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

Project description

News and Announcements

  • 2019-12-10:
    • ktrain v0.7.x is released and now uses TensorFlow Keras (i.e., tf.keras) instead of stand-alone Keras. If you're using custom Keras models with ktrain, you must change all keras references to tensorflow.keras. That is, don't import Keras like this: from keras.layers import Dense. Do this instead: from tensorflow.keras.layers import Dense. If you mix calls to tf.keras with Keras, you will experience problems. Supported versions of TensorFlow include 1.14 and 2.0.
  • 2019-11-12:
  • Coming Soon:

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:

Tutorials

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

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

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

  1. Ensure Tensorflow 1.14 or TensorFlow 2 is installed if it is not already

For GPU: pip3 install "tensorflow_gpu>=1.14,<=2"

For CPU: pip3 install "tensorflow>=1.14,<=2"

  1. Install ktrain: pip3 install ktrain

The ktrain package can be used with TensorFlow versions 1.14 and 2.0. If using TensorFlow 2.0, ktrain presently runs in 1.x mode using tf.compat.v1.disable_v2_behavior. In the future, this will be removed and only TensorFlow 2 will be supported.

This code was tested on Ubuntu 18.04 LTS using TensorFlow 1.14 and TensorFlow 2 (Keras version 2.2.4-tf).


Creator: Arun S. Maiya

Email: arun [at] maiya [dot] net

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