%global _empty_manifest_terminate_build 0
Name:		python-tf-keras-vis
Version:	0.8.5
Release:	1
Summary:	Neural network visualization toolkit for tf.keras
License:	MIT License
URL:		https://github.com/keisen/tf-keras-vis
Source0:	https://mirrors.aliyun.com/pypi/web/packages/fc/98/a542ccb528a764302b7db123c9ff19736879a81d5d326a5d2899f99561e8/tf-keras-vis-0.8.5.tar.gz
BuildArch:	noarch

Requires:	python3-scipy
Requires:	python3-pillow
Requires:	python3-deprecated
Requires:	python3-imageio
Requires:	python3-packaging
Requires:	python3-importlib-metadata
Requires:	python3-flake8
Requires:	python3-flake8-docstrings
Requires:	python3-isort
Requires:	python3-yapf
Requires:	python3-pytest
Requires:	python3-pytest-pycodestyle
Requires:	python3-pytest-cov
Requires:	python3-pytest-env
Requires:	python3-pytest-xdist
Requires:	python3-sphinx
Requires:	python3-sphinx-autobuild
Requires:	python3-sphinx-rtd-theme
Requires:	python3-myst-parser
Requires:	python3-nbsphinx
Requires:	python3-pandoc
Requires:	python3-jupyterlab
Requires:	python3-matplotlib

%description
# [tf-keras-vis](https://keisen.github.io/tf-keras-vis-docs/)

<!-- sec.0 -->

[![Downloads](https://pepy.tech/badge/tf-keras-vis)](https://pepy.tech/project/tf-keras-vis)
[![Python](https://img.shields.io/pypi/pyversions/tf-keras-vis.svg?style=plastic)](https://badge.fury.io/py/tf-keras-vis)
[![PyPI version](https://badge.fury.io/py/tf-keras-vis.svg)](https://badge.fury.io/py/tf-keras-vis)
[![Python package](https://github.com/keisen/tf-keras-vis/actions/workflows/python-package.yml/badge.svg)](https://github.com/keisen/tf-keras-vis/actions/workflows/python-package.yml)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Documentation](https://img.shields.io/badge/api-reference-blue.svg)](https://keisen.github.io/tf-keras-vis-docs/)

<!-- ./sec.0 -->

 <!-- sec.1 -->

<!-- ./sec.1 -->

## Web documents

https://keisen.github.io/tf-keras-vis-docs/


## Overview

<!-- sec.2 -->

tf-keras-vis is a visualization toolkit for debugging `tf.keras.Model` in Tensorflow2.0+.
Currently supported methods for visualization include:

* Feature Visualization
   - ActivationMaximization ([web](https://distill.pub/2017/feature-visualization/), [github](https://github.com/raghakot/keras-vis))
* Class Activation Maps
   - GradCAM ([paper](https://arxiv.org/pdf/1610.02391v1.pdf))
   - GradCAM++ ([paper](https://arxiv.org/pdf/1710.11063.pdf))
   - ScoreCAM ([paper](https://arxiv.org/pdf/1910.01279.pdf), [github](https://github.com/haofanwang/Score-CAM))
   - Faster-ScoreCAM ([github](https://github.com/tabayashi0117/Score-CAM/blob/master/README.md#faster-score-cam))
   - LayerCAM ([paper](http://mftp.mmcheng.net/Papers/21TIP_LayerCAM.pdf), [github](https://github.com/PengtaoJiang/LayerCAM)) :new::zap:
* Saliency Maps
   - Vanilla Saliency ([paper](https://arxiv.org/pdf/1312.6034.pdf))
   - SmoothGrad ([paper](https://arxiv.org/pdf/1706.03825.pdf))

tf-keras-vis is designed to be light-weight, flexible and ease of use.
All visualizations have the features as follows:

* Support **N-dim image inputs**, that's, not only support pictures but also such as 3D images.
* Support **batch wise** processing, so, be able to efficiently process multiple input images.
* Support the model that have either **multiple inputs** or **multiple outputs**, or both.
* Support the **mixed-precision** model.

