ktrain 0.2.1

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

ktrain

ktrain is a lightweight wrapper for the deep learning library Keras 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 both text classification (e.g., BERT, NBSVM, fastText, GRUs with pretrained word vectors) and image classification (e.g., ResNet, Wide ResNet, Inception)
• load and preprocess text and image data from a variety of formats
• inspect data points that were misclassified 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 A1: Additional tricks, which covers topics such as examining misclassifications, inspecting intermediate output of Keras models for debugging, and built-in callbacks.

A Medium post providing a broad overview of ktrain is here:

ktrain: A Lightweight Wrapper for Keras to Help Train Neural Networks by Arun Maiya.

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))

# 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') 

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 1.10 backend. There are a few portions of the code that may explicitly depend on TensorFlow, but such dependencies are kept to a minimum.

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Creator: Arun S. Maiya

Email: arun [at] maiya [dot] net