Accuracy score in pyTorch LSTM

Question

I have been running this LSTM tutorial on the wikigold.conll NER data set `training_data` contains a list of tuples of sequences and tags, for example: ``` training_data = [ ("They also have a song called \" wake up \"".split(), ["O", "O", "O", "O", "O", "O", "I-MISC", "I-MISC", "I-MISC", "I-MISC"]), ("Major General John C. Scheidt Jr.".split(), ["O", "O", "I-PER", "I-PER", "I-PER"]) ] ``` And I wrote down this function ``` def predict(indices): """Gets a list of indices of training_data, and returns a list of predicted lists of tags""" for index in indicies: inputs = prepare_sequence(training_data[index][0], word_to_ix) tag_scores = model(inputs) values, target = torch.max(tag_scores, 1) yield target ``` This way I can get the predicted labels for specific indices in the training data. However, how do I evaluate the accuracy score across all training data. Accuracy being, the amount of words correctly classified across all sentences divided by the word count. This is what I came up w…

Accepted Answer

I would use `numpy` in order to not iterate the list in pure python.

The results are the same, but it runs much faster

```
def accuracy_score(y_true, y_pred):
    y_pred = np.concatenate(tuple(y_pred))
    y_true = np.concatenate(tuple([[t for t in y] for y in y_true])).reshape(y_pred.shape)
    return (y_true == y_pred).sum() / float(len(y_true))
```

And this is how to use it:

```
#original code:
y_pred = list(predict([s for s, t in training_data]))
y_true = [t for s, t in training_data]
#numpy accuracy score
print(accuracy_score(y_true, y_pred))
```

Accuracy score in pyTorch LSTM

Solution

Problem