Update mobilenet_shallow.py

llvm/projects/keras/src/mobilenet_shallow.py

@@ -41,18 +41,18 @@ def get_mobilenet(alpha=1, depth_multiplier=1):
     model = Sequential()
     
     def _conv_block(filters, alpha, kernel=(3, 3), strides=(1, 1)):
-        channel_axis = 1 if K.image_data_format() == 'channels_first' else -1
+        channel_axis = 1
         filters = int(filters * alpha)
         model.add(Conv2D(filters, kernel,
                           padding='same',
                           use_bias=False,
                           strides=strides, 
-                        input_shape=(32, 32, 3)))
+                        input_shape=(3, 32, 32)))
         model.add(BatchNormalization(axis=channel_axis))
-        model.add(ReLU())
+        model.add(Activation('relu'))
     
     def _depthwise_conv_block(pointwise_conv_filters, alpha, depth_multiplier=1, strides=(1, 1)):
-        channel_axis = 1 if K.image_data_format() == 'channels_first' else -1
+        channel_axis = 1
         pointwise_conv_filters = int(pointwise_conv_filters * alpha)
 
         model.add(DepthwiseConv2D((3, 3),
@@ -61,13 +61,13 @@ def get_mobilenet(alpha=1, depth_multiplier=1):
                                    strides=strides,
                                    use_bias=False))    
         model.add(BatchNormalization(axis=channel_axis))
-        model.add(ReLU())
+        model.add(Activation('relu'))
         model.add(Conv2D(pointwise_conv_filters, (1, 1),
                           padding='same',
                           use_bias=False,
                           strides=(1, 1)))
         model.add(BatchNormalization(axis=channel_axis))
-        model.add(ReLU())
+        model.add(Activation('relu'))
         
 
     _conv_block(32, alpha, strides=(1, 1))