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deeptrain-0.6.0
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مشاهده بیشتر
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مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
Full knowledge and control of the train state
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | deeptrain-0.6.0 |
| نام | deeptrain |
| نسخه کتابخانه | 0.6.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | OverLordGoldDragon |
| ایمیل نویسنده | 16495490+OverLordGoldDragon@users.noreply.github.com |
| آدرس صفحه اصلی | https://github.com/OverLordGoldDragon/deeptrain |
| آدرس اینترنتی | https://pypi.org/project/deeptrain/ |
| مجوز | MIT |
<p align="center"><img src="https://user-images.githubusercontent.com/16495490/89590797-bf379000-d859-11ea-8414-1e08aee3a95c.png" width="300"></p>
# DeepTrain
[](https://travis-ci.com/OverLordGoldDragon/deeptrain)
[](https://coveralls.io/github/OverLordGoldDragon/deeptrain)
[](https://www.codacy.com/manual/OverLordGoldDragon/deeptrain?utm_source=github.com&utm_medium=referral&utm_content=OverLordGoldDragon/deeptrain&utm_campaign=Badge_Grade)
[](https://badge.fury.io/py/keras-adamw)
[](https://deeptrain.readthedocs.io/en/latest/?badge=latest)
[](https://opensource.org/licenses/MIT)
Full knowledge and control of the train state.
## Features
DeepTrain is founded on **control** and **introspection**: full knowledge and manipulation of the train state.
### Train Loop
- **Resumability**: interrupt-protection, can pause mid-training
- **Tracking & reproducibility**: save & load model, train state, random seeds, and hyperparameter info
### Data Pipeline
- **Flexible batch_size**: can differ from that of loaded files, will split/combine ([ex](https://deeptrain.readthedocs.io/en/latest/examples/misc/flexible_batch_size.html))
- **Faster SSD loading**: load larger batches to maximize read speed utility
- **Stateful timeseries**: splits up a batch into windows, and `reset_states()` (RNNs) at end ([ex](https://deeptrain.readthedocs.io/en/latest/examples/misc/timeseries.html))
### Introspection & Utilities
- **Model**: auto descriptive naming ([ex](https://deeptrain.readthedocs.io/en/latest/examples/misc/model_auto_naming.html)); gradients, weights, activations visuals ([ex](https://deeptrain.readthedocs.io/en/latest/examples/callbacks/mnist.html#Init-&-train))
- **Train state**: image log of key attributes for easy reference ([ex](https://deeptrain.readthedocs.io/en/latest/examples/advanced.html#Inspect-generated-logs)); batches marked w/ "set nums" - know what's being fit and when
- **Algorithms, preprocesing, calibration**: tools for inspecting & manipulating data and models
[Complete list](https://deeptrain.readthedocs.io/en/latest/why_deeptrain.html)
## When is DeepTrain suitable (and not)?
Training _few_ models _thoroughly_: closely tracking model and train attributes to debug performance and inform next steps.
DeepTrain is _not_ for models that take under an hour to train, or for training hundreds of models at once.
## What does DeepTrain do?
Abstract away boilerplate train loop and data loading code, *without* making it into a black box. Code is written intuitively and fully documented.
Everything about the train state can be seen via *dedicated attributes*; which batch is being fit and when, how long until an epoch ends, intermediate metrics, etc.
DeepTrain is *not* a "wrapper" around TF; while currently only supporting TF, fitting and data logic is framework-agnostic.
## How it works
<p align="center"><img src="https://raw.githubusercontent.com/OverLordGoldDragon/deeptrain/master/docs/source/_images/train_loop.png" width="700"></p>
<img src="https://raw.githubusercontent.com/OverLordGoldDragon/deeptrain/master/docs/source/_images/train_val.gif" width="450" align="right">
1. We define `tg = TrainGenerator(**configs)`,
2. call `tg.train()`.<br>
3. `get_data()` is called, returning data & labels,<br>
4. fed to `model.fit()`, returning `metrics`,<br>
5. which are then printed, recorded.<br>
6. The loop repeats, or `validate()` is called.<br>
Once `validate()` finishes, training may checkpoint, and `train()` is called again. Internally, data loads with `DataGenerator.load_data()` (using e.g. `np.load`).
That's the high-level overview; details [here](https://deeptrain.readthedocs.io/en/latest/how_works.html). Callbacks & other behavior can be configured for every stage of training.
