MATLAB Answers

Is it possible to implement a LSTM layer after a CNN layer?

194 views (last 30 days)
Sofía
Sofía on 26 Apr 2018
Commented: suraj sahoo on 11 Nov 2019
I'm trying to implement a CNN layer + a LSTM layer, but I have an error: "Network: Incompatible layer types". Is it not possible to implement this combination in MATLAB or am I just writing it not properly?
My code:
layers = [ ...
sequenceInputLayer(inputSize)
convolution2dLayer(3,8,'Padding','same')
batchNormalizationLayer
reluLayer
maxPooling2dLayer(2,'Stride',2)
lstmLayer(numHiddenUnits,'OutputMode','last')
fullyConnectedLayer(numClasses)
softmaxLayer
classificationLayer
];
Error:
Error using trainNetwork (line 154)
Invalid network.
Caused by:
Network: Incompatible layer types. The network contains layer types not supported with recurrent layers.
Detected recurrent layers:
layer 6 (LSTM)
Detected incompatible layers:
layer 2 (Convolution)
layer 3 (Batch Normalization)
layer 5 (Max Pooling)
Layer 2: Input size mismatch. Size of input to this layer is different from the expected input size.
Inputs to this layer:
from layer 1 (output size 500)

  1 Comment

Sign in to comment.

Accepted Answer

Mona
Mona on 19 Sep 2018
As far as I know, no, you can't combine the two. You can train a CNN independently on your training data, then use the learned features as an input to your LSTM. However, learning and updating CNN weights while training an LSTM is unfortunately not possible.

  0 Comments

Sign in to comment.

More Answers (4)

charu
charu on 9 Jul 2018
use bilstmLayer layer instead of lstm layer as in example
inputSize = 12;
numHiddenUnits = 100;
numClasses = 9;
layers = [ ...
sequenceInputLayer(inputSize)
bilstmLayer(numHiddenUnits,'OutputMode','last')
fullyConnectedLayer(numClasses)
softmaxLayer
classificationLayer]

  1 Comment

Guillaume  JUBIEN
Guillaume JUBIEN on 3 Sep 2018
I have the same problem by using a bilstm Layer. The error message is :
if true
Error using trainNetwork (line 154)
Invalid network.
Error in test_spa_REG (line 168)
net = trainNetwork(XTR,TTR,Layers,options);
Caused by:
Network: Incompatible layer types. The network contains layer types not supported with recurrent layers.
Detected recurrent layers:
layer 9 (BiLSTM)
Detected incompatible layers:
layer 1 (Image Input)
layer 2 (Transposed Convolution)
layer 'temp1' (Convolution)
layer 5 (Average Pooling)
and 1 other layers.
Layer 10: Input size mismatch. Size of input to this layer is different from the expected input size.
Inputs to this layer:
from layer 9 (output size 20)
Is it possible to combine CNN with LSTM layer ?

Sign in to comment.


Shounak Mitra
Shounak Mitra on 11 Jul 2019
Hello Everyone,
As of 19a, MATLAB supports workflows containing both CNN and LSTM layers.
Please check the link that contains an example showing the CNN+LSTM workflow --> https://www.mathworks.com/help/deeplearning/examples/classify-videos-using-deep-learning.html

  2 Comments

Bhavna Rajasekaran
Bhavna Rajasekaran on 8 Nov 2019
Is it possible to implement LSTM regression on an image (N-by-M array) such that the output is also a 2-dimesional array? Which means that the Predictors are an N-by-M array of sequences?

Sign in to comment.


sotiraw sotiroglou
sotiraw sotiroglou on 24 Mar 2019
Matlab 2019a is out. And it claims it can do this cnn - rnn combination.
Could someone give us an example?

  0 Comments

Sign in to comment.


sotiraw sotiroglou
sotiraw sotiroglou on 24 Mar 2019
Matlab 2019a is out there , and it claims it can do this rnn cnn combination.
I dont know the details, but i write this answer to encourage everyone with the same issue to search and maybe help with an example

  0 Comments

Sign in to comment.

Sign in to answer this question.