在我的 LSTM 中出现不可广播的错误

Question

所以，我一直在尝试在这个 csv 文件上应用 LSTM CSV File that im trying to train

然而，它似乎是自我训练的，但在训练之后，它导致我的测试文件出现问题 Error 1 或者，如果我稍微修改一下它，那么我会得到另一个错误，提示“值错误：无法将大小为 1047835 的数组重塑为形状”

这是我正在执行的代码：-

import math
import matplotlib.pyplot as plt
import keras
import pandas as pd
import numpy as np
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "-1" #Had to use CPU because of gpus capability was 3.0

from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Dropout
from keras.layers import *
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from keras.callbacks import EarlyStopping 
  


df=pd.read_csv(r'C:\Users\LambertThePrick\Desktop\Databysir\LSTM.csv')
print(df.shape)
print(df.head(5))
#df.head(5)  
TrainPart=df.iloc[:800,1:3].values
test_set=df.iloc[800:,1:3].values

scaler=MinMaxScaler(feature_range=(0,1))
Trainpart_scaled=scaler.fit_transform(TrainPart)

print(Trainpart_scaled) 

X_Train=[]
Y_Train=[]
for i in range(60,800):
    X_Train.append(Trainpart_scaled[i-60:i,0])
    Y_Train.append(Trainpart_scaled[i,0])
X_Train,Y_Train=np.array(X_Train),np.array(Y_Train)

X_Train = np.reshape(X_Train, (X_Train.shape[0], X_Train.shape[1], 1))
# print(X_train = np.reshape(X_Train, (X_Train.shape[0], X_Train.shape[1], 1)))
#(740, 60, 1) 

model = Sequential()
#Adding the first LSTM layer and some Dropout regularisation
model.add(LSTM(units = 50, return_sequences = True, input_shape = (X_Train.shape[1], 1)))
model.add(Dropout(0.2))
# Adding a second LSTM layer and some Dropout regularisation
model.add(LSTM(units = 50, return_sequences = True))
model.add(Dropout(0.2))
# Adding a third LSTM layer and some Dropout regularisation
model.add(LSTM(units = 50, return_sequences = True))
model.add(Dropout(0.2))
# Adding a fourth LSTM layer and some Dropout regularisation
model.add(LSTM(units = 50))
model.add(Dropout(0.2))
# Adding the output layer
model.add(Dense(units = 1))

# Compiling the RNN
model.compile(optimizer = 'adam', loss = 'mean_squared_error')

# Fitting the RNN to the Training set
model.fit(X_Train, Y_Train, epochs = 100, batch_size = 32) 



#THIS IS EXPT AFTER THIS 


dataset_train = df.iloc[:800, 1:3]
dataset_test = df.iloc[800:, 1:3]
dataset_total = pd.concat((dataset_train, dataset_test), axis = 0)
inputs = dataset_total[len(dataset_total) - len(dataset_test) - 60:].values
inputs = inputs.reshape(-1,1)
inputs = scaler.transform(inputs)
X_Test = []
for i in range(60, 800):
    X_Test.append(inputs[i-60:i, 0])
X_Test = np.array(X_Test)
X_Test = np.reshape(X_Test, (X_Test.shape[0], X_Test.shape[1], 1))
print(X_Test.shape) 

predicted_stock_price = model.predict(X_Test)
predicted_stock_price = scaler.inverse_transform(predicted_stock_price) 
plt.plot(df.loc[800:, 'Date'],dataset_test.values, color = 'red', label = 'Real ASTL Stock Price')
plt.plot(df.loc[800:, 'Date'],predicted_stock_price, color = 'blue', label = 'Predicted ASTL Stock Price')
plt.xticks(np.arange(0,459,50))
plt.title('ASTL Stock Price Prediction')
plt.xlabel('Time')
plt.ylabel('ASTL Stock Price')
plt.legend()
plt.show()

Answer 1

您在重塑过程中有一段时间最终得到了非整数除法。举个例子：

import numpy as np

data = np.zeros(3936)
out = data.reshape((-1,1,24,2))

效果很好，因为 3936/24/2 结果是一个整数，82 。

但在这个例子中

import numpy as np

data = np.zeros(34345)
out = data.reshape((-1,1,24,2))

您最终得到错误消息 ValueError: cannot reshape array of size 34345 into shape (1,24,2)，因为除法没有得到整数。

因此，按照您的方式循环必然会导致该类型的事件。

在我的 LSTM 中出现不可广播的错误

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