@@ -318,6 +318,16 @@ def my_dropout(x):
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scaled = tf .image .resize_nearest_neighbor (scaled , size = (9 , 12 ))
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save (inp , scaled , 'resize_nearest_neighbor' )
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################################################################################
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+ inp = tf .placeholder (tf .float32 , [2 , 10 , 11 , 3 ], 'input' )
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+ resized = tf .image .resize_nearest_neighbor (inp , size = (15 , 13 ), align_corners = True , name = 'resize_nearest_neighbor' ,
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+ half_pixel_centers = False )
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+ save (inp , resized , ('resize_nearest_neighbor' , 'align_corners' ), is_gen_data = False )
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+ ################################################################################
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+ inp = tf .placeholder (tf .float32 , [2 , 10 , 11 , 3 ], 'input' )
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+ resized = tf .image .resize_nearest_neighbor (inp , size = (15 , 13 ), align_corners = False , name = 'resize_nearest_neighbor' ,
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+ half_pixel_centers = True )
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+ save (inp , resized , ('resize_nearest_neighbor' , 'half_pixel' ), is_gen_data = False )
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+ ################################################################################
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inp = tf .placeholder (tf .float32 , [1 , 2 , 3 , 4 ], 'input' )
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bn = tf .layers .batch_normalization (inp , training = isTraining , fused = False , name = 'batch_norm' ,
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beta_initializer = tf .random_normal_initializer (),
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