And in ActivationMaximization,

* Support Optimizers that are built to tf.keras.

<!-- ./sec.2 -->

### Visualizations

<!-- sec.3 -->

#### Dense Unit

<img src='https://github.com/keisen/tf-keras-vis/raw/master/docs/examples/images/visualize-dense-layer.png' width='600px' />

#### Convolutional Filter

<img src='https://github.com/keisen/tf-keras-vis/raw/master/docs/examples/images/visualize-filters.png' width='600px' />

#### Class Activation Map

<img src='https://github.com/keisen/tf-keras-vis/raw/master/docs/examples/images/gradcam_plus_plus.png' width='600px' />

The images above are generated by `GradCAM++`.

#### Saliency Map

<img src='https://github.com/keisen/tf-keras-vis/raw/master/docs/examples/images/smoothgrad.png' width='600px' />

The images above are generated by `SmoothGrad`.

<!-- ./sec.3 -->

## Usage

### ActivationMaximization (Visualizing Convolutional Filter)

<!-- ActivationMaximization -->

```python
import tensorflow as tf
from tensorflow.keras.applications import VGG16
from matplotlib import pyplot as plt
from tf_keras_vis.activation_maximization import ActivationMaximization
from tf_keras_vis.activation_maximization.callbacks import Progress
from tf_keras_vis.activation_maximization.input_modifiers import Jitter, Rotate2D
from tf_keras_vis.activation_maximization.regularizers import TotalVariation2D, Norm
from tf_keras_vis.utils.model_modifiers import ExtractIntermediateLayer, ReplaceToLinear
from tf_keras_vis.utils.scores import CategoricalScore

# Create the visualization instance.
# All visualization classes accept a model and model-modifier, which, for example,
#     replaces the activation of last layer to linear function so on, in constructor.
activation_maximization = \
   ActivationMaximization(VGG16(),
                          model_modifier=[ExtractIntermediateLayer('block5_conv3'),
                                          ReplaceToLinear()],
                          clone=False)

# You can use Score class to specify visualizing target you want.
# And add regularizers or input-modifiers as needed.
activations = \
   activation_maximization(CategoricalScore(FILTER_INDEX),
                           steps=200,
                           input_modifiers=[Jitter(jitter=16), Rotate2D(degree=1)],
                           regularizers=[TotalVariation2D(weight=1.0),
                                         Norm(weight=0.3, p=1)],
                           optimizer=tf.keras.optimizers.RMSprop(1.0, 0.999),
                           callbacks=[Progress()])

## Since v0.6.0, calling `astype()` is NOT necessary.
# activations = activations[0].astype(np.uint8)

# Render
plt.imshow(activations[0])
```

<!-- ./ActivationMaximization -->

### Gradcam++

<!-- Gradcam++ -->

```python
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import cm
from tf_keras_vis.gradcam_plus_plus import GradcamPlusPlus
from tf_keras_vis.utils.model_modifiers import ReplaceToLinear
from tf_keras_vis.utils.scores import CategoricalScore

# Create GradCAM++ object
gradcam = GradcamPlusPlus(YOUR_MODEL_INSTANCE,
                          model_modifier=ReplaceToLinear(),
                          clone=True)

# Generate cam with GradCAM++
cam = gradcam(CategoricalScore(CATEGORICAL_INDEX),
              SEED_INPUT)

## Since v0.6.0, calling `normalize()` is NOT necessary.
# cam = normalize(cam)

plt.imshow(SEED_INPUT_IMAGE)
heatmap = np.uint8(cm.jet(cam[0])[..., :3] * 255)
plt.imshow(heatmap, cmap='jet', alpha=0.5) # overlay
```

<!-- ./Gradcam++ -->

Please see the guides below for more details:

