Hi, I am working on the further research section of chapter 4. I have built my own implementation of the Learner but keep getting a matrix multiplication problem (RuntimeError: mat1 and mat2 shapes cannot be multiplied (5376x28 and 784x30)). Here is my code:
Learner
class Lrner:
def __init__(self, dls, model, opt,loss_fn, metric):
self.dl_train = dls[0]
self.dl_valid = dls[1]
self.model = model
self.opt = opt(self.model.parameters(),lr=0.1)
self.loss = loss_fn
self.metric = metric
def cal_grad(self, xb, yb):
preds = self.model(xb)
loss = self.loss(preds, yb)
loss.backward()
def train_epoch(self):
for xb, yb in self.dl_train:
self.cal_grad(xb, yb)
self.opt.step()
self.opt.zero_grad()
def validate_epoch(self):
accs = [self.metric(self.model(xb),yb) for xb, yb in self.dl_valid]
return round(torch.stack(accs).mean().item(),4)
def fit(self, epochs):
for i in range(epochs):
self.train_epoch()
print(self.validate_epoch(),end=' ')
Model
simple_net = nn.Sequential(
nn.Linear(28*28,30),
nn.ReLU(),
nn.Linear(30,1),
nn.Sigmoid()
)
Dataloaders
I am using the same dataloaders created in the chapter 4 exercises
dls = DataLoaders(dl, valid_dl)
Error
I instantiate my class:
learner = Lrner(dls, simple_net, SGD, F.cross_entropy, accuracy)
I check the model:
In: learner.model
Out:
Sequential(
(0): Linear(in_features=784, out_features=30, bias=True)
(1): ReLU()
(2): Linear(in_features=30, out_features=1, bias=True)
(3): Sigmoid()
)
I try to fit it for one epoch:
In: learner.fit(1)
Out:
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
/var/folders/f1/bcv5hnqs1rd944j8hkw5gyb40000gn/T/ipykernel_3189/3857249191.py in <module>
----> 1 learner.fit(1)
/var/folders/f1/bcv5hnqs1rd944j8hkw5gyb40000gn/T/ipykernel_3189/1438402721.py in fit(self, epochs)
25 def fit(self, epochs):
26 for i in range(epochs):
---> 27 self.train_epoch()
28 print(self.validate_epoch(),end=' ')
/var/folders/f1/bcv5hnqs1rd944j8hkw5gyb40000gn/T/ipykernel_3189/1438402721.py in train_epoch(self)
15 def train_epoch(self):
16 for xb, yb in self.dl_train:
---> 17 self.cal_grad(xb, yb)
18 self.opt.step()
19 self.opt.zero_grad()
/var/folders/f1/bcv5hnqs1rd944j8hkw5gyb40000gn/T/ipykernel_3189/1438402721.py in cal_grad(self, xb, yb)
9
10 def cal_grad(self, xb, yb):
---> 11 preds = self.model(xb)
12 loss = self.loss(preds, yb)
13 loss.backward()
~/opt/anaconda3/envs/fastai/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1108 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1109 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1110 return forward_call(*input, **kwargs)
1111 # Do not call functions when jit is used
1112 full_backward_hooks, non_full_backward_hooks = [], []
~/opt/anaconda3/envs/fastai/lib/python3.7/site-packages/torch/nn/modules/container.py in forward(self, input)
139 def forward(self, input):
140 for module in self:
--> 141 input = module(input)
142 return input
143
~/opt/anaconda3/envs/fastai/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1108 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1109 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1110 return forward_call(*input, **kwargs)
1111 # Do not call functions when jit is used
1112 full_backward_hooks, non_full_backward_hooks = [], []
~/opt/anaconda3/envs/fastai/lib/python3.7/site-packages/torch/nn/modules/linear.py in forward(self, input)
101
102 def forward(self, input: Tensor) -> Tensor:
--> 103 return F.linear(input, self.weight, self.bias)
104
105 def extra_repr(self) -> str:
~/opt/anaconda3/envs/fastai/lib/python3.7/site-packages/fastai/torch_core.py in __torch_function__(self, func, types, args, kwargs)
339 convert=False
340 if _torch_handled(args, self._opt, func): convert,types = type(self),(torch.Tensor,)
--> 341 res = super().__torch_function__(func, types, args=args, kwargs=kwargs)
342 if convert: res = convert(res)
343 if isinstance(res, TensorBase): res.set_meta(self, as_copy=True)
~/opt/anaconda3/envs/fastai/lib/python3.7/site-packages/torch/_tensor.py in __torch_function__(cls, func, types, args, kwargs)
1140
1141 with _C.DisableTorchFunction():
-> 1142 ret = func(*args, **kwargs)
1143 if func in get_default_nowrap_functions():
1144 return ret
RuntimeError: mat1 and mat2 shapes cannot be multiplied (5376x28 and 784x30)
I understand that the error refers to a multiplication of two matrices. I assume the (784 x 30) matrix is the inputs (28*28,30) but I have no idea where the (5376x28) matrix comes from.
Troubleshooting
I have tried testing with a single fully connected linear layer nn.Linear(28*28,1) and copied the code from this response in the FastAi forums but haven’t been able to get anywhere.
Would anyone know where the (5376x28) matrix comes from and how to fix this?