def noisy_forward(self,
data,
noise=tf.constant(0.0),
update_batch_stats=False):
with tf.name_scope("forward"):
protos = concat(
[self.protos,
tf.zeros_like(self.protos[:, 0:1, :])], 1)
encoded = self.phi(data + noise,
update_batch_stats=update_batch_stats)
logits = compute_logits_radii(protos, tf.expand_dims(encoded, 0),
self.radii)
return logits
def predict(self):
"""See `model.py` for documentation."""
nclasses = self.nway
num_cluster_steps = self.config.num_cluster_steps
h_train, h_unlabel, h_test = self.get_encoded_inputs(
self.x_train, self.x_unlabel, self.x_test)
y_train = self.y_train
protos = self._compute_protos(nclasses, h_train, y_train)
# Distractor class has a zero vector as prototype.
protos = concat([protos, tf.zeros_like(protos[:, 0:1, :])], 1)
# Hard assignment for training images.
prob_train = [None] * (nclasses + 1)
for kk in range(nclasses):
# [B, N, 1]
prob_train[kk] = tf.expand_dims(
tf.cast(tf.equal(y_train, kk), h_train.dtype), 2)
prob_train[-1] = tf.zeros_like(prob_train[0])
prob_train = concat(prob_train, 2)
# Initialize cluster radii.
radii = [None] * (nclasses + 1)
y_train_shape = tf.shape(y_train)
bsize = y_train_shape[0]
for kk in range(nclasses):
radii[kk] = tf.ones([bsize, 1]) * 1.0
# Distractor class has a larger radius.
if FLAGS.learn_radius:
log_distractor_radius = tf.get_variable(
"log_distractor_radius",
shape=[],
dtype=tf.float32,
initializer=tf.constant_initializer(np.log(FLAGS.init_radius)))
distractor_radius = tf.exp(log_distractor_radius)
else:
distractor_radius = FLAGS.init_radius
distractor_radius = tf.cond(
tf.shape(self._x_unlabel)[1] > 0, lambda: distractor_radius,
lambda: 100000.0)
# distractor_radius = tf.Print(distractor_radius, [distractor_radius])
radii[-1] = tf.ones([bsize, 1]) * distractor_radius
radii = concat(radii, 1) # [B, K]
h_all = concat([h_train, h_unlabel], 1)
logits_list = []
logits_list.append(compute_logits_radii(protos, h_test, radii))
# Run clustering.
for tt in range(num_cluster_steps):
# Label assignment.
prob_unlabel = assign_cluster_radii(protos, h_unlabel, radii)
prob_all = concat([prob_train, prob_unlabel], 1)
protos = update_cluster(h_all, prob_all)
logits_list.append(compute_logits_radii(protos, h_test, radii))
# Distractor evaluation.
is_distractor = tf.equal(tf.argmax(prob_unlabel, axis=-1), nclasses)
pred_non_distractor = 1.0 - tf.to_float(is_distractor)
acc, recall, precision = eval_distractor(pred_non_distractor,
self.y_unlabel)
self._non_distractor_acc = acc
self._distractor_recall = recall
self._distractor_precision = precision
self._distractor_pred = 1.0 - tf.exp(prob_unlabel[:, :, -1])
return logits_list