ktrain 0.6.1

ktrain is a lightweight wrapper for Keras to help train neural networks

News and Announcements

• 2019-11-12:
  • ktrain v0.6.x is released and includes pre-canned support for learning from unlabeled or partially labeled text data.
• 2019-10-16:
  • ktrain v0.5.x is released and includes pre-canned support for node classification in graphs.
• Coming Soon:
  • better support for custom data formats and models
  • support for using ktrain with tf.keras

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ktrain

ktrain is a lightweight wrapper for the deep learning library Keras (and other libraries) to help build, train, and deploy neural networks. With only a few lines of code, ktrain allows you to easily and quickly:

• 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
• employ fast and easy-to-use pre-canned models for text, vision, and graph data:
  • text data:
    • Text Classification: BERT, NBSVM, fastText, GRUs with pretrained word vectors, and other models _{^{[example notebook]}}
    • Sequence Labeling: Bidirectional LSTM-CRF with optional pretrained word embeddings _{^{[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 semantically similar to it from a larger corpus _{^{[example notebook]}}
  • vision data:
    • image classification (e.g., ResNet, Wide ResNet, Inception) _{^{[example notebook]}}
  • graph data:
    • graph node classification (e.g., GraphSAGE) _{^{[example notebook]}}
• perform multilingual text classification (e.g., Chinese Sentiment Analysis with BERT, Arabic Sentiment Analysis with NBSVM)
• 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

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

Explainable AI in Practice

Using ktrain on Google Colab? See this simple demo of Multiclass Text Classification with BERT.

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')

# 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')

Additional examples can be found here.

Installation

pip3 install ktrain

This code was tested on Ubuntu 18.04 LTS using Keras 2.2.4 with a TensorFlow 1.14 backend.

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Creator: Arun S. Maiya

Email: arun [at] maiya [dot] net