def z_est(self): print "Layer ", self.layer_number, ": ", self.output_nodes, " -> ", self.input_nodes, ", denoising cost: ", self._denoising_cost u = unlabeled(self.input_layer.activation_train) u = LadderLayer.batch_normalization( u, self.input_layer.mean_clean_unlabeled, self.input_layer.variance_clean_unlabeled) return self._g_gauss(unlabeled(self.input_layer.z_corrupted), u)
def unsupervised_cost_train(self): cost = tf.reduce_mean( tf.reduce_sum( tf.square(self.z_est_bn - unlabeled(self.input_layer.z_clean)), 1)) # TODO: input_nodes may change... return (cost / self.input_nodes) * self._denoising_cost
def unsupervised_cost_train(self): cost = tf.reduce_mean(tf.reduce_sum(tf.square(self.z_est_bn - unlabeled(self.input_layer.z_clean)), 1)) # TODO: input_nodes may change... return (cost / self.input_nodes) * self._denoising_cost
def z_est(self): print "Layer ", self.layer_number, ": ", self.output_nodes, " -> ", self.input_nodes, ", denoising cost: ", self._denoising_cost u = unlabeled(self.input_layer.activation_train) u = LadderLayer.batch_normalization(u, self.input_layer.mean_clean_unlabeled, self.input_layer.variance_clean_unlabeled) return self._g_gauss(unlabeled(self.input_layer.z_corrupted), u)