如何解决AttributeError:“ tensorflow.python.framework.ops.EagerTensor”对象没有为Python中的LSTM模型显示“ assign”属性
我试图用LSTM模型实施时间序列预测。我使用的导入-
import tensorflow as tf
from tensorflow import keras
import numpy as np
import pandas as pd
import os
import glob
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense,LSTM
import datetime as dt
from datetime import datetime
from keras.callbacks import EarlyStopping,ReduceLROnPlateau,ModelCheckpoint,TensorBoard
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import MinMaxScaler
为了构建模型,我使用了LSTM。在这里,我将顺序LSTM用于时间序列预测。 以下代码用于建模-
model = Sequential()
# Adding 1st LSTM layer
model.add(LSTM(units=64,return_sequences=True,input_shape=(n_past,df2.shape[1]-1)))
# Adding 2nd LSTM layer
model.add(LSTM(units=10,return_sequences=False))
# Adding Dropout
model.add(Dropout(0.25))
# Output layer
model.add(Dense(units=1,activation='linear')
# Compiling the Neural Network
model.compile(Adam(learning_rate=0.01),loss='mean_squared_error')
在下面的代码中,我尝试了培训。
es = EarlyStopping(monitor='val_loss',min_delta=1e-10,patience=10,verbose=1)
rlr = ReduceLROnPlateau(monitor='val_loss',factor=0.5,verbose=1)
mcp = ModelCheckpoint(filepath='weights.h5',monitor='val_loss',verbose=1,save_best_only=True,save_weights_only=True)
tb = TensorBoard('logs')
history = model.fit(X_train,y_train,shuffle=True,epochs=30,callbacks=[es,rlr,mcp,tb],validation_split=0.2,batch_size=64)
因此,我正在尝试使用LSTM对系统进行建模。但是显示“ AttributeError”。我无法使用此代码解决问题。因此,请帮助我找出错误。
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-65-37e13cc5a806> in <module>
5 tb = TensorBoard('logs')
6
----> 7 history = model.fit(X_train,batch_size=256)
8
AttributeError: 'tensorflow.python.framework.ops.EagerTensor' object has no attribute 'assign'
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