Hello,
I’ve had a model running without error. I increased my training data size and now, I’m getting the below error.
The error shows up after the last epoch is ran on the training data.
I read to change torch.no_grad() to roof_model.eval() but that didn’t seem to help.
Traceback (most recent call last):
File "<ipython-input-70-5d1647d029f8>", line 33, in <module>
y_pred = roof_model(images, num_tensor, col_tensor)
File "C:\Users\JORDAN.HOWELL.GITDIR\AppData\Local\Continuum\anaconda3\envs\torch_env\lib\site-packages\torch\nn\modules\module.py", line 532, in __call__
result = self.forward(*input, **kwargs)
File "<ipython-input-69-2bc68a827381>", line 31, in forward
x = self.fc2_b(x)
File "C:\Users\JORDAN.HOWELL.GITDIR\AppData\Local\Continuum\anaconda3\envs\torch_env\lib\site-packages\torch\nn\modules\module.py", line 532, in __call__
result = self.forward(*input, **kwargs)
File "C:\Users\JORDAN.HOWELL.GITDIR\AppData\Local\Continuum\anaconda3\envs\torch_env\lib\site-packages\torch\nn\modules\batchnorm.py", line 107, in forward
exponential_average_factor, self.eps)
File "C:\Users\JORDAN.HOWELL.GITDIR\AppData\Local\Continuum\anaconda3\envs\torch_env\lib\site-packages\torch\nn\functional.py", line 1666, in batch_norm
raise ValueError('Expected more than 1 value per channel when training, got input size {}'.format(size))
ValueError: Expected more than 1 value per channel when training, got input size torch.Size([1, 512])
Here is my model:
class Image_Model(nn.Module):
def __init__(self, embedding_size):
super().__init__()
self.all_embeddings = nn.ModuleList([nn.Embedding(categories,
size)
for categories,
size in embedding_size])
n_emb = sum([y for x, y in embedding_size])
self.n_emb = n_emb
self.embedding_dropout = nn.Dropout(p = 0.04)
self.cnn = models.resnet50(pretrained=True)
for param in self.cnn.parameters():
param_requires_grad = False
self.fc2 = nn.Sequential(nn.Linear(1042, 512))
self.fc2_b = nn.BatchNorm1d(512)
self.fc3 = nn.Dropout(p=.04)
self.fc4 = nn.Sequential(nn.Linear(512, 256))
self.fc4_b = nn.BatchNorm1d(256)
self.fc5 = nn.Dropout(p = 0.04)
self.fc6 = nn.Sequential(nn.Linear(256, 2))
def forward(self, image, numerical_columns, cat_columns):
x = self.cnn(image)
e = [emb_layer(cat_columns[:,i]) for i,
emb_layer in enumerate(self.all_embeddings)]
e = torch.cat(e, 1)
x = torch.cat((x, numerical_columns), dim = 1)
x = torch.cat((x, e), dim = 1)
x = F.relu(self.fc2(x))
x = self.fc2_b(x)
x = F.relu(self.fc3(x))
x = F.relu(self.fc4(x))
x = self.fc4_b(x)
x = F.relu(self.fc5(x))
x = F.relu(self.fc6(x))
x = F.log_softmax(x)
return x
# Loss and optimizer
torch.manual_seed(1010)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
roof_model = Image_Model(embeddings).to(device)
criterion = torch.nn.NLLLoss().to(device)
optimizer = torch.optim.Adam(roof_model.parameters(), lr=0.001)
scheduler = ReduceLROnPlateau(optimizer, 'min', patience = 2, verbose = True,
min_lr = .0000000001)
Both test and train are running in batches of 10. I’m not sure what could have changed this.