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deeptrain-0.6.0


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توضیحات

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 [![Build Status](https://travis-ci.com/OverLordGoldDragon/deeptrain.svg?branch=master)](https://travis-ci.com/OverLordGoldDragon/deeptrain) [![Coverage Status](https://coveralls.io/repos/github/OverLordGoldDragon/deeptrain/badge.svg?branch=master&service=github)](https://coveralls.io/github/OverLordGoldDragon/deeptrain) [![Codacy Badge](https://app.codacy.com/project/badge/Grade/b3ddf578cd674c268004b0c445c2d695)](https://www.codacy.com/manual/OverLordGoldDragon/deeptrain?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=OverLordGoldDragon/deeptrain&amp;utm_campaign=Badge_Grade) [![PyPI version](https://badge.fury.io/py/deeptrain.svg)](https://badge.fury.io/py/keras-adamw) [![Documentation Status](https://readthedocs.org/projects/deeptrain/badge/?version=latest)](https://deeptrain.readthedocs.io/en/latest/?badge=latest) ![](https://img.shields.io/badge/keras-tensorflow-blue.svg) ![](https://img.shields.io/badge/keras-tf.keras-blue.svg) [![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](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