Tensorflow 索引器：带“stateful=True”的LSTM

我尝试使用LSTM网络，使用重置回调来实现预期的未来 预测如下：

import numpy as np, pandas as pd, matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense, LSTM
from keras.callbacks import LambdaCallback
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler

raw = np.sin(2*np.pi*np.arange(1024)/float(1024/2)).reshape(-1,1)

scaler = MinMaxScaler(feature_range=(-1, 1))
scaled = scaler.fit_transform(raw)

data = pd.DataFrame(scaled)


window_size = 3
data_s = data.copy()
for i in range(window_size):
    data = pd.concat([data, data_s.shift(-(i+1))], axis = 1)   
data.dropna(axis=0, inplace=True)

ds = data.values

n_rows = ds.shape[0]
ts = int(n_rows * 0.8)

train_data = ds[:ts,:]
test_data = ds[ts:,:]

train_X = train_data[:,:-1]
train_y = train_data[:,-1]
test_X = test_data[:,:-1]
test_y = test_data[:,-1]

print (train_X.shape)
print (train_y.shape)
print (test_X.shape)
print (test_y.shape)

batch_size = 3
n_feats = 1

train_X = train_X.reshape(train_X.shape[0], batch_size, n_feats)
test_X = test_X.reshape(test_X.shape[0], batch_size, n_feats)
print(train_X.shape, train_y.shape)

regressor = Sequential()
regressor.add(LSTM(units = 64, batch_input_shape=(1, batch_size, n_feats),
                   activation = 'sigmoid',  
                   stateful=True, return_sequences=False))
regressor.add(Dense(units = 1))

regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')

resetCallback = LambdaCallback(on_epoch_begin=lambda epoch,logs: regressor.reset_states())

regressor.fit(train_X, train_y, batch_size=1, epochs = 1, callbacks=[resetCallback])

previous_inputs = test_X                                    
regressor.reset_states()

previous_predictions = regressor.predict(previous_inputs, batch_size=1)
previous_predictions = scaler.inverse_transform(previous_predictions).reshape(-1)
test_y = scaler.inverse_transform(test_y.reshape(-1,1)).reshape(-1)

plt.plot(test_y, color = 'blue')
plt.plot(previous_predictions, color = 'red')
plt.show()

inputs = test_X
future_predicitons = regressor.predict(inputs, batch_size=1)

n_futures = 7
regressor.reset_states()
predictions = regressor.predict(previous_inputs, batch_size=1)
print (predictions)

future_predicts = []
currentStep = predictions[:,-1:,:] 
for i in range(n_futures):
    currentStep = regressor.predict(currentStep, batch_size=1)
    future_predicts.append(currentStep)  
regressor.reset_states()

future_predicts = np.array(future_predicts, batch_size=1).reshape(-1,1)
future_predicts = scaler.inverse_transform(future_predicts).reshape(-1)

all_predicts = np.concatenate([predicts, future_predicts])

plt.plot(all_predicts, color='red')
plt.show()

但是我犯了以下错误。我想不出如何解决预期预测的问题

    currentStep = predictions[:,-1:,:]
IndexError: too many indices for array

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PS此代码已根据定义回归器时使用的return_sequences=False改编

所以，回归器返回的是2D，没有步长的张量，不是3D

所以你不能像以前那样用三个指数从预测中得到元素

可能性：

如果return_sequences=False，则每个预测将只是最后一步。 当return_sequences=True时，每个预测都将包含步骤，即使只有一个步骤。

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你想从预测中得到什么[：，-1:，：]？我们可以在聊天中讨论这个问题吗：是的，这是预期的，现在你需要一个包含所有时间步长的3D输出。如果您遵循我的github中的代码，您将看到我的培训阶段一次涉及所有步骤。检查链接，这是正确的：是的，它确实在培训输出之后有未来的预测。我真正信任的唯一链接是ipynb文件，是的，它确实在训练输出之后有一个未来预测模块。我的意思是，TestBookLSTM.ipynb文件。-未来预测部分从[72]中的内容开始：。