH):
for step, (x, y) in enumerate(loader):
b_x = Variable(x)
b_y = Variable(y)
b_y = b_y.squeeze
output = cnn(b_x)
loss = loss_f(output, b_y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
Traceback (most recent call last):
File "C:/Users/Bryan Zoe/PycharmProjects/MNIST_TEST/PyTorch/test1.py", line 118, in loss = loss_f(output, b_y)
File "C:\Users\Bryan Zoe\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 491, in __ call __ result = self.forward(*input, **kwargs)
File "C:\Users\Bryan Zoe\Anaconda3\lib\site-packages\torch\nn\modules\loss.py", line 757, in forward _assert_no_grad(target)
File "C:\Users\Bryan Zoe\Anaconda3\lib\site-packages\torch\nn\modules\loss.py", line 11, in _assert_no_grad assert not tensor.requires_grad, \
AttributeError: 'builtin_function_or_method' object has no attribute 'requires_grad'
最佳答案
您没有调用挤压方法,这应该可以工作
b_y = b_y.squeeze()
关于pytorch - 属性错误: 'builtin_function_or_method' object has no attribute 'requires_grad' ,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/50939730/