commit #40: change weight int -> he normal for general

Author: BIGBALLON

2_Network_in_Network/Network_in_Network_bn_keras.py

@@ -15,6 +15,7 @@
 iterations    = 391
 num_classes   = 10
 dropout       = 0.5
+weight_decay  = 0.0001
 log_filepath  = './nin_bn'
 
 def color_preprocessing(x_train,x_test):
@@ -40,39 +41,39 @@ def scheduler(epoch):
 def build_model():
   model = Sequential()
 
-  model.add(Conv2D(192, (5, 5), padding='same', kernel_regularizer=keras.regularizers.l2(0.0001), kernel_initializer=RandomNormal(stddev = 0.01), input_shape=x_train.shape[1:]))
+  model.add(Conv2D(192, (5, 5), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal", input_shape=x_train.shape[1:]))
   model.add(BatchNormalization())
   model.add(Activation('relu'))
-  model.add(Conv2D(160, (1, 1), padding='same', kernel_regularizer=keras.regularizers.l2(0.0001), kernel_initializer=RandomNormal(stddev = 0.05)))
+  model.add(Conv2D(160, (1, 1), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
   model.add(BatchNormalization())
   model.add(Activation('relu'))
-  model.add(Conv2D(96, (1, 1), padding='same', kernel_regularizer=keras.regularizers.l2(0.0001), kernel_initializer=RandomNormal(stddev = 0.05)))
+  model.add(Conv2D(96, (1, 1), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
   model.add(BatchNormalization())
   model.add(Activation('relu'))
   model.add(MaxPooling2D(pool_size=(3, 3),strides=(2,2),padding = 'same'))
 
   model.add(Dropout(dropout))
 
-  model.add(Conv2D(192, (5, 5), padding='same', kernel_regularizer=keras.regularizers.l2(0.0001), kernel_initializer=RandomNormal(stddev = 0.05)))
+  model.add(Conv2D(192, (5, 5), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
   model.add(BatchNormalization())
   model.add(Activation('relu'))
-  model.add(Conv2D(192, (1, 1),padding='same', kernel_regularizer=keras.regularizers.l2(0.0001), kernel_initializer=RandomNormal(stddev = 0.05)))
+  model.add(Conv2D(192, (1, 1),padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
   model.add(BatchNormalization())
   model.add(Activation('relu'))
-  model.add(Conv2D(192, (1, 1),padding='same', kernel_regularizer=keras.regularizers.l2(0.0001), kernel_initializer=RandomNormal(stddev = 0.05)))
+  model.add(Conv2D(192, (1, 1),padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
   model.add(BatchNormalization())
   model.add(Activation('relu'))
   model.add(MaxPooling2D(pool_size=(3, 3),strides=(2,2),padding = 'same'))
 
   model.add(Dropout(dropout))
 
-  model.add(Conv2D(192, (3, 3), padding='same', kernel_regularizer=keras.regularizers.l2(0.0001), kernel_initializer=RandomNormal(stddev = 0.05)))
+  model.add(Conv2D(192, (3, 3), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
   model.add(BatchNormalization())
   model.add(Activation('relu'))
-  model.add(Conv2D(192, (1, 1), padding='same', kernel_regularizer=keras.regularizers.l2(0.0001), kernel_initializer=RandomNormal(stddev = 0.05)))
+  model.add(Conv2D(192, (1, 1), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
   model.add(BatchNormalization())
   model.add(Activation('relu'))
-  model.add(Conv2D(10, (1, 1), padding='same', kernel_regularizer=keras.regularizers.l2(0.0001), kernel_initializer=RandomNormal(stddev = 0.05)))
+  model.add(Conv2D(10, (1, 1), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
   model.add(BatchNormalization())
   model.add(Activation('relu'))
 

2_Network_in_Network/Network_in_Network_keras.py

@@ -15,6 +15,7 @@
 iterations    = 391
 num_classes   = 10
 dropout       = 0.5
+weight_decay  = 0.0001
 log_filepath  = './nin'
 
 def color_preprocessing(x_train,x_test):
@@ -29,42 +30,40 @@ def color_preprocessing(x_train,x_test):
     return x_train, x_test
 
 def scheduler(epoch):
-    if epoch <= 60:
-        return 0.05
-    if epoch <= 120:
+    if epoch <= 80:
         return 0.01
-    if epoch <= 160:    
-        return 0.002
-    return 0.0004
+    if epoch <= 140:
+        return 0.005
+    return 0.001
 
 def build_model():
   model = Sequential()
 
-  model.add(Conv2D(192, (5, 5), padding='same', kernel_regularizer=keras.regularizers.l2(0.0001), kernel_initializer=RandomNormal(stddev = 0.01), input_shape=x_train.shape[1:]))
+  model.add(Conv2D(192, (5, 5), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal", input_shape=x_train.shape[1:]))
   model.add(Activation('relu'))
-  model.add(Conv2D(160, (1, 1), padding='same', kernel_regularizer=keras.regularizers.l2(0.0001), kernel_initializer=RandomNormal(stddev = 0.05)))
+  model.add(Conv2D(160, (1, 1), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
   model.add(Activation('relu'))
-  model.add(Conv2D(96, (1, 1), padding='same', kernel_regularizer=keras.regularizers.l2(0.0001), kernel_initializer=RandomNormal(stddev = 0.05)))
+  model.add(Conv2D(96, (1, 1), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
   model.add(Activation('relu'))
   model.add(MaxPooling2D(pool_size=(3, 3),strides=(2,2),padding = 'same'))
 
   model.add(Dropout(dropout))
 
-  model.add(Conv2D(192, (5, 5), padding='same', kernel_regularizer=keras.regularizers.l2(0.0001), kernel_initializer=RandomNormal(stddev = 0.05)))
+  model.add(Conv2D(192, (5, 5), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
   model.add(Activation('relu'))
-  model.add(Conv2D(192, (1, 1),padding='same', kernel_regularizer=keras.regularizers.l2(0.0001), kernel_initializer=RandomNormal(stddev = 0.05)))
+  model.add(Conv2D(192, (1, 1),padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
   model.add(Activation('relu'))
-  model.add(Conv2D(192, (1, 1),padding='same', kernel_regularizer=keras.regularizers.l2(0.0001), kernel_initializer=RandomNormal(stddev = 0.05)))
+  model.add(Conv2D(192, (1, 1),padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
   model.add(Activation('relu'))
   model.add(MaxPooling2D(pool_size=(3, 3),strides=(2,2),padding = 'same'))
 
   model.add(Dropout(dropout))
 
-  model.add(Conv2D(192, (3, 3), padding='same', kernel_regularizer=keras.regularizers.l2(0.0001), kernel_initializer=RandomNormal(stddev = 0.05)))
+  model.add(Conv2D(192, (3, 3), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
   model.add(Activation('relu'))
-  model.add(Conv2D(192, (1, 1), padding='same', kernel_regularizer=keras.regularizers.l2(0.0001), kernel_initializer=RandomNormal(stddev = 0.05)))
+  model.add(Conv2D(192, (1, 1), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
   model.add(Activation('relu'))
-  model.add(Conv2D(10, (1, 1), padding='same', kernel_regularizer=keras.regularizers.l2(0.0001), kernel_initializer=RandomNormal(stddev = 0.05)))
+  model.add(Conv2D(10, (1, 1), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
   model.add(Activation('relu'))
 
   model.add(GlobalAveragePooling2D())