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توضیحات

Deep Neural Network inspection: view weights, gradients and activations

ویژگی	مقدار
سیستم عامل	-
نام فایل	dnnviewer-0.1.0.dev9
نام	dnnviewer
نسخه کتابخانه	0.1.0.dev9
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	A. Hue
ایمیل نویسنده	-
آدرس صفحه اصلی	https://github.com/tonio73/dnnviewer
آدرس اینترنتی	https://pypi.org/project/dnnviewer/
مجوز	MIT

# Deep Neural Network viewer A **dashboard to inspect deep neural network models**, providing interactive view on the layer and unit weights and gradients, as well as activation maps. Current version is targeted at the **image classification**. 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. ![Screenshot](assets/screenshots/screen5.png) # 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 <img src="assets/screenshots/select_model2.png" alt="Model selection UI" style="zoom:40%;" /> 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}` ### Loading a single model Keras models are loaded from *Tensorflow Checkpoint* or *HDF5* format with option `--model-keras <file>` #### CIFAR-10 Convolutional neural network (at the beginning of training) ```shell $ dnnviewer --model-keras dnnviewer-data/models/CIFAR-10_CNN5-Reg.tf --test-dataset cifar-10 ``` #### MNIST Convolutional neural network based on LeNet5 ```shell $ dnnviewer --model-keras dnnviewer-data/models/MNIST_LeNet60.h5 --test-dataset mnist ``` ### Loading several epochs of a model Series of models along training epochs are loaded using the argument `--sequence-keras <path>` and the pattern `{model}_{epoch}` within the provided path. See below on how to generate these checkpoints. #### Fashion MNIST convolutionnal network ```shell $ dnnviewer --sequence-keras "dnnviewer-data/models/FashionMNIST_checkpoints/model1_{epoch}" --test-dataset fashion-mnist ``` # 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)

نیازمندی

مقدار	نام
-	numpy
>=2.0	tensorflow
-	tensorflow-datasets
>=4.0	plotly
-	dash
-	dash-core-components
-	dash-html-components
-	dash-bootstrap-components
-	pillow

زبان مورد نیاز

مقدار	نام
>=3.5	Python

نحوه نصب

نصب پکیج whl dnnviewer-0.1.0.dev9:

pip install dnnviewer-0.1.0.dev9.whl

نصب پکیج tar.gz dnnviewer-0.1.0.dev9:

pip install dnnviewer-0.1.0.dev9.tar.gz