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Neural network visualization toolkit for tf.keras

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Downloads Python PyPI version Python package License: MIT Documentation

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

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.


Dense Unit

Convolutional Filter

Class Activation Map

The images above are generated by GradCAM++.

Saliency Map

The images above are generated by SmoothGrad.


ActivationMaximization (Visualizing Convolutional Filter)

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 = \

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

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

# Render


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,

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

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

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

Please see the guides below for more details:

Getting Started Guides

[NOTES] If you have ever used 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.


  • Python 3.7+
  • Tensorflow 2.0+


  • PyPI
$ pip install tf-keras-vis tensorflow
  • Source (for development)
$ git clone
$ cd tf-keras-vis
$ pip install -e .[develop] tensorflow

Use Cases

  • 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.


  • 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

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