gpkg.keras.mnist 0.6.0.dev1

gpkg.keras.mnist
################

*MNIST models in Keras (Guild AI)*

Models
######

_check
======

Operations
^^^^^^^^^^

acgan
-----

all
---

cnn
---

denoising-autoencoder
---------------------

hierarchical-rnn
----------------

irnn
----

mlp
---

net2net
-------

siamese
-------

swwae
-----


acgan
=====

*Auxiliary Classifier Generative Adversarial Network (ACGAN) for MNIST in
Keras*

Operations
^^^^^^^^^^

train
-----
*Train the model*

Flags
`````
**batch-size**
*Training batch size (default is 100)*

**beta-1**
*Beta 1 (default is 0.5)*

**epochs**
*Number of epochs to train (default is 100)*

**learning-rate**
*Learning rate (default is 0.0002)*

References
^^^^^^^^^^

- https://github.com/keras-team/keras/blob/master/examples/mnist_acgan.py
- https://arxiv.org/abs/1511.06434

cnn
===

*Convolutional neural network (CNN) classifier for MNIST in Keras*

Operations
^^^^^^^^^^

train
-----
*Train the model*

Flags
`````
**batch-size**
*Training batch size (default is 128)*

**epochs**
*Number of epochs to train (default is 12)*

References
^^^^^^^^^^

- https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py

denoising-autoencoder
=====================

*Denoising autoencoder for MNIST in Keras*

Operations
^^^^^^^^^^

train
-----
*Train the model*

Flags
`````
**batch-size**
*Training batch size (default is 128)*

**epochs**
*Number of epochs to train (default is 30)*

References
^^^^^^^^^^

- https://github.com/keras-team/keras/blob/master/examples/mnist_denoising_autoencoder.py

hierarchical-rnn
================

*Hierarchical RNN (HRNN) classifier for MNIST in Keras*

Operations
^^^^^^^^^^

train
-----
*Train the model*

Flags
`````
**batch-size**
*Training batch size (default is 32)*

**epochs**
*Number of epochs to train (default is 5)*

References
^^^^^^^^^^

- https://github.com/keras-team/keras/blob/master/examples/mnist_hierarchical_rnn.py
- https://arxiv.org/abs/1506.01057
- http://ieeexplore.ieee.org/document/7298714/

irnn
====

*Implementation of 'A Simple Way to Initialize Recurrent Networks of Rectified
Linear Units' with MNIST in Keras*

Operations
^^^^^^^^^^

train
-----
*Train the model*

Flags
`````
**batch-size**
*Training batch size (default is 32)*

**epochs**
*Number of epochs to train (default is 200)*

**learning-rate**
*Learning rate (default is 1.0e-06)*

References
^^^^^^^^^^

- https://github.com/keras-team/keras/blob/master/examples/mnist_irnn.py
- http://arxiv.org/pdf/1504.00941v2.pdf

mlp
===

*Multilayer perceptron (MLP) classifier for MNIST in Keras*

Operations
^^^^^^^^^^

train
-----
*Train the model*

Flags
`````
**batch-size**
*Training batch size (default is 128)*

**epochs**
*Number of epochs to train (default is 20)*

References
^^^^^^^^^^

- https://github.com/keras-team/keras/blob/master/examples/mnist_mlp.py

net2net
=======

*Implementation of 'Net2Net: Accelerating Learning via Knowledge Transfer'
with MNIST in Keras*

Operations
^^^^^^^^^^

train
-----
*Train the model*

Flags
`````
**batch-size**
*Training batch size (default is 32)*

**epochs**
*Number of epochs to train (default is 3)*

References
^^^^^^^^^^

- https://github.com/keras-team/keras/blob/master/examples/mnist_net2net.py
- http://arxiv.org/abs/1511.05641

siamese
=======

*Siamese MLP classifier for MNIST in Keras*

Operations
^^^^^^^^^^

train
-----
*Train the model*

Flags
`````
**batch-size**
*Training batch size (default is 128)*

**epochs**
*Number of epochs to train (default is 20)*

References
^^^^^^^^^^

- https://github.com/keras-team/keras/blob/master/examples/mnist_siamese.py
- http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf

swwae
=====

*Stacked what-where autoencoder for MNIST in Keras*

Operations
^^^^^^^^^^

train
-----
*Train the model*

Flags
`````
**batch-size**
*Training batch size (default is 128)*

**epochs**
*Number of epochs to train (default is 5)*

**pool-size**
*kernel size used for the MaxPooling2D (default is 2)

Choices: [2, 3]

*

References
^^^^^^^^^^

- https://github.com/keras-team/keras/blob/master/examples/mnist_swwae.py
- https://arxiv.org/abs/1311.2901v3
- https://arxiv.org/abs/1506.02351v8