def explain(self,
validation_data,
model,
class_index,
num_samples=5,
noise=1.0):
"""
Compute SmoothGrad for a specific class index
Args:
validation_data (Tuple[np.ndarray, Optional[np.ndarray]]): Validation data
to perform the method on. Tuple containing (x, y).
model (tf.keras.Model): tf.keras model to inspect
class_index (int): Index of targeted class
num_samples (int): Number of noisy samples to generate for each input image
noise (float): Standard deviation for noise normal distribution
Returns:
np.ndarray: Grid of all the smoothed gradients
"""
images, _ = validation_data
noisy_images = SmoothGrad.generate_noisy_images(
images, num_samples, noise)
smoothed_gradients = SmoothGrad.get_averaged_gradients(
noisy_images, model, class_index, num_samples)
grayscale_gradients = transform_to_normalized_grayscale(
tf.abs(smoothed_gradients)).numpy()
grid = grid_display(grayscale_gradients)
return grid
def test_should_transform_gradients_to_grayscale():
gradients = tf.random.uniform((4, 28, 28, 3))
grayscale_gradients = transform_to_normalized_grayscale(gradients)
expected_output_shape = (4, 28, 28)
assert grayscale_gradients.shape == expected_output_shape
def explain(self, validation_data, model, class_index, n_steps=10):
"""
Compute Integrated Gradients for a specific class index
Args:
validation_data (Tuple[np.ndarray, Optional[np.ndarray]]): Validation data
to perform the method on. Tuple containing (x, y).
model (tf.keras.Model): tf.keras model to inspect
class_index (int): Index of targeted class
n_steps (int): Number of steps in the path
Returns:
np.ndarray: Grid of all the integrated gradients
"""
images, _ = validation_data
interpolated_images = IntegratedGradients.generate_interpolations(
np.array(images), n_steps)
integrated_gradients = IntegratedGradients.get_integrated_gradients(
interpolated_images, model, class_index, n_steps)
grayscale_integrated_gradients = transform_to_normalized_grayscale(
tf.abs(integrated_gradients)).numpy()
grid = grid_display(grayscale_integrated_gradients)
return grid
def explain_score_model(self, validation_data, score_model, class_index):
"""
Perform gradients backpropagation for a given input
Args:
validation_data (Tuple[np.ndarray, Optional[np.ndarray]]): Validation data
to perform the method on. Tuple containing (x, y).
score_model (tf.keras.Model): tf.keras model to inspect. The last layer
should not have any activation function.
class_index (int): Index of targeted class
Returns:
numpy.ndarray: Grid of all the gradients
"""
images, _ = validation_data
gradients = self.compute_gradients(images, score_model, class_index)
grayscale_gradients = transform_to_normalized_grayscale(
tf.abs(gradients)).numpy()
grid = grid_display(grayscale_gradients)
return grid