Question

我尝试使用stateful=True的LSTM网络，如下所示：

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))
data = pd.DataFrame(raw)

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)

print (data)
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)

（816,3）（816）（205,3）（205）

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=(train_X.shape[0], batch_size, n_feats),
                   activation = 'sigmoid',  
                   stateful=True, return_sequences=True))

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=7, epochs = 1, callbacks=[resetCallback])

previous_inputs = test_X                                    
regressor.reset_states()

previous_predictions = regressor.predict(previous_inputs).reshape(-1)
test_y = test_y.reshape(-1)
plt.plot(test_y, color = 'blue')
plt.plot(previous_predictions, color = 'red')
plt.show()

然而，我得到了：

ValueError: Error when checking target: expected dense_1 to have 3 dimensions, but got array with shape (816, 1)

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

Answer 1

两个小错误：

你有

^(11|111|11[01]*11)$

此LSTM将返回一个3D矢量，但您的y是2D，它会抛出一个值误差。您可以使用regressor.add(LSTM(units = 64, batch_input_shape=(train_X.shape[0], batch_size, n_feats), activation = 'sigmoid', stateful=True, return_sequences=True))解决此问题。我不确定您最初在return_sequences=False内部train_X.shape[0]为什么，整个集合中的样本数量不应影响每个批次的大小。

batch_input

在此之后你有

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

在有状态网络中，您只能输入一些分区批量大小的输入。由于7不划分816，我们将其更改为1：

regressor.fit(train_X, train_y, batch_size=7, epochs = 1, callbacks=[resetCallback])

你的预测也是如此。您必须指定regressor.fit(train_X, train_y, batch_size=1, epochs = 1, callbacks=[resetCallback])：

batch_size=1

ValueError：在LSTM的情况下，`stateful = True`

1 个答案: