why :C

miquelmarti · May 23, 2016 · f1be5a6 · f1be5a6
1 parent 5b431db
commit f1be5a6
Showing 1 changed file with 14 additions and 7 deletions.
diff --git a/tiny_imagenet_classifier.py b/tiny_imagenet_classifier.py
@@ -173,29 +173,36 @@
     model.add(Activation('relu'))
     model.add(Convolution2D(96, 3, 3, border_mode='same'))
     model.add(Activation('relu'))
-
-    model.add(Convolution2D(96, 3, 3, border_mode='same', subsample=(2,2)))
+    model.add(Convolution2D(96, 3, 3, border_mode='same'))
     model.add(Activation('relu'))
+    model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2)))
+
+    # model.add(Convolution2D(96, 3, 3, border_mode='same', subsample=(2,2)))
+    # model.add(Activation('relu'))
 
     model.add(Convolution2D(192, 3, 3, border_mode='same'))
     model.add(Activation('relu'))
     model.add(Convolution2D(192, 3, 3, border_mode='same'))
     model.add(Activation('relu'))
-
-    model.add(Convolution2D(192, 3, 3, border_mode='same', subsample=(2,2)))
+    model.add(Convolution2D(192, 3, 3, border_mode='same'))
     model.add(Activation('relu'))
 
+    model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2)))
+
+
+    # model.add(Convolution2D(192, 3, 3, border_mode='same', subsample=(2,2)))
+    # model.add(Activation('relu'))
+
     model.add(Convolution2D(192, 3, 3, border_mode='same'))
     model.add(Activation('relu'))
     model.add(Convolution2D(192, 1, 1, border_mode='same'))
     model.add(Activation('relu'))
     model.add(Convolution2D(10, 1, 1, border_mode='same'))
     model.add(Activation('relu'))
     AveragePooling2D(pool_size=(6, 6), strides=None, border_mode='same', dim_ordering='th')
-    # model.add(Activation('relu'))
     model.add(Flatten())
-    # model.add(Dense(512))
-    # model.add(Activation('relu'))
+    model.add(Dense(512))
+    model.add(Activation('relu'))
     model.add(Dense(10))#pretrained weights assume only 100 outputs, we need to train this layer from scratch
 
     # model.add(AveragePooling2D(pool_size=(6, 6), strides=None, border_mode='valid', dim_ordering='th'))