Deep Learning Class¶
Contains core classes and methods for initializing the deep learning 3D CNN model with different variants of the loss function, inputs are provided from the modelconfig_train.py file
-
class
dlmfg.core.core_model.DLModel(model_type, output_dimension, optimizer, loss_function, regularizer_coeff, output_type='regression')[source]¶ Deep Learning Model Class
- Parameters
model_type (str (required)) – Type of model to be used for training 3D CNN with MSE loss, 3D CNN with hetreoskedastic aleatoric loss, 3D CNN with a mixture density network (GMM) output
output_dimension (int (required)) – Number of output nodes for the network equal to number of KCCs for the assembly in case MSE is used as loss function
optimizer (keras.optimizer (required)) – The optimizer to be used while model training, refer: https://keras.io/optimizers/ for more information
loss_function (keras.losses (required)) – The loss function to be optimized by training the model, refer: https://keras.io/losses/ for more information
regularizer_coeff (float (required)) – The L2 norm regularization coefficient value used in the penultimate fully connected layer of the model, refer: https://keras.io/regularizers/ for more information
output_type (str) – The output type of the model which can be regression or classification, this is used to define the output layer of the model, defaults to regression (classification: softmax, regression: linear)
-
cnn_model_3d(voxel_dim, deviation_channels)[source]¶ Build the 3D Model using the specified loss function, the inputs are parsed from the assemblyconfig_<case_study_name>.py file
-
cnn_model_3d_aleatoric(voxel_dim, deviation_channels)[source]¶ Build the 3D Model with a heteroeskedastic aleatoric loss, this enables different standard deviation of each predicted value, to be used when the expected sensor noise is heteroskedastic
-
cnn_model_3d_mdn(voxel_dim, deviation_channels, num_of_mixtures=5)[source]¶ Build the 3D Model with a Mixture Density Network output the gives parameters of a Gaussian Mixture Model as output, to be used if the system is expected to be collinear (Multi-Stage Assembly Systems) i.e. a single input can have multiple outputs Functions for predicting and sampling from a MDN.py need to used when deploying a MDN based model refer https://publications.aston.ac.uk/id/eprint/373/1/NCRG_94_004.pdf for more details on the working of a MDN model refer https://arxiv.org/pdf/1709.02249.pdf to understand how a MDN model can be leveraged to estimate the epistemic and aleatoric unceratninty present in manufacturing sytems based on the data collected
- Parameters
voxel_dim (int (required)) – The voxel dimension of the input, reuired to build input to the 3D CNN model
voxel_channels (int (required)) – The number of voxel channels in the input structure, required to build input to the 3D CNN model
number_of_mixtures (int) – The number of mixtures in the Gaussian Mixture Model output, defaults to 5, can be increased if higher collinearity is expected