### Getting Started Guides

<!-- examples -->

* [Saliency and CAMs](https://keisen.github.io/tf-keras-vis-docs/examples/attentions.html)
* [Visualize Dense Layer](https://keisen.github.io/tf-keras-vis-docs/examples/visualize_dense_layer.html)
* [Visualize Convolutional Filer](https://keisen.github.io/tf-keras-vis-docs/examples/visualize_conv_filters.html)

<!-- ./examples -->

**[NOTES]**
If you have ever used [keras-vis](https://github.com/raghakot/keras-vis), you may feel that tf-keras-vis is similar with keras-vis.
Actually tf-keras-vis derived from keras-vis, and both provided visualization methods are almost the same.
But please notice that tf-keras-vis APIs does NOT have compatibility with keras-vis.


## Requirements

<!-- sec.5 -->

* Python 3.7+
* Tensorflow 2.0+


<!-- ./sec.5 -->

## Installation

<!-- sec.6 -->

* PyPI

```bash
$ pip install tf-keras-vis tensorflow
```

* Source (for development)

```bash
$ git clone https://github.com/keisen/tf-keras-vis.git
$ cd tf-keras-vis
$ pip install -e .[develop] tensorflow
```

<!-- ./sec.6 -->

## Use Cases

<!-- sec.7 -->

* [chitra](https://github.com/aniketmaurya/chitra)
   * A Deep Learning Computer Vision library for easy data loading, model building and model interpretation with GradCAM/GradCAM++.


## Known Issues

* With InceptionV3, ActivationMaximization doesn't work well, that's, it might generate meaninglessly blur image.
* With cascading model, Gradcam and Gradcam++ don't work well, that's, it might occur some error. So we recommend to use FasterScoreCAM in this case.
* `channels-first` models and data is unsupported.


## ToDo

* Guides
   * Visualizing multiple attention or activation images at once utilizing batch-system of model
   * Define various score functions
   * Visualizing attentions with multiple inputs models
   * Visualizing attentions with multiple outputs models
   * Advanced score functions
   * Tuning Activation Maximization
   * Visualizing attentions for N-dim image inputs
*  We're going to add some methods such as below
   - Deep Dream
   - Style transfer


%package -n python3-tf-keras-vis
Summary:	Neural network visualization toolkit for tf.keras
Provides:	python-tf-keras-vis
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-tf-keras-vis
# [tf-keras-vis](https://keisen.github.io/tf-keras-vis-docs/)

<!-- sec.0 -->

[![Downloads](https://pepy.tech/badge/tf-keras-vis)](https://pepy.tech/project/tf-keras-vis)
[![Python](https://img.shields.io/pypi/pyversions/tf-keras-vis.svg?style=plastic)](https://badge.fury.io/py/tf-keras-vis)
[![PyPI version](https://badge.fury.io/py/tf-keras-vis.svg)](https://badge.fury.io/py/tf-keras-vis)
[![Python package](https://github.com/keisen/tf-keras-vis/actions/workflows/python-package.yml/badge.svg)](https://github.com/keisen/tf-keras-vis/actions/workflows/python-package.yml)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Documentation](https://img.shields.io/badge/api-reference-blue.svg)](https://keisen.github.io/tf-keras-vis-docs/)

<!-- ./sec.0 -->

 <!-- sec.1 -->

<!-- ./sec.1 -->

## Web documents

https://keisen.github.io/tf-keras-vis-docs/


## Overview

<!-- sec.2 -->

tf-keras-vis is a visualization toolkit for debugging `tf.keras.Model` in Tensorflow2.0+.
Currently supported methods for visualization include:

