Vizdnn
Interpretable Deep Neural network is the next phase of understanding the working of neural network. This library suffices the need to vizualize neural network layer with simple python pip package.
Pip Install
pip install vizdnn
Use
from vizdnn import vizdnn
import keras
visualize_neural_network = vizdnn(keras.applications.resnet.ResNet50() , "network_layer_name" , "test_image_name.jpg")
vis_layer = visualize_neural_network.get_layer()
visualize_neural_network.viz_feature_map(vis_layer)
Sample Viz
- Sample test image
- Vizualization from Resnet50 conv1_bn Layer.
- Vizualization from VGG16 block2_conv1 Layer.
Metadata
Release files for vizdnn 0.0.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| vizdnn-0.0.4.tar.gz | 2.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| vizdnn-0.0.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 5.3 kB
Release files / vizdnn-0.0.4.tar.gz
| Download URL | vizdnn-0.0.4.tar.gz |
|---|---|
| Size | 2.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
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twine/2.0.0 pkginfo/1.5.0.1 requests/2.20.1 setuptools/41.4.0 requests-toolbelt/0.8.0 tqdm/4.19.1 CPython/3.6.8
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Release files / vizdnn-0.0.4-py3-none-any.whl
| Download URL | vizdnn-0.0.4-py3-none-any.whl |
|---|---|
| Size | 3.2 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
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| Uploaded via |
twine/2.0.0 pkginfo/1.5.0.1 requests/2.20.1 setuptools/41.4.0 requests-toolbelt/0.8.0 tqdm/4.19.1 CPython/3.6.8
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