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tf-explain

tf-explain implements interpretability methods as Tensorflow 2.x callbacks to ease neural network's understanding. See Introducing tf-explain, Interpretability for Tensorflow 2.0

Documentation: https://tf-explain.readthedocs.io

Installation

tf-explain is available on PyPi as an alpha release. To install it:

virtualenv venv -p python3.6
pip install tf-explain

tf-explain is compatible with Tensorflow 2.x. It is not declared as a dependency to let you choose between full and standalone-CPU versions. Additionally to the previous install, run:

# For CPU or GPU
pip install tensorflow==2.2.0

Opencv is also a dependency. To install it, run:

# For CPU or GPU
pip install opencv-python

Quickstart

tf-explain offers 2 ways to apply interpretability methods. The full list of methods is the Available Methods section.

On trained model

The best option is probably to load a trained model and apply the methods on it.

# Load pretrained model or your own
model = tf.keras.applications.vgg16.VGG16(weights="imagenet", include_top=True)

# Load a sample image (or multiple ones)
img = tf.keras.preprocessing.image.load_img(IMAGE_PATH, target_size=(224, 224))
img = tf.keras.preprocessing.image.img_to_array(img)
data = ([img], None)

# Start explainer
explainer = GradCAM()
grid = explainer.explain(data, model, class_index=281)  # 281 is the tabby cat index in ImageNet

explainer.save(grid, ".", "grad_cam.png")

During training

If you want to follow your model during the training, you can also use it as a Keras Callback, and see the results directly in TensorBoard.

from tf_explain.callbacks.grad_cam import GradCAMCallback

model = [...]

callbacks = [
    GradCAMCallback(
        validation_data=(x_val, y_val),
        class_index=0,
        output_dir=output_dir,
    )
]

model.fit(x_train, y_train, batch_size=2, epochs=2, callbacks=callbacks)

Activations Visualization

Visualize how a given input comes out of a specific activation layer

from tf_explain.callbacks.activations_visualization import ActivationsVisualizationCallback

model = [...]

callbacks = [
    ActivationsVisualizationCallback(
        validation_data=(x_val, y_val),
        layers_name=["activation_1"],
        output_dir=output_dir,
    ),
]

model.fit(x_train, y_train, batch_size=2, epochs=2, callbacks=callbacks)

Vanilla Gradients

Visualize gradients importance on input image

from tf_explain.callbacks.vanilla_gradients import VanillaGradientsCallback

model = [...]

callbacks = [
    VanillaGradientsCallback(
        validation_data=(x_val, y_val),
        class_index=0,
        output_dir=output_dir,
    ),
]

model.fit(x_train, y_train, batch_size=2, epochs=2, callbacks=callbacks)

Gradients*Inputs

Variant of Vanilla Gradients ponderating gradients with input values

from tf_explain.callbacks.gradients_inputs import GradientsInputsCallback

model = [...]

callbacks = [
    GradientsInputsCallback(
        validation_data=(x_val, y_val),
        class_index=0,
        output_dir=output_dir,
    ),
]

model.fit(x_train, y_train, batch_size=2, epochs=2, callbacks=callbacks)

Occlusion Sensitivity

Visualize how parts of the image affects neural network's confidence by occluding parts iteratively

from tf_explain.callbacks.occlusion_sensitivity import OcclusionSensitivityCallback

model = [...]

callbacks = [
    OcclusionSensitivityCallback(
        validation_data=(x_val, y_val),
        class_index=0,
        patch_size=4,
        output_dir=output_dir,
    ),
]

model.fit(x_train, y_train, batch_size=2, epochs=2, callbacks=callbacks)

<div align="center"> <img src="./docs/assets/occlusion_sensitivity.png" width="200" /> Occlusion Sensitivity for Tabby class (stripes differentiate tabby cat from other ImageNet cat classes) </div>

Grad CAM

Visualize how parts of the image affects neural network's output by looking into the activation maps

From Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

from tf_explain.callbacks.grad_cam import GradCAMCallback

model = [...]

callbacks = [
    GradCAMCallback(
        validation_data=(x_val, y_val),
        class_index=0,
        output_dir=output_dir,
    )
]

model.fit(x_train, y_train, batch_size=2, epochs=2, callbacks=callbacks)

SmoothGrad

Visualize stabilized gradients on the inputs towards the decision

From SmoothGrad: removing noise by adding noise

from tf_explain.callbacks.smoothgrad import SmoothGradCallback

model = [...]

callbacks = [
    SmoothGradCallback(
        validation_data=(x_val, y_val),
        class_index=0,
        num_samples=20,
        noise=1.,
        output_dir=output_dir,
    )
]

model.fit(x_train, y_train, batch_size=2, epochs=2, callbacks=callbacks)

Integrated Gradients

Visualize an average of the gradients along the construction of the input towards the decision

From Axiomatic Attribution for Deep Networks

from tf_explain.callbacks.integrated_gradients import IntegratedGradientsCallback

model = [...]

callbacks = [
    IntegratedGradientsCallback(
        validation_data=(x_val, y_val),
        class_index=0,
        n_steps=20,
        output_dir=output_dir,
    )
]

model.fit(x_train, y_train, batch_size=2, epochs=2, callbacks=callbacks)

Roadmap

Contributing

To contribute to the project, please read the dedicated section.