# Train the model
training_iter = data_iter(training_set, batch_size)
train_eval_iter = eval_iter(training_set[:256], batch_size)
validation_iter = eval_iter(validation_set, batch_size)
total_batches = int(len(training_set) / batch_size)
#total_batches = 100
# In[441]:
tls.training_loop(batch_size,
total_batches,
hyper_params['alphabet_size'],
hyper_params['l0'],
num_epochs,
model,
loss,
optimizer,
training_iter,
validation_iter,
train_eval_iter,
save_model_path,
comet,
cuda=True)
# Loading best model and calculating accuracy on test set
tls.load_checkpoint(model, save_model_path)
test_set = dataGenerator(data_path + 'test.txt',
test=True,
max_length=hyper_params['l0'])
test_iter = eval_iter(test_set, batch_size)
test_acc = tls.evaluate(model,
print(model)
# Train the model
training_iter = data_iter(training_set, batch_size)
train_eval_iter = eval_iter(training_set[:256], batch_size)
validation_iter = eval_iter(validation_set, batch_size)
total_batches = int(len(training_set) / batch_size)
tls.training_loop(batch_size,
total_batches,
alphabet_size,
l0,
num_epochs,
model,
loss,
optimizer,
training_iter,
validation_iter,
train_eval_iter,
save_path,
comet,
cuda=True)
# Loading best model and calculating accuracy on test set
tls.load_checkpoint(model, save_path)
test_set = dataGeneratorTest(list_subword_without_end,
file_name=data_path + 'test.txt',
max_length=l0)
test_iter = eval_iter(test_set, batch_size)
test_acc = tls.evaluate(model,