* Feature Visualization
   - ActivationMaximization ([web](https://distill.pub/2017/feature-visualization/), [github](https://github.com/raghakot/keras-vis))
* Class Activation Maps
   - GradCAM ([paper](https://arxiv.org/pdf/1610.02391v1.pdf))
   - GradCAM++ ([paper](https://arxiv.org/pdf/1710.11063.pdf))
   - ScoreCAM ([paper](https://arxiv.org/pdf/1910.01279.pdf), [github](https://github.com/haofanwang/Score-CAM))
   - Faster-ScoreCAM ([github](https://github.com/tabayashi0117/Score-CAM/blob/master/README.md#faster-score-cam))
   - LayerCAM ([paper](http://mftp.mmcheng.net/Papers/21TIP_LayerCAM.pdf), [github](https://github.com/PengtaoJiang/LayerCAM)) :new::zap:
* Saliency Maps
   - Vanilla Saliency ([paper](https://arxiv.org/pdf/1312.6034.pdf))
   - SmoothGrad ([paper](https://arxiv.org/pdf/1706.03825.pdf))

tf-keras-vis is designed to be light-weight, flexible and ease of use.
All visualizations have the features as follows:

* Support **N-dim image inputs**, that's, not only support pictures but also such as 3D images.
* Support **batch wise** processing, so, be able to efficiently process multiple input images.
* Support the model that have either **multiple inputs** or **multiple outputs**, or both.
* Support the **mixed-precision** model.

And in ActivationMaximization,

* Support Optimizers that are built to tf.keras.

<!-- ./sec.2 -->

### Visualizations

<!-- sec.3 -->

#### Dense Unit

<img src='https://github.com/keisen/tf-keras-vis/raw/master/docs/examples/images/visualize-dense-layer.png' width='600px' />

#### Convolutional Filter

<img src='https://github.com/keisen/tf-keras-vis/raw/master/docs/examples/images/visualize-filters.png' width='600px' />

#### Class Activation Map

<img src='https://github.com/keisen/tf-keras-vis/raw/master/docs/examples/images/gradcam_plus_plus.png' width='600px' />

The images above are generated by `GradCAM++`.

#### Saliency Map

<img src='https://github.com/keisen/tf-keras-vis/raw/master/docs/examples/images/smoothgrad.png' width='600px' />

The images above are generated by `SmoothGrad`.

<!-- ./sec.3 -->

## Usage

### ActivationMaximization (Visualizing Convolutional Filter)

<!-- ActivationMaximization -->

```python
import tensorflow as tf
from tensorflow.keras.applications import VGG16
from matplotlib import pyplot as plt
from tf_keras_vis.activation_maximization import ActivationMaximization
from tf_keras_vis.activation_maximization.callbacks import Progress
from tf_keras_vis.activation_maximization.input_modifiers import Jitter, Rotate2D
from tf_keras_vis.activation_maximization.regularizers import TotalVariation2D, Norm
from tf_keras_vis.utils.model_modifiers import ExtractIntermediateLayer, ReplaceToLinear
from tf_keras_vis.utils.scores import CategoricalScore

# Create the visualization instance.
# All visualization classes accept a model and model-modifier, which, for example,
#     replaces the activation of last layer to linear function so on, in constructor.
activation_maximization = \
   ActivationMaximization(VGG16(),
                          model_modifier=[ExtractIntermediateLayer('block5_conv3'),
                                          ReplaceToLinear()],
                          clone=False)

# You can use Score class to specify visualizing target you want.
# And add regularizers or input-modifiers as needed.
activations = \
   activation_maximization(CategoricalScore(FILTER_INDEX),
                           steps=200,
                           input_modifiers=[Jitter(jitter=16), Rotate2D(degree=1)],
                           regularizers=[TotalVariation2D(weight=1.0),
                                         Norm(weight=0.3, p=1)],
                           optimizer=tf.keras.optimizers.RMSprop(1.0, 0.999),
                           callbacks=[Progress()])

## Since v0.6.0, calling `astype()` is NOT necessary.
# activations = activations[0].astype(np.uint8)

# Render
plt.imshow(activations[0])
```

