Skip to main content

A PyTorch based software platform for teaching the Deep Learning class at Purdue University

Project description

Consult the module API page at

https://engineering.purdue.edu/kak/distDLS/DLStudio-2.2.8.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


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

DLStudio-2.2.8.tar.gz (19.6 MB view details)

Uploaded Source

File details

Details for the file DLStudio-2.2.8.tar.gz.

File metadata

  • Download URL: DLStudio-2.2.8.tar.gz
  • Upload date:
  • Size: 19.6 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: Python-urllib/3.8

File hashes

Hashes for DLStudio-2.2.8.tar.gz
Algorithm Hash digest
SHA256 3a9e98555711eb5aa9e98dcae0616c54731a6a2e051bb80f1681301c16f987a4
MD5 c30b4b0f786a44581470f8106b6753ec
BLAKE2b-256 ae266a35924fcfc6ec824cffc3f252dea8f4dcf02e6d52fd16f2e66580c1c059

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page