tf-explain

tf-explain implements interpretability methods as Tensorflow 2.0 callbacks to ease neural network's understanding.

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. It is not declared as a dependency to let you choose between CPU and GPU versions. Additionally to the previous install, run:

# For CPU version
pip install tensorflow==2.0.0-beta1
# For GPU version
pip install tensorflow-gpu==2.0.0-beta1

Available Methods

1. Activations Visualization
2. Occlusion Sensitivity
3. Grad CAM (Class Activation Maps)

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)

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),
        patch_size=4,
        class_index=0,
        output_dir=output_dir,
    ),
]

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

Occlusion Sensitivity for Tabby class (stripes differentiate tabby cat from other ImageNet cat classes)

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),
        layer_name="activation_1",
        class_index=0,
        output_dir=output_dir,
    )
]

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

Roadmap

Next features are listed as issues with the roadmap label.