The Jupyter Notebook for Chapters 7, 8, and 9 are missing. The other files and the Notebooks for Chapters 8.5 and 9.5 are still Visible. Can the files please be reuploaded? Thanks.

The variable model in the function don't be parament. How to use?

Hi,

after having inserted the missing train_loader argument in the find_lr() function (we should open a PR for this after having solved this error), I get a RuntimeError in the current version of Chapter6.ipynb.

After running

torch.save(audionet.state_dict(), "audionet.pth")
optimizer = optim.Adam(audionet.parameters(), lr=0.001)
### added: , train_loader, device="cuda"
logs,losses = find_lr(audionet, nn.CrossEntropyLoss(), optimizer, train_loader, device="cuda")
plt.plot(logs,losses)

I get this error:

---------------------------------------------------------------------------

RuntimeError                              Traceback (most recent call last)

<ipython-input-47-d21b94029709> in <module>()
      2 optimizer = optim.Adam(audionet.parameters(), lr=0.001)
      3 ### added: , train_loader, device="cuda"
----> 4 logs,losses = find_lr(audionet, nn.CrossEntropyLoss(), optimizer, train_loader, device="cuda")
      5 plt.plot(logs,losses)

4 frames

<ipython-input-41-3824bd6072de> in find_lr(model, loss_fn, optimizer, train_loader, init_value, final_value, device)
     49         targets = targets.to(device)
     50         optimizer.zero_grad()
---> 51         outputs = model(inputs)
     52         loss = loss_fn(outputs, targets)
     53 

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    530             result = self._slow_forward(*input, **kwargs)
    531         else:
--> 532             result = self.forward(*input, **kwargs)
    533         for hook in self._forward_hooks.values():
    534             hook_result = hook(self, input, result)

<ipython-input-44-db3925c72c5e> in forward(self, x)
     31         x = F.relu(self.bn4(x))
     32         x = self.pool4(x)
---> 33         x = self.avgPool(x)
     34         x = x.squeeze(-1)
     35         x = self.fc1(x)

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    530             result = self._slow_forward(*input, **kwargs)
    531         else:
--> 532             result = self.forward(*input, **kwargs)
    533         for hook in self._forward_hooks.values():
    534             hook_result = hook(self, input, result)

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/pooling.py in forward(self, input)
    484         return F.avg_pool1d(
    485             input, self.kernel_size, self.stride, self.padding, self.ceil_mode,
--> 486             self.count_include_pad)
    487 
    488 

RuntimeError: Given input size: (512x1x1). Calculated output size: (512x1x0). Output size is too small

The same happens when I run

train(audionet, optimizer, torch.nn.CrossEntropyLoss(),train_loader, valid_loader, epochs=20)

I ran the code in Colab.

Hi,

when I try to download the images for chapter2 through the download.py script the images are not available.
It looks like the links are broken.

Is it possible to look into this, because not only chapter 2 but also other chapters are based on this and it would make a lot of the book useless.

The notebook for chapter 5 needs a line like the following:

LABEL.build_vocab(train, max_size=2)

Otherwise, when you run the train method there's a no vocab error that I don't have handy just now.

Learning

Pointer, Ian. Programming PyTorch for Deep Learning (Kindle Location 2). O'Reilly Media. Kindle Edition.

This is the title of the book I bought that uses this GitHub repo - got really confused trying to restart my kindle reading on different device using the title from GitHub - how about noting the two titles on the GitHub - you made me wonder if my 73 year old brain was starting to mis fire.

the cell with

audionet.save("audionet.pth")

fails with error message
AttributeError: 'AudioNet' object has no attribute 'save'

So of course later attempts to load this pth also fail.

Running the code on Ubuntu 19.10 and an Ananconda python setup

where is casper ??

Starting to think you got behind schedule and these last chapters are a joke.