<!-- ./ActivationMaximization -->

### Gradcam++

<!-- Gradcam++ -->

```python
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import cm
from tf_keras_vis.gradcam_plus_plus import GradcamPlusPlus
from tf_keras_vis.utils.model_modifiers import ReplaceToLinear
from tf_keras_vis.utils.scores import CategoricalScore

# Create GradCAM++ object
gradcam = GradcamPlusPlus(YOUR_MODEL_INSTANCE,
                          model_modifier=ReplaceToLinear(),
                          clone=True)

# Generate cam with GradCAM++
cam = gradcam(CategoricalScore(CATEGORICAL_INDEX),
              SEED_INPUT)

## Since v0.6.0, calling `normalize()` is NOT necessary.
# cam = normalize(cam)

plt.imshow(SEED_INPUT_IMAGE)
heatmap = np.uint8(cm.jet(cam[0])[..., :3] * 255)
plt.imshow(heatmap, cmap='jet', alpha=0.5) # overlay
```

<!-- ./Gradcam++ -->

Please see the guides below for more details:

### Getting Started Guides

<!-- examples -->

* [Saliency and CAMs](https://keisen.github.io/tf-keras-vis-docs/examples/attentions.html)
* [Visualize Dense Layer](https://keisen.github.io/tf-keras-vis-docs/examples/visualize_dense_layer.html)
* [Visualize Convolutional Filer](https://keisen.github.io/tf-keras-vis-docs/examples/visualize_conv_filters.html)

<!-- ./examples -->

**[NOTES]**
If you have ever used [keras-vis](https://github.com/raghakot/keras-vis), you may feel that tf-keras-vis is similar with keras-vis.
Actually tf-keras-vis derived from keras-vis, and both provided visualization methods are almost the same.
But please notice that tf-keras-vis APIs does NOT have compatibility with keras-vis.


## Requirements

<!-- sec.5 -->

* Python 3.7+
* Tensorflow 2.0+


<!-- ./sec.5 -->

## Installation

<!-- sec.6 -->

* PyPI

```bash
$ pip install tf-keras-vis tensorflow
```

* Source (for development)

```bash
$ git clone https://github.com/keisen/tf-keras-vis.git
$ cd tf-keras-vis
$ pip install -e .[develop] tensorflow
```

<!-- ./sec.6 -->

## Use Cases

<!-- sec.7 -->

* [chitra](https://github.com/aniketmaurya/chitra)
   * A Deep Learning Computer Vision library for easy data loading, model building and model interpretation with GradCAM/GradCAM++.


## Known Issues

* With InceptionV3, ActivationMaximization doesn't work well, that's, it might generate meaninglessly blur image.
* With cascading model, Gradcam and Gradcam++ don't work well, that's, it might occur some error. So we recommend to use FasterScoreCAM in this case.
* `channels-first` models and data is unsupported.


## ToDo

* Guides
   * Visualizing multiple attention or activation images at once utilizing batch-system of model
   * Define various score functions
   * Visualizing attentions with multiple inputs models
   * Visualizing attentions with multiple outputs models
   * Advanced score functions
   * Tuning Activation Maximization
   * Visualizing attentions for N-dim image inputs
*  We're going to add some methods such as below
   - Deep Dream
   - Style transfer


%package help
Summary:	Development documents and examples for tf-keras-vis
Provides:	python3-tf-keras-vis-doc
%description help
# [tf-keras-vis](https://keisen.github.io/tf-keras-vis-docs/)

<!-- sec.0 -->

[![Downloads](https://pepy.tech/badge/tf-keras-vis)](https://pepy.tech/project/tf-keras-vis)
[![Python](https://img.shields.io/pypi/pyversions/tf-keras-vis.svg?style=plastic)](https://badge.fury.io/py/tf-keras-vis)
[![PyPI version](https://badge.fury.io/py/tf-keras-vis.svg)](https://badge.fury.io/py/tf-keras-vis)
[![Python package](https://github.com/keisen/tf-keras-vis/actions/workflows/python-package.yml/badge.svg)](https://github.com/keisen/tf-keras-vis/actions/workflows/python-package.yml)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Documentation](https://img.shields.io/badge/api-reference-blue.svg)](https://keisen.github.io/tf-keras-vis-docs/)

