Overview | Tutorials | Examples | Installation | How to Cite

ktrain

News and Announcements

2020-06-03:
- ktrain v0.16.x is released and includes support for Zero-Shot Learning, where documents can be classified into user-provided topics without any training examples. See the example notebook. _{^{(This feature currently requires that PyTorch be installed.)}}

# Zero-Shot Topic Classification in ktrain (NOTE: Zero-Shot Learning uses PyTorch instead of TensorFlow)

from ktrain import text 
zsl = text.ZeroShotClassifier()
topic_strings=['politics', 'elections', 'sports', 'films', 'television']
doc = 'I am unhappy with decisions of the government and will definitely vote in 2020.'
zsl.predict(doc, topic_strings=topic_strings, include_labels=True)
# output:
# [('politics', 0.9829113483428955),
#  ('elections', 0.9880988001823425),
#  ('sports', 0.00030677582253701985),
#  ('films', 0.0008969294722191989),
#  ('television', 0.00045271270209923387)]

2020-05-13:
- ktrain v0.15.x is released and includes support for:
  - image regression: See the example notebook on age prediction from photos.
  - sentence pair classification: See this example notebook on using BERT for paraphrase detection._{^{(Sentence pair classification included in v0.15.1, but not v0.15.0.)}}
  - tf.data.Datasets: See the example notebook on using tf.data.Datasets in ktrain for custom models and data formats.
2020-04-15:
- ktrain v0.14.x is released and now includes support for open-domain question-answering. See the example QA notebook

Overview

ktrain is a lightweight wrapper for the deep learning library TensorFlow Keras (and other libraries) to help build, train, and deploy neural networks and other machine learning models. 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, ktrain allows you to easily and quickly:

employ fast, accurate, and easy-to-use pre-canned models for text, vision, and graph data:
- text data:
  - Text Classification: BERT, DistilBERT, NBSVM, fastText, and other models _{^{[example notebook]}}
  - Text Regression: BERT, DistilBERT, Embedding-based linear text regression, fastText, and other models _{^{[example notebook]}}
  - Sequence Labeling (NER): Bidirectional LSTM with optional CRF layer and various embedding schemes such as pretrained BERT and fasttext word embeddings and character embeddings _{^{[example notebook]}}
  - Ready-to-Use NER models for English, Chinese, and Russian with no training required _{^{[example notebook]}}
  - Sentence Pair Classification for tasks like paraphrase detection _{^{[example notebook]}}
  - Unsupervised Topic Modeling with LDA _{^{[example notebook]}}
  - Document Similarity with One-Class Learning: given some documents of interest, find and score new documents that are semantically similar to them using One-Class Text Classification _{^{[example notebook]}}
  - Document Recommendation Engine: given text from a sample document, recommend documents that are thematically-related to it from a larger corpus _{^{[example notebook]}}
  - Text Summarization: summarize long documents with a pretrained BART model - no training required _{^{[example notebook]}}
  - Open-Domain Question-Answering: ask a large text corpus questions and receive exact answers _{^{[example notebook]}}
  - Zero-Shot Learning: classify documents into user-provided topics without training examples _{^{[example notebook]}}
- vision data:
  - image classification (e.g., ResNet, Wide ResNet, Inception) _{^{[example notebook]}}
  - image regression for predicting numerical targets from photos (e.g., age prediction) _{^{[example notebook]}}
- graph data:
  - node classification with graph neural networks (GraphSAGE) _{^{[example notebook]}}
  - link prediction with graph neural networks (GraphSAGE) _{^{[example notebook]}}
estimate an optimal learning rate for your model given your data using a Learning Rate Finder
utilize learning rate schedules such as the triangular policy, the 1cycle policy, and SGDR to effectively minimize loss and improve generalization
build text classifiers for any language (e.g., Chinese Sentiment Analysis with BERT, Arabic Sentiment Analysis with NBSVM)
easily train NER models for any language (e.g., Dutch NER )
load and preprocess text and image data from a variety of formats
inspect data points that were misclassified and provide explanations to help improve your model
leverage a simple prediction API for saving and deploying both models and data-preprocessing steps to make predictions on new raw data

Tutorials

Please see the following tutorial notebooks for a guide on how to use ktrain on your projects:

Tutorial 1: Introduction
Tutorial 2: Tuning Learning Rates
Tutorial 3: Image Classification
Tutorial 4: Text Classification
Tutorial 5: Learning from Unlabeled Text Data
Tutorial 6: Text Sequence Tagging for Named Entity Recognition
Tutorial 7: Graph Node Classification with Graph Neural Networks
Tutorial A1: Additional tricks, which covers topics such as previewing data augmentation schemes, inspecting intermediate output of Keras models for debugging, setting global weight decay, and use of built-in and custom callbacks.
Tutorial A2: Explaining Predictions and Misclassifications
Tutorial A3: Text Classification with Hugging Face Transformers
Tutorial A4: Using Custom Data Formats and Models: Text Regression with Extra Regressors

Some blog tutorials about ktrain are shown below:

ktrain: A Lightweight Wrapper for Keras to Help Train Neural Networks

BERT Text Classification in 3 Lines of Code

Text Classification with Hugging Face Transformers in TensorFlow 2 (Without Tears)

Build an Open-Domain Question-Answering System With BERT in 3 Lines of Code

Examples

Tasks such as text classification and image classification can be accomplished easily with only a few lines of code.