I got a copy of casper from a post of Ian's - but the code for the cam notebook fails early.,

Giving up on your book.

transfer_model.fc = nn.Sequential(nn.Linear(transfer_model.fc.in_features, 500),
nn.ReLU(),
nn.Dropout(),
nn.Linear(500,2))

AttributeError: 'Sequential' object has no attribute 'in_features'

issue: no ./train directory

def check_image(path):
try:
im = Image.open(path)
return True
except:
return False
img_transforms = transforms.Compose([
transforms.Resize((64,64)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225] )
])
train_data_path = "./train/"
train_data = torchvision.datasets.ImageFolder(root=train_data_path,transform=img_transforms, is_valid_file=check_image)
val_data_path = "./val/"
val_data = torchvision.datasets.ImageFolder(root=val_data_path,transform=img_transforms, is_valid_file=check_image)
batch_size=64
train_data_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size,shuffle=True)
val_data_loader = torch.utils.data.DataLoader(val_data, batch_size=batch_size, shuffle=True)
if torch.cuda.is_available():
device = torch.device("cuda")
else:
device = torch.device("cpu")

FileNotFoundErrorTraceback (most recent call last)
in ()
12 ])
13 train_data_path = "./train/"
---> 14 train_data = torchvision.datasets.ImageFolder(root=train_data_path,transform=img_transforms, is_valid_file=check_image)
15 val_data_path = "./val/"
16 val_data = torchvision.datasets.ImageFolder(root=val_data_path,transform=img_transforms, is_valid_file=check_image)

~/anaconda3/lib/python3.6/site-packages/torchvision/datasets/folder.py in init(self, root, transform, target_transform, loader, is_valid_file)
207 transform=transform,
208 target_transform=target_transform,
--> 209 is_valid_file=is_valid_file)
210 self.imgs = self.samples

~/anaconda3/lib/python3.6/site-packages/torchvision/datasets/folder.py in init(self, root, loader, extensions, transform, target_transform, is_valid_file)
91 super(DatasetFolder, self).init(root, transform=transform,
92 target_transform=target_transform)
---> 93 classes, class_to_idx = self._find_classes(self.root)
94 samples = make_dataset(self.root, class_to_idx, extensions, is_valid_file)
95 if len(samples) == 0:

~/anaconda3/lib/python3.6/site-packages/torchvision/datasets/folder.py in _find_classes(self, dir)
120 if sys.version_info >= (3, 5):
121 # Faster and available in Python 3.5 and above
--> 122 classes = [d.name for d in os.scandir(dir) if d.is_dir()]
123 else:
124 classes = [d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d))]

FileNotFoundError: [Errno 2] No such file or directory: './train/'

Chapter 6: The ESC-50 folder name are different between book and repo code

In the book, tell me to get the wav file from 2 ways, one is to use git clone, the command in the book is below:

git clone https://github.com/karoldvl/ESC-50

The other is to download the zip file directly and unzip it.

Both will create the folder with the default name ESC-50.

but in the repo code, it used another name esc50 in the file Chapter 6.ipynb:

device="cuda"
bs=64
PATH_TO_ESC50 = Path.cwd() / 'esc50'

It confused me for a long while, every times I failed, get 0 result, I checked and checked again, and finally found the reason.

I suggest to update the repo code

I'd propose to change the PrecomputedESC50 class, since it did not work for me.

1. Change the glob() argument to glob('{}{}*.wav.png'.format(path.name, dpi)) so it can catch files with naming style like train50_2-95567-A-23.wav.png. Before I got an empty Data Loader.

2. Add .convert('RGB') - I got the same Error as last time you fixed that on another part.

Hi, I've been trying to follow the code examples from the class activation mapping workflow (chapter 7) but have a hard time understanding how this code fits with what comes before:

fts = sf[0].features[idx]
        prob = np.exp(to_np(log_prob))
        preds = np.argmax(prob[idx])
        fts_np = to_np(fts)
        f2=np.dot(np.rollaxis(fts_np,0,3), prob[idx])
        f2-=f2.min()
        f2/=f2.max()
        f2
plt.imshow(dx)
plt.imshow(scipy.misc.imresize(f2, dx.shape), alpha=0.5, cmap='jet');

Could you please post the code that is consistent with previous naming, or add a file in the repository to demonstrate this flow ?

Thanks !

In chapter 2, we are supposed to download a dataset by running a file named "download.py," but a lot of pics from it are unavailable. Can you please create an archive with this dataset and post a link?

At the top of page 21, we have

batch_size=64
train_data_loader = data.DataLoader(train_data, batch_size=batch_size)  ## line 19, 'data' not defined
val_data_loader = data.DataLoader(val_data, batch_size=batch_size)
test_data_loader = data.DataLoader(test_data, batch_size=batch_size)

which python will complain about, because there has been no 'data' package import.

