def test_mcm_3_classes(dtype):
actuals = tf.constant([[1, 0, 1], [0, 1, 0], [1, 0, 1], [0, 1, 0]], dtype=dtype)
preds = tf.constant([[1, 0, 0], [0, 1, 1], [1, 0, 0], [0, 1, 1]], dtype=dtype)
# Initialize
mcm_obj = MultiLabelConfusionMatrix(num_classes=3, dtype=dtype)
mcm_obj.update_state(actuals, preds)
# Check results
check_results(mcm_obj, [[[2, 0], [0, 2]], [[2, 0], [0, 2]], [[0, 2], [2, 0]]])
def test_config():
mcm_obj = MultiLabelConfusionMatrix(num_classes=3)
assert mcm_obj.num_classes == 3
assert mcm_obj.dtype == tf.float32
# Check save and restore config
mcm_obj2 = MultiLabelConfusionMatrix.from_config(mcm_obj.get_config())
assert mcm_obj2.num_classes == 3
assert mcm_obj2.dtype == tf.float32
def test_config(self):
mcm_obj = MultiLabelConfusionMatrix(num_classes=3)
self.assertEqual(mcm_obj.num_classes, 3)
self.assertEqual(mcm_obj.dtype, tf.int32)
# Check save and restore config
mcm_obj2 = MultiLabelConfusionMatrix.from_config(mcm_obj.get_config())
self.assertEqual(mcm_obj2.num_classes, 3)
self.assertEqual(mcm_obj2.dtype, tf.int32)
def test_multiclass(dtype):
actuals = tf.constant(
[
[1, 0, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
[0, 1, 0, 0],
[0, 1, 0, 0],
[1, 0, 0, 0],
[0, 0, 1, 0],
[1, 0, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
],
dtype=dtype,
)
preds = tf.constant(
[
[1, 0, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
[1, 0, 0, 0],
[1, 0, 0, 0],
[1, 0, 0, 0],
[0, 0, 1, 0],
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 0, 1],
],
dtype=dtype,
)
# Initialize
mcm_obj = MultiLabelConfusionMatrix(num_classes=4, dtype=dtype)
mcm_obj.update_state(actuals, preds)
# Check results
check_results(
mcm_obj,
[
[[5, 2], [0, 3]],
[[7, 1], [2, 0]],
[[7, 0], [1, 2]],
[[8, 0], [0, 2]],
],
)
def initialize_vars(self, n_classes, input_dtype):
mcm_obj = MultiLabelConfusionMatrix(num_classes=n_classes,
dtype=input_dtype)
self.evaluate(tf.compat.v1.variables_initializer(mcm_obj.variables))
return mcm_obj