<!-- ./sec.0 -->

 <!-- sec.1 -->

<!-- ./sec.1 -->

## Web documents

https://keisen.github.io/tf-keras-vis-docs/


## Overview

<!-- sec.2 -->

tf-keras-vis is a visualization toolkit for debugging `tf.keras.Model` in Tensorflow2.0+.
Currently supported methods for visualization include:

* Feature Visualization
   - ActivationMaximization ([web](https://distill.pub/2017/feature-visualization/), [github](https://github.com/raghakot/keras-vis))
* Class Activation Maps
   - GradCAM ([paper](https://arxiv.org/pdf/1610.02391v1.pdf))
   - GradCAM++ ([paper](https://arxiv.org/pdf/1710.11063.pdf))
   - ScoreCAM ([paper](https://arxiv.org/pdf/1910.01279.pdf), [github](https://github.com/haofanwang/Score-CAM))
   - Faster-ScoreCAM ([github](https://github.com/tabayashi0117/Score-CAM/blob/master/README.md#faster-score-cam))
   - LayerCAM ([paper](http://mftp.mmcheng.net/Papers/21TIP_LayerCAM.pdf), [github](https://github.com/PengtaoJiang/LayerCAM)) :new::zap:
* Saliency Maps
   - Vanilla Saliency ([paper](https://arxiv.org/pdf/1312.6034.pdf))
   - SmoothGrad ([paper](https://arxiv.org/pdf/1706.03825.pdf))

tf-keras-vis is designed to be light-weight, flexible and ease of use.
All visualizations have the features as follows:

* Support **N-dim image inputs**, that's, not only support pictures but also such as 3D images.
* Support **batch wise** processing, so, be able to efficiently process multiple input images.
* Support the model that have either **multiple inputs** or **multiple outputs**, or both.
* Support the **mixed-precision** model.

And in ActivationMaximization,

* Support Optimizers that are built to tf.keras.

<!-- ./sec.2 -->

### Visualizations

<!-- sec.3 -->

#### Dense Unit

<img src='https://github.com/keisen/tf-keras-vis/raw/master/docs/examples/images/visualize-dense-layer.png' width='600px' />

#### Convolutional Filter

<img src='https://github.com/keisen/tf-keras-vis/raw/master/docs/examples/images/visualize-filters.png' width='600px' />

#### Class Activation Map

<img src='https://github.com/keisen/tf-keras-vis/raw/master/docs/examples/images/gradcam_plus_plus.png' width='600px' />

The images above are generated by `GradCAM++`.

#### Saliency Map

<img src='https://github.com/keisen/tf-keras-vis/raw/master/docs/examples/images/smoothgrad.png' width='600px' />

The images above are generated by `SmoothGrad`.

<!-- ./sec.3 -->

## Usage

### ActivationMaximization (Visualizing Convolutional Filter)

<!-- ActivationMaximization -->

```python
import tensorflow as tf
from tensorflow.keras.applications import VGG16
from matplotlib import pyplot as plt
from tf_keras_vis.activation_maximization import ActivationMaximization
from tf_keras_vis.activation_maximization.callbacks import Progress
from tf_keras_vis.activation_maximization.input_modifiers import Jitter, Rotate2D
from tf_keras_vis.activation_maximization.regularizers import TotalVariation2D, Norm
from tf_keras_vis.utils.model_modifiers import ExtractIntermediateLayer, ReplaceToLinear
from tf_keras_vis.utils.scores import CategoricalScore

# Create the visualization instance.
# All visualization classes accept a model and model-modifier, which, for example,
#     replaces the activation of last layer to linear function so on, in constructor.
activation_maximization = \
   ActivationMaximization(VGG16(),
                          model_modifier=[ExtractIntermediateLayer('block5_conv3'),
                                          ReplaceToLinear()],
                          clone=False)

# You can use Score class to specify visualizing target you want.
# And add regularizers or input-modifiers as needed.
activations = \
   activation_maximization(CategoricalScore(FILTER_INDEX),
                           steps=200,
                           input_modifiers=[Jitter(jitter=16), Rotate2D(degree=1)],
                           regularizers=[TotalVariation2D(weight=1.0),
                                         Norm(weight=0.3, p=1)],
                           optimizer=tf.keras.optimizers.RMSprop(1.0, 0.999),
                           callbacks=[Progress()])

## Since v0.6.0, calling `astype()` is NOT necessary.
# activations = activations[0].astype(np.uint8)

# Render
plt.imshow(activations[0])
```