$ python ./prep.py
Traceback (most recent call last):
  File "./prep.py", line 19, in <module>
    train_data_loader = data.DataLoader(train_data, batch_size=batch_size)
NameError: name 'data' is not defined

where is the data package with the DataLoader definition supposed to come from?

Dear @falloutdurham,

would you mind to comment where to find casper.jpg. I copied your casper.jpg from the notebook to my drive and used that pic, but I had to adjust one line to make it work:

tensor = preprocess(image)

to

tensor = preprocess(image.convert('RGB'))

Maybe you can add the image to the directory and check whether the rgb conversion is necessary for the added one.

Replacing the model from Chapter 2 with the one of Chapter 3, results in the following message when the prediction algorithm is called:
RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x36 and 9216x4096)
EDIT: Found solution, using .unsqueeze(0)

I'm downloading images for Chapter 2, there are a number of explicit errors like:

Error downloading http://farm1.static.flickr.com/27/57455726_8ccf14753f.jpg
Error downloading http://farm1.static.flickr.com/185/426029368_3a9612f006.jpg
Error downloading http://farm2.static.flickr.com/1133/1141687717_890fe14d8e.jpg
Error downloading http://farm1.static.flickr.com/62/204035942_de0d323af5.jpg
Error downloading http://farm2.static.flickr.com/1172/1323667952_a7a74975c4.jpg
Error downloading http://farm1.static.flickr.com/42/84450056_bb5974a64f.jpg
Error downloading http://farm4.static.flickr.com/3001/2776498771_20527f258b.jpg
Error downloading http://farm4.static.flickr.com/3159/2824589595_07ee2443a3.jpg
Error downloading http://farm4.static.flickr.com/3206/2970698030_1021311f52.jpg
Error downloading http://farm2.static.flickr.com/1125/1348034256_cc50f5b446.jpg
Error downloading http://farm3.static.flickr.com/2416/1833444691_dc3d1017db.jpg
Error downloading http://farm3.static.flickr.com/2077/1834276056_4e86bacfe3.jpg
Error downloading http://farm2.static.flickr.com/1231/1455910975_07315bf34e.jpg
Error downloading http://farm2.static.flickr.com/1424/747119762_615603a9a0.jpg
Error downloading http://farm1.static.flickr.com/79/233477721_0126cc0331.jpg
Error downloading http://farm3.static.flickr.com/2056/2021607548_e0835d3552.jpg
Error downloading http://farm2.static.flickr.com/1312/1287462891_f1a3e27b50.jpg
Error downloading http://farm2.static.flickr.com/1322/1348044264_3e0ed6611e.jpg
Error downloading http://farm4.static.flickr.com/3183/2791133165_5df1d47be5.jpg
Error downloading http://farm4.static.flickr.com/3088/2776496087_1973f8dced.jpg
Error downloading http://farm4.static.flickr.com/3224/2777354896_176a518b8c.jpg
Error downloading http://farm2.static.flickr.com/1103/840200833_be72b99848.jpg

There are also invalid 0 byte jpeg files created in the target directories.

It would help students if there was a quick explanation in the README on github. helping us with:

• should we worry or not?
• actions to take to recover - if necessary

Excellent tutorial !

Cheers

-- Peter G

As title says, how do you evaluate a CNN? I tried use the same approach on chapter 2 but I can't. I get the following.

`---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
in
11 img = img_transforms(img).to(device)
12 cnnet.eval()
---> 13 prediction = F.softmax(cnnet(img), dim=1)
14 prediction = prediction.argmax()
15 cats_pred.append(labels[prediction])

D:\Users\gusta\anaconda3\envs\book-1\lib\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(),

in forward(self, x)
30
31 def forward(self, x):
---> 32 x = self.features(x)
33 x = self.avgpool(x)
34 x = torch.flatten(x, 1)

D:\Users\gusta\anaconda3\envs\book-1\lib\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(),

D:\Users\gusta\anaconda3\envs\book-1\lib\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

D:\Users\gusta\anaconda3\envs\book-1\lib\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(),

D:\Users\gusta\anaconda3\envs\book-1\lib\site-packages\torch\nn\modules\conv.py in forward(self, input)
421
422 def forward(self, input: Tensor) -> Tensor:
--> 423 return self._conv_forward(input, self.weight)
424
425 class Conv3d(_ConvNd):

D:\Users\gusta\anaconda3\envs\book-1\lib\site-packages\torch\nn\modules\conv.py in _conv_forward(self, input, weight)
417 weight, self.bias, self.stride,
418 _pair(0), self.dilation, self.groups)
--> 419 return F.conv2d(input, weight, self.bias, self.stride,
420 self.padding, self.dilation, self.groups)
421

RuntimeError: Expected 4-dimensional input for 4-dimensional weight [64, 3, 11, 11], but got 3-dimensional input of size [3, 64, 64] instead
`

Hello,

I can't open the notebook directly in GitHub

Sorry, something went wrong. Reload?

