GradCAM

class tf_keras_vis.gradcam.Gradcam(model, model_modifier=None, clone=True)

Bases: ModelVisualization

Grad-CAM

References

• Grad-CAM: Why did you say that? Visual Explanations from Deep Networks via Gradient-based Localization (https://arxiv.org/pdf/1610.02391v1.pdf)

Parameters:

• model – A tf.keras.Model instance. When model_modifier is NOT None, this model will be cloned with tf.keras.models.clone_model function and then will be modified by model_modifier according to needs.

• model_modifier – A tf_keras_vis.utils.model_modifiers.ModelModifier instance, a function or a list of them. We recommend to apply tf_keras_vis.utils.model_modifiers.ReplaceToLinear to all visualizations (except tf_keras_vis.scorecam.Scorecam) when the model output is softmax. Defaults to None.

• clone – A bool that indicates whether or not it clones the model. When False, the model won’t be cloned. Note that, although when True, the model won’t be clone if model_modifier is None. Defaults to True.

__call__(score, seed_input, penultimate_layer=None, seek_penultimate_conv_layer=True, gradient_modifier=None, activation_modifier=lambda cam: ..., training=False, expand_cam=True, normalize_cam=True, unconnected_gradients=tf.UnconnectedGradients.NONE)

Generate gradient based class activation maps (CAM) by using positive gradient of penultimate_layer with respect to score.

Parameters:

• score –

  A tf_keras_vis.utils.scores.Score instance, function or a list of them. For example of the Score instance to specify visualizing target:

  scores = CategoricalScore([1, 294, 413])
  

  The code above means the same with the one below:

  score = lambda outputs: (outputs[0][1], outputs[1][294], outputs[2][413])
  

  When the model has multiple outputs, you MUST pass a list of Score instances or functions. For example:

  from tf_keras_vis.utils.scores import CategoricalScore, InactiveScore
  score = [
      CategoricalScore([1, 23]),  # For 1st model output
      InactiveScore(),            # For 2nd model output
      ...
  ]
  

• seed_input – A tf.Tensor, numpy.ndarray or a list of them to input in the model. That’s when the model has multiple inputs, you MUST pass a list of tensors.

• penultimate_layer – An index or name of the layer, or the tf.keras.layers.Layer instance itself. When None, it means the same with -1. If the layer specified by this option is not convolutional layer, penultimate_layer will work as the offset to seek convolutional layer. Defaults to None.

• seek_penultimate_conv_layer – A bool that indicates whether or not seeks a penultimate layer when the layer specified by penultimate_layer is not convolutional layer. Defaults to True.

• gradient_modifier – A function to modify gradients. Defaults to None.

• activation_modifier – A function to modify the Class Activation Map (CAM). Defaults to lambda cam: K.relu(cam).

• training – A bool that indicates whether the model’s training-mode on or off. Defaults to False.

• expand_cam – True to resize CAM to the same as input image size. Note! When False, even if the model has multiple inputs, return only a CAM. Defaults to True.

• normalize_cam – When True, CAM will be normalized. Defaults to True.

• unconnected_gradients – Specifies the gradient value returned when the given input tensors are unconnected. Defaults to tf.UnconnectedGradients.NONE.

Returns:

An numpy.ndarray or a list of them. They are the Class Activation Maps (CAMs) that indicate the seed_input regions whose change would most contribute the score value.

Raises:

ValueError – When there is any invalid arguments.

Return type:

ndarray | list