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+/visualkeras-0.0.2.tar.gz
diff --git a/python-visualkeras.spec b/python-visualkeras.spec
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+%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
diff --git a/sources b/sources
new file mode 100644
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--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+7490325cf34ee4253ae3350ae392db11  visualkeras-0.0.2.tar.gz