Hello @MUHAMMED FURKAN YILMAZ
Based on the model description, I have created a sample model using Keras (version 2.6.0), which is as follows.
from keras.models import Sequential
from keras.layers import LSTM, Dense
model = Sequential()
model.add(LSTM(64, input_shape=(50, 5), return_sequences=True))
model.add(LSTM(32))
model.add(Dense(1, activation='linear'))
model.compile(optimizer='adam', loss='mean_squared_error')
model.save('model.h5')
It seems the error occurred because the model expects input with dimensions (50, 5), but the input was provided with dimensions (num_window, 50, 5). I have found a workaround to resolve the given error. You can iterate through each window one by one, so the model input becomes (50, 5).
Here I have attach a code snippet for better understanding.
net = importKerasNetwork('model.h5');
% Define the dimensions
num_window = 100; % You can change this to the desired number of windows
% Generate random data
% This will create a 3D array with dimensions (num_window, 50, 5)
data = rand(num_window, 5, 50);
% Display the size of the generated data to confirm
disp(size(Data));
% Make predictions using the imported Keras model
for i=1:num_window
predictions = predict(net, reshape(data(i,:,:),5,50))
end
I hope this will be helpful.