I can't open the notebook with Google Colab

Error when I open the cloned repo:
Cannot read property 'id' of undefined
TypeError: Cannot read property 'id' of undefined
at XG (https://colab.research.google.com/v2/external/external_polymer_binary_l10n__de.js?vrz=colab-20210428-060103-RC00_370868387:1827:201)
at ya.program_ (https://colab.research.google.com/v2/external/external_polymer_binary_l10n__de.js?vrz=colab-20210428-060103-RC00_370868387:1825:247)
at Aa (https://colab.research.google.com/v2/external/external_polymer_binary_l10n__de.js?vrz=colab-20210428-060103-RC00_370868387:19:336)
at ya.next_ (https://colab.research.google.com/v2/external/external_polymer_binary_l10n__de.js?vrz=colab-20210428-060103-RC00_370868387:17:503)
at Ca.next (https://colab.research.google.com/v2/external/external_polymer_binary_l10n__de.js?vrz=colab-20210428-060103-RC00_370868387:20:206)
at f (https://colab.research.google.com/v2/external/external_polymer_binary_l10n__de.js?vrz=colab-20210428-060103-RC00_370868387:61:101)

The server.py from chapter 8 cannot be run as a whole, since definitions & functions are missing for some lines.

I tried to follow the version from the book as well and adapted the code and completed it where necessary, so that you can run the printed shell commands from the book and get a prediction. I propose to change the server.py (which I named catfish_server.py like in the book) and add a catfish_model.py to load the model inside the server file (see PR).

Hello, about the code in chapter 2, in the training section, I wonder why the loss calculation is different from that on the book.

def train(model, optimizer, loss_fn, train_loader, val_loader, epochs=5, device="cpu"):
    for epoch in range(1, epochs+1):

[...]

from github:

        training_loss += loss.data.item() * inputs.size(0)
training_loss /= len(train_loader.dataset)

Here you multiply the loss.data.item() * input.size(0), which is the number of elements in the batch input (64 elements). That's because by default, "the losses are averaged over each loss element in the batch.", so you calculate a cumulative loss for the entire batch.
Then you divide the training_loss by the total number of the elments in the dataset (about 800 elements). So this is a medium loss for each element.

from the book:

        training_loss += loss.data.item()
training_loss /= len(train_iterator)

The book code seems wrong in this case, and github code is right. Because in the last row you had to divide by the length of the batch?

[...]

from github:

            valid_loss += loss.data.item() * inputs.size(0)
            correct = torch.eq(torch.max(F.softmax(output, dim=1), dim=1)[1], targets)
            num_correct += torch.sum(correct).item()
            num_examples += correct.shape[0]
        valid_loss /= len(val_loader.dataset)

from the book:

            valid_loss += loss.data.item()
            correct = torch.eq(torch.max(F.softmax(output), dim=1)[1],
            target).view(-1)
            num_correct += torch.sum(correct).item()
            num_examples += correct.shape[0]
            valid_loss /= len(valid_iterator)

I've got this error when training the model from Chapter 2:

  File ".../Python/3.7.6/site-packages/PIL/ImageFile.py", line 220, in load
    raise IOError("image file is truncated (%d bytes not processed)" % len(b))
IOError: image file is truncated (57 bytes not processed)

I googled it and immediately found a solution here: https://stackoverflow.com/questions/12984426/python-pil-ioerror-image-file-truncated-with-big-images

So I added these lines before the training part and it helped.

from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES=True

Hopefully, it will help somebody else :-) I can submit a PR, if necessary.

I've got some issues on the newly changed Chapter 6.ipynb, since it failed to run at some parts.

First I'd propose to add the excecution of the precompute_spectrograms() function and add the paths being used in that function (and later in PrecomputedESC50), so that the precomputed spectrograms are saved on disk for further processing (see PR).

