from tffm import TFFMRegressor
import tensorflow as tf
os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu)
model = TFFMRegressor(
order=dim,
rank=args.embedding_dim,
optimizer=tf.train.AdagradOptimizer(learning_rate=args.lr),
n_epochs=args.train_epochs,
# batch_size=1076946,
batch_size=4096,
init_std=0.001,
reg=args.weight_decay,
input_type='sparse',
log_dir=os.path.join(args.save, save_name),
)
model.fit(train_queue[0], train_queue[1], show_progress=True)
inferences = model.predict(test_queue[0])
mse = mean_squared_error(test_queue[1], inferences)
rmse = np.sqrt(mse)
logging.info('rmse: %.4f[%.4f]' % (rmse, time() - start))
elif args.mode == 'autoneural':
start = time()
if dim == 2:
model = AutoNeural(num_users, num_items, args.embedding_dim,
args.weight_decay).cuda()
elif dim == 3:
model = AutoNeural_Triple(num_ps, num_qs, num_rs,
args.embedding_dim,
args.weight_decay).cuda()
embedding_oprimizer = torch.optim.Adagrad(model.embedding_parameters(),
episode_rewards_sum = sum(epoche_rewards)
max_reward = max(episode_rewards_sum, max_reward)
if episode_rewards_sum > 0:
suc_count += 1
print("-----------------------")
print("Episode: ", epoch)
print("Reward: ", episode_rewards_sum)
print("Max reward during train: ", max_reward)
print("-----------------------")
epoche_rewards = model.calc_reward(epoche_rewards)
replBuffer.append(epoche_observations, epoche_actions,
epoche_rewards)
model.fit(epoche_observations, epoche_actions, epoche_rewards,
replBuffer)
epoche_observations = []
epoche_actions = []
epoche_rewards = []
training_version = load_version + (
epochs_count - current_epoch) // save_period
save_path = "res/{}/{}/LunarLander-v2.ckpt".format(
train_model_name, training_version)
model.save_model(save_path)
break
# Save new observation
episode_rewards_sum = sum(epoche_rewards)
if episode_rewards_sum < min_reward:
done = True
if time.clock() - time_begin > time_limit:
done = True
if done:
episode_rewards_sum = sum(epoche_rewards)
max_reward = max(episode_rewards_sum, max_reward)
print("-----------------------")
print("Episode: ", epoch)
print("Reward: ", episode_rewards_sum)
print("Max reward during train: ", max_reward)
print("-----------------------")
model.fit(episode_actions=epoche_actions,
episode_rewards=epoche_rewards,
episode_observations=epoche_observations)
epoche_observations = []
epoche_actions = []
epoche_rewards = []
model.save_model(save_path)
break
# Save new observation
state = state_