ABOUT
This is a mini deep learning framework created by Kaikeba @ Beijing
Features
- Define Dynamic Computing Graph
- Define Neural Network Operator
- Define Linear, Sigmoid, L2_loss
- Auto-Diff Computing
- Auto-Feedforward and Backward
Remain
- Classification
- Cross-Entropy
- CNN, RNN
Connection
wechat: fortymiles Mail: minchiuan@zju.edu.cn
1 maintainer
Release files for kaikeba-flow 0.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| kaikeba_flow-0.0.1.tar.gz | 3.5 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| kaikeba_flow-0.0.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 8.7 kB
Release files / kaikeba_flow-0.0.1.tar.gz
| Download URL | kaikeba_flow-0.0.1.tar.gz |
|---|---|
| Size | 3.5 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/3.2.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/51.1.0.post20201221 requests-toolbelt/0.9.1 tqdm/4.31.1 CPython/3.7.3
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Release files / kaikeba_flow-0.0.1-py3-none-any.whl
| Download URL | kaikeba_flow-0.0.1-py3-none-any.whl |
|---|---|
| Size | 5.2 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.2.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/51.1.0.post20201221 requests-toolbelt/0.9.1 tqdm/4.31.1 CPython/3.7.3
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