New features brought to torchtext are making the code obsolete (fields, dictionaries etc). You can still run the code for a little bit, using modified imports (from torchtext.legacy import data). Are you going to update the code for the 5th chapter, or is the job too long? Will we have an updated version of the book? thanks in advance.

'<' not supported between instances of 'Example' and 'Example' when running line:
train(epochs=5, model=model, optimizer=optimizer, criterion=criterion, train_iterator=train_iterator, valid_iterator=valid_iterator) (which by the way does not appear in the notebook).
Any idea how to fix ?

Great, great book by the way :)

hello when I use other images folder as dog and cat, with 5000 in cat and dog train, and 500 in each val, 100 in test, the predication result only give me dog feedback, even if I use val image to test

Hi,

While executing the cell below:

LABEL = data.LabelField()
TWEET = data.Field(tokenize='spacy', lower=True)

fields = [('score',None), ('id',None),('date',None),('query',None),
      ('name',None),
      ('tweet', TWEET),('category',None),('label',LABEL)] 

I get the following error:

OSError: [E050] Can't find model 'en'. It doesn't seem to be a shortcut link, a Python package or a valid path to a data directory.

Thank you.

py:28: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.

Need dim parameter added to softmax calls.

ImportError

When I run the tensorboard.py I get the following ImportError:

Traceback (most recent call last):
  File "/home/marcus/Uebersezungen/beginners-pytorch-deep-learning/venv/lib/python3.6/site-packages/torch/utils/tensorboard/__init__.py", line 2, in <module>
    from tensorboard.summary.writer.record_writer import RecordWriter  # noqa F401
  File "/home/marcus/Uebersezungen/beginners-pytorch-deep-learning/chapter7/tensorboard.py", line 3, in <module>
    from torch.utils.tensorboard import SummaryWriter
ImportError: cannot import name 'SummaryWriter'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "tensorboard.py", line 3, in <module>
    from torch.utils.tensorboard import SummaryWriter
  File "/home/marcus/Uebersezungen/beginners-pytorch-deep-learning/venv/lib/python3.6/site-packages/torch/utils/tensorboard/__init__.py", line 4, in <module>
    raise ImportError('TensorBoard logging requires TensorBoard with Python summary writer installed. '
ImportError: TensorBoard logging requires TensorBoard with Python summary writer installed. This should be available in 1.14 or above.
 marcus@marcus-XPS-13-7390  ~/Uebersezungen/beginners-pytorch-deep-learning/chapter7  master ✚ ● ?  python3 tensorboard.py                                                         1 ↵  478  15:58:52 
Traceback (most recent call last):
  File "/home/marcus/Uebersezungen/beginners-pytorch-deep-learning/venv/lib/python3.6/site-packages/torch/utils/tensorboard/__init__.py", line 2, in <module>
    from tensorboard.summary.writer.record_writer import RecordWriter  # noqa F401
  File "/home/marcus/Uebersezungen/beginners-pytorch-deep-learning/chapter7/tensorboard.py", line 3, in <module>
    from torch.utils.tensorboard import SummaryWriter
ImportError: cannot import name 'SummaryWriter'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "tensorboard.py", line 3, in <module>
    from torch.utils.tensorboard import SummaryWriter
  File "/home/marcus/Uebersezungen/beginners-pytorch-deep-learning/venv/lib/python3.6/site-packages/torch/utils/tensorboard/__init__.py", line 4, in <module>
    raise ImportError('TensorBoard logging requires TensorBoard with Python summary writer installed. '
ImportError: TensorBoard logging requires TensorBoard with Python summary writer installed. This should be available in 1.14 or above.

Versions

I work on Ubuntu 18.04 with tensorboard 2.2.1 and torch 1.4.0 installed

My solution

I had to rename tensorboard.py to make it work, think its due to a naming conflict with the ```from torch.utils.tensorboard import SummaryWriter``` import, but maybe Im wrong.

Does anyone have the same issue? Do I have the wrong dependencies installed?

