IndexError: LSTM with "stateful=True"

Question

我尝试使用 LSTM 网络为预期的未来使用重置回调预测如下：

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()

但我收到以下错误。我不知道如何解决预期的预测。

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

PS 此代码改编自 https://github.com/danmoller/TestRepo/blob/master/testing%20the%20blog%20code%20-%20train%20and%20pred.ipynb

Answer 1

当您定义回归量时，您使用了 return_sequences=False。

因此，回归器返回的是 2D 张量（没有阶梯），而不是 3D。

所以你不能像你那样使用三个索引从 predictions 中获取元素。

可能性：

使用return_sequences=False，每次预测都只是最后一步。
使用 return_sequences=True，每个预测都将包含步骤，即使只有一个步骤。

IndexError: LSTM with "stateful=True"

IndexError: LSTM with "stateful=True"

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