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I tried running this and I got this error:
raceback (most recent call last):
File "/Users/brianweston/Documents/Stanford_CS/CS231N_CNN/Project/cs231n_final_project/scripts/training/train_FMNIST.py", line 109, in
for batch_features, _ in train_loader:
File "/Users/brianweston/miniforge3/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 530, in next
data = self._next_data()
File "/Users/brianweston/miniforge3/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 570, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "/Users/brianweston/miniforge3/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch
return self.collate_fn(data)
File "/Users/brianweston/miniforge3/lib/python3.8/site-packages/torch/utils/data/_utils/collate.py", line 172, in default_collate
return [default_collate(samples) for samples in transposed] # Backwards compatibility.
File "/Users/brianweston/miniforge3/lib/python3.8/site-packages/torch/utils/data/_utils/collate.py", line 172, in
return [default_collate(samples) for samples in transposed] # Backwards compatibility.
File "/Users/brianweston/miniforge3/lib/python3.8/site-packages/torch/utils/data/_utils/collate.py", line 180, in default_collate
raise TypeError(default_collate_err_msg_format.format(elem_type))
TypeError: default_collate: batch must contain tensors, numpy arrays, numbers, dicts or lists; found <class 'PIL.Image.Image'>
(base) brianweston@MBP training % /Users/brianweston/miniforge3/bin/python /Users/brianweston/Documents/Stanford_CS/CS231N_CNN/Project/cs231n_final_project/scripts/training/train_FMNIST.py
Traceback (most recent call last):
File "/Users/brianweston/Documents/Stanford_CS/CS231N_CNN/Project/cs231n_final_project/scripts/training/train_FMNIST.py", line 109, in
for batch_features, _ in train_loader:
File "/Users/brianweston/miniforge3/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 530, in next
data = self._next_data()
File "/Users/brianweston/miniforge3/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 570, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "/Users/brianweston/miniforge3/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch
return self.collate_fn(data)
File "/Users/brianweston/miniforge3/lib/python3.8/site-packages/torch/utils/data/_utils/collate.py", line 172, in default_collate
return [default_collate(samples) for samples in transposed] # Backwards compatibility.
File "/Users/brianweston/miniforge3/lib/python3.8/site-packages/torch/utils/data/_utils/collate.py", line 172, in
return [default_collate(samples) for samples in transposed] # Backwards compatibility.
File "/Users/brianweston/miniforge3/lib/python3.8/site-packages/torch/utils/data/_utils/collate.py", line 180, in default_collate
raise TypeError(default_collate_err_msg_format.format(elem_type))
TypeError: default_collate: batch must contain tensors, numpy arrays, numbers, dicts or lists; found <class 'PIL.Image.Image'>
(base) brianweston@MBP training % /Users/brianweston/miniforge3/bin/python /Users/brianweston/Documents/Stanford_CS/CS231N_CNN/Project/cs231n_final_project/scripts/training/train_FMNIST.py
Traceback (most recent call last):
File "/Users/brianweston/Documents/Stanford_CS/CS231N_CNN/Project/cs231n_final_project/scripts/training/train_FMNIST.py", line 60, in
ax.imshow(np.array(img), cmap='gist_gray')
File "/Users/brianweston/miniforge3/lib/python3.8/site-packages/matplotlib/_api/deprecation.py", line 456, in wrapper
return func(*args, **kwargs)
File "/Users/brianweston/miniforge3/lib/python3.8/site-packages/matplotlib/init.py", line 1412, in inner
return func(ax, *map(sanitize_sequence, args), **kwargs)
File "/Users/brianweston/miniforge3/lib/python3.8/site-packages/matplotlib/axes/_axes.py", line 5488, in imshow
im.set_data(X)
File "/Users/brianweston/miniforge3/lib/python3.8/site-packages/matplotlib/image.py", line 715, in set_data
raise TypeError("Invalid shape {} for image data"
TypeError: Invalid shape (1, 28, 28) for image data
Any idea what the fix is?
Kernel dies when running the Visualize Results, no error code, no changes. Everything prior to this successfully ran.
I didn't quite understand your question, but by
forward method
input traverse through the network with your mentioned architecture and give us an output which can be then used to calculate loss and optimize all weights through the optimization process (using calculated gradients from the lastloss.backward
method)forward
: tensor multiplicationmodel
: uses forward to calculate outputloss
: how far the generated output is from the original(target)loss.backward
: calculate gradients of every participated item in the existed neural networkoptimizer.step
: update every tensor (W, b) in the network