Flowws Modules for Machine Learning Experiments with Keras

flowws-keras-experimental is a set of flowws modules to train machine learning models using tensorflow and keras. It contains basic modules to perform common tasks such as loading data and specifying model architectures.

Installation

Install flowws-keras-experimental from PyPI:

pip install flowws-keras-experimental

Alternatively, install from source:

pip install git+https://github.com/klarh/flowws-keras-experimental.git#egg=flowws-keras-experimental

API Documentation

Browse more detailed documentation online or build the sphinx documentation from source:

git clone https://github.com/klarh/flowws-keras-experimental
cd flowws-keras-experimental/doc
pip install -r requirements.txt
make html

Base Modules

Image-Related Tasks

Data sources

class flowws_keras_experimental.images.CIFAR100(**kwargs)

Use the CIFAR100 dataset from keras.

Parameters:

coarse_labels – If True, use 20 coarse-grained labels instead of 100 fine-grained labels

class flowws_keras_experimental.images.CIFAR10(**kwargs)

Use the CIFAR10 dataset from keras.

class flowws_keras_experimental.images.ImagenetDirectory(**kwargs)

Load ImageNet images from a specified directory.

Parameters:

• base – Base directory storing images

• validation_fraction – Fraction of files to be used in validation set

• augmentations – Names of augmentations to perform on each image (use “null” for none)

• batch_size – Batch size for training and validation

• train_epoch_scaling – Factor to scale the number of batches considered to be part of an epoch by (train set)

• val_epoch_scaling – Factor to scale the number of batches considered to be part of an epoch by (validation set)

• test_epoch_scaling – Factor to scale the number of batches considered to be part of an epoch by (test set)

class flowws_keras_experimental.images.MNIST(**kwargs)

Use the MNIST dataset from keras.

Architectures

class flowws_keras_experimental.images.Encoder(**kwargs)

Specify simple CNN-based encoder layers for image-type data.

Parameters:

• convolution_widths – Number of channels to build for each convolution layer

• dropout – Dropout frequency for encoder

• final_pool – Type of final pooling to apply for encoding, if any: one of “max”, “avg”

• separable_convolutions – If True, use separable convolutions

• normalize_before_activation – If True, place batch normalization before the post-conv activation

• encoding_dropout – Dropout rate for final encoding output, if any

class flowws_keras_experimental.images.MobileNetV2(**kwargs)

Use the MobileNetV2 architecture as provided by keras.

Parameters:

dropout – Dropout probability to use (if any)

class flowws_keras_experimental.images.ResNet(**kwargs)

Use the ResNet architecture as provided by keras.

Parameters:

• size – Size of the ResNet to build (i.e. 50, 101, etc)

• dropout – Dropout probability to use (if any)

Indices and tables

• Index

• Module Index

• Search Page