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-1.1.3.html
for all information related to this module, including information related to 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)
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