def one_shot_validation(episode_file=lib["validation_episode_list"], data_x=val_x, data_keys=val_keys, data_labels=val_labels, normalize=True, print_normalization=False):
episode_dict = generate_unimodal_image_episodes.read_in_episodes(episode_file)
correct = 0
total = 0
episode_numbers = np.arange(1, len(episode_dict)+1)
np.random.shuffle(episode_numbers)
saver = tf.train.Saver()
with tf.Session() as sesh:
saver.restore(sesh, model_fn)
for episode in episode_numbers:
episode_num = str(episode)
query = episode_dict[episode_num]["query"]
query_data, query_keys, query_lab = generate_unimodal_image_episodes.episode_data(
query["keys"], data_x, data_keys, data_labels
)
query_iterator = batching_library.unflattened_image_iterator(
query_data, len(query_data), shuffle_batches_every_epoch=False
)
query_labels = [query_lab[i] for i in query_iterator.indices]
support_set = episode_dict[episode_num]["support_set"]
S_data, S_keys, S_lab = generate_unimodal_image_episodes.episode_data(
support_set["keys"], data_x, data_keys, data_labels
)
S_iterator = batching_library.unflattened_image_iterator(
S_data, len(S_data), shuffle_batches_every_epoch=False
)
S_labels = [S_lab[i] for i in S_iterator.indices]
for feats in query_iterator:
lat = sesh.run(
[latent], feed_dict={X: feats}
)[0]
for feats in S_iterator:
S_lat = sesh.run(
[latent], feed_dict={X: feats}
)[0]
if normalize:
latents = (lat - lat.mean(axis=0))/lat.std(axis=0)
s_latents = (S_lat - S_lat.mean(axis=0))/S_lat.std(axis=0)
if print_normalization:
evaluation_library.normalization_visualization(
lat, latents, labels, 300,
path.join(lib["output_fn"], lib["model_name"])
)
else:
latents = lat
s_latents = S_lat
distances = cdist(latents, s_latents, "cosine")
indexes = np.argmin(distances, axis=1)
label_matches = few_shot_learning_library.label_matches_grid_generation_2D(query_labels, S_labels)
for i in range(len(indexes)):
total += 1
if label_matches[i, indexes[i]]:
correct += 1
return [-correct/total]
def unimodal_image_test(episode_fn=lib["image_episode_1_list"],
data_x=image_test_x,
data_keys=image_test_keys,
data_labels=image_test_labels,
normalize=True,
k=1):
episode_dict = generate_unimodal_image_episodes.read_in_episodes(
episode_fn)
correct = 0
total = 0
episode_numbers = np.arange(1, len(episode_dict) + 1)
np.random.shuffle(episode_numbers)
saver = tf.train.Saver()
with tf.Session() as sesh:
saver.restore(sesh, model_fn)
for episode in tqdm(
episode_numbers,
desc=
f'\t{k}-shot unimodal image classification tests on {len(episode_numbers)} episodes for random seed {rnd_seed:2.0f}',
ncols=COL_LENGTH):
episode_num = str(episode)
query = episode_dict[episode_num]["query"]
query_data, query_keys, query_lab = generate_unimodal_image_episodes.episode_data(
query["keys"], data_x, data_keys, data_labels)
query_iterator = batching_library.unflattened_image_iterator(
query_data,
len(query_data),
shuffle_batches_every_epoch=False)
query_labels = [
query_lab[i] for i in query_iterator.indices
]
support_set = episode_dict[episode_num][
"support_set"]
S_data, S_keys, S_lab = generate_unimodal_image_episodes.episode_data(
support_set["keys"], data_x, data_keys,
data_labels)
S_iterator = batching_library.unflattened_image_iterator(
S_data,
len(S_data),
shuffle_batches_every_epoch=False)
S_labels = [S_lab[i] for i in S_iterator.indices]
for feats in query_iterator:
lat = sesh.run([image_latent],
feed_dict={
image_X: feats,
train_flag: False
})[0]
for feats in S_iterator:
S_lat = sesh.run([image_latent],
feed_dict={
image_X: feats,
train_flag: False
})[0]
if normalize:
latents = (lat -
lat.mean(axis=0)) / lat.std(axis=0)
s_latents = (S_lat - S_lat.mean(axis=0)
) / S_lat.std(axis=0)
else:
latents = lat
s_latents = S_lat
distances = cdist(latents, s_latents, "cosine")
indexes = np.argmin(distances, axis=1)
label_matches = few_shot_learning_library.label_matches_grid_generation_2D(
query_labels, S_labels)
for i in range(len(indexes)):
total += 1
if label_matches[i, indexes[i]]:
correct += 1
return correct / total