model_dir=MODEL_PATH))
#regressor = learn.Estimator(model_fn=lstm_model, model_dir=MODEL_PATH)
# 生成数据
train_X, train_y = generate_data(normalize_data[0:train_length])
test_X, test_y = generate_data(normalize_data[train_length:data_length])
train_X = np.transpose(train_X, [0, 2, 1])
train_y = np.transpose(train_y, [0, 2, 1])
test_X = np.transpose(test_X, [0, 2, 1])
test_y = np.transpose(test_y, [0, 2, 1])
# 拟合数据
# In[]
#regressor.fit(train_X, train_y, batch_size=BATCH_SIZE, steps=TRAINING_STEPS)
# 计算预测值
# In[]
#predicted = [[pred] for pred in regressor.predict(test_X)]
regressor.score(test_X, test_y)
predicted_list = list(regressor.predict(test_X))
# In[]
def final_data_for_plot(predicted_list, test_y):
test_y_list = test_y.reshape(test_y.shape[0] * test_y.shape[1], 1).tolist()
final_predicted_list = []
final_test_y_list = []
for i in range(0, len(predicted_list) - PREDICT_STEPS + 1):
if i % (PREDICT_STEPS * PREDICT_STEPS) == 0:
final_predicted_list.extend(predicted_list[i:i + PREDICT_STEPS])
final_test_y_list.extend(test_y_list[i:i + PREDICT_STEPS])
final_predicted = np.array(final_predicted_list).reshape(
def test_func(test_X, test_y, model_path="Models/model_sin"):
regressor = SKCompat(
learn.Estimator(model_fn=lstm_model, model_dir=model_path))
a = regressor.score(x=test_X, y=test_y)
predicted_data = [[pred] for pred in regressor.predict(test_X)]
return predicted_data