add include_top==True to weights check when importing model

keras_squeezenet/squeezenet.py

@@ -25,7 +25,7 @@ def fire_module(x, fire_id, squeeze=16, expand=64):
         channel_axis = 1
     else:
         channel_axis = 3
-    
+
     x = Convolution2D(squeeze, (1, 1), padding='valid', name=s_id + sq1x1)(x)
     x = Activation('relu', name=s_id + relu + sq1x1)(x)
 
@@ -47,13 +47,13 @@ def SqueezeNet(include_top=True, weights='imagenet',
                classes=1000):
     """Instantiates the SqueezeNet architecture.
     """
-        
+
     if weights not in {'imagenet', None}:
         raise ValueError('The `weights` argument should be either '
                          '`None` (random initialization) or `imagenet` '
                          '(pre-training on ImageNet).')
 
-    if weights == 'imagenet' and classes != 1000:
+    if weights == 'imagenet' and classes != 1000 and include_top==True:
         raise ValueError('If using `weights` as imagenet with `include_top`'
                          ' as true, `classes` should be 1000')
 
@@ -89,11 +89,11 @@ def SqueezeNet(include_top=True, weights='imagenet',
     x = fire_module(x, fire_id=7, squeeze=48, expand=192)
     x = fire_module(x, fire_id=8, squeeze=64, expand=256)
     x = fire_module(x, fire_id=9, squeeze=64, expand=256)
-    
+
     if include_top:
-        # It's not obvious where to cut the network... 
+        # It's not obvious where to cut the network...
         # Could do the 8th or 9th layer... some work recommends cutting earlier layers.
-    
+
         x = Dropout(0.5, name='drop9')(x)
 
         x = Convolution2D(classes, (1, 1), padding='valid', name='conv10')(x)
@@ -129,7 +129,7 @@ def SqueezeNet(include_top=True, weights='imagenet',
             weights_path = get_file('squeezenet_weights_tf_dim_ordering_tf_kernels_notop.h5',
                                     WEIGHTS_PATH_NO_TOP,
                                     cache_subdir='models')
-            
+
         model.load_weights(weights_path)
         if K.backend() == 'theano':
             layer_utils.convert_all_kernels_in_model(model)