def get_gradients(self, loss, params):
"""Returns gradients of `loss` with respect to `params`.
Arguments:
loss: Loss tensor.
params: List of variables.
Returns:
List of gradient tensors.
Raises:
ValueError: In case any gradient cannot be computed (e.g. if gradient
function not implemented).
"""
grads = K.gradients(loss, params)
if None in grads:
raise ValueError('An operation has `None` for gradient. '
'Please make sure that all of your ops have a '
'gradient defined (i.e. are differentiable). '
'Common ops without gradient: '
'K.argmax, K.round, K.eval.')
if hasattr(self, 'clipnorm') and self.clipnorm > 0:
norm = K.sqrt(sum([K.sum(K.square(g)) for g in grads]))
grads = [clip_norm(g, self.clipnorm, norm) for g in grads]
if hasattr(self, 'clipvalue') and self.clipvalue > 0:
grads = [K.clip(g, -self.clipvalue, self.clipvalue) for g in grads]
return grads
def get_gradients(self, loss, params):
"""Returns gradients of `loss` with respect to `params`.
Arguments:
loss: Loss tensor.
params: List of variables.
Returns:
List of gradient tensors.
Raises:
ValueError: In case any gradient cannot be computed (e.g. if gradient
function not implemented).
"""
grads = K.gradients(loss, params)
if None in grads:
raise ValueError('An operation has `None` for gradient. '
'Please make sure that all of your ops have a '
'gradient defined (i.e. are differentiable). '
'Common ops without gradient: '
'K.argmax, K.round, K.eval.')
if hasattr(self, 'clipnorm') and self.clipnorm > 0:
norm = K.sqrt(sum([K.sum(K.square(g)) for g in grads]))
grads = [clip_norm(g, self.clipnorm, norm) for g in grads]
if hasattr(self, 'clipvalue') and self.clipvalue > 0:
grads = [K.clip(g, -self.clipvalue, self.clipvalue) for g in grads]
return grads
def get_gradients(self, loss, params):
grads = K.gradients(loss, params)
if hasattr(self, 'clipnorm') and self.clipnorm > 0:
norm = K.sqrt(sum([K.sum(K.square(g)) for g in grads]))
grads = [clip_norm(g, self.clipnorm, norm) for g in grads]
if hasattr(self, 'clipvalue') and self.clipvalue > 0:
grads = [K.clip(g, -self.clipvalue, self.clipvalue) for g in grads]
return grads
def get_gradients(self, loss, params):
grads = K.gradients(loss, params)
if hasattr(self, 'clipnorm') and self.clipnorm > 0:
norm = K.sqrt(sum([K.sum(K.square(g)) for g in grads]))
grads = [clip_norm(g, self.clipnorm, norm) for g in grads]
if hasattr(self, 'clipvalue') and self.clipvalue > 0:
grads = [K.clip(g, -self.clipvalue, self.clipvalue) for g in grads]
return grads
style_reference_features = layer_features[1:nb_tensors - 1, :, :, :]
sl2 = []
for j in range(nb_style_images):
sl2.append(style_loss(style_reference_features[j], combination_features,shape))
for j in range(nb_style_images):
s1 = sl1[j] = sl2[j]
# Improvement 4
# Geometric weighted scaling of style loss
loss += (style_weights[j] / (2 ** (nb_layers - (i + 1)))) * s1
loss += total_variation_weight * total_variation_loss(combination_image)
# get the gradients of the generated image wrt the loss
grads = K.gradients(loss, combination_image)
outputs = [loss]
if type(grads) in {list, tuple}:
outputs += grads
else:
outputs.append(grads)
f_outputs = K.function([combination_image], outputs)
def eval_loss_and_grads(x):
x = x.reshape((1, img_width, img_height, 3))
outs = f_outputs([x])
loss_value = outs[0]
if len(outs[1:]) == 1:
