I have a network for semantic segmentation and the last layer of my model applies a sigmoid activation, so all predictions are scaled between 0-1. There is this validation metric tf.keras.metrics.MeanIoU(num_classes), which compares classified predictions (0 or 1) with validation (0 or 1). So if i make a prediction and apply this metric, will it automatically map the continuous predictions to binary with threshold = 0.5? Are there any possibilities to manually define the threshold?
tf.keras.metrics.MeanIoU with sigmoid layer
3.4k Views Asked by WillemBoone At
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There are 2 best solutions below
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Try this(remember to replace the space with tab):
def mean_iou(y_true, y_pred):
th = 0.5
y_pred_ = tf.to_int32(y_pred > th)
score, up_opt = tf.metrics.mean_iou(y_true, y_pred_, 2)
K.get_session().run(tf.local_variables_initializer())
with tf.control_dependencies([up_opt]):
score = tf.identity(score)
return score
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No,
tf.keras.metrics.MeanIoU
will not automatically map the continuous predictions to binary with threshold = 0.5.It will convert the continuous predictions to its binary, by taking the binary digit before decimal point as predictions like
0.99
as0
,0.50
as0
,0.01
as0
,1.99
as1
,1.01
as1
etc whennum_classes=2
. So basically if your predicted values are between0
to1
andnum_classes=2
, then everything is considered0
unless the prediction is1
.Below are the experiments to justify the behavior in
tensorflow version 2.2.0
:All binary result :
Output -
Change one prediction to continuous 0.99 - Here it considers
0.99
as0
.Output -
Change one prediction to continuous 0.01 - Here it considers
0.01
as0
.Output -
Change one prediction to continuous 1.99 - Here it considers
1.99
as1
.Output -
So ideal way is to define a function to convert the continuous to binary before evaluating the
MeanIoU
.Hope this answers your question. Happy Learning.