def fprop(self, x, y, **kwargs):
x_adv = self.attack(x)
d1 = self.model.fprop(x, **kwargs)
d2 = self.model.fprop(x_adv, **kwargs)
pairing_loss = [
tf.reduce_mean(tf.square(a - b))
for a, b in zip(d1[Model.O_FEATURES], d2[Model.O_FEATURES])
]
pairing_loss = tf.reduce_mean(pairing_loss)
loss = tf.reduce_mean(
softmax_cross_entropy_with_logits(labels=y,
logits=d1[Model.O_LOGITS]))
loss += tf.reduce_mean(
softmax_cross_entropy_with_logits(labels=y,
logits=d2[Model.O_LOGITS]))
return loss + self.weight * pairing_loss
def fprop(self, x, y, **kwargs):
x_adv = self.attack(x)
d1 = self.model.fprop(x, **kwargs)
d2 = self.model.fprop(x_adv, **kwargs)
pairing_loss = [
tf.reduce_mean(tf.square(a - b))
for a, b in zip(d1[Model.O_FEATURES], d2[Model.O_FEATURES])
]
pairing_loss = tf.reduce_mean(pairing_loss)
loss = softmax_cross_entropy_with_logits(labels=y,
logits=d1[Model.O_LOGITS])
loss += softmax_cross_entropy_with_logits(labels=y,
logits=d2[Model.O_LOGITS])
warnings.warn("LossFeaturePairing is deprecated, switch to "
"FeaturePairing. LossFeaturePairing may be removed "
"on or after 2019-03-06.")
return loss + self.weight * pairing_loss
def fprop(self, x, y, **kwargs):
mix = tf.distributions.Beta(self.beta, self.beta)
mix = mix.sample([tf.shape(x)[0]] + [1] * (len(x.shape) - 1))
xm = x + mix * (x[::-1] - x)
ym = y + mix * (y[::-1] - y)
logits = self.model.get_logits(xm, **kwargs)
loss = softmax_cross_entropy_with_logits(labels=ym, logits=logits)
warnings.warn("LossMixUp is deprecated, switch to "
"MixUp. LossFeaturePairing may be removed "
"on or after 2019-03-06.")
return loss
def attack_softmax_cross_entropy(y, probs, mean=True):
"""
Define target loss for an Attack.
:param y: 2D tensor, one hot labels.
:param probs: 2D tensor, probability distribution output from the model.
:param mean: bool, reduce mean loss when true.
:return: return mean of loss if True, otherwise return vector with per
sample loss
"""
logits = probs.op.inputs[0] if probs.op.type == 'Softmax' else probs
out = softmax_cross_entropy_with_logits(logits=logits, labels=y)
return tf.reduce_mean(out) if mean else out
def fprop(self, x, y, **kwargs):
with tf.device('/CPU:0'):
# Prevent error complaining GPU kernels unavailable for this.
mix = tf.distributions.Beta(self.beta, self.beta)
mix = mix.sample([tf.shape(x)[0]] + [1] * (len(x.shape) - 1))
mix = tf.maximum(mix, 1 - mix)
xm = x + mix * (x[::-1] - x)
ym = y + mix * (y[::-1] - y)
logits = self.model.get_logits(xm, **kwargs)
loss = tf.reduce_mean(
softmax_cross_entropy_with_logits(labels=ym, logits=logits))
return loss
def fprop(self, x, y, **kwargs):
kwargs.update(self.kwargs)
if self.attack is not None:
x = x, self.attack(x)
else:
x = x,
# Catching RuntimeError: Variable -= value not supported by tf.eager.
try:
y -= self.smoothing * (y - 1. / tf.cast(y.shape[-1], y.dtype))
except RuntimeError:
y.assign_sub(self.smoothing *
(y - 1. / tf.cast(y.shape[-1], y.dtype)))
logits = [self.model.get_logits(x, **kwargs) for x in x]
loss = sum(
tf.reduce_mean(
softmax_cross_entropy_with_logits(labels=y, logits=logit))
for logit in logits)
return loss
def fprop(self, x, y, **kwargs):
if self.attack is not None:
x = x, self.attack(x)
else:
x = x,
# Catching RuntimeError: Variable -= value not supported by tf.eager.
try:
y -= self.smoothing * (y - 1. / tf.cast(y.shape[-1], tf.float32))
except RuntimeError:
y.assign_sub(self.smoothing *
(y - 1. / tf.cast(y.shape[-1], tf.float32)))
logits = [self.model.get_logits(x, **kwargs) for x in x]
loss = sum(
softmax_cross_entropy_with_logits(labels=y, logits=logit)
for logit in logits)
warnings.warn("LossCrossEntropy is deprecated, switch to "
"CrossEntropy. LossCrossEntropy may be removed on "
"or after 2019-03-06.")
return loss
def model_loss(y, model, mean=True):
"""
Define loss of TF graph
:param y: correct labels
:param model: output of the model
:param mean: boolean indicating whether should return mean of loss
or vector of losses for each input of the batch
:return: return mean of loss if True, otherwise return vector with per
sample loss
"""
warnings.warn('This function is deprecated.')
op = model.op
if op.type == "Softmax":
logits, = op.inputs
else:
logits = model
out = softmax_cross_entropy_with_logits(logits=logits, labels=y)
if mean:
out = reduce_mean(out)
return out