kyungjunlee / codemixedsentimentdetection Goto Github PK

print("-"*50)
print("Running LSTM, One Hot Embeddings, Untrainable")

config = {
    "NAME": "LSTM_OneHot_Untrainable",
    "N_EPOCHS": 1,
    "EMB_TRAIN": False,
    "N_VOCAB": len(text_field.vocab.itos),
    "N_EMBED" : len(text_field.vocab.itos),
    "N_HIDDEN" : 512,
    "N_OUTPUT" : 3,
    "N_LAYERS" : 2,
    "LR": 0.001
}
model = OneHotLSTM(config)
pp.pprint(config)
print("-"*50)
run_loop(model, config)
print("-"*50)
print("END TRAINING")
print("-"*50)
print("START TESTING")
print("-"*50)
test(model, config)
print("-"*50)
print("END TESTING")
print("-"*50)

--------------------------------------------------
Running LSTM, One Hot Embeddings, Untrainable
{   'EMB_TRAIN': False,
    'LR': 0.001,
    'NAME': 'LSTM_OneHot_Untrainable',
    'N_EMBED': 899,
    'N_EPOCHS': 1,
    'N_HIDDEN': 512,
    'N_LAYERS': 2,
    'N_OUTPUT': 3,
    'N_VOCAB': 899}
--------------------------------------------------
BEGIN TRAINING
--------------------------------------------------
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-34-f17180894fb6> in <module>()
     16 pp.pprint(config)
     17 print("-"*50)
---> 18 run_loop(model, config)
     19 print("-"*50)
     20 print("END TRAINING")

4 frames
/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py in cross_entropy(input, target, weight, size_average, ignore_index, reduce, reduction)
   2822     if size_average is not None or reduce is not None:
   2823         reduction = _Reduction.legacy_get_string(size_average, reduce)
-> 2824     return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
   2825 
   2826 

TypeError: cross_entropy_loss(): argument 'input' (position 1) must be Tensor, not tuple