Helpers for importing individual layers
You're likely to want to import parameter values from your trained neural networks from outside of Julia. get_conv_params and get_matrix_params are helper functions enabling you to import individual layers.
Index
Public Interface
MIPVerify.get_conv_params โ Method.get_conv_params(param_dict, layer_name, expected_size; matrix_name, bias_name, expected_stride)
Helper function to import the parameters for a convolution layer from param_dict as a Conv2d object.
The default format for the key is 'layer_name/weight' and 'layer_name/bias'; you can customize this by passing in the named arguments matrix_name and bias_name respectively. The expected parameter names will then be 'layer_name/matrix_name' and 'layer_name/bias_name'
Arguments
param_dict::Dict{String}: Dictionary mapping parameter names to array of weights / biases.layer_name::String: Identifies parameter in dictionary.expected_size::NTuple{4, Int}: Tuple of length 4 corresponding to the expected size of the weights of the layer.
MIPVerify.get_matrix_params โ Method.get_matrix_params(param_dict, layer_name, expected_size; matrix_name, bias_name)
Helper function to import the parameters for a layer carrying out matrix multiplication (e.g. fully connected layer / softmax layer) from param_dict as a Linear object.
The default format for the key is 'layer_name/weight' and 'layer_name/bias'; you can customize this by passing in the named arguments matrix_name and bias_name respectively. The expected parameter names will then be 'layer_name/matrix_name' and 'layer_name/bias_name'
Arguments
param_dict::Dict{String}: Dictionary mapping parameter names to array of weights / biases.layer_name::String: Identifies parameter in dictionary.expected_size::NTuple{2, Int}: Tuple of length 2 corresponding to the expected size of the weights of the layer.