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I was using your model trainer, but with considerably less data than from the given example
Trying to train for 3 classes
Approx. 50 sentences in the training set for each class
and what parameters should I provide in order to make a functioning/effective model.
Normally when I run with the given parameters we get an error, and so we were wondering if there was a better way to go about doing that.
Errors include:
for the following paramters
{
"num_epochs": 1,
"batch_size": 20,
"num_filters": 32,
"filter_sizes": "3,4,5",
"embedding_dim": 50,
"l2_reg_lambda": 0.0,
"evaluate_every": 200,
"dropout_keep_prob": 0.5
}
logging.critical('Accuracy on test set is {} based on the best model {}'.format(test_accuracy, path))
UnboundLocalError: local variable 'path' referenced before assignment
The text was updated successfully, but these errors were encountered:
Hi,
I was using your model trainer, but with considerably less data than from the given example
and what parameters should I provide in order to make a functioning/effective model.
Normally when I run with the given parameters we get an error, and so we were wondering if there was a better way to go about doing that.
Errors include:
{
"num_epochs": 1,
"batch_size": 20,
"num_filters": 32,
"filter_sizes": "3,4,5",
"embedding_dim": 50,
"l2_reg_lambda": 0.0,
"evaluate_every": 200,
"dropout_keep_prob": 0.5
}
logging.critical('Accuracy on test set is {} based on the best model {}'.format(test_accuracy, path))
UnboundLocalError: local variable 'path' referenced before assignment
The text was updated successfully, but these errors were encountered: