了解将密集层连接到LSTM [英] Understanding connecting a Dense layer to LSTM
问题描述
我无法正确地将密集层连接到LSTM层.我的Y值范围是0-1，所以S形对我来说似乎是合乎逻辑的.
I am unable to correctly connect my dense layer to my LSTM layers. My Y values range from 0-1 so sigmoid seems logical to me.
我得到了错误:
检查目标时出错:预期density_4具有2维，但是 得到了形状为(993，300，1)的数组
Error when checking target: expected dense_4 to have 2 dimensions, but got array with shape (993, 300, 1)
在我看来，我输入的形状正确的总DF为(350700，2413)，我将其重塑为(1169，300，2413)//不包括Y值.我只是似乎无法弄清楚如何使稠密层起作用并将S形曲线应用于我的Y.
To me it seems i have the input shape correct total DF is (350700 , 2413) which i reshape to a ( 1169 , 300 , 2413 ) // not including Y value. I just can't seem to figure out how to get the dense layer working and apply the sigmoid to my Y.
通过火车测试拆分，我的y_train为(993，300，1)，这是我的错误的主要问题，但我似乎无法理解自己做错了什么. x_train是(933，300，2413)x_test =(176，300，2413)y_test =(176，300，1)
With the train test split i have a y_train of (993, 300, 1) which is the main issue of my error but i can't seem to understand what i have done wrong. x_train is ( 933, 300, 2413) x_test = ( 176, 300 , 2413) y_test= (176, 300, 1)
这是我建立的网络.后端张量流(也使用theano相同的问题)
Here is the network i have set up. backend tensorflow (also used theano same issue)
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
lstm_21 (LSTM) (None, 300, 1000) 13656000
_________________________________________________________________
lstm_22 (LSTM) (None, 300, 500) 3002000
_________________________________________________________________
lstm_23 (LSTM) (None, 300, 250) 751000
_________________________________________________________________
lstm_24 (LSTM) (None, 300, 100) 140400
_________________________________________________________________
lstm_25 (LSTM) (None, 50) 30200
_________________________________________________________________
dense_4 (Dense) (None, 1) 51
=================================================================
Total params: 17,579,651
Trainable params: 17,579,651
Non-trainable params: 0
_________________________________________________________________
这是我的代码.
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers.advanced_activations import LeakyReLU
from keras.layers import Dense, Activation, LSTM, Flatten
from keras import backend as K
from sklearn.model_selection import train_test_split
aa = aa[np.isfinite(aa['Y1'])]
aa=aa[-350700:]
Y=aa['Y1'].values.reshape(1169,300,1) #break into 1169 samples @ 300 timestamps
aa.drop(drop1, axis=1, inplace=True) #drop the Y1 feature and others not needed.
features=aa.shape[1]
X=aa.values.reshape(1169,300,features)
seed = 7
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.15, random_state=seed)
model = Sequential()
model.add(LSTM(1000, input_shape=(300,features),activation='relu',return_sequences=True))
model.add(LSTM(500,activation='relu',return_sequences=True))
model.add(LSTM(250,activation='relu',return_sequences=True))
model.add(LSTM(100, activation='relu',return_sequences=True))
model.add(LSTM(50,activation='relu',return_sequences=False))
model.add(Dense(1,activation='sigmoid'))
model.compile(loss='mae',
optimizer='adam',
metrics=['mse', 'mae', 'mape'])
print(model.summary())
# evaluate model with standardized dataset
model.fit(X_train, y_train, validation_data=(X_test,y_test), epochs=15000)
推荐答案
您的数据"与您的最后一层形状"不兼容.
Your "data" is not compatible with your "last layer shape".
- 您需要形状为
(993,1)的Y_train-对整个序列进行分类 - 或者您需要将
return_sequences=True保留在所有" LSTM层中-对每个时间步进行分类
- Either you need
Y_trainwith shape(993,1)- Classifying the entire sequence - Or you need to keep
return_sequences=Truein "all" LSTM layers - Classifying each time step
正确的方法取决于您要执行的操作.
What is correct depends you what you're trying to do.
这篇关于了解将密集层连接到LSTM的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
