path: root/python-visualkeras.spec

%global _empty_manifest_terminate_build 0
Name:		python-visualkeras
Version:	0.0.2
Release:	1
Summary:	Architecture visualization of Keras models
License:	MIT License
URL:		https://github.com/paulgavrikov/visualkeras
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/e5/e6/ae948673eaed8515e4c1dd24d8ca40df85f14493914fc5d2e9c09b82fa33/visualkeras-0.0.2.tar.gz
BuildArch:	noarch

Requires:	python3-pillow
Requires:	python3-numpy
Requires:	python3-aggdraw

%description
# visualkeras for Keras / TensorFlow

[![Wheel Status](https://pypip.in/wheel/visualkeras/badge.svg?style=flat)](https://pypi.python.org/pypi/visualkeras)
[![Latest Version](https://pypip.in/version/visualkeras/badge.svg?style=flat)](https://pypi.python.org/pypi/visualkeras)
[![Build Status](https://travis-ci.org/paulgavrikov/visualkeras.svg?branch=master)](https://travis-ci.org/paulgavrikov/visualkeras)

## Introduction
Visualkeras is a Python package to help visualize Keras (either standalone or included in tensorflow) neural network architectures. It allows easy styling to fit most 
needs. This module supports layered style architecture generation which is great for CNNs (Convolutional Neural 
Networks), and a graph style architecture, which works great for most models including plain feed-forward networks.

## Model Support

|  Mode | Sequential  |  Functional |  Subclassed models |
|---|---|---|---|
| `visualkeras.layered_view()` | yes<sup>(1)</sup> | partially<sup>(1,2)</sup> |  not tested |
| `visualkeras.graph_view()` | yes | yes |  not tested |

<sup>1</sup>: Any tensor with more than 3 dimensions will be rendered as 3D tensor with elongated z-axis.

<sup>2</sup>: Only linear models where each layer has no more than one in or output. Non-linear models will be shown in sequential order.

## Installation
To install published releases from PyPi execute:
```bash
pip install visualkeras
```
To update visualkeras to the latest version, add the `--upgrade` flag to the above commands.

If you want the latest (potentially unstable) features you can also directly install from the github master branch:
```bash
pip install git+https://github.com/paulgavrikov/visualkeras
```

## Usage

Generating neural network architectures is easy:
```python
import visualkeras

model = ...

visualkeras.layered_view(model).show() # display using your system viewer
visualkeras.layered_view(model, to_file='output.png') # write to disk
visualkeras.layered_view(model, to_file='output.png').show() # write and show
```

To help understand some of the most important parameters we are going to use a VGG16 CNN architecture (see [example.py](examples/example.py)).

###### Default
```python
visualkeras.layered_view(model)
```
![Default view of a VGG16 CNN](figures/vgg16.png)

###### Legend

You can set the legend parameter to describe the relationship between color and layer types. It is also possible to pass
a custom `PIL.ImageFont` to use (or just leave it out and visualkeras will use the default PIL font). Please note that 
you may need to provide the full path of the desired font depending on your OS.

```python
from PIL import ImageFont

font = ImageFont.truetype("arial.ttf", 32)  # using comic sans is strictly prohibited!
visualkeras.layered_view(model, legend=True, font=font)  # font is optional!
```
![Layered view of a VGG16 CNN with legend](figures/vgg16_legend.png)

###### Flat Style
```python
visualkeras.layered_view(model, draw_volume=False)
```
![Flat view of a VGG16 CNN](figures/vgg16_flat.png)

###### Spacing and logic grouping
The global distance between two layers can be controlled with `spacing`. To generate logical groups a special dummy 
keras layer `visualkeras.SpacingDummyLayer()` can be added.
```python

model = ...
...
model.add(visualkeras.SpacingDummyLayer(spacing=100))
...

visualkeras.layered_view(model, spacing=0)
```
![Spaced and grouped view of a VGG16 CNN](figures/vgg16_spacing_layers.png)


