def get_heatmaps(img_id, alexnet, title):
base_model = alexnet.base_model
top_layer_model = alexnet.model
labels = get_labels()
path = get_path_from_id(img_id)
strongest_filter = get_strongest_filter(img_id, layer=5)
true_label = labels[img_id]
print(strongest_filter, true_label)
predictions = AlexNet(base_model=base_model).predict(path)
print(decode_classnames_json(predictions))
print(decode_classnumber(predictions))
print(true_label)
# DeconvOutput(preprocess_image_batch_grey_square(image_paths=path, square_x=50, square_y=50)).save_as('Occlusion',
# title + '.JPEG')
activations = np.zeros((30, 30))
class_prop = np.zeros((30, 30))
for x in range(0, 30):
print(x)
for y in range(0, 30):
prep_image = preprocess_image_batch_grey_square(
path, 13 + x * 7, 13 + y * 7)
activation = get_summed_activation_of_feature_map(
top_layer_model, strongest_filter, prep_image)
prediction = base_model.predict(prep_image)
activations[x, y] = activation
class_prop[x, y] = prediction[0][true_label]
print('done')
fig, ax = plt.subplots()
cax = ax.imshow(activations, interpolation='nearest', cmap='plasma')
plt.axis('off')
# Add colorbar, make sure to specify tick locations to match desired ticklabels
cbar = fig.colorbar(cax)
plt.show()
fig, ax = plt.subplots()
cax = ax.imshow(class_prop, interpolation='nearest', cmap='plasma')
plt.axis('off')
# Add colorbar, make sure to specify tick locations to match desired ticklabels
cbar = fig.colorbar(cax)
plt.show()
def top_classes(self, img_path, top=5):
preds = self.predict(img_path)
return decode_classnumber(preds, top)
if __name__ == "__main__":
img_path = 'Example_JPG/Elephant.jpg'
# Usage of alexnet_model
im = preprocess_image_batch([img_path])
model = alexnet_model()
out_model = model.predict(im)
# Usage of AlexNet()
out_wrapper = AlexNet().predict(img_path)
assert (out_model == out_wrapper).all()
# Decode one-hot vector to most probable class names
print(decode_classnames_json(out_wrapper))
print(decode_classnumber(out_wrapper))
# Plot and print information about model
plot_and_print = True
if plot_and_print:
plot_model(model, to_file='alexnet_model.png', show_shapes=True)
print(model.summary())
testimages = [
'Example_JPG/Elephant.jpg', 'Example_JPG/RoadBike.jpg',
'Example_JPG/Trump.jpg'
]
model = alexnet_model()
preds = AlexNet(base_model=model).top_classes(testimages)
print(preds)
for pred in preds: