Update min_size as the minimum size to make the bottleneck size 1x1 (…

…#22)
keras-team · Jul 28, 2018 · 8ad3241 · 8ad3241
1 parent c457429
commit 8ad3241
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Showing 6 changed files with 7 additions and 8 deletions.
diff --git a/keras_applications/densenet.py b/keras_applications/densenet.py
@@ -184,7 +184,7 @@ def DenseNet(blocks,
     # Determine proper input shape
     input_shape = _obtain_input_shape(input_shape,
                                       default_size=224,
-                                      min_size=221,
+                                      min_size=32,
                                       data_format=backend.image_data_format(),
                                       require_flatten=include_top,
                                       weights=weights)

diff --git a/keras_applications/inception_resnet_v2.py b/keras_applications/inception_resnet_v2.py
@@ -236,7 +236,7 @@ def InceptionResNetV2(include_top=True,
     input_shape = _obtain_input_shape(
         input_shape,
         default_size=299,
-        min_size=139,
+        min_size=75,
         data_format=backend.image_data_format(),
         require_flatten=False,
         weights=weights)

diff --git a/keras_applications/inception_v3.py b/keras_applications/inception_v3.py
@@ -145,7 +145,7 @@ def InceptionV3(include_top=True,
     input_shape = _obtain_input_shape(
         input_shape,
         default_size=299,
-        min_size=139,
+        min_size=75,
         data_format=backend.image_data_format(),
         require_flatten=False,
         weights=weights)

diff --git a/keras_applications/resnet50.py b/keras_applications/resnet50.py
@@ -202,7 +202,7 @@ def ResNet50(include_top=True,
     # Determine proper input shape
     input_shape = _obtain_input_shape(input_shape,
                                       default_size=224,
-                                      min_size=197,
+                                      min_size=32,
                                       data_format=backend.image_data_format(),
                                       require_flatten=include_top,
                                       weights=weights)
@@ -250,8 +250,7 @@ def ResNet50(include_top=True,
     x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')
 
     if include_top:
-        x = layers.AveragePooling2D((7, 7), name='avg_pool')(x)
-        x = layers.Flatten()(x)
+        x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
         x = layers.Dense(classes, activation='softmax', name='fc1000')(x)
     else:
         if pooling == 'avg':

diff --git a/keras_applications/vgg16.py b/keras_applications/vgg16.py
@@ -96,7 +96,7 @@ def VGG16(include_top=True,
     # Determine proper input shape
     input_shape = _obtain_input_shape(input_shape,
                                       default_size=224,
-                                      min_size=48,
+                                      min_size=32,
                                       data_format=backend.image_data_format(),
                                       require_flatten=include_top,
                                       weights=weights)

diff --git a/keras_applications/vgg19.py b/keras_applications/vgg19.py
@@ -96,7 +96,7 @@ def VGG19(include_top=True,
     # Determine proper input shape
     input_shape = _obtain_input_shape(input_shape,
                                       default_size=224,
-                                      min_size=48,
+                                      min_size=32,
                                       data_format=backend.image_data_format(),
                                       require_flatten=include_top,
                                       weights=weights)