Efficient explaining AI algorithms for Keras models.
pip install tensorflow
pip install git+https://github.com/lucasdavid/keras-explainable.gitThis example illustrate how to explain predictions of a Convolutional Neural Network (CNN) using Grad-CAM. This can be easily achieved with the following example:
import keras_explainable as ke
model = tf.keras.applications.ResNet50V2(...)
model = ke.inspection.expose(model)
scores, cams = ke.gradcam(model, x, y, batch_size=32)| Method | Kind | Description | Reference |
|---|---|---|---|
| Gradient Back-propagation | gradient | Computes the gradient of the output activation unit being explained with respect to each unit in the input signal. | docs paper |
| Full-Gradient | gradient | Adds the individual contributions of each bias factor in the model to the extracted gradient, forming the "full gradient" representation. | docs paper |
| CAM | CAM | Creates class-specific maps by linearly combining the activation maps advent from the last convolutional layer, scaled by their contributions to the unit of interest. | docs paper |
| Grad-CAM | CAM | Linear combination of activation maps, weighted by the gradient of the output unit with respect to the maps themselves. | docs paper |
| Grad-CAM++ | CAM | Weights pixels in the activation maps in order to counterbalance, resulting in similar activation intensity over multiple instances of objects. | docs paper |
| Score-CAM | CAM | Combines activation maps considering their contribution towards activation, when used to mask Activation maps are used to mask the input signal, which is feed-forwarded and activation intensity is computed for the new . Maps are combined weighted by their relative activation retention. | docs paper |
| SmoothGrad | Meta | Consecutive applications of an AI explaining method, adding Gaussian noise to the input signal each time. | docs paper |
| TTA | Meta | Consecutive applications of an AI explaining method, applying augmentation to the input signal each time. | docs paper |