https://keisen.github.io/tf-keras-vis-docs/
tf-keras-vis is a visualization toolkit for debugging tf.keras.Model in Tensorflow2.0+.
Currently supported methods for visualization include:
- Feature Visualization
- Class Activation Maps
- Saliency Maps
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.
The images above are generated by GradCAM++.
The images above are generated by SmoothGrad.
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])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) # overlayPlease see the guides below for more details:
[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 https://github.com/keisen/tf-keras-vis.git
$ cd tf-keras-vis
$ pip install -e .[develop] tensorflow- chitra
- A Deep Learning Computer Vision library for easy data loading, model building and model interpretation with GradCAM/GradCAM++.
- 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-firstmodels 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
