From 0fea3e8f79f838c45d5bd228af1782983d5fc69d Mon Sep 17 00:00:00 2001
From: Bright Chang <75425626@qq.com>
Date: Thu, 27 Dec 2018 17:12:44 +0800
Subject: [PATCH] changes are needed since tensorflow has changed!

mul->multiply
add logits and labels to sigmoid cross entropy function
---
 model.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/model.py b/model.py
index d59f46b..bebe445 100644
--- a/model.py
+++ b/model.py
@@ -119,13 +119,13 @@ def __init__(self, model_params, num_emoji, embeddings_array, use_embeddings=Tru
         v_col = tf.nn.dropout(v_col, (1 - model_params.dropout))
 
         # Calculate the predicted score, a.k.a. dot product (here)
-        self.score = tf.reduce_sum(tf.mul(v_row, v_col), 1)
+        self.score = tf.reduce_sum(tf.multiply(v_row, v_col), 1)
 
         # Probability of match
         self.prob = tf.sigmoid(self.score)
 
         # Calculate the cross-entropy loss
-        self.loss = tf.nn.sigmoid_cross_entropy_with_logits(self.score, self.y)
+        self.loss = tf.nn.sigmoid_cross_entropy_with_logits(logits=self.score, labels=self.y)
 
     # train the model using the appropriate parameters
     def train(self, kb, hooks, session):