lstmLayer(numHiddenUnits) fullyConnectedLayer(numResponses) regressionLayer]; %% 学習オプションの設定 opts = trainingOptions('adam',... 'MaxEpochs',250,... 'GradientThreshold', 1,... 'InitialLearnRate', 0.005,... 'LearnRateSchedule','piecewise',... ...
>LSTM cell's hidden units need to pass return sequences This is the default for us:lstmLayer(32,"Name","LSTM1")will return the sequence of hidden states, it is setting the"OutputMode"to the default value"sequence". I notice your Keras LSTM also hasreturn_state=True.We support this as...
lstmLayer(50, 'OutputMode', 'sequence') % 50 hidden units fullyConnectedLayer(2) % Output: RSRP & RSRQ regressionLayer]; % For regression task options = trainingOptions('adam', ... 'MaxEpochs', 200, ... 'GradientThreshold', 1, ... ...
num_hidden_units = 350; layers = [ featureInputLayer(6); lstmLayer(num_hidden_units,'OutputMode','last') fullyConnectedLayer(num_responses) ]; %Training Options options = trainingOptions("adam",... MaxEpochs=params.MaxEpochs,...
numHiddenUnits = 128; layers = [ sequenceInputLayer(numChannels) lstmLayer(numHiddenUnits) fullyConnectedLayer(numResponses) ]; However, MATLAB gives an error saying Size of predictions and targets must match. Size of predictions: 1(C) × 128(B) × 20(T) Size of targets: 1(C) × 128...
1. Define the SE BLock as a custom layer: functionseLayer = seBlock(numHiddenUnits, name) seLayer = [ fullyConnectedLayer(numHiddenUnits,'Name', [name'_fc1']) reluLayer('Name', [name'_relu']) fullyConnectedLayer(numHiddenUnits,'Name', [name'_fc2']) ...