CNN-LSTM MATLAB

As an AI language model, I do not have access to MATLAB software, but I can provide you with a general overview of how a CNN-LSTM model can be implemented in MATLAB.

1. Load and preprocess the data: Load the data into MATLAB and preprocess it to make it suitable for the CNN-LSTM model. This may involve resizing the images, normalizing the pixel values, and splitting the data into training, validation, and test sets.

2. Build the CNN layers: Define the convolutional layers of the model using the convolution2dLayer function in MATLAB. Specify the number of filters, filter size, stride, and padding for each layer.

3. Build the LSTM layers: Define the LSTM layers of the model using the LSTMLayer function in MATLAB. Specify the number of hidden units in each layer.

4. Combine the CNN and LSTM layers: Use the CNN layers as the input to the LSTM layers. This can be done using the sequenceInputLayer function in MATLAB.

5. Define the output layer: Define the output layer of the model using the fullyConnectedLayer function in MATLAB. Specify the number of output classes.

6. Train the model: Train the CNN-LSTM model using the trainNetwork function in MATLAB. Specify the training options, such as the optimizer, learning rate, and mini-batch size.

7. Test the model: Test the model on the test set using the classify function in MATLAB. Evaluate the performance of the model using metrics such as accuracy, precision, and recall.

Overall, implementing a CNN-LSTM model in MATLAB involves defining the CNN and LSTM layers, combining them using the sequenceInputLayer function, and training and testing the model using the trainNetwork and classify functions.