Hello,

I am working on a next word prediction model using a regularised LSTM architecture. I am struggling to understand why my model performs bad on inference. I have trained a LSTM model on the Penn Tree Bank dataset reaching about 94 in test perplexity, while this is not the best result I think it should give some correct predictions.

When I run my prediction functions on the test file, I get 0 matches however I do see predictions. I am not sure if exact matches are not supposed to happen or if my function itself has some error. Here is my code:

```
#Model Loading
PATH = "./ptb_model.pth"
model = Model(vocab_size, hidden_size, layer_num, dropout, winit, lstm_type)
model = torch.load(PATH)
print("Vocab Size: {}".format(vocab_size))
unique_index_word = dict((i,c) for i,c in enumerate(sorted(vocab)))
def predict(string, model):
batch_size = 1
model.eval()
string = clean_text(string)
string = tokenize_text(string)
string = to_lower_case_list(string)
hidden = model.state_init(batch_size)
try:
for w in string:
ix = torch.tensor([[unique_word_index[w]]])
output, hidden= model(ix, hidden)
_ , top_ix = torch.topk(output[0], k=10)
choices = top_ix.tolist()
choice = np.random.choice(choices[0])
out_word = unique_index_word[choice]
except:
out_word = "Error"
return out_word
with open("ptb.test.txt", 'r') as f:
lines = f.readlines()
matches = 0
total = 0
for line in lines:
line = line.strip(" ")
text = line.split(" ")
if len(text) > 7:
#print(text)
exact = text[7]
text = text[:7]
text = " ".join(text)
out = predict(text, model)
print("Sequence: {}".format(text))
print("Prediction: {} Exact: {}".format(out, exact))
print("*"*10)
total += 1
if out == exact:
matches += 1
print(matches/total)
```

I am also not sure how to include an accuracy measure in my training process. Here is my code for the perplexity:

```
def perplexity(data, model):
with torch.no_grad():
losses = []
states = model.state_init(batch_size)
accuracy = 0
for x, y in data:
scores, states = model(x, states)
loss = nll_loss(scores, y)
ps = torch.exp(scores)
#print(y)
#print(scores)
#equality = (y.data[0] == ps.max(dim = 1))
#accuracy += equality.type(torch.FloatTensor).mean()
#print(accuracy)
losses.append(loss.data.item()/batch_size)
return np.exp(np.mean(losses)), np.mean(losses), accuracy
```

If I’m not wrong, since I have 0 matches on inference, wouldn’t my model accuracy be 0? This just seems a bit strange to me. If any one knows what’s wrong, I would be very grateful!