###### Custom color map
It is possible to provide a custom color map for fill and outline per layer type.
```python
from tensorflow.python.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D, ZeroPadding2D
from collections import defaultdict

color_map = defaultdict(dict)
color_map[Conv2D]['fill'] = 'orange'
color_map[ZeroPadding2D]['fill'] = 'gray'
color_map[Dropout]['fill'] = 'pink'
color_map[MaxPooling2D]['fill'] = 'red'
color_map[Dense]['fill'] = 'green'
color_map[Flatten]['fill'] = 'teal'

visualkeras.layered_view(model, color_map=color_map)
```
![Custom colored view of a VGG16 CNN](figures/vgg16_color_map.png)

###### Hiding layers
Some models may consist of too many layers to visualize or to comprehend the model. In this case it can be helpful to 
hide (ignore) certain layers of the keras model without modifying it. Visualkeras allows ignoring layers by their type
 (`type_ignore`) or index in the keras layer sequence (`index_ignore`).
```python
visualkeras.layered_view(model, type_ignore=[ZeroPadding2D, Dropout, Flatten])
```
![Simplified view of a VGG16 CNN](figures/vgg16_type_ignore.png)

###### Scaling dimensions
Visualkeras computes the size of each layer by the output shape. Values are transformed into pixels. Then, scaling is 
applied. By default visualkeras will enlarge the x and y dimension and reduce the size of the z dimensions as this has 
deemed visually most appealing. However, it is possible to control scaling using `scale_xy` and `scale_z`. Additionally, 
to prevent to small or large options minimum and maximum values can be set (`min_xy`, `min_z`, `max_xy`, `max_z`).  
```python
visualkeras.layered_view(model, scale_xy=1, scale_z=1, max_z=1000)
```
![True scale view of a VGG16 CNN](figures/vgg16_scaling.png)
_Note: Scaled models may hide the true complexity of a layer, but are visually more appealing._





%package -n python3-visualkeras
Summary:	Architecture visualization of Keras models
Provides:	python-visualkeras
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-visualkeras
# visualkeras for Keras / TensorFlow

[![Wheel Status](https://pypip.in/wheel/visualkeras/badge.svg?style=flat)](https://pypi.python.org/pypi/visualkeras)
[![Latest Version](https://pypip.in/version/visualkeras/badge.svg?style=flat)](https://pypi.python.org/pypi/visualkeras)
[![Build Status](https://travis-ci.org/paulgavrikov/visualkeras.svg?branch=master)](https://travis-ci.org/paulgavrikov/visualkeras)

## Introduction
Visualkeras is a Python package to help visualize Keras (either standalone or included in tensorflow) neural network architectures. It allows easy styling to fit most 
needs. This module supports layered style architecture generation which is great for CNNs (Convolutional Neural 
Networks), and a graph style architecture, which works great for most models including plain feed-forward networks.

## Model Support

|  Mode | Sequential  |  Functional |  Subclassed models |
|---|---|---|---|
| `visualkeras.layered_view()` | yes<sup>(1)</sup> | partially<sup>(1,2)</sup> |  not tested |
| `visualkeras.graph_view()` | yes | yes |  not tested |

<sup>1</sup>: Any tensor with more than 3 dimensions will be rendered as 3D tensor with elongated z-axis.

<sup>2</sup>: Only linear models where each layer has no more than one in or output. Non-linear models will be shown in sequential order.

## Installation
To install published releases from PyPi execute:
```bash
pip install visualkeras
```
To update visualkeras to the latest version, add the `--upgrade` flag to the above commands.

If you want the latest (potentially unstable) features you can also directly install from the github master branch:
```bash
pip install git+https://github.com/paulgavrikov/visualkeras
```

## Usage

Generating neural network architectures is easy:
```python
import visualkeras

model = ...

visualkeras.layered_view(model).show() # display using your system viewer
visualkeras.layered_view(model, to_file='output.png') # write to disk
visualkeras.layered_view(model, to_file='output.png').show() # write and show
```

To help understand some of the most important parameters we are going to use a VGG16 CNN architecture (see [example.py](examples/example.py)).

