def grad_cam(self, value, index):
for classe in self.labels:
grad_cam = GradCAM()
grid = grad_cam.explain((value, self.setY[index]),
self.model,
class_index=classe,
layer_name=self.target_layers)
name = "n_{}_".format(index) + str(classe) + "_grad_cam.png"
grad_cam.save(grid, ".", sg.PATH_GRAD + name)
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))
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.ylim([0.5, 1])
plt.legend(loc='lower right')
test_loss, test_acc = model.evaluate(images_vali, labels_vali, verbose=2)
print(test_acc)
"""Our simple CNN has achieved a test accuracy of over 70%. Not bad for a few lines of code! For another CNN style,
see an example using the Keras subclassing API and a `tf.GradientTape`
[here](https://www.tensorflow.org/tutorials/quickstart/advanced)."""
#%%
# Instantiation of the explainer
explainer = GradCAM()
for class_index in range(7):
ind_in_images_vali = np.where(labels_vali == class_index)[0]
img_explain = images_vali[ind_in_images_vali[0:25], :, :, :]
# print(img_single.size)
data = (img_explain, None)
# Save output
output_dir = '.'
output_name = 'grad_cam_class_%d.png' % class_index
output = explainer.explain(data, model, "conv3", class_index)
explainer.save(output, output_dir, output_name)
import tensorflow as tf
from tf_explain.core.grad_cam import GradCAM
IMAGE_PATH = './cat.jpg'
if __name__ == '__main__':
model = tf.keras.applications.vgg16.VGG16(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 = ([img], None)
tabby_cat_class_index = 281
explainer = GradCAM()
# Compute GradCAM on VGG16
grid = explainer.explain(data, model, 'block5_conv3',
tabby_cat_class_index)
explainer.save(grid, '.', 'grad_cam.png')
import tensorflow as tf
from tf_explain.core.grad_cam import GradCAM
IMAGE_PATH = "./cat.jpg"
if __name__ == "__main__":
model = tf.keras.applications.vgg16.VGG16(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)
print(img.shape)
model.summary()
data = ([img], None)
tabby_cat_class_index = 281
explainer = GradCAM()
# Compute GradCAM on VGG16
grid = explainer.explain(data, model, "block5_conv3",
tabby_cat_class_index)
explainer.save(grid, ".", "grad_cam.png")
