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DLTKDL-51.0.8
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مشاهده بیشترتوضیحات
DLTKDL is a wrapper for TensorFlow and Keras that makes deep learning and AI more accessible and easier to apply
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | DLTKDL-51.0.8 |
| نام | DLTKDL |
| نسخه کتابخانه | 51.0.8 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | DLTK |
| ایمیل نویسنده | connect@qubitai.tech |
| آدرس صفحه اصلی | https://github.com/dltk-ai/Deep-Learning |
| آدرس اینترنتی | https://pypi.org/project/DLTKDL/ |
| مجوز | GPL-3.0 License |
**DLTK Deep Learning API or DLTKDL** is an open source lightweight wrapper for the deep learning library TensorFlow and Keras (and other libraries) to help build, train, and deploy neural networks and other machine learning models. Inspired by ML framework extensions like fastai and ludwig, it is designed to make deep learning and AI more accessible and easier to apply for both newcomers and experienced practitioners. With only a few lines of code, dltkdl allows you to easily and quickly:
**Installation Instructions**
Make sure pip is up-to-date with:
Install TensorFlow 2 if it is not already installed (e.g., pip install tensorflow==2.3.0)
Install dltkdl:
```
git clone https://github.com/dltk-ai/Deep-Learning
cd Deep-Learning
python setup.py install or pip install .
```
The above should be all you need on Linux systems and cloud computing environments like Google Colab and AWS EC2. If you are using dltkdl on a Windows computer, you can follow these more [detailed instructions](https://github.com/dltk-ai/Deep-Learning/blob/master/FAQ.md) that include some extra steps.
Some important things to note about installation:
If using dltkdl on a local machine with a GPU (versus Google Colab, for example), you'll need to install GPU support for TensorFlow 2.
On Google Colab, TensorFlow 2 should be already installed. You should be able to use dltkdl with any version of TensorFlow 2. However, we currently recommend using TensorFlow 2.3.0 (if possible) due to a TensorFlow bug that will not be fixed until TensorFlow 2.4 that affects the Learning-Rate-Finder.
Since some dltkdl dependencies have not yet been migrated to tf.keras in TensorFlow 2 (or may have other issues), dltkdl is temporarily using forked versions of some libraries. If not installed, dltkdl will complain when a method or function needing either of these libraries is invoked.
### Available Deep Learning algorithms:
#### Artificial Neural Network(ANN) :
1. **Multilayer perceptron or mlp**: a configurable multilayer perceptron with categorical variable embeddings.
#### Convolutional Neural Network(CNN) :
1. **pretrained_resnet50**: 50-layer Residual Network (pretrained on ImageNet)
2. **resnet50**: 50-layer Residual Network (randomly initialized)
3. **pretrained_mobilenet**: MobileNet Neural Network (pretrained on ImageNet)
4. **mobilenet**: MobileNet Neural Network (randomly initialized)
5. **pretrained_inception**: Inception Version 3 (pretrained on ImageNet)
6. **inception**: Inception Version 3 (randomly initialized)
7. **wrn22**: 22-layer Wide Residual Network (randomly initialized)
8. **default_cnn**: a default Convolutional Neural Network
#### Recurrent neural Network(RNN) and Long short-term memory(LSTM) :
1. **fasttext**: a fastText-like model [http://arxiv.org/pdf/1607.01759.pdf]
2. **logreg**: logistic regression using a trainable Embedding layer
3. **nbsvm**: NBSVM model [http://www.aclweb.org/anthology/P12-2018]
4. **bigru**: Bidirectional GRU with pretrained word vectors [https://arxiv.org/abs/1712.09405]
5. **standard_gru**: simple 2-layer GRU with randomly initialized embeddings
6. **bert**: Bidirectional Encoder Representations from Transformers (BERT) [https://arxiv.org/abs/1810.04805]
7. **distilbert**: distilled, smaller, and faster BERT from Hugging Face [https://arxiv.org/abs/1910.01108]
### Examples:
You can also chckout step-by step implementation by following python notebooks under tutorial folder.