I copyed the code to pycharm, but it can not run, here are the code:

import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data
import torch.nn.functional as F
import torchvision
from torchvision import transforms
from PIL import Image, ImageFile

ImageFile.LOAD_TRUNCATED_IMAGES = True

def check_image(path):

try:

im = Image.open(path)

return True

except:

return False

check_image = True

img_transforms = transforms.Compose([
transforms.Resize((64, 64)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])

train_data_path = "./train/"
train_data = torchvision.datasets.ImageFolder(root=train_data_path, transform=img_transforms)
val_data_path = "./val/"
val_data = torchvision.datasets.ImageFolder(root=val_data_path, transform=img_transforms)
test_data_path = "./test/"
test_data = torchvision.datasets.ImageFolder(root=test_data_path, transform=img_transforms)

batch_size = 64

train_data_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size)
val_data_loader = torch.utils.data.DataLoader(val_data, batch_size=batch_size)
test_data_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size)

class SimpleNet(nn.Module):

def __init__(self):
    super(SimpleNet, self).__init__()
    self.fc1 = nn.Linear(12288, 84)
    self.fc2 = nn.Linear(84, 50)
    self.fc3 = nn.Linear(50, 2)

def forward(self, x):
    x = x.view(-1, 12288)
    x = F.relu(self.fc1(x))
    x = F.relu(self.fc2(x))
    x = self.fc3(x)
    return x

simplenet = SimpleNet()

optimizer = optim.Adam(simplenet.parameters(), lr=0.001)

if torch.cuda.is_available():
device = torch.device("cuda")
else:
device = torch.device("cpu")

simplenet.to(device)

def train(model, optimizer, loss_fn, train_loader, val_loader, epochs, device):
for epoch in range(1, epochs + 1):
training_loss = 0.0
valid_loss = 0.0
model.train()
for batch in train_loader:
optimizer.zero_grad()
inputs, targets = batch
inputs = inputs.to(device)
targets = targets.to(device)
output = model(inputs)
loss = loss_fn(output, targets)
loss.backward()
optimizer.step()
training_loss += loss.data.item() * inputs.size(0)
training_loss /= len(train_loader.dataset)

    model.eval()
    num_correct = 0
    num_examples = 0
    for batch in val_loader:
        inputs, targets = batch
        inputs = inputs.to(device)
        output = model(inputs)
        targets = targets.to(device)
        loss = loss_fn(output, targets)
        valid_loss += loss.data.item() * inputs.size(0)
        correct = torch.eq(torch.max(F.softmax(output, dim=1), dim=1)[1], targets)
        num_correct += torch.sum(correct).item()
        num_examples += correct.shape[0]
    valid_loss /= len(val_loader.dataset)

    print(
        'Epoch: {}, Training Loss: {:.2f}, Validation Loss: {:.2f}, accuracy = {:.2f}'.format(epoch, training_loss,
                                                                                              valid_loss,
                                                                                              num_correct / num_examples))

epoch = 5

train(simplenet, optimizer, torch.nn.CrossEntropyLoss(), train_data_loader, val_data_loader, epoch, device)

mistake information:

Traceback (most recent call last):
File "C:\Users\Administrator\Desktop\disk_file\DL\1\5.py", line 110, in
train(simplenet, optimizer, torch.nn.CrossEntropyLoss(), train_data_loader, val_data_loader, epoch, device)
File "C:\Users\Administrator\Desktop\disk_file\DL\1\5.py", line 75, in train
for batch in train_loader:
File "C:\Users\Administrator\AppData\Roaming\Python\Python39\site-packages\torch\utils\data\dataloader.py", line 521, in next
data = self._next_data()
File "C:\Users\Administrator\AppData\Roaming\Python\Python39\site-packages\torch\utils\data\dataloader.py", line 561, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "C:\Users\Administrator\AppData\Roaming\Python\Python39\site-packages\torch\utils\data_utils\fetch.py", line 49, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "C:\Users\Administrator\AppData\Roaming\Python\Python39\site-packages\torch\utils\data_utils\fetch.py", line 49, in
data = [self.dataset[idx] for idx in possibly_batched_index]
File "C:\Users\Administrator\AppData\Roaming\Python\Python39\site-packages\torchvision\datasets\folder.py", line 232, in getitem
sample = self.loader(path)
File "C:\Users\Administrator\AppData\Roaming\Python\Python39\site-packages\torchvision\datasets\folder.py", line 269, in default_loader
return pil_loader(path)
File "C:\Users\Administrator\AppData\Roaming\Python\Python39\site-packages\torchvision\datasets\folder.py", line 250, in pil_loader
img = Image.open(f)
File "C:\Program Files\1\lib\site-packages\PIL\Image.py", line 3030, in open
raise UnidentifiedImageError(
PIL.UnidentifiedImageError: cannot identify image file <_io.BufferedReader name='./train/cat\1004525_cba96ba3c3.jpg'>

