From 7c19245824f562cdedb7c96fd1b9da964bd1bd9a Mon Sep 17 00:00:00 2001 From: Michael Oliver Date: Wed, 16 Aug 2017 17:41:04 -0700 Subject: [PATCH] Update normalization.py --- keras_contrib/layers/normalization.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/keras_contrib/layers/normalization.py b/keras_contrib/layers/normalization.py index a9aba89..28bc261 100644 --- a/keras_contrib/layers/normalization.py +++ b/keras_contrib/layers/normalization.py @@ -318,14 +318,12 @@ class BatchRenormalization(Layer): K.moving_average_update(self.running_variance, std_batch ** 2, self.momentum)], inputs) # update r_max and d_max - t_val = K.get_value(self.t) - r_val = self.r_max_value / (1 + (self.r_max_value - 1) * K.exp(-t_val)) - d_val = self.d_max_value / (1 + ((self.d_max_value / 1e-3) - 1) * K.exp(-(2 * t_val))) - t_val += float(self.t_delta) + r_val = self.r_max_value / (1 + (self.r_max_value - 1) * K.exp(-self.t)) + d_val = self.d_max_value / (1 + ((self.d_max_value / 1e-3) - 1) * K.exp(-(2 * self.t))) self.add_update([K.update(self.r_max, r_val), K.update(self.d_max, d_val), - K.update(self.t, t_val)], inputs) + K.update_add(self.t, K.variable(np.array([self.t_delta])))], x) if training in {0, False}: return x_normed