def learn_pairs(label_interest=5, n_jumps_test=50):
"""
:param label_interest: MNIST label of interest
:param n_jumps_test: how many test saccades to be made for one image;
as we increase `n_jumps_test`, we expect overlap with L23 train history to decrease in time,
since during the training we observe only the most significant features in an image.
Ideally, we'd like the overlap not to decrease much in time.
"""
images, labels = load_mnist.load_images(images_number=100)
world = World()
poppy = Agent()
images_interest = images[labels == label_interest]
for image in images_interest:
world.add_image(image)
poppy.cortex.reset_activations()
l23_train = poppy.learn_pairs(world, label_interest)
world.reset()
if n_jumps_test == 0:
l23_test = poppy.learn_pairs(world, label=label_interest)
else:
l23_test = []
poppy.sense_data(world)
for saccade in range(n_jumps_test):
poppy.sense_data(world)
l23_test.append(poppy.cortex.V1.layers['L23'].cells.copy())
l23_test = np.vstack(l23_test)
overlap = np.dot(l23_train, l23_test.T)
overlap = (overlap * 255 /
poppy.cortex.V1.layers['L23'].n_active).astype(np.uint8)
cv2.imshow('overlap', overlap)
cv2_step()
def one_image(label_interest=5):
images, labels = load_mnist.load_images(images_number=100)
world = World()
poppy = Agent()
image_interest = images[labels == label_interest][0]
world.add_image(image_interest)
poppy.cortex.reset_activations()
poppy.cortex.display = True
while True:
poppy.sense_data(world)
poppy.cortex.associate(label=label_interest)
