```
def yolo_filter_boxes(boxes, box_confidence, box_class_probs, threshold = .6):
mentor edit: code removed
```

## error

TypeError Traceback (most recent call last)

in

4 boxes = tf.random.normal([19, 19, 5, 4], mean=1, stddev=4, seed = 1)

5 box_class_probs = tf.random.normal([19, 19, 5, 80], mean=1, stddev=4, seed = 1)

----> 6 scores, boxes, classes = yolo_filter_boxes(boxes, box_confidence, box_class_probs, threshold = 0.5)

7 print("scores[2] = " + str(scores[2].numpy()))

8 print("boxes[2] = " + str(boxes[2].numpy()))

in yolo_filter_boxes(boxes, box_confidence, box_class_probs, threshold)

36 # same dimension as box_class_scores, and be True for the boxes you want to keep (with probability >= threshold)

37 ## (≈ 1 line)

—> 38 filtering_mask =(box_class_scores >= threshold)

39

40 # Step 4: Apply the mask to box_class_scores, boxes and box_classes

/opt/conda/lib/python3.7/site-packages/tensorflow/python/ops/gen_math_ops.py in greater_equal(x, y, name)

4053 _result = pywrap_tfe.TFE_Py_FastPathExecute(

4054 _ctx._context_handle, tld.device_name, “GreaterEqual”, name,

→ 4055 tld.op_callbacks, x, y)

4056 return _result

4057 except _core._NotOkStatusException as e:

TypeError: Cannot convert 0.5 to EagerTensor of dtype int64