The variational-autoencoder from libennext

Semi-Supervised Learning with Deep Generative Models

Chainer implementation of Variational AutoEncoder(VAE) model M1, M2, M1+M2

この記事で実装したコードです。

Requirements

Chainer 1.8+
sklearn

To visualize results, you need

matplotlib.patches
PIL
pandas

Download MNIST

run mnist-tools.py to download and extract MNIST.

How to label my own dataset?

You can provide label information by filename.

format:

{label_id}_{unique_filename}.{extension}

regex:

([0-9]+)_.+\.(bmp|png|jpg)

e.g. MNIST

M1

Parameters

params	value
OS	Windows 7
GPU	GeForce GTX 970M
ndim_z	2
encoder_apply_dropout	False
decoder_apply_dropout	False
encoder_apply_batchnorm	True
decoder_apply_batchnorm	True
encoder_apply_batchnorm_to_input	True
decoder_apply_batchnorm_to_input	True
encoder_units	[600, 600]
decoder_units	[600, 600]
gradient_clipping	1.0
learning_rate	0.0003
gradient_momentum	0.9
gradient_clipping	1.0
nonlinear	softplus

Result

Latent space

M2

Parameters

params	value
OS	Windows 7
GPU	GeForce GTX 970M
ndim_z	50
encoder_xy_z_apply_dropout	False
encoder_x_y_apply_dropout	False
decoder_apply_dropout	False
encoder_xy_z_apply_batchnorm_to_input	True
encoder_x_y_apply_batchnorm_to_input	True
decoder_apply_batchnorm_to_input	True
encoder_xy_z_apply_batchnorm	True
encoder_x_y_apply_batchnorm	True
decoder_apply_batchnorm	True
encoder_xy_z_hidden_units	[500]
encoder_x_y_hidden_units	[500]
decoder_hidden_units	[500]
batchnorm_before_activation	True
gradient_clipping	5.0
learning_rate	0.0003
gradient_momentum	0.9
gradient_clipping	1.0
nonlinear	softplus

Result

Classification

Training details

data	#
labeled	100
unlabeled	49900
validation	10000
test	10000

*	#
epochs	490
minutes	1412
weight updates per epoch	2000

Validation accuracy:

Test accuracy: 0.9018

Analogies

run analogy.py after training

Model was trained with...

data	#
labeled	100
unlabeled	49900

data	#
labeled	10000
unlabeled	40000

data	#
labeled	50000
unlabeled	0

M1+M2

Parameters

M1

params	value
OS	Windows 7
GPU	GeForce GTX 970M
ndim_z	2
encoder_apply_dropout	False
decoder_apply_dropout	False
encoder_apply_batchnorm	True
decoder_apply_batchnorm	True
encoder_apply_batchnorm_to_input	True
decoder_apply_batchnorm_to_input	True
encoder_units	[600, 600]
decoder_units	[600, 600]
gradient_clipping	1.0
learning_rate	0.0003
gradient_momentum	0.9
gradient_clipping	1.0
nonlinear	softplus

We trained M1 for 500 epochs before starting training of M2.

*	#
epochs	500
minutes	860
weight updates per epoch	2000

M2

params	value
OS	Windows 7
GPU	GeForce GTX 970M
ndim_z	50
encoder_xy_z_apply_dropout	False
encoder_x_y_apply_dropout	False
decoder_apply_dropout	False
encoder_xy_z_apply_batchnorm_to_input	True
encoder_x_y_apply_batchnorm_to_input	True
decoder_apply_batchnorm_to_input	True
encoder_xy_z_apply_batchnorm	True
encoder_x_y_apply_batchnorm	True
decoder_apply_batchnorm	True
encoder_xy_z_hidden_units	[500]
encoder_x_y_hidden_units	[500]
decoder_hidden_units	[500]
batchnorm_before_activation	True
gradient_clipping	5.0
learning_rate	0.0003
gradient_momentum	0.9
gradient_clipping	1.0
nonlinear	softplus

Result

Classification

Training details

data	#
labeled	100
unlabeled	49900
validation	10000
test	10000

*	#
epochs	600
minutes	4920
weight updates per epoch	5000

Validation accuracy:

Test accuracy

seed1: 0.954

seed2: 0.951

libennext / variational-autoencoder Goto Github PK

variational-autoencoder's Introduction

Semi-Supervised Learning with Deep Generative Models

Requirements

Download MNIST

How to label my own dataset?

M1

Parameters

Result

Latent space

M2

Parameters

Result

Classification

Training details

Validation accuracy:

Test accuracy: 0.9018

Analogies

M1+M2

Parameters

M1

M2

Result

Classification

Training details

Validation accuracy:

Test accuracy

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