add mobilenet

get_logits_from_xception.ipynb

@@ -122,7 +122,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "399it [03:53,  1.70it/s]"
+      "399it [04:04,  1.63it/s]"
      ]
     }
    ],
@@ -155,16 +155,14 @@
      "text": [
       "\n",
       "0it [00:00, ?it/s]\u001b[A\n",
-      "1it [00:00,  1.71it/s]\u001b[A\n",
-      "2it [00:01,  1.71it/s]\u001b[A\n",
-      "3it [00:01,  1.71it/s]\u001b[A\n",
-      "4it [00:02,  1.71it/s]\u001b[A\n",
-      "5it [00:02,  1.71it/s]\u001b[A\n",
-      "6it [00:03,  1.71it/s]\u001b[A\n",
-      "7it [00:04,  1.71it/s]\u001b[A\n",
-      "8it [00:04,  1.71it/s]\u001b[A\n",
-      "9it [00:05,  1.71it/s]\u001b[A\n",
-      "79it [00:46,  1.70it/s]"
+      "1it [00:00,  1.63it/s]\u001b[A\n",
+      "2it [00:01,  1.63it/s]\u001b[A\n",
+      "3it [00:01,  1.63it/s]\u001b[A\n",
+      "4it [00:02,  1.63it/s]\u001b[A\n",
+      "5it [00:03,  1.63it/s]\u001b[A\n",
+      "6it [00:03,  1.63it/s]\u001b[A\n",
+      "7it [00:04,  1.63it/s]\u001b[A\n",
+      "79it [00:48,  1.63it/s]"
      ]
     }
    ],

mobilenet.py

@@ -0,0 +1,23 @@
+import keras
+from keras.applications.mobilenet import MobileNet
+from keras.models import Model
+from keras.layers import Activation, GlobalAveragePooling2D, Dropout, Dense, Input
+
+
+def get_mobilenet(input_size, alpha, weight_decay, dropout):
+    input_shape = (input_size, input_size, 3)
+    base_model = MobileNet(
+        include_top=False, weights='imagenet', 
+        input_shape=input_shape, alpha=alpha
+    )
+    x = base_model.output
+    x = GlobalAveragePooling2D()(x)
+    x = Dropout(dropout)(x)
+    logits = Dense(256, kernel_regularizer=keras.regularizers.l2(weight_decay))(x)
+    probabilities = Activation('softmax')(logits)
+    model = Model(base_model.input, probabilities)
+
+    for layer in model.layers[:-2]:
+        layer.trainable = False
+
+    return model

squeezenet.py

@@ -76,7 +76,7 @@ def SqueezeNet(weight_decay, image_size=224):
     x = fire_module('6', x, squeeze=48, expand=192) # 13, 13, 384
     x = fire_module('7', x, squeeze=48, expand=192) # 13, 13, 384
     x = fire_module('8', x, squeeze=64, expand=256) # 13, 13, 512
-    x = fire_module('9', x, 64, 256, weight_decay, trainable=False) # 13, 13, 512
+    x = fire_module('9', x, squeeze=64, expand=256) # 13, 13, 512
 
     x = Dropout(0.5)(x)
     x = Convolution2D(