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updated sub to subtract so code run under tf=14+ #12

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updated sub to subtract so code run under tf=14+ #12

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6 changes: 3 additions & 3 deletions NeuralFM.py
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Original file line number Diff line number Diff line change
Expand Up @@ -118,7 +118,7 @@ def _init_graph(self):
self.squared_sum_features_emb = tf.reduce_sum(self.squared_features_emb, 1) # None * K

# ________ FM __________
self.FM = 0.5 * tf.sub(self.summed_features_emb_square, self.squared_sum_features_emb) # None * K
self.FM = 0.5 * tf.subtract(self.summed_features_emb_square, self.squared_sum_features_emb) # None * K
if self.batch_norm:
self.FM = self.batch_norm_layer(self.FM, train_phase=self.train_phase, scope_bn='bn_fm')
self.FM = tf.nn.dropout(self.FM, self.dropout_keep[-1]) # dropout at the bilinear interactin layer
Expand All @@ -141,9 +141,9 @@ def _init_graph(self):
# Compute the loss.
if self.loss_type == 'square_loss':
if self.lamda_bilinear > 0:
self.loss = tf.nn.l2_loss(tf.sub(self.train_labels, self.out)) + tf.contrib.layers.l2_regularizer(self.lamda_bilinear)(self.weights['feature_embeddings']) # regulizer
self.loss = tf.nn.l2_loss(tf.subtract(self.train_labels, self.out)) + tf.contrib.layers.l2_regularizer(self.lamda_bilinear)(self.weights['feature_embeddings']) # regulizer
else:
self.loss = tf.nn.l2_loss(tf.sub(self.train_labels, self.out))
self.loss = tf.nn.l2_loss(tf.subtract(self.train_labels, self.out))
elif self.loss_type == 'log_loss':
self.out = tf.sigmoid(self.out)
if self.lambda_bilinear > 0:
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