<!-- ./ActivationMaximization -->

### Gradcam++

<!-- Gradcam++ -->

```python
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import cm
from tf_keras_vis.gradcam_plus_plus import GradcamPlusPlus
from tf_keras_vis.utils.model_modifiers import ReplaceToLinear
from tf_keras_vis.utils.scores import CategoricalScore

# Create GradCAM++ object
gradcam = GradcamPlusPlus(YOUR_MODEL_INSTANCE,
                          model_modifier=ReplaceToLinear(),
                          clone=True)

# Generate cam with GradCAM++
cam = gradcam(CategoricalScore(CATEGORICAL_INDEX),
              SEED_INPUT)

## Since v0.6.0, calling `normalize()` is NOT necessary.
# cam = normalize(cam)

plt.imshow(SEED_INPUT_IMAGE)
heatmap = np.uint8(cm.jet(cam[0])[..., :3] * 255)
plt.imshow(heatmap, cmap='jet', alpha=0.5) # overlay
```

<!-- ./Gradcam++ -->

Please see the guides below for more details:

### Getting Started Guides

<!-- examples -->

* [Saliency and CAMs](https://keisen.github.io/tf-keras-vis-docs/examples/attentions.html)
* [Visualize Dense Layer](https://keisen.github.io/tf-keras-vis-docs/examples/visualize_dense_layer.html)
* [Visualize Convolutional Filer](https://keisen.github.io/tf-keras-vis-docs/examples/visualize_conv_filters.html)

<!-- ./examples -->

**[NOTES]**
If you have ever used [keras-vis](https://github.com/raghakot/keras-vis), you may feel that tf-keras-vis is similar with keras-vis.
Actually tf-keras-vis derived from keras-vis, and both provided visualization methods are almost the same.
But please notice that tf-keras-vis APIs does NOT have compatibility with keras-vis.


## Requirements

<!-- sec.5 -->

* Python 3.7+
* Tensorflow 2.0+


<!-- ./sec.5 -->

## Installation

<!-- sec.6 -->

* PyPI

```bash
$ pip install tf-keras-vis tensorflow
```

* Source (for development)

```bash
$ git clone https://github.com/keisen/tf-keras-vis.git
$ cd tf-keras-vis
$ pip install -e .[develop] tensorflow
```

<!-- ./sec.6 -->

## Use Cases

<!-- sec.7 -->

* [chitra](https://github.com/aniketmaurya/chitra)
   * A Deep Learning Computer Vision library for easy data loading, model building and model interpretation with GradCAM/GradCAM++.


## Known Issues

* With InceptionV3, ActivationMaximization doesn't work well, that's, it might generate meaninglessly blur image.
* With cascading model, Gradcam and Gradcam++ don't work well, that's, it might occur some error. So we recommend to use FasterScoreCAM in this case.
* `channels-first` models and data is unsupported.


## ToDo

* Guides
   * Visualizing multiple attention or activation images at once utilizing batch-system of model
   * Define various score functions
   * Visualizing attentions with multiple inputs models
   * Visualizing attentions with multiple outputs models
   * Advanced score functions
   * Tuning Activation Maximization
   * Visualizing attentions for N-dim image inputs
*  We're going to add some methods such as below
   - Deep Dream
   - Style transfer


%prep
%autosetup -n tf-keras-vis-0.8.5

%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-tf-keras-vis -f filelist.lst
%dir %{python3_sitelib}/*

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

%changelog
* Fri Jun 09 2023 Python_Bot <Python_Bot@openeuler.org> - 0.8.5-1
- Package Spec generated