Creating Config

There are multiple scenarios where we need to create configuration like in context of deep learning

  1. Model Config
  2. Training Config
  3. Inference Config
>>> from classification_report import Config
>>> training_config = Config(lr=0.1, batch_size=32, device="GPU")
>>> model_config = Config(number_layers=3, num_head=32)
>>> model_config.number_layers
>>> training_config.lr

The benefit of using these config files are the can be saved as JSON file and can be loaded from a JSON file

Save Configuration

>>> training_config.save_config_json("training_config.json")
Configuration Saved

Load Configuration

>>> training_config = Config.load_config_json("training_config.json") # Execute the saving code first.
>>> training_config.lr

For other methods in Config check the API references.

Creating HyperParameter

Since we can have many configs. It is necessary to combine all of them in place so that they can all be in one place and one can easily track all the HyperParameter to compare against multiple experiments.

HyperParameter class inherits from Config class so it supports all the method of Config.

>>> from classification_report import Config, HyperParameters
>>> model_config = Config(**{'hid_dim': 512,'n_layers': 8,'n_heads': 8,'pf_dim': 2048,'dropout': 0.1})
>>> training_config = Config(num_epochs=15, max_lr=0.09, batch_size=64)
>>> inference_config = Config(batch_size=16)
>>> hyper = HyperParameters(model_config = model_config, training_config=training_config,
>>> hyper.model_config.hid_dim
>>> hyper.training_config.num_epochs

Similar to Config we can also save and load this HyperParameter which is a collection of configs.

while saving the HyperParameter it writes only in one JSON file.

>>> hyper.save_config_json("hyper.json")
Configuration Saved
>>> h = HyperParameters.load_config_json("hyper.json")
Saved Configuration Loaded
>>> h.training_config.num_epochs

Creating Report

There are demo_notebooks which can be executed to see the full potential of the this library.

Simple MNIST with Simple Reporting Example

Simple MNIST with Detail Reporting Example

This two notebook covers almost all the features of the library but to see details of it head over to API References

Visualising Report

As the report is generated on the fly while the model is training. All the visualization can be seen using tensorboard.

Whenever this library is executed a runs folder is created on the top-level and tensorboard uses that runs folder track.

This runs folder contains all the experiment and can be used to compare different experiments and can be shared among your teammates for studying.

The ideal way to visualize is to first execute the tensorboard from the same directory level from where all the notebooks are created.

tensorboard --logdir=runs



Manually Visualize a pre-existing experiment on your local.

Install the library After installing the library execute these commands.

git clone https://github.com/aman5319/Classification-Report # clone library 
cd Classification-Report/demo_notebook # change the directory
tensorboard --logdir=runs # execute tensorboard