for text in data_loader:
out = model(text)
pred.append(out)
pred = torch.stack(pred)
pred = list(np.array(pred.detach()))
for i in range(len(pred)):
str1 = str(int(pred[i][0])) + ' ' + str(d[i])
f.write(str1)
f.write('\n')
if __name__ == "__main__":
epochs = 10
d = DataLoader(batch_size=64)
text_tr, labels_tr, maxLen_tr = d.load_data('train')
v = d.createVocab(text_tr)
enc_data_tr = d.padText(text_tr, maxLen_tr, v)
train_loader = d.createTensor(enc_data_tr, labels_tr)
train_acc, model = train_main(train_loader, v, epochs)
text_ts, labels_ts, maxLen_ts = d.load_data('test')
enc_data_ts = d.padText(text_ts, maxLen_ts, v)
test_loader = d.createTensor(enc_data_ts, labels_ts, batch_size=1)
test_acc, pred, test_labels = evaluate(test_loader, model)
print("The training accuracy is:")
print(train_acc)
print("The testing accuracy is:")
print(test_acc)
