我正在尝试使用PyTorch。我收到此错误:
RuntimeError: Expected object of scalar type Double but got scalar type Float for argument #2 'weight' in call to _thnn_conv2d_forward
这是我的代码(从在线教程中无耻复制):
class Net(Module):
def __init__(self):
super(Net,self).__init__()
self.cnn_layers = Sequential(
Conv2d(1,4,kernel_size=3,stride=1,padding=1),
BatchNorm2d(4),
ReLU(inplace=True),
MaxPool2d(kernel_size=2,stride=2),
Conv2d(4,4,kernel_size=3,stride=1,padding=1),
BatchNorm2d(4),
ReLU(inplace=True),
MaxPool2d(kernel_size=2,stride=2)
)
self.linear_layers = Sequential(
Linear(900,10)
)
def forward(self,x):
# self.weights = self.weights.double()
x = self.cnn_layers(x)
x = x.view(x.size(0),-1)
x = self.linear_layers(x)
return x
tdata = dt.Data("train")
train_x = torch.from_numpy(tdata.get_train()[0].reshape(925,1,300,300))
train_y = torch.from_numpy(tdata.get_train()[1].astype(int))
val_x = torch.from_numpy(tdata.get_test()[0].reshape(102,1,300,300))
val_y = torch.from_numpy(tdata.get_test()[1].astype(int))
print(val_y.shape)
plt.imshow(tdata.get_train()[0][100],cmap='gray')
plt.show()
model = Net()
# defining the optimizer
optimizer = Adam(model.parameters(), lr=0.07)
# defining the loss function
criterion = CrossEntropyLoss()
# checking if GPU is available
if torch.cuda.is_available():
model = model.cuda()
criterion = criterion.cuda()
print(model)
def train(epoch):
model.train()
tr_loss = 0
# getting the training set
x_train, y_train = Variable(train_x.double()), Variable(train_y.double())
# getting the validation set
x_val, y_val = Variable(val_x), Variable(val_y)
# converting the data into GPU format
if torch.cuda.is_available():
x_train = x_train.cuda()
y_train = y_train.cuda()
x_val = x_val.cuda()
y_val = y_val.cuda()
# clearing the Gradients of the model parameters
optimizer.zero_grad()
# prediction for training and validation set
output_train = model(x_train.double())
output_val = model(x_val)
# computing the training and validation loss
loss_train = criterion(output_train, y_train)
loss_val = criterion(output_val, y_val)
train_losses.append(loss_train)
val_losses.append(loss_val)
# computing the updated weights of all the model parameters
loss_train.backward()
optimizer.step()
tr_loss = loss_train.item()
if epoch%2 == 0:
# printing the validation loss
print('Epoch : ',epoch+1, '\t', 'loss :', loss_val)
n_epochs = 25
# empty list to store training losses
train_losses = []
# empty list to store validation losses
val_losses = []
# training the model
for epoch in range(n_epochs):
train(epoch)
tdata.get_train()和tdata.get_test()返回一个元组(numpy(dtype ='double'),numpy(dtype ='int'))我认为权重是一个内部数据结构。因此,其类型应由PyTorch自行调整。这里有什么问题?
你可能只是添加.to(torch.float32)
到您train_x
和val_x
张量
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我来说两句