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An educational module to make it easier to design experimental deep-learning networks in PyTorch

Project description

Consult the module API page at

https://engineering.purdue.edu/kak/distDLS/DLStudio-2.1.6.html

for all information related to this module, including the information about the latest changes to the code.

convo_layers_config = "1x[128,3,3,1]-MaxPool(2) 1x[16,5,5,1]-MaxPool(2)"
fc_layers_config = [-1,1024,10]

dls = DLStudio(
                  dataroot = "/home/kak/ImageDatasets/CIFAR-10/",
                  image_size = [32,32],
                  convo_layers_config = convo_layers_config,
                  fc_layers_config = fc_layers_config,
                  path_saved_model = "./saved_model",
                  momentum = 0.9,
                  learning_rate = 1e-3,
                  epochs = 2,
                  batch_size = 4,
                  classes = ('plane','car','bird','cat','deer','dog','frog','horse','ship','truck'),
                  use_gpu = True,
                  debug_train = 0,
                  debug_test = 1
              )

configs_for_all_convo_layers = dls.parse_config_string_for_convo_layers()
convo_layers = dls.build_convo_layers2( configs_for_all_convo_layers )
fc_layers = dls.build_fc_layers()
model = dls.Net(convo_layers, fc_layers)
dls.show_network_summary(model)
dls.load_cifar_10_dataset()
dls.run_code_for_training(model)
dls.run_code_for_testing(model)

Project details


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