semvec/pypi-packages huggingface dataset:
machine-data-hub 0.1.1
Python-based Command Line Tool to use the Machine Data Hub
License: MIT
Requires Python: >=3.7.1,<4.0.0

[![Documentation Status](https://readthedocs.org/projects/machine-data-hub/badge/?version=latest)](https://machine-data-hub.readthedocs.io/en/latest/?badge=latest)

# Machine Data Hub
Python Client to use the [Machine Data Hub](https://machinedatahub.ai), an open source project
to make state of the art machine learning and artificial intelligence techniques more accessible to engineering domains 
by providing suitable benchmark datasets for advancing prognostics
pycopy-linecache 0.0.2
Dummy linecache module for Pycopy
License: MIT

This is a dummy implementation of a module for the standard library of
Pycopy project (https://github.com/pfalcon/pycopy).
It contains zero or very little functionality, and primarily intended to
avoid import errors (using idea that even if an application imports a
module, it may be not using it onevery code path, so may work at least
partially). It is expected that more complete implementation of the module
will be provided later. Please help with the development if you are
interested in this module.
pycopy-tempfile 0.2
tempfile module for Pycopy
License: MIT

This is a module for the standard library of Pycopy project:
https://github.com/pfalcon/pycopy . This module was implemented from
scratch specifically to target Pycopy, with efficient and lean design
in mind. Note that this module is likely work in progress and likely
supports just a subset of CPython’s corresponding module. Please help
with the development if you are interested in this module.
training-dashboard 0.1.0
No bullshit, dead simple training visualizer for tf-keras.
License: MIT

# Training Dashboard
A no bullshit, dead simple training visualizer for tf-keras.


## Usage

TrainingDashboard is meant to be used as a callback passed to the fit() function.

```python
  from training_dashboard import TrainingDashboard
  callback = TrainingDashboard(validation=True,
                               min_loss=0,
                               metrics=["accuracy", "auc"],
                               batch_step=10,
                               min_metric_dict={"accuracy": 0, "auc": 0},
                               max_metric_dict={"accuracy": 1, "auc": 1})
  model.fit(x_train,
            y_train,
            batch_size=512,
            epochs=25,
            verbose=1,
            validation_split=0.2,
            callbacks=[callback])
```

## Example Output

<img src="https://i.imgur.com/D91YmwT.gif" width="700"/>