sum -> add

bnaul · Feb 28, 2017 · fbe7873 · fbe7873
1 parent 8d34c7e
commit fbe7873
Show file tree

Hide file tree

Showing 4 changed files with 9 additions and 9 deletions.
diff --git a/examples/babi_rnn.py b/examples/babi_rnn.py
@@ -192,7 +192,7 @@ def vectorize_stories(data, word_idx, story_maxlen, query_maxlen):
 encoded_question = RNN(EMBED_HIDDEN_SIZE)(encoded_question)
 encoded_question = layers.RepeatVector(story_maxlen)(encoded_question)
 
-merged = layers.sum([encoded_sentence, encoded_question])
+merged = layers.add([encoded_sentence, encoded_question])
 merged = RNN(EMBED_HIDDEN_SIZE)(merged)
 merged = layers.Dropout(0.3)(merged)
 preds = layers.Dense(vocab_size, activation='softmax')(merged)

diff --git a/keras/applications/resnet50.py b/keras/applications/resnet50.py
@@ -71,7 +71,7 @@ def identity_block(input_tensor, kernel_size, filters, stage, block):
     x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)
     x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
 
-    x = layers.sum([x, input_tensor])
+    x = layers.add([x, input_tensor])
     x = Activation('relu')(x)
     return x
 
@@ -117,7 +117,7 @@ def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2))
                       name=conv_name_base + '1')(input_tensor)
     shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut)
 
-    x = layers.sum([x, shortcut])
+    x = layers.add([x, shortcut])
     x = Activation('relu')(x)
     return x
 

diff --git a/keras/applications/xception.py b/keras/applications/xception.py
@@ -156,7 +156,7 @@ def Xception(include_top=True, weights='imagenet',
     x = BatchNormalization(name='block2_sepconv2_bn')(x)
 
     x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block2_pool')(x)
-    x = layers.sum([x, residual])
+    x = layers.add([x, residual])
 
     residual = Conv2D(256, (1, 1), strides=(2, 2),
                       padding='same', use_bias=False)(x)
@@ -170,7 +170,7 @@ def Xception(include_top=True, weights='imagenet',
     x = BatchNormalization(name='block3_sepconv2_bn')(x)
 
     x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block3_pool')(x)
-    x = layers.sum([x, residual])
+    x = layers.add([x, residual])
 
     residual = Conv2D(728, (1, 1), strides=(2, 2),
                       padding='same', use_bias=False)(x)
@@ -184,7 +184,7 @@ def Xception(include_top=True, weights='imagenet',
     x = BatchNormalization(name='block4_sepconv2_bn')(x)
 
     x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block4_pool')(x)
-    x = layers.sum([x, residual])
+    x = layers.add([x, residual])
 
     for i in range(8):
         residual = x
@@ -200,7 +200,7 @@ def Xception(include_top=True, weights='imagenet',
         x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv3')(x)
         x = BatchNormalization(name=prefix + '_sepconv3_bn')(x)
 
-        x = layers.sum([x, residual])
+        x = layers.add([x, residual])
 
     residual = Conv2D(1024, (1, 1), strides=(2, 2),
                       padding='same', use_bias=False)(x)
@@ -214,7 +214,7 @@ def Xception(include_top=True, weights='imagenet',
     x = BatchNormalization(name='block13_sepconv2_bn')(x)
 
     x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block13_pool')(x)
-    x = layers.sum([x, residual])
+    x = layers.add([x, residual])
 
     x = SeparableConv2D(1536, (3, 3), padding='same', use_bias=False, name='block14_sepconv1')(x)
     x = BatchNormalization(name='block14_sepconv1_bn')(x)

diff --git a/tests/keras/engine/test_topology.py b/tests/keras/engine/test_topology.py
@@ -479,7 +479,7 @@ def test_recursion():
 
         # test merge
         layers.concatenate([j_tf, k_tf], axis=1)
-        layers.sum([j_tf, k_tf])
+        layers.add([j_tf, k_tf])
 
         # test tensor input
         x = tf.placeholder(shape=(None, 2), dtype=K.floatx())