def get_updates(self, params, constraints, loss): grads = self.get_gradients(loss, params) self.updates = [K.update_add(self.iterations, 1)] lr = self.lr if self.initial_decay > 0: lr *= (1. / (1. + self.decay * self.iterations)) t = self.iterations + 1 lr_t = lr / (1. - K.pow(self.beta_1, t)) shapes = [K.int_shape(p) for p in params] # zero init of 1st moment ms = [K.zeros(shape) for shape in shapes] # zero init of exponentially weighted infinity norm us = [K.zeros(shape) for shape in shapes] self.weights = [self.iterations] + ms + us for p, g, m, u in zip(params, grads, ms, us): m_t = (self.beta_1 * m) + (1. - self.beta_1) * g u_t = K.maximum(self.beta_2 * u, K.abs(g)) p_t = p - lr_t * m_t / (u_t + self.epsilon) self.updates.append(K.update(m, m_t)) self.updates.append(K.update(u, u_t)) new_p = p_t # apply constraints if p in constraints: c = constraints[p] new_p = c(new_p) self.updates.append(K.update(p, new_p)) return self.updates
def get_updates(self, loss, params): grads = self.get_gradients(loss, params) self.updates = [K.update_add(self.iterations, 1)] lr = self.lr if self.initial_decay > 0: lr *= (1. / (1. + self.decay * K.cast(self.iterations, K.dtype(self.decay)))) t = K.cast(self.iterations, K.floatx()) + 1 lr_t = lr / (1. - K.pow(self.beta_1, t)) shapes = [K.int_shape(p) for p in params] # zero init of 1st moment ms = [K.zeros(shape) for shape in shapes] # zero init of exponentially weighted infinity norm us = [K.zeros(shape) for shape in shapes] self.weights = [self.iterations] + ms + us for p, g, m, u in zip(params, grads, ms, us): m_t = (self.beta_1 * m) + (1. - self.beta_1) * g u_t = K.maximum(self.beta_2 * u, K.abs(g)) p_t = p - lr_t * m_t / (u_t + self.epsilon) self.updates.append(K.update(m, m_t)) self.updates.append(K.update(u, u_t)) new_p = p_t # Apply constraints. if getattr(p, 'constraint', None) is not None: new_p = p.constraint(new_p) self.updates.append(K.update(p, new_p)) return self.updates
def __call__(self, x): regularization = 0. if self.l1: regularization += K.sum(self.l1 * K.abs(x)) if self.l2: regularization += K.sum(self.l2 * K.square(x)) return regularization
def __call__(self, x): regularization = 0. if self.l1: regularization += K.sum(self.l1 * K.abs(x)) if self.l2: regularization += K.sum(self.l2 * K.square(x)) return regularization
def call(self, inputs, mask=None): pos = K.relu(inputs) if K.backend() == 'theano': neg = (K.pattern_broadcast(self.alpha, self.param_broadcast) * (inputs - K.abs(inputs)) * 0.5) else: neg = -self.alpha * K.relu(-inputs) return pos + neg
def call(self, inputs, mask=None): pos = K.relu(inputs) if K.backend() == 'theano': neg = (K.pattern_broadcast(self.alpha, self.param_broadcast) * (inputs - K.abs(inputs)) * 0.5) else: neg = -self.alpha * K.relu(-inputs) return pos + neg
def mean_absolute_percentage_error(y_true, y_pred): # Equivalent to MAE, but sometimes easier to interpret. diff = K.abs((y_true - y_pred) / K.clip(K.abs(y_true), K.epsilon(), None)) return 100. * K.mean(diff, axis=-1)
def mean_absolute_error(y_true, y_pred): return K.mean(K.abs(y_pred - y_true), axis=-1)
def sample_mean_absolute_percentage_error(y_true, y_pred): diff = K.abs((y_true - y_pred) / K.clip(K.abs(y_true) + K.abs(y_pred), K.epsilon(), None)) return 200. * K.mean(diff, axis=-1)
def mean_absolute_percentage_error(y_true, y_pred): # Equivalent to MAE, but sometimes easier to interpret. diff = K.abs((y_true - y_pred) / K.clip(K.abs(y_true), K.epsilon(), None)) return 100. * K.mean(diff, axis=-1)
def mean_absolute_error(y_true, y_pred): return K.mean(K.abs(y_pred - y_true), axis=-1)