# **Parameters**:
#
# - model_fn: 학습 및 예측에 사용할 모델
# - n_classes: label에 해당하는 클래스 수 (0: prediction, 1이상: classification) 확인필요
# - verbose: 과정 출력
# - steps: 학습 스텝
# - optimizer: 최적화 기법 ("SGD", "Adam", "Adagrad")
# - learning_rate: learning rate
# - batch_size: batch size
#
#
# In[ ]:
X, y = generate_data(np.sin,
np.linspace(0, 100, 10000),
TIMESTEPS,
seperate=False)
# create a lstm instance and validation monitor
validation_monitor = learn.monitors.ValidationMonitor(
X['val'], y['val'], every_n_steps=PRINT_STEPS, early_stopping_rounds=1000)
# ## Generate a dataset
#
# 1. generate_data: 학습에 사용될 데이터를 특정 함수를 이용하여 만듭니다.
# - fct: 데이터를 생성할 함수
# - x: 함수값을 관측할 위치
# - time_steps: 관측(observation)
# - seperate: check multimodal
# 1. ValidationMonitor: training 이후, validation 과정을 모니터링
# - x
LOG_DIR = 'resources/logs/'
TIMESTEPS = 1
RNN_LAYERS = [{'num_units': 400}]
DENSE_LAYERS = None
TRAINING_STEPS = 500
PRINT_STEPS = TRAINING_STEPS # / 10
BATCH_SIZE = 100
regressor = SKCompat(
learn.Estimator(model_fn=lstm_model(TIMESTEPS, RNN_LAYERS,
DENSE_LAYERS), ))
# model_dir=LOG_DIR)
X, y = generate_data(np.sin,
np.linspace(0, 100, 10000, dtype=np.float32),
TIMESTEPS,
seperate=False)
noise_train = np.asmatrix(np.random.normal(0, 0.2, len(y['train'])),
dtype=np.float32)
noise_val = np.asmatrix(np.random.normal(0, 0.2, len(y['val'])),
dtype=np.float32)
noise_test = np.asmatrix(np.random.normal(0, 0.2, len(y['test'])),
dtype=np.float32) #asmatrix
noise_train = np.transpose(noise_train)
noise_val = np.transpose(noise_val)
noise_test = np.transpose(noise_test)
y['train'] = np.add(y['train'], noise_train)
y['val'] = np.add(y['val'], noise_val)