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
def mean_squared_logarithmic_error(y_true, y_pred):
first_log = K.log(K.clip(y_pred, K.epsilon(), np.inf) + 1.)
second_log = K.log(K.clip(y_true, K.epsilon(), np.inf) + 1.)
return K.mean(K.square(first_log - second_log), axis=-1)
def mean_absolute_percentage_error(y_true, y_pred):
diff = K.abs(
(y_true - y_pred) / K.clip(K.abs(y_true), K.epsilon(), np.inf))
return 100. * K.mean(diff, axis=-1)
def mean_squared_logarithmic_error(y_true, y_pred):
first_log = K.log(K.clip(y_pred, K.epsilon(), np.inf) + 1.)
second_log = K.log(K.clip(y_true, K.epsilon(), np.inf) + 1.)
return K.mean(K.square(first_log - second_log), axis=-1)
def mean_absolute_percentage_error(y_true, y_pred):
diff = K.abs((y_true - y_pred) / K.clip(K.abs(y_true), K.epsilon(), np.inf))
return 100. * K.mean(diff, axis=-1)
def __call__(self, p):
norms = K.sqrt(K.sum(K.square(p), axis=0))
desired = K.clip(norms, 0, self.m)
p = p * (desired / (1e-7 + norms))
return p
def __call__(self, p):
norms = K.sqrt(K.sum(K.square(p), axis=0))
desired = K.clip(norms, 0, self.m)
p = p * (desired / (1e-7 + norms))
return p