Example: Text Classification of IMDb Movie Reviews Using BERT

import ktrain
from ktrain import text as txt

# load data
(x_train, y_train), (x_test, y_test), preproc = txt.texts_from_folder('data/aclImdb', maxlen=500, 
                                                                     preprocess_mode='bert',
                                                                     train_test_names=['train', 'test'],
                                                                     classes=['pos', 'neg'])

# load model
model = txt.text_classifier('bert', (x_train, y_train), preproc=preproc)

# wrap model and data in ktrain.Learner object
learner = ktrain.get_learner(model, 
                             train_data=(x_train, y_train), 
                             val_data=(x_test, y_test), 
                             batch_size=6)

# 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 1cycle learning rate schedule for 3 epochs
learner.fit_onecycle(2e-5, 3)

Example: Classifying Images of Dogs and Cats Using a Pretrained ResNet50 model

import ktrain
from ktrain 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 ktrain.Learner object
learner = ktrain.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: Sequence Labeling for Named Entity Recognition using a randomly initialized Bidirectional LSTM CRF model

import ktrain
from ktrain import text as txt

# load data
(trn, val, preproc) = txt.entities_from_txt('data/ner_dataset.csv',
                                            sentence_column='Sentence #',
                                            word_column='Word',
                                            tag_column='Tag', 
                                            data_format='gmb',
                                            use_char=True) # enable character embeddings

# load model
model = txt.sequence_tagger('bilstm-crf', preproc)

# wrap model and data in ktrain.Learner object
learner = ktrain.get_learner(model, train_data=trn, val_data=val)


# conventional training for 1 epoch using a learning rate of 0.001 (Keras default for Adam optmizer)
learner.fit(1e-3, 1)

Example: Node Classification on Cora Citation Graph using a GraphSAGE model

import ktrain
from ktrain import graph as gr

# load data with supervision ratio of 10%
(trn, val, preproc)  = gr.graph_nodes_from_csv(
                                               'cora.content', # node attributes/labels
                                               'cora.cites',   # edge list
                                               sample_size=20, 
                                               holdout_pct=None, 
                                               holdout_for_inductive=False,
                                              train_pct=0.1, sep='\t')

# load model
model=gr.graph_node_classifier('graphsage', trn)

# wrap model and data in ktrain.Learner object
learner = ktrain.get_learner(model, train_data=trn, val_data=val, batch_size=64)


# find good learning rate
learner.lr_find(max_epochs=100) # 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(0.01, checkpoint_folder='/tmp/saved_weights')

Example: Text Classification with Hugging Face Transformers on 20 Newsgroups Dataset Using DistilBERT

# 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 ktrain
from ktrain 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 = ktrain.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 from learner.validate()
#                        precision    recall  f1-score   support
#
#           alt.atheism       0.92      0.93      0.93       319
#         comp.graphics       0.97      0.97      0.97       389
#               sci.med       0.97      0.95      0.96       396
#soc.religion.christian       0.96      0.96      0.96       398
#
#              accuracy                           0.96      1502
#             macro avg       0.95      0.96      0.95      1502
#          weighted avg       0.96      0.96      0.96      1502

Example: NER With BioBERT Embeddings

# NER with BioBERT embeddings
import ktrain
from ktrain import text as txt
x_train= [['IL-2', 'responsiveness', 'requires', 'three', 'distinct', 'elements', 'within', 'the', 'enhancer', '.'], ...]
y_train=[['B-protein', 'O', 'O', 'O', 'O', 'B-DNA', 'O', 'O', 'B-DNA', 'O'], ...]
(trn, val, preproc) = txt.entities_from_array(x_train, y_train)
model = txt.sequence_tagger('bilstm-bert', preproc, bert_model='monologg/biobert_v1.1_pubmed')
learner = ktrain.get_learner(model, train_data=trn, val_data=val, batch_size=128)
learner.fit(0.01, 1, cycle_len=5)

Using ktrain on Google Colab? See these Colab examples:

Additional examples can be found here.

Installation

ktrain currently uses TensorFlow 2.1.0, which will be installed automatically when installing ktrain. While ktrain will probably work with other versions of TensorFlow 2.x, v2.1.0 is the current recommended and tested version.

Make sure pip is up-to-date with: pip3 install -U pip
Install ktrain: pip3 install ktrain

Some things to note:

ktrain will automatically install TensorFlow 2 as a dependency.
Since some ktrain dependencies have not yet been migrated to tf.keras in TensorFlow 2 (or may have other issues), ktrain is temporarily using forked versions of some libraries. Specifically, ktrain uses forked versions of the eli5 and stellargraph libraries. If not installed, ktrain will complain when a method or function needing either of these libraries is invoked. To install these forked versions, you can do the following:

pip3 install git+https://github.com/amaiya/eli5@tfkeras_0_10_1
pip3 install git+https://github.com/amaiya/stellargraph@no_tf_dep_082

This code was tested on Ubuntu 18.04 LTS using TensorFlow 2.1.0

How to Cite

Please cite the following paper when using ktrain:

@article{maiya2020ktrain,
         title={ktrain: A Low-Code Library for Augmented Machine Learning},
         author={Arun S. Maiya},
         journal={arXiv},
         year={2020},
         volume={arXiv:2004.10703 [cs.LG]}
}

Creator: Arun S. Maiya

Email: arun [at] maiya [dot] net

kumar-abhishek/ktrain