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2 Answers
JO295.00
3

Here is another implementations, which is more useful, when you've got images, not vectors

import tensorflow as tf

def IoU(y_pred, y_true):
    I = tf.reduce_sum(y_pred * y_true, axis=(1, 2))
    U = tf.reduce_sum(y_pred + y_true, axis=(1, 2)) - I
    return tf.reduce_mean(I / U)
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LP262.00
0

Great. Thank you. It works now.

JW326.00
4

If you've fimilar with true positive, false positive, true negative and false negatives, then here is a code, which will calculate all these. useing these values, you can find a lot of metrics like IoU, precision, recall, F2 score etc.

y_pred = tf.nn.sigmoid(_output)
y_pred_class = tf.to_float(y_pred > 0.5)

correct_prediction = tf.equal(y_pred_class, y_true)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

tp = tf.reduce_sum(y_pred_class * y_true)
fp = tf.reduce_sum(tf.nn.relu(y_pred - y_true))
fn = tf.reduce_sum(tf.nn.relu(y_true - y_pred))

iou = tp / (tp + fp + fn)
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LP262.00
0

This one works as well. Thank you, John.

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