###### Default
```python
visualkeras.layered_view(model)
```
![Default view of a VGG16 CNN](figures/vgg16.png)

###### Legend

You can set the legend parameter to describe the relationship between color and layer types. It is also possible to pass
a custom `PIL.ImageFont` to use (or just leave it out and visualkeras will use the default PIL font). Please note that 
you may need to provide the full path of the desired font depending on your OS.

```python
from PIL import ImageFont

font = ImageFont.truetype("arial.ttf", 32)  # using comic sans is strictly prohibited!
visualkeras.layered_view(model, legend=True, font=font)  # font is optional!
```
![Layered view of a VGG16 CNN with legend](figures/vgg16_legend.png)

###### Flat Style
```python
visualkeras.layered_view(model, draw_volume=False)
```
![Flat view of a VGG16 CNN](figures/vgg16_flat.png)

###### Spacing and logic grouping
The global distance between two layers can be controlled with `spacing`. To generate logical groups a special dummy 
keras layer `visualkeras.SpacingDummyLayer()` can be added.
```python

model = ...
...
model.add(visualkeras.SpacingDummyLayer(spacing=100))
...

visualkeras.layered_view(model, spacing=0)
```
![Spaced and grouped view of a VGG16 CNN](figures/vgg16_spacing_layers.png)


###### Custom color map
It is possible to provide a custom color map for fill and outline per layer type.
```python
from tensorflow.python.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D, ZeroPadding2D
from collections import defaultdict

color_map = defaultdict(dict)
color_map[Conv2D]['fill'] = 'orange'
color_map[ZeroPadding2D]['fill'] = 'gray'
color_map[Dropout]['fill'] = 'pink'
color_map[MaxPooling2D]['fill'] = 'red'
color_map[Dense]['fill'] = 'green'
color_map[Flatten]['fill'] = 'teal'

visualkeras.layered_view(model, color_map=color_map)
```
![Custom colored view of a VGG16 CNN](figures/vgg16_color_map.png)

###### Hiding layers
Some models may consist of too many layers to visualize or to comprehend the model. In this case it can be helpful to 
hide (ignore) certain layers of the keras model without modifying it. Visualkeras allows ignoring layers by their type
 (`type_ignore`) or index in the keras layer sequence (`index_ignore`).
```python
visualkeras.layered_view(model, type_ignore=[ZeroPadding2D, Dropout, Flatten])
```
![Simplified view of a VGG16 CNN](figures/vgg16_type_ignore.png)

###### Scaling dimensions
Visualkeras computes the size of each layer by the output shape. Values are transformed into pixels. Then, scaling is 
applied. By default visualkeras will enlarge the x and y dimension and reduce the size of the z dimensions as this has 
deemed visually most appealing. However, it is possible to control scaling using `scale_xy` and `scale_z`. Additionally, 
to prevent to small or large options minimum and maximum values can be set (`min_xy`, `min_z`, `max_xy`, `max_z`).  
```python
visualkeras.layered_view(model, scale_xy=1, scale_z=1, max_z=1000)
```
![True scale view of a VGG16 CNN](figures/vgg16_scaling.png)
_Note: Scaled models may hide the true complexity of a layer, but are visually more appealing._





%package help
Summary:	Development documents and examples for visualkeras
Provides:	python3-visualkeras-doc
%description help
# visualkeras for Keras / TensorFlow

[![Wheel Status](https://pypip.in/wheel/visualkeras/badge.svg?style=flat)](https://pypi.python.org/pypi/visualkeras)
[![Latest Version](https://pypip.in/version/visualkeras/badge.svg?style=flat)](https://pypi.python.org/pypi/visualkeras)
[![Build Status](https://travis-ci.org/paulgavrikov/visualkeras.svg?branch=master)](https://travis-ci.org/paulgavrikov/visualkeras)

## Introduction
Visualkeras is a Python package to help visualize Keras (either standalone or included in tensorflow) neural network architectures. It allows easy styling to fit most 
needs. This module supports layered style architecture generation which is great for CNNs (Convolutional Neural 
Networks), and a graph style architecture, which works great for most models including plain feed-forward networks.

## Model Support

|  Mode | Sequential  |  Functional |  Subclassed models |
|---|---|---|---|
| `visualkeras.layered_view()` | yes<sup>(1)</sup> | partially<sup>(1,2)</sup> |  not tested |
| `visualkeras.graph_view()` | yes | yes |  not tested |

<sup>1</sup>: Any tensor with more than 3 dimensions will be rendered as 3D tensor with elongated z-axis.

