def run(sample_num, dqn_model, isRandom):
# Initialize LSTM model, allocate the cuda memory
model_LSTM = MyLSTM(n_letters, hidden_size_LSTM, nlayers_LSTM, True, True,
hidden_dropout_prob_LSTM, bidirectional_LSTM,
batch_size_LSTM, cuda_LSTM)
model_LSTM.cuda()
# Load the data based on selection(sampled data or random data)
if not isRandom:
with open(
'sampled_data/data_sampled_' + str(dqn_model) + '_' +
str(sample_num), 'rb') as b:
dataset_train = pickle.load(b)
write_loss = open('sampled_data/data_sampled_dqn_loss_' +
str(dqn_model) + '_' + str(sample_num) + '.csv',
'w',
encoding='UTF-8',
newline='')
else:
with open(
'sampled_data/data_sampled_random_' + str(dqn_model) + '_' +
str(sample_num), 'rb') as b:
dataset_train = pickle.load(b)
write_loss = open('sampled_data/data_sampled_random_loss_' +
str(dqn_model) + '_' + str(sample_num) + '.csv',
'w',
encoding='UTF-8',
newline='')
writer = csv.DictWriter(
write_loss,
fieldnames=['Epoch', 'Train_loss', 'Train_ppl', 'Val_loss'])
# LSTM Training Part
# At any point, you can hit Ctrl + C to break out of training early.
try:
for epoch in range(1, n_epoch + 1):
print("# Epoch", epoch)
model_LSTM, train_loss, train_ppl = w_t_RL.train(
model_LSTM, dataset_train,
epoch) # Train LSTM based on dataset_labelled
val_loss = w_t_RL.evaluate(model_LSTM, dataset_val,
epoch) # Evaluate current loss
writer.writerow({
'Epoch': str(epoch),
'Train_loss': str(train_loss),
'Train_ppl': str(train_ppl),
'Val_loss': str(val_loss)
})
except KeyboardInterrupt:
print('-' * 89)
print('Exiting from training early')
# write_loss.close()
write_loss.close()
choice = random.randint(0,
N_options - 1) # Choose data randomly
# dataset_train.append(data_list[choice]) # Add selected data into train dataset
dataset_train = np.concatenate([dataset_train, data_list[choice]])
# Update seen lists
state = create_feature(data, uni_seen_list, bi_seen_list,
tri_seen_list, i_10 * 10 + i + 1, True)
state_list.append(state)
print(state)
loss_prev = w_t_RL.evaluate(model_LSTM, dataset_val,
i_ep) # Evaluate previous loss
model_LSTM, loss_train, _ = w_t_RL.train(
model_LSTM, dataset_train,
i_ep) # train LSTM based on dataset_labelled
loss_curr = w_t_RL.evaluate(model_LSTM, dataset_val,
i_ep) # Evaluate current loss
reward = (
loss_prev - loss_curr
) / 10 # Reward(Difference between previous loss and current loss)
print("# loss_prev, loss_cur, reward :", loss_prev, loss_curr, reward)
'''
# Save replay memory with "terminal" state when dataset is exhausted
if i == len(dataset)//N_options-1:
replay_memory.append([state,reward,"terminal"])
break;
state_prev = state # Save previous state
def train_LSTM(dataset, w_t_model):
return w_t_RL.train(w_t_model, dataset)
def run(sample_num, dqn_model, isRandom):
# Initialize LSTM model, allocate the cuda memory
model_LSTM = MyLSTM(n_letters, hidden_size_LSTM, nlayers_LSTM, True, True,
hidden_dropout_prob_LSTM, bidirectional_LSTM,
batch_size_LSTM, cuda_LSTM)
model_LSTM.cuda()
# Load the data based on selection(sampled data or random data)
if not isRandom:
with open(
'sampled_data/data_sampled_' + str(dqn_model) + '_' +
str(sample_num), 'rb') as b:
dataset_train = pickle.load(b)
with open(
'sampled_data/value_sampled_' + str(dqn_model) + '_' +
str(sample_num), 'rb') as b:
dataset_value_dqn = pickle.load(b)
write_loss = open('sampled_data/data_sampled_dqn_loss_' +
str(dqn_model) + '_' + str(sample_num) + '.csv',
'w',
encoding='UTF-8',
newline='')
write_val_diff = open('sampled_data/val_sampled_dqn_diff_' +
str(dqn_model) + '_' + str(sample_num) + '.csv',
'w',
encoding='UTF-8',
newline='')
else:
with open(
'sampled_data/data_sampled_random_' + str(dqn_model) + '_' +
str(sample_num), 'rb') as b:
dataset_train = pickle.load(b)
with open(
'sampled_data/value_sampled_random_' + str(dqn_model) + '_' +
str(sample_num), 'rb') as b:
dataset_value_dqn = pickle.load(b)
write_loss = open('sampled_data/data_sampled_random_loss_' +
str(dqn_model) + '_' + str(sample_num) + '.csv',
'w',
encoding='UTF-8',
newline='')
write_val_diff = open('sampled_data/val_sampled_random_diff_' +
str(dqn_model) + '_' + str(sample_num) + '.csv',
'w',
encoding='UTF-8',
newline='')
writer = csv.DictWriter(
write_loss,
fieldnames=['Epoch', 'Train_loss', 'Train_ppl', 'Val_loss'])
writer_value = csv.DictWriter(
write_val_diff, fieldnames=['Epoch', 'Iteration', 'Reward', 'Value'])
# LSTM Training Part
# At any point, you can hit Ctrl + C to break out of training early.
try:
for epoch in range(1, n_epoch + 1):
print("# Epoch", epoch)
for i in range(len(dataset_train)
): # Loop through groups of N_options options
loss_prev = w_t_RL.evaluate(model_LSTM, dataset_val,
epoch) # Evaluate previous loss
model_LSTM, train_loss, train_ppl = w_t_RL.train(
model_LSTM, [dataset_train[i]],
epoch) # Train LSTM based on dataset_labelled
loss_curr = w_t_RL.evaluate(model_LSTM, dataset_val,
epoch) # Evaluate current loss
reward = loss_prev - loss_curr # Reward(Difference between previous loss and current loss)
writer_value.writerow({
'Epoch': str(epoch),
'Iteration': str(i),
'Reward': str(reward),
'Value': str(dataset_value_dqn[i])
})
# print (reward, dataset_value_dqn[i])
writer.writerow({
'Epoch': str(epoch),
'Train_loss': str(train_loss),
'Train_ppl': str(train_ppl),
'Val_loss': str(loss_curr)
})
except KeyboardInterrupt:
print('-' * 89)
print('Exiting from training early')
# write_loss.close()
write_loss.close()