Update kernel_initializer for ResNet50

Author: taehoonlee

keras_applications/resnet50.py

@@ -59,16 +59,21 @@ def identity_block(input_tensor, kernel_size, filters, stage, block):
     bn_name_base = 'bn' + str(stage) + block + '_branch'
 
     x = layers.Conv2D(filters1, (1, 1),
+                      kernel_initializer='he_normal',
                       name=conv_name_base + '2a')(input_tensor)
     x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
     x = layers.Activation('relu')(x)
 
     x = layers.Conv2D(filters2, kernel_size,
-                      padding='same', name=conv_name_base + '2b')(x)
+                      padding='same',
+                      kernel_initializer='he_normal',
+                      name=conv_name_base + '2b')(x)
     x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
     x = layers.Activation('relu')(x)
 
-    x = layers.Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)
+    x = layers.Conv2D(filters3, (1, 1),
+                      kernel_initializer='he_normal',
+                      name=conv_name_base + '2c')(x)
     x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
 
     x = layers.add([x, input_tensor])
@@ -109,19 +114,24 @@ def conv_block(input_tensor,
     bn_name_base = 'bn' + str(stage) + block + '_branch'
 
     x = layers.Conv2D(filters1, (1, 1), strides=strides,
+                      kernel_initializer='he_normal',
                       name=conv_name_base + '2a')(input_tensor)
     x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
     x = layers.Activation('relu')(x)
 
     x = layers.Conv2D(filters2, kernel_size, padding='same',
+                      kernel_initializer='he_normal',
                       name=conv_name_base + '2b')(x)
     x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
     x = layers.Activation('relu')(x)
 
-    x = layers.Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)
+    x = layers.Conv2D(filters3, (1, 1),
+                      kernel_initializer='he_normal',
+                      name=conv_name_base + '2c')(x)
     x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
 
     shortcut = layers.Conv2D(filters3, (1, 1), strides=strides,
+                             kernel_initializer='he_normal',
                              name=conv_name_base + '1')(input_tensor)
     shortcut = layers.BatchNormalization(
         axis=bn_axis, name=bn_name_base + '1')(shortcut)
@@ -214,6 +224,7 @@ def ResNet50(include_top=True,
     x = layers.Conv2D(64, (7, 7),
                       strides=(2, 2),
                       padding='valid',
+                      kernel_initializer='he_normal',
                       name='conv1')(x)
     x = layers.BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
     x = layers.Activation('relu')(x)