I am getting this error while writing the following class code. The error message is attached below the code.

class GPReviewDataset(data.dataset):

def init(self, review, target, tokenizer, max_len): self.review = review self.target = target self.tokenizer = tokenizer self.max_len = max_len

def len(self): # return the number of reviews we have return len(self.reviews)

def getitem(self, item): # takes the index and the reviews review = str(self.reviews[item])

encoding = self.tokenizer.encode_plus(
  review,
  add_special_tokens=True,
  max_length=self.max_len,
  return_token_type_ids=False,
  pad_to_max_length=True,
  return_attention_mask=True,
  return_tensors='pt'
)

return {
  'review_text': review,
  'input_ids': encoding['input_ids'].flatten(),
  'attention_mask': encoding['attention_mask'].flatten(),
  'targets': torch.tensor(self.target, dtype=torch.long)
}

I am getting the following error for 04.first-neural-network.ipynb notebook:

--------------------------------------------------
RuntimeError     Traceback (most recent call last)
<ipython-input-28-8756d5fbee9e> in <module>
     10 
     11     if epoch % 100 == 0:
---> 12       train_acc = calculate_accuracy(y_train, y_pred)
     13 
     14       y_test_pred = net(X_test)

<ipython-input-24-51ba3ab94870> in calculate_accuracy(y_true, y_pred)
      1 def calculate_accuracy(y_true, y_pred):
      2   predicted = y_pred.ge(.5).view(-1)
----> 3   return (y_true == predicted).sum().float() / len(y_true)

RuntimeError: Expected object of scalar type Float but got scalar type Byte for argument #2 'other'

Change this line -> bert_model = BertModel.from_pretrained(PRE_TRAINED_MODEL_NAME) to bert_model = BertModel.from_pretrained(PRE_TRAINED_MODEL_NAME, return_dict=False)

as a result the dict will not return str rather will return tensor.

It will help you with other parts of code as well.

Hi. Thanks to this code, I was able to write a multi-label classification model well. By the way, can you tell me how to export the model made like this using torch.onnx? An error occurred when I used the normal torch.onnx.export method.

My code :

test_comment="hello"  
encoding = tokenizer.encode_plus(
    test_comment,
    add_special_tokens=True,
    max_length=63,
    return_token_type_ids=False,
    padding="max_length",
    return_attention_mask=True,
    return_tensors='pt',
  )



torch.onnx.export(trained_model,
                    (encoding["input_ids"], encoding["attention_mask"]),
                    'model.onnx',
                    export_params=True,
                    do_constant_folding=True,
                    opset_version=11,
                    input_names=['input_ids', 'attention_mask'],
                    output_names=['output'],
)

error :

RuntimeError                              Traceback (most recent call last)
<ipython-input-49-9c2e1e064898> in <module>
----> 1 torch.onnx.export(trained_model,
      2                     (encoding["input_ids"], encoding["attention_mask"]),
      3                     'model.onnx',
      4                     export_params=True,
      5                     do_constant_folding=True,

~/anaconda3/envs/myenv1/lib/python3.8/site-packages/torch/onnx/__init__.py in export(model, args, f, export_params, verbose, training, input_names, output_names, aten, export_raw_ir, operator_export_type, opset_version, _retain_param_name, do_constant_folding, example_outputs, strip_doc_string, dynamic_axes, keep_initializers_as_inputs, custom_opsets, enable_onnx_checker, use_external_data_format)
    273 
    274     from torch.onnx import utils
--> 275     return utils.export(model, args, f, export_params, verbose, training,
    276                         input_names, output_names, aten, export_raw_ir,
    277                         operator_export_type, opset_version, _retain_param_name,

~/anaconda3/envs/myenv1/lib/python3.8/site-packages/torch/onnx/utils.py in export(model, args, f, export_params, verbose, training, input_names, output_names, aten, export_raw_ir, operator_export_type, opset_version, _retain_param_name, do_constant_folding, example_outputs, strip_doc_string, dynamic_axes, keep_initializers_as_inputs, custom_opsets, enable_onnx_checker, use_external_data_format)
     86         else:
     87             operator_export_type = OperatorExportTypes.ONNX
---> 88     _export(model, args, f, export_params, verbose, training, input_names, output_names,
     89             operator_export_type=operator_export_type, opset_version=opset_version,
     90             _retain_param_name=_retain_param_name, do_constant_folding=do_constant_folding,

~/anaconda3/envs/myenv1/lib/python3.8/site-packages/torch/onnx/utils.py in _export(model, args, f, export_params, verbose, training, input_names, output_names, operator_export_type, export_type, example_outputs, opset_version, _retain_param_name, do_constant_folding, strip_doc_string, dynamic_axes, keep_initializers_as_inputs, fixed_batch_size, custom_opsets, add_node_names, enable_onnx_checker, use_external_data_format, onnx_shape_inference)
    687 
...
    128             wrapper,
    129             in_vars + module_state,

RuntimeError: output 1 (0
[ CPULongType{} ]) of traced region did not have observable data dependence with trace inputs; this probably indicates your program cannot be understood by the tracer.

Thank you in advance for your reply.

Hi, When running the notebook 11.multi-label-text-classification-with-bert.ipynb I encounter the following error... pytorch_lightning.utilities.exceptions.MisconfigurationException: ModelCheckpoint(monitor='val_loss') not found in the returned metrics: ['train_loss']. HINT: Did you call self.log('val_loss', tensor) in the LightningModule?

Do you know how I could fix this?

Hi, This question is related to notebook 6 on ECG anomaly detection.

I would like to know if there is any study on how much is the accuracy improvement on using LSTM autoencoder versus ANN encoder. Let me know if you have any info.

Regards, Debapriya

Great work you did in the Multi-label Text Classification! Thanks!! I have a similar problem as you except that II only have one column for the labels (for example, you have [1,0,0,0,0,0] as label of the toxic classe but with my data I have only one value as the number of the class. For example with the first class I have 1 in the column and the number goes to 10). This causes a problem in the training (trainer.fit), the code keep telling me that the target size (torch.Size([16]) which is the epoch number) is different from the input size (which is torch.Size([16, 10]) and the 10 here is the number of the classes) Can you please tell me where can I make the changes so the code will run?