--- a
+++ b/SegNet/SegNetCMR/evaluation.py
@@ -0,0 +1,21 @@
+import tensorflow as tf
+
+
+def loss_calc(logits, labels):
+    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels)
+    loss = tf.reduce_mean(cross_entropy)
+    tf.summary.scalar('loss', loss)
+    return loss
+
+
+def evaluation(logits, labels):
+    correct_prediction = tf.equal(tf.argmax(logits, 3) > 0, labels > 0)
+    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
+    tf.summary.scalar('accuracy', accuracy)
+    return accuracy
+
+def IoU_calc(logits, labels):
+    inter = tf.reduce_sum(tf.cast((tf.argmax(logits, 3) > 0) & (labels > 0), tf.float32), [1, 2])
+    union = tf.reduce_sum(tf.cast((tf.argmax(logits, 3) > 0) | (labels > 0), tf.float32), [1, 2])
+    IoU = tf.reduce_mean(tf.cast(inter/union, tf.float32))
+    return IoU
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