Hi,
It seems the error you're encountering is due to the weights and biases not being initialized.
You can verify this by inspecting the layer properties using “layer(2).Weights.” In the code provided, the “Weights” and “Biases” are uninitialized.
To resolve this issue, you can explicitly initialize the weights and biases and apply them when defining the “add_conv” layer.
Refer to the code snippet below that demonstrates how to initialize weights and biases:
% Load the network and convert to layer graph
net = squeezenet('Weights', 'imagenet');
lgraph = layerGraph(net);
% Disconnect the specified layers
lgraph = disconnectLayers(lgraph, "fire8-concat", "fire9-squeeze1x1");
% Define the input channels based on the architecture
inputChannels = 512; % This should match the number of output channels from the previous layer
% Initialize weights and biases
weights = randn([1, 1, inputChannels, 512]) * 0.01; % Random initialization
bias = zeros([1, 1, 512]); % Zero initialization
% Define the new layers with initialized weights and biases
layer = [
maxPooling2dLayer([3 3], "Name", "pool6", "Padding", "same", "Stride", [2 2])
convolution2dLayer([1 1], 512, "Name", "add_conv", "Padding", [1 1 1 1], "Stride", [2 2], ...
'Weights', weights, 'Bias', bias)
reluLayer("Name", "relu_add_conv")
];
% Add the new layers to the graph
lgraph = addLayers(lgraph, layer);
% Connect the layers
lgraph = connectLayers(lgraph, 'fire8-concat', 'pool6');
lgraph = connectLayers(lgraph, 'relu_add_conv', 'fire9-squeeze1x1');
% Assemble the network
newbaseNetwork = assembleNetwork(lgraph);
For more information on initializing the weights and biases explicitly, you can refer to the “Parameters and Initialization” section in the below mentioned documentation:
Hope it helps!