<sup>2</sup>: Only linear models where each layer has no more than one in or output. Non-linear models will be shown in sequential order.

## Installation
To install published releases from PyPi execute:
```bash
pip install visualkeras
```
To update visualkeras to the latest version, add the `--upgrade` flag to the above commands.

If you want the latest (potentially unstable) features you can also directly install from the github master branch:
```bash
pip install git+https://github.com/paulgavrikov/visualkeras
```

## Usage

Generating neural network architectures is easy:
```python
import visualkeras

model = ...

visualkeras.layered_view(model).show() # display using your system viewer
visualkeras.layered_view(model, to_file='output.png') # write to disk
visualkeras.layered_view(model, to_file='output.png').show() # write and show
```

To help understand some of the most important parameters we are going to use a VGG16 CNN architecture (see [example.py](examples/example.py)).

###### Default
```python
visualkeras.layered_view(model)
```
![Default view of a VGG16 CNN](figures/vgg16.png)

###### Legend

You can set the legend parameter to describe the relationship between color and layer types. It is also possible to pass
a custom `PIL.ImageFont` to use (or just leave it out and visualkeras will use the default PIL font). Please note that 
you may need to provide the full path of the desired font depending on your OS.

```python
from PIL import ImageFont

font = ImageFont.truetype("arial.ttf", 32)  # using comic sans is strictly prohibited!
visualkeras.layered_view(model, legend=True, font=font)  # font is optional!
```
![Layered view of a VGG16 CNN with legend](figures/vgg16_legend.png)

###### Flat Style
```python
visualkeras.layered_view(model, draw_volume=False)
```
![Flat view of a VGG16 CNN](figures/vgg16_flat.png)

###### Spacing and logic grouping
The global distance between two layers can be controlled with `spacing`. To generate logical groups a special dummy 
keras layer `visualkeras.SpacingDummyLayer()` can be added.
```python

model = ...
...
model.add(visualkeras.SpacingDummyLayer(spacing=100))
...

visualkeras.layered_view(model, spacing=0)
```
![Spaced and grouped view of a VGG16 CNN](figures/vgg16_spacing_layers.png)


###### Custom color map
It is possible to provide a custom color map for fill and outline per layer type.
```python
from tensorflow.python.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D, ZeroPadding2D
from collections import defaultdict

color_map = defaultdict(dict)
color_map[Conv2D]['fill'] = 'orange'
color_map[ZeroPadding2D]['fill'] = 'gray'
color_map[Dropout]['fill'] = 'pink'
color_map[MaxPooling2D]['fill'] = 'red'
color_map[Dense]['fill'] = 'green'
color_map[Flatten]['fill'] = 'teal'

visualkeras.layered_view(model, color_map=color_map)
```
![Custom colored view of a VGG16 CNN](figures/vgg16_color_map.png)

###### Hiding layers
Some models may consist of too many layers to visualize or to comprehend the model. In this case it can be helpful to 
hide (ignore) certain layers of the keras model without modifying it. Visualkeras allows ignoring layers by their type
 (`type_ignore`) or index in the keras layer sequence (`index_ignore`).
```python
visualkeras.layered_view(model, type_ignore=[ZeroPadding2D, Dropout, Flatten])
```
![Simplified view of a VGG16 CNN](figures/vgg16_type_ignore.png)

###### Scaling dimensions
Visualkeras computes the size of each layer by the output shape. Values are transformed into pixels. Then, scaling is 
applied. By default visualkeras will enlarge the x and y dimension and reduce the size of the z dimensions as this has 
deemed visually most appealing. However, it is possible to control scaling using `scale_xy` and `scale_z`. Additionally, 
to prevent to small or large options minimum and maximum values can be set (`min_xy`, `min_z`, `max_xy`, `max_z`).  
```python
visualkeras.layered_view(model, scale_xy=1, scale_z=1, max_z=1000)
```
![True scale view of a VGG16 CNN](figures/vgg16_scaling.png)
_Note: Scaled models may hide the true complexity of a layer, but are visually more appealing._





%prep
%autosetup -n visualkeras-0.0.2

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-visualkeras -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.2-1
- Package Spec generated