Skip to main content

The official implementation of the 2pCePd-Net model. pBPf Fusion block coming soon...

Project description

This is the official Pytorch implementation of 2pCePd-Net model published in IEEE Transactions on Instrumentation and Measurement

DOI: 10.1109/TIM.2025.3569005

Model Sample Usage Code:

from two_pcepd import model

network, optimizer = model.create_net(in_ch, out_ch, dim, n)

in_ch -> the number of channels in input

out_ch -> the number of channels in output

dim -> a dimensional parameter to modulate the number of parameters in the model. Default value is 64. Reducing or increasing the parameters would modulate the model accordingly.

n -> number of paths. Default value is 2. Can be modulated to increase or decrease the complexity of the model.

network -> the returned model

optimizer -> the optimizer associated with network is returned. Default value is Adam Optimizer with learning rate set to 1e^-4

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

two_pcepd-0.1.5.tar.gz (16.6 kB view details)

Uploaded Source

File details

Details for the file two_pcepd-0.1.5.tar.gz.

File metadata

  • Download URL: two_pcepd-0.1.5.tar.gz
  • Upload date:
  • Size: 16.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.9.12

File hashes

Hashes for two_pcepd-0.1.5.tar.gz
Algorithm Hash digest
SHA256 06282219f541e6314c20f13fdc5dfdda2c421eabd905a1ade21157e89e7d28ff
MD5 e74b1ec93bcd2690610259f97d15bc52
BLAKE2b-256 f35bae030b3185f8211bef527d54067880dbce3e73960fea7ffbb0a198021e72

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