Example **Hourse price prediction using Multilayer perceptron Neural Network**
```
import urllib.request
import pandas as pd
import numpy as np
pd.set_option('display.max_columns', None)
import dltkdl
from dltkdl import tabledata
train_df = pd.read_csv('train.csv', index_col=0)
# print(train_df)
train_df.drop(['Alley', 'PoolQC', 'MiscFeature', 'Fence', 'FireplaceQu', 'Utilities'], 1, inplace=True)
trn, val, preproc = tabledata.load_from_dataframe(train_df, is_regression=True, label_columns='SalePrice',
random_state=42)
# Invoking multilayer perceptron NN
model = tabledata.tabular_regression_model('mlp', trn)
learner = dltkdl.get_learner(model, train_data=trn, val_data=val, batch_size=128)
learner.lr_find(show_plot=True, max_epochs=16)
learner.autofit(1e-1)
print(learner.evaluate(test_data=val))
```
Example: **Classifying Images of Dogs and Cats Using a Pretrained ResNet50 model**
```
import dltkdl
from dltkdl import vision as vis
# load data
(train_data, val_data, preproc) = vis.images_from_folder(
datadir='data/dogscats',
data_aug = vis.get_data_aug(horizontal_flip=True),
train_test_names=['train', 'valid'],
target_size=(224,224), color_mode='rgb')
# load model
model = vis.image_classifier('pretrained_resnet50', train_data, val_data, freeze_layers=80)
# wrap model and data in dltkdl.Learner object
learner = dltkdl.get_learner(model=model, train_data=train_data, val_data=val_data,
workers=8, use_multiprocessing=False, batch_size=64)
# find good learning rate
learner.lr_find() # briefly simulate training to find good learning rate
learner.lr_plot() # visually identify best learning rate
# train using triangular policy with ModelCheckpoint and implicit ReduceLROnPlateau and EarlyStopping
learner.autofit(1e-4, checkpoint_folder='/tmp/saved_weights')
```
Example: **Language translation(English to Dutch)**
```
from dltkdl import text
translator = text.Translator(model_name='Helsinki-NLP/opus-mt-en-nl')
src_text = '''My name is Sarah and I live in London.'''
print(translator.translate(src_text))
Output: Mijn naam is Sarah en ik woon in Londen.
```
Example: **Text classification**
```
# load text data
categories = ['alt.atheism', 'soc.religion.christian', 'comp.graphics', 'sci.med']
from sklearn.datasets import fetch_20newsgroups
train_b = fetch_20newsgroups(subset='train', categories=categories, shuffle=True)
test_b = fetch_20newsgroups(subset='test', categories=categories, shuffle=True)
(x_train, y_train) = (train_b.data, train_b.target)
(x_test, y_test) = (test_b.data, test_b.target)
# build, train, and validate model (Transformer is wrapper around transformers library)
import dltkdl
from dltkdl import text
MODEL_NAME = 'distilbert-base-uncased'
t = text.Transformer(MODEL_NAME, maxlen=500, class_names=train_b.target_names)
trn = t.preprocess_train(x_train, y_train)
val = t.preprocess_test(x_test, y_test)
model = t.get_classifier()
learner = dltkdl.get_learner(model, train_data=trn, val_data=val, batch_size=6)
learner.fit_onecycle(5e-5, 4)
learner.validate(class_names=t.get_classes()) # class_names must be string values
Output:
precision recall f1-score support
alt.atheism 0.91 0.93 0.92 319
comp.graphics 0.98 0.96 0.97 389
sci.med 0.97 0.96 0.96 396
soc.religion.christian 0.95 0.97 0.96 398
accuracy 0.96 1502
macro avg 0.95 0.95 0.95 1502
weighted avg 0.96 0.96 0.96 1502
array([[296, 1, 6, 16],
[ 12, 372, 5, 0],
[ 8, 6, 379, 3],
[ 8, 2, 0, 388]])
```
## Founding Member
[](https://github.com/shreeramiyer)
## Lead Maintainer
[](https://github.com/GHub4Naveen)
## Core Maintainer
[](https://github.com/dltk-ai)
## Core Contributers
[](https://github.com/SivaramVeluri15)
[](https://github.com/vishnupeesapati)
[](https://github.com/EpuriHarika/)
[](https://github.com/nageshsinghc4)
[](https://github.com/appareddyraja)
[](https://github.com/shakeeldhada)
[](https://github.com/thomasbinish)
[](https://github.com/kavyavelagapudi252)
## License
The content of this project itself is licensed under [GNU LGPL, Version 3 (LGPL-3)](https://github.com/dltk-ai/Deep-Learning/blob/master/LICENSE)
نیازمندی
| مقدار | نام |
|---|---|
| ==2.3.0 | tensorflow |
| ==1.5.2 | scipy |
| - | matplotlib |
| ==1.0.1 | pandas |
| ==0.1.21 | fastprogress |
| ==0.86.0 | keras-bert |
| - | requests |
| - | joblib |
| - | langdetect |
| - | theano |
| - | jieba |
| - | cchardet |
| - | networkx |
| - | bokeh |
| ==0.0.19 | seqeval |
| - | packaging |
| ==3.1.0 | transformers |
| - | ipython |
| - | syntok |
| - | whoosh |
| - | scikit-learn |
| - | numba |
نحوه نصب
نصب پکیج whl DLTKDL-51.0.8:
pip install DLTKDL-51.0.8.whl
نصب پکیج tar.gz DLTKDL-51.0.8:
pip install DLTKDL-51.0.8.tar.gz