https://github.com/tonio73/dnnviewer
Deep Neural Network viewer
https://github.com/tonio73/dnnviewer
convolutional-neural-networks deep-learning deep-neural-networks image-classification
Last synced: 3 months ago
JSON representation
Deep Neural Network viewer
- Host: GitHub
- URL: https://github.com/tonio73/dnnviewer
- Owner: tonio73
- License: mit
- Created: 2020-03-22T20:06:04.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2020-07-05T14:52:53.000Z (about 6 years ago)
- Last Synced: 2025-12-15T14:15:09.874Z (7 months ago)
- Topics: convolutional-neural-networks, deep-learning, deep-neural-networks, image-classification
- Language: Jupyter Notebook
- Homepage: https://tonio73.github.io/dnnviewer/
- Size: 78.2 MB
- Stars: 19
- Watchers: 1
- Forks: 2
- Open Issues: 19
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Deep Neural Network viewer
## A dashboard to inspect deep neural network models
**DNN Viewer** is providing interactive view on the layer and unit weights and gradients, as well as activation maps.
**DNN Viewer** is distinctive to existing tools since it is linking architecture, parameters, test data and performance.
Current version is targeted at the **classification** task. However, coming version will target more diverse tasks.
This project is for learning and teaching purpose, do not try to display a network with hundreds of layers.
# Install
Install with PIP
```shell script
$ pip install dnnviewer
```
Run `dnnviewer` with one of the examples below, or with you own model (see below for capabilities and limitations)
Access the web application at http://127.0.0.1:8050
# Running the program
Currently accepted input formats are Keras Sequential models written to file in Checkpoint format or HDF5. A series of checkpoints along training epochs is also accepted as exemplified below.
Some test models are provided in the GIT repository `_dnnviewer-data_` to clone from Github or download a zip from the [repository page](https://github.com/tonio73/dnnviewer-data), a full description of the models and their design is available in the repository [readme](https://github.com/tonio73/dnnviewer-data/blob/master/README.md).
```shell script
$ git clone https://github.com/tonio73/dnnviewer-data.git
```
Test data is provided by Keras.
### Selecting the model within the application`
Launch the application with command line `--model-directories` that set a comma separated list of directory paths where the models are located
```shell
$ dnnviewer --model-directories dnnviewer-data/models,dnnviewer-data/models/FashionMNIST_checkpoints
```
Then select the network model and the corresponding test data (optional) on the user interface
Models containing the '{epoch}' tag are sequences over epochs. They are detected based on the pattern set by
command line option `--sequence-pattern` whose default is `{model}_{epoch}`
# Generating the models
## From Tensorflow 2.0 Keras
Note: Only Sequential models are currently supported.
### Save a single model
Use the `save()`method of _keras.models.Model_ class the output file format is either Tensorflow Checkpoint or HDF5 based on the extension.
```python
model1.save('models/MNIST_LeNet60.h5')
```
### Save models during training
The Keras standard callback `tensorflow.keras.callbacks.ModelCheckpoint` is saving the model every epoch or a defined period of epochs:
```python
from tensorflow import keras
from tensorflow.keras.callbacks import ModelCheckpoint
model1 = keras.models.Sequential()
#...
callbacks = [
ModelCheckpoint(
filepath='checkpoints_cnn-mnistfashion/model1_{epoch}',
save_best_only=False,
verbose=1)
]
hist1 = model1.fit(train_images, train_labels,
epochs=nEpochs, validation_split=0.2, batch_size=batch_size,
verbose=0, callbacks=callbacks)
```
# Current capabilities
- Load **Tensorflow Keras Sequential** models and create a display of the network
- Targeted at image classification task (assume image as input, class as output)
- Display series of models over training epochs
- Interactive display and unit weights through connections within the network and histograms
- Supported layers
- Dense
- Convolution 2D
- Flatten
- Input
- Following layers are added as attributes to the previous or next layer
- Dropout, ActivityRegularization, SpatialDropout1D/2D/3D
- All pooling layers
- BatchNormalization
- Activation
- Unsupported layers
- Convolution 1D and 3D
- Transpose convolution 2D and 3D
- Reshape, Permute, RepeatVector, Lambda, Masking
- Recurrent layers (LSTM, GRU...)
- Embedding layers
- Merge layers
# Developer documentation
See [developer.md](docs/developer.md)