Available Methods¶
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 on the inputs towards the decision.
From Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
from tf_explain.callbacks.vanilla_gradients import VanillaGradients
model = [...]
callbacks = [
VanillaGradients(
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)
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)
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)