## Examples
<a href="https://deeptrain.readthedocs.io/en/latest/examples/advanced.html">MNIST AutoEncoder</a> | <a href="https://deeptrain.readthedocs.io/en/latest/examples/misc/timeseries.html">Timeseries Classification</a> | <a href="https://deeptrain.readthedocs.io/en/latest/examples/misc/model_health.html">Health Monitoring</a>
:----------------:|:-----------------:|:-----------------:
<a href="https://deeptrain.readthedocs.io/en/latest/examples/advanced.html"><img src="https://raw.githubusercontent.com/OverLordGoldDragon/deeptrain/master/docs/source/_images/mnist.gif" width="210" height="210"><a>|<a href="https://deeptrain.readthedocs.io/en/latest/examples/misc/timeseries.html"><img src="https://raw.githubusercontent.com/OverLordGoldDragon/deeptrain/master/docs/source/_images/ecg2.png" width="210" height="210"></a>|<a href="https://deeptrain.readthedocs.io/en/latest/examples/misc/model_health.html"><img src="https://raw.githubusercontent.com/OverLordGoldDragon/deeptrain/master/docs/source/_images/model_health.png" width="210" height="210"></a>
<a href="https://deeptrain.readthedocs.io/en/latest/examples/callbacks/mnist.html">Tracking Weights</a> | <a href="https://deeptrain.readthedocs.io/en/latest/examples/misc/reproducibility.html">Reproducibility</a> | <a href="https://deeptrain.readthedocs.io/en/latest/examples/misc/flexible_batch_size.html">Flexible batch_size</a>
:----------------:|:----------------:|:----------------:|
<a href="https://deeptrain.readthedocs.io/en/latest/examples/callbacks/mnist.html"><img src="https://raw.githubusercontent.com/OverLordGoldDragon/deeptrain/master/docs/source/_images/gradients.gif" width="210" height="210"></a>|<a href="https://deeptrain.readthedocs.io/en/latest/examples/misc/reproducibility.html"><img src="https://raw.githubusercontent.com/OverLordGoldDragon/deeptrain/master/docs/source/_images/reproducibility.png" width="210" height="210"></a>|<a href="https://deeptrain.readthedocs.io/en/latest/examples/misc/flexible_batch_size.html"><img src="https://raw.githubusercontent.com/OverLordGoldDragon/deeptrain/master/docs/source/_images/flexible_batch_size.png" width="210" height="210"></a>
## Installation
`pip install deeptrain` (without data; see [how to run examples](https://deeptrain.readthedocs.io/en/latest/how_to.html#run-examples)), or clone repository
## Quickstart
To run, DeepTrain requires (1) a compiled model; (2) data directories (train & val). Below is a minimalistic example.
Checkpointing, visualizing, callbacks & more can be accomplished via additional arguments; see [Basic](https://deeptrain.readthedocs.io/en/latest/examples/basic.html) and [Advanced](https://deeptrain.readthedocs.io/en/latest/examples/advanced.html) examples.
Also see [Recommended Usage](https://deeptrain.readthedocs.io/en/latest/recommended_usage.html).
```python
from tensorflow.keras.layers import Input, Dense
from tensorflow.keras.models import Model
from deeptrain import TrainGenerator, DataGenerator
ipt = Input((16,))
out = Dense(10, 'softmax')(ipt)
model = Model(ipt, out)
model.compile('adam', 'categorical_crossentropy')
dg = DataGenerator(data_path="data/train", labels_path="data/train/labels.npy")
vdg = DataGenerator(data_path="data/val", labels_path="data/val/labels.npy")
tg = TrainGenerator(model, dg, vdg, epochs=3, logs_dir="logs/")
tg.train()
```
## In future releases
- `MetaTrainer`: direct support for dynamic model recompiling with changing hyperparameters, and optimizing thereof
- PyTorch support
نیازمندی
| مقدار | نام |
|---|---|
| - | numpy |
| - | matplotlib |
| - | tensorflow |
| - | h5py |
| - | see-rnn |
| >=41.0.0 | setuptools |
| >=1.15.0 | twine |
| - | Keras |
| - | coverage |
| - | pytest |
| - | pytest-cov |
| - | pycode |
| - | pandas |
| - | pillow |
| - | sklearn |
| - | py-lz4framed |
| >=41.0.0 | setuptools |
| >=1.15.0 | twine |
| - | Keras |
| - | coverage |
| - | pytest |
| - | pytest-cov |
| - | pycode |
| - | pandas |
| - | pillow |
| - | sklearn |
| - | py-lz4framed |
نحوه نصب
نصب پکیج whl deeptrain-0.6.0:
pip install deeptrain-0.6.0.whl
نصب پکیج tar.gz deeptrain-0.6.0:
pip install deeptrain-0.6.0.tar.gz