I am getting the following error. If I click on the manual download link.

400. That’s an error.

The requested URL was not found on this server. That’s all we know.

https://github.com/joosthub/PyTorchNLPBook/blob/db9fc8fe48a2416b36b21dde0dfce787c6ade2b2/chapters/chapter_4/4_4_cnn_surnames/4_4_Classifying_Surnames_with_a_CNN.ipynb#L552

code checks is not available to switch to false (but starts as false)

isn't this wrong if data_size not multiple of batch_size? shoudn't it be :

    def get_num_batches(self, batch_size):
        """Given a batch size, return the number of batches in the dataset
        
        Args:
            batch_size (int)
        Returns:
            number of batches in the dataset
        """
        return int(np.ceil(len(self)/batch_size))

Uses pandas groupby and filtering functionality to create subset dataframe in place of manual creation through loops.

Also improves efficiency of execution from Wall time of over 1 min to 120ms

In fact, F.dropout(xx, p=0.5) works both in model's train mode and eval mode. You should write F.dropout(xx, p=0.5, training=self.training) or when you excute a cell more than one time, the result is different. I heard this book from the CS224 class, the professor said that it's a great book and if we don't read the book, he will be sad. Even now there seems no one maintaining this book. I want to say "Thank you, Great authors!"

An error occurred when I ran below code in Chapter3, could anyone please help to figure it out?

`classifier = classifier.to(args.device)

loss_func = nn.BCEWithLogitsLoss() optimizer = optim.Adam(classifier.parameters(), lr=args.learning_rate) scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer=optimizer, mode='min', factor=0.5, patience=1)

train_state = make_train_state(args)

epoch_bar = tqdm.notebook.tqdm(desc='training routine', total=args.num_epochs, position=0)

dataset.set_split('train') train_bar = tqdm.notebook.tqdm(desc='split=train', total=dataset.get_num_batches(args.batch_size), position=1, leave=True) dataset.set_split('val') val_bar = tqdm.notebook.tqdm(desc='split=val', total=dataset.get_num_batches(args.batch_size), position=1, leave=True)

try: for epoch_index in range(args.num_epochs): train_state['epoch_index'] = epoch_index

    # Iterate over training dataset

    # setup: batch generator, set loss and acc to 0, set train mode on
    dataset.set_split('train')
    batch_generator = generate_batches(dataset, 
                                       batch_size=args.batch_size, 
                                       device=args.device)
    running_loss = 0.0
    running_acc = 0.0
    classifier.train()

    for batch_index, batch_dict in enumerate(batch_generator):
        # the training routine is these 5 steps:

        # --------------------------------------
        # step 1. zero the gradients
        optimizer.zero_grad()

        # step 2. compute the output
        y_pred = classifier(x_in=batch_dict['x_data'].float())

        # step 3. compute the loss
        loss = loss_func(y_pred, batch_dict['y_target'].float())
        loss_t = loss.item()
        running_loss += (loss_t - running_loss) / (batch_index + 1)

        # step 4. use loss to produce gradients
        loss.backward()

        # step 5. use optimizer to take gradient step
        optimizer.step()
        # -----------------------------------------
        # compute the accuracy
        acc_t = compute_accuracy(y_pred, batch_dict['y_target'])
        running_acc += (acc_t - running_acc) / (batch_index + 1)

        # update bar
        train_bar.set_postfix(loss=running_loss, 
                              acc=running_acc, 
                              epoch=epoch_index)
        train_bar.update()

    train_state['train_loss'].append(running_loss)
    train_state['train_acc'].append(running_acc)

    # Iterate over val dataset

    # setup: batch generator, set loss and acc to 0; set eval mode on
    dataset.set_split('val')
    batch_generator = generate_batches(dataset, 
                                       batch_size=args.batch_size, 
                                       device=args.device)
    running_loss = 0.
    running_acc = 0.
    classifier.eval()

    for batch_index, batch_dict in enumerate(batch_generator):
        
        # compute the output
        y_pred = classifier(x_in=batch_dict['x_data'].float())

        # compute the loss
        loss = loss_func(y_pred, batch_dict['y_target'].float())
        loss_t = loss.item()
        running_loss += (loss_t - running_loss) / (batch_index + 1)

        # compute the accuracy
        acc_t = compute_accuracy(y_pred, batch_dict['y_target'])
        running_acc += (acc_t - running_acc) / (batch_index + 1)
        
        val_bar.set_postfix(loss=running_loss, 
                            acc=running_acc, 
                            epoch=epoch_index)
        val_bar.update()

    train_state['val_loss'].append(running_loss)
    train_state['val_acc'].append(running_acc)

    train_state = update_train_state(args=args, 
                                     model=classifier,
                                     train_state=train_state)

    scheduler.step(train_state['val_loss'][-1])

    train_bar.n = 0
    val_bar.n = 0
    epoch_bar.update()

    if train_state['stop_early']:
        break

    train_bar.n = 0
    val_bar.n = 0
    epoch_bar.update()

except KeyboardInterrupt: print("Exiting loop")`

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

I can't understand the code running_loss += (loss_t - running_loss) / (batch_index + 1) I think loss_t means per current batch loss ,loss_t is less than running loss (loss_t - running_loss ?) can anyone explain it what the codes mean ?

raw_train.csv

https://drive.google.com/open?id=1xeUnqkhuzGGzZKThzPeXe2Vf6Uu_g_xM gives a 404 error

Please provide update link to exact dataset used in the book, or to an entirely new set of yelp CSV-formatted datasets (train, test, and reviews_with_splits_lite)

download.py doesn't work. And when I tried to go by link to train set from .md(https://github.com/delip/PyTorchNLPBook/blob/master/data/README.md): `404. That’s an error.

The requested URL was not found on this server. That’s all we know.`

What I should do for download your examples?

file glove.6B.100d.txt from kaggle [link] the appropriate from_embeddings_file method:

def from_embeddings_file(cls, embedding_file):
    """Instantiate from pre-trained vector file.

    Vector file should be of the format:
        word0 x0_0 x0_1 x0_2 x0_3 ... x0_N
        word1 x1_0 x1_1 x1_2 x1_3 ... x1_N

    Args:
        embedding_file (str): location of the file
    Returns: 
        instance of PretrainedEmbeddigns
    """
    word_to_index = {}
    word_vectors = []

    with open(embedding_file, encoding="utf8") as fp:
        for line in fp.readlines():
            line = line.split(" ")
            word = line[0]
            vec = np.array([float(x) for x in line[1:]])

            word_to_index[word] = len(word_to_index)
            word_vectors.append(vec)

    return cls(word_to_index, word_vectors)

The early_stopping_best_val never gets updated so the loss is ALWAYS smaller and early stopping never happens this line of code is missing in the if-else statement in update_train_stage function:

train_state['early_stopping_best_val'] = loss_t

Hi, thanks for providing the code. I found a small typo in 3_5_Classifying_Yelp_Review_Sentiment.py. In the ReviewVectorizer class, the comment of vectorize should be one-hot rather than one-hit.