def test_should_save_output_grid(output_dir):
grid = np.random.random((208, 208))
explainer = ExtractActivations()
explainer.save(grid, output_dir, "output.png")
assert len(list(output_dir.glob("output.png"))) == 1
def save_all_explainer(validation_data,
model,
name_conv,
n_conv=1,
dir_save_im='./',
save_name='outputs'):
explainerGradCam = GradCAM()
explainerActiv = ExtractActivations()
explainerOccl = OcclusionSensitivity()
explainerSmoothGrad = SmoothGrad()
for i in range(1, n_conv + 1):
output = explainerActiv.explain(validation_data, model,
'{}_{}'.format(name_conv, i))
explainerActiv.save(output, dir_save_im,
'{}-activ-conv{}.jpg'.format(save_name, i))
output = explainerGradCam.explain(validation_data, model,
'{}_{}'.format(name_conv, i), 0)
explainerGradCam.save(output, dir_save_im,
'{}-gradCam0-conv{}.jpg'.format(save_name, i))
output = explainerSmoothGrad.explain(validation_data, model, 0)
explainerSmoothGrad.save(output, dir_save_im,
'{}-smooth0.jpg'.format(save_name))
output = explainerSmoothGrad.explain(validation_data, model, 1)
explainerSmoothGrad.save(output, dir_save_im,
'{}-smooth1.jpg'.format(save_name))
output = explainerOccl.explain(validation_data, model, 0, 5)
explainerOccl.save(output, dir_save_im,
'{}-occlSens0.jpg'.format(save_name))
output = explainerOccl.explain(validation_data, model, 1, 5)
explainerOccl.save(output, dir_save_im,
'{}-occlSens1.jpg'.format(save_name))
import numpy as np
import tensorflow as tf
from tf_explain.core.activations import ExtractActivations
target_layers = ['activation_6'] # Could be either the output of a Conv2D, or an activation
IMAGE_PATH = './cat.jpg'
if __name__ == '__main__':
model = tf.keras.applications.resnet50.ResNet50(weights='imagenet', include_top=True)
img = tf.keras.preprocessing.image.load_img(IMAGE_PATH, target_size=(224, 224))
img = tf.keras.preprocessing.image.img_to_array(img)
model.summary()
data = (np.array([img]), None)
explainer = ExtractActivations()
# Compute Activations of layer activation_1
grid = explainer.explain(data, model, target_layers)
explainer.save(grid, '.', 'activations.png')
index = np.random.randint(0, len(X_test_reshaped))
# image = input_test[index].reshape((1, 32, 32, 3))
# image = np.expand_dims(X_test_reshaped[index],0)
image = X_test_reshaped[index:index + 10]
label = image
print('val:', image.shape)
data = ([image])
explainer = ExtractActivations()
layers_of_interest = ['conv2d_1']
grid = explainer.explain(validation_data=data,
model=complete_model,
layers_name=['conv2d_1'])
print(grid.shape)
explainer.save(grid, '.', 'conv2d_1.png')
grid = explainer.explain(validation_data=data,
model=complete_model,
layers_name=['conv2d_2'])
print(grid.shape)
explainer.save(grid, '.', 'conv2d_2.png')
grid = explainer.explain(validation_data=data,
model=complete_model,
layers_name=['conv2d_3'])
print(grid.shape)
explainer.save(grid, '.', 'conv2d_3.png')
grid = explainer.explain(validation_data=data,
model=complete_model,
import numpy as np
import tensorflow as tf
from tf_explain.core.activations import ExtractActivations
target_layers = ["conv1_relu"
] # Could be either the output of a Conv2D, or an activation
IMAGE_PATH = "./cat.jpg"
if __name__ == "__main__":
model = tf.keras.applications.resnet50.ResNet50(weights="imagenet",
include_top=True)
img = tf.keras.preprocessing.image.load_img(IMAGE_PATH,
target_size=(224, 224))
img = tf.keras.preprocessing.image.img_to_array(img)
model.summary()
data = (np.array([img]), None)
explainer = ExtractActivations()
# Compute Activations of layer activation_1
grid = explainer.explain(data, model, target_layers)
explainer.save(grid, ".", "activations.png")
# Fit data to model
history = model.fit(input_train,
target_train,
batch_size=batch_size,
epochs=no_epochs,
verbose=verbosity,
validation_split=validation_split)
#callbacks=callbacks)
# In[10]:
# Define the Activation Visualization explainer
index = 250
image = input_test[index].reshape((1, 32, 32, 3))
label = target_test[index]
data = ([image], [label])
explainer = ExtractActivations()
grid = explainer.explain(data, model, layers_name='visualization_layer')
explainer.save(grid, '.', 'act.png')
# In[11]:
# Generate generalization metrics
score = model.evaluate(input_test, target_test, verbose=0)
print(f'Test loss: {score[0]} / Test accuracy: {score[1]}')
# In[ ]:
# In[ ]:
def extract_activations(self, value, index):
extract_activations = ExtractActivations()
grid = extract_activations.explain((value, self.setY[index]),
self.model, self.target_layers)
name = "n_{}_gradients_input.png".format(index)
extract_activations.save(grid, ".", sg.PATH_ACTIVATION + name)