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我正在研究带有数字的经典示例。我想创建我的第一个神经网络来预测数字图像 {0,1,2,3,4,5,6,7,8,9} 的标签。所以 train.txt 的第一列有标签，所有其他列都是每个标签的特征。我定义了一个类来导入我的数据：
class DigitDataset(Dataset):
"""Digit dataset."""
def __init__(self, file_path, transform=None):
"""
Args:
csv_file (string): Path to the csv file with annotations.
root_dir (string): Directory with all the images.
transform (callable, optional): Optional transform to be applied
on a sample.
"""
self.data = pd.read_csv(file_path, header = None, sep =" ")
self.transform = transform
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
labels = self.data.iloc[idx,0]
images = self.data.iloc[idx,1:-1].values.astype(np.uint8).reshape((1,16,16))
if self.transform is not None:
sample = self.transform(sample)
return images, labels
然后我运行这些命令将我的数据集分成批次，定义模型和损失：
train_dataset = DigitDataset("train.txt")
train_loader = DataLoader(train_dataset, batch_size=64,
shuffle=True, num_workers=4)
# Model creation with neural net Sequential model
model=nn.Sequential(nn.Linear(256, 128), # 1 layer:- 256 input 128 o/p
nn.ReLU(), # Defining Regular linear unit as activation
nn.Linear(128,64), # 2 Layer:- 128 Input and 64 O/p
nn.Tanh(), # Defining Regular linear unit as activation
nn.Linear(64,10), # 3 Layer:- 64 Input and 10 O/P as (0-9)
nn.LogSoftmax(dim=1) # Defining the log softmax to find the probablities
for the last output unit
)
# defining the negative log-likelihood loss for calculating loss
criterion = nn.NLLLoss()
images, labels = next(iter(train_loader))
images = images.view(images.shape[0], -1)
logps = model(images) #log probabilities
loss = criterion(logps, labels) #calculate the NLL-loss
我接受了错误：
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-2-7f4160c1f086> in <module>
47 images = images.view(images.shape[0], -1)
48
---> 49 logps = model(images) #log probabilities
50 loss = criterion(logps, labels) #calculate the NLL-loss
~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self,
*input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/container.py in forward(self, input)
115 def forward(self, input):
116 for module in self:
--> 117 input = module(input)
118 return input
119
~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self,
*input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/linear.py in forward(self, input)
91
92 def forward(self, input: Tensor) -> Tensor:
---> 93 return F.linear(input, self.weight, self.bias)
94
95 def extra_repr(self) -> str:
~/anaconda3/lib/python3.8/site-packages/torch/nn/functional.py in linear(input, weight, bias)
1688 if input.dim() == 2 and bias is not None:
1689 # fused op is marginally faster
-> 1690 ret = torch.addmm(bias, input, weight.t())
1691 else:
1692 output = input.matmul(weight.t())
RuntimeError: expected scalar type Float but found Byte
你知道哪里出了问题吗？感谢您的耐心等待和帮助！
原文由 CyberMathIdiot 发布，翻译遵循 CC BY-SA 4.0 许可协议
python
此行是导致错误的原因：
images是uint8(byte) 而神经网络需要输入作为浮点数，以便使用整数梯度计算反向传播（你不能那些不是连续的和不可微分的）。您可以使用
torchvision.transforms.functional.to_tensor将图像转换为float和[0, 1]像这样：或者简单地除以
255得到值到[0, 1]。