When running the example notebook for chapter 2, the call of ImageFolder for train, val and test data uses the keyword argument is_valid_file=check_img. ImageFolder passes as argument path to check_img(path) only the filenames without the relative pathes like ../cat/ or ../fish/ and therefore the test in check_img always throws an exception. I start the notebook in ./ and the keyword argument root="./train/" etc. seems to allow ImageFolder to correctly find the images in the two classes cat and fish, as the following final error message of the call of ImageFolder suggests: FileNotFoundError: Found no valid file for the classes cat, fish.. But path contains only the filenames of the images without the relative path w.r.t. ./ (or root).

The notebook runs without any other issues when is_valid_file=check_img is removed from the call of ImageFolder. I wonder whether I missunderstood something trivial or wether this is a bug. (I am using python v3.8.10, pytorch v1.10.0, torchvision v0.11.1)

2 problems:
1 test not defined
2 shown below

device="cuda"
bs=64
PATH_TO_ESC50 = Path.cwd() / 'esc50'
test

train_esc50 = ESC50(PATH_TO_ESC50 / "train")
valid_esc50 = ESC50(PATH_TO_ESC50 / "valid")
test_esc50 = ESC50(PATH_TO_ESC50 / "test")

train_loader = torch.utils.data.DataLoader(train_esc50, batch_size = bs, shuffle = True)
valid_loader = torch.utils.data.DataLoader(valid_esc50, batch_size = bs, shuffle = True)
test_loader = torch.utils.data.DataLoader(test_esc50, batch_size = bs, shuffle = True)

ValueErrorTraceback (most recent call last)
in ()
8 test_esc50 = ESC50(PATH_TO_ESC50 / "test")
9
---> 10 train_loader = torch.utils.data.DataLoader(train_esc50, batch_size = bs, shuffle = True)
11 valid_loader = torch.utils.data.DataLoader(valid_esc50, batch_size = bs, shuffle = True)
12 test_loader = torch.utils.data.DataLoader(test_esc50, batch_size = bs, shuffle = True)

~/anaconda3/lib/python3.6/site-packages/torch/utils/data/dataloader.py in init(self, dataset, batch_size, shuffle, sampler, batch_sampler, num_workers, collate_fn, pin_memory, drop_last, timeout, worker_init_fn, multiprocessing_context)
211 else: # map-style
212 if shuffle:
--> 213 sampler = RandomSampler(dataset)
214 else:
215 sampler = SequentialSampler(dataset)

~/anaconda3/lib/python3.6/site-packages/torch/utils/data/sampler.py in init(self, data_source, replacement, num_samples)
92 if not isinstance(self.num_samples, int) or self.num_samples <= 0:
93 raise ValueError("num_samples should be a positive integer "
---> 94 "value, but got num_samples={}".format(self.num_samples))
95
96 @Property

ValueError: num_samples should be a positive integer value, but got num_samples=0

The following pictures seem to be missing. Is this to be expected?

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I just downgraded my torch and other packages to the correct version. Seems this doesn't work.

after installing with 'pip install googletrans' to the anaconda env for pytorch
the line:
sentences = ['The cat sat on the mat']
translations_fr = translator.translate(sentences, dest='fr')

throws an error:
File "myuser\anaconda3\envs\PyTc\lib\site-packages\googletrans\gtoken.py", line 62, in _update
code = self.RE_TKK.search(r.text).group(1).replace('var ', '')

AttributeError: 'NoneType' object has no attribute 'group'

how can i make this nice tool work?!

Hi,
I do not find the solution, why len(train_loader.dataset) is 0 and leads to this error.
Thank you for help.
Brds,
Patrick

[9] train(audionet, optimizer, torch.nn.CrossEntropyLoss(), train_loader, valid_loader, epochs=20, device=device)

ZeroDivisionError Traceback (most recent call last)
in
----> 1 train(audionet, optimizer, torch.nn.CrossEntropyLoss(), train_loader, valid_loader, epochs=20, device=device)

in train(model, optimizer, loss_fn, train_loader, val_loader, epochs, device)
14 optimizer.step()
15 training_loss += loss.data.item() * inputs.size(0)
---> 16 training_loss /= len(train_loader.dataset)
17
18 model.eval()

ZeroDivisionError: float division by zero