From 17bd72d5f6bcae0951d6987f9acd490799223445 Mon Sep 17 00:00:00 2001 From: Harshwardhan Praveen Date: Wed, 22 Mar 2023 18:08:24 -0400 Subject: [PATCH] Bug fix related to tf.compact.v1.repeat (https://github.com/google/automl/issues/159) --- greenlearning/loss_function.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/greenlearning/loss_function.py b/greenlearning/loss_function.py index 1884687..2c84dec 100644 --- a/greenlearning/loss_function.py +++ b/greenlearning/loss_function.py @@ -37,8 +37,8 @@ def build(self): Lu = [] Lf = [] for i in range(d): - xG = tf.reshape(tf.repeat(tf.reshape(self.xU[:,i], (1, Nu)), Nf, 0), (Nu*Nf,1)) - yG = tf.reshape(tf.repeat(tf.reshape(self.xF[:,i], (Nf, 1)), Nu, 1), (Nu*Nf,1)) + xG = tf.reshape(tf.tile(tf.reshape(self.xU[:,i], (1, Nu)), [Nf, 1]), (Nu*Nf,1)) + yG = tf.reshape(tf.tile(tf.reshape(self.xF[:,i], (Nf, 1)), [1, Nu]), (Nu*Nf,1)) Lu.append(xG) Lf.append(yG) training_G = tf.concat(Lu+Lf, 1) @@ -75,7 +75,7 @@ def build(self): self.N_output = self.N[i].evaluate(self.xU) # Difference with u - loss_N = tf.repeat(self.N_output, tf.shape(self.u)[1], 1) + loss_N = tf.tile(self.N_output, [1, tf.shape(self.u)[1]]) relative_error = tf.divide(tf.reduce_sum(tf.multiply(self.weights_x, tf.square(self.u[:,:,i] - self.loss_i - loss_N)),0), \ tf.reduce_sum(tf.multiply(self.weights_x, tf.square(self.u[:,:,i])),0)) self.loss = self.loss + tf.reduce_mean(relative_error)