def test_multiplex_float(self):
a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0])
b = tf.constant([10.0, 20.0, 30.0, 40.0, 50.0])
cond = tf.constant([True, False, True, False, True], dtype=bool)
# expected result is [1.0, 20.0, 3.0, 40.0, 5.0]
expect = np.where(self.evaluate(cond), self.evaluate(a),
self.evaluate(b))
result = multiplex_1_op.multiplex(cond, a, b)
self.assertAllEqual(result, expect)
def test_multiplex_int(self):
a = tf.constant([1, 2, 3, 4, 5])
b = tf.constant([10, 20, 30, 40, 50])
cond = tf.constant([True, False, True, False, True], dtype=bool)
expect = np.where(self.evaluate(cond), self.evaluate(a),
self.evaluate(b))
# expected result is [1, 20, 3, 40, 5]
result = multiplex_1_op.multiplex(cond, a, b)
self.assertAllEqual(result, expect)
def test_multiplex_2d(self):
a = tf.constant([[1, 2, 3], [4, 5, 6]])
b = tf.constant([[10, 20, 30], [40, 50, 60]])
cond = tf.constant([[True, False, True], [False, True, False]],
dtype=bool)
expect = np.where(self.evaluate(cond), self.evaluate(a),
self.evaluate(b))
# expected result is [[1, 20], [3, 40]]
result = multiplex_1_op.multiplex(cond, a, b)
self.assertAllEqual(result, expect)
def test_multiplex_bad_shape(self):
a = tf.constant([[1, 2, 3], [4, 5, 6]]) # shape (2,3)
b = tf.constant([[10, 20], [30, 40], [50, 60]]) # shape (3,2)
cond = tf.constant([[True, False, True], [False, True, False]],
dtype=bool)
with self.assertRaisesRegex(
(errors_impl.InvalidArgumentError, ValueError),
# Eager mode raises InvalidArgumentError with the following message
r'(a_values and b_values must have the same shape.'
r' a_values shape: \[2,3\] b_values shape: \[3,2\]'
r')|('
# Graph mode raises ValueError with the following message
r'Dimension 0 in both shapes must be equal, '
r'but are 2 and 3\. Shapes are \[2,3\] and \[3,2\])\.'):
self.evaluate(multiplex_1_op.multiplex(cond, a, b))
def test_multiplex_bad_size(self):
a = tf.constant([1, 2, 3, 4, 5]) # longer than b
b = tf.constant([10, 20]) # shorter than a
cond = tf.constant([True, False, True, False, True], dtype=bool)
with self.assertRaisesRegex(
(errors_impl.InvalidArgumentError, ValueError),
# Eager mode raises InvalidArgumentError with the following message
r'(a_values and b_values must have the same shape. '
r'a_values shape: \[5\] b_values shape: \[2\](?s).* '
r'\[Op:Examples1>MultiplexDense\]'
r')|('
# Graph mode raises ValueError with the following message
r'Dimension 0 in both shapes must be equal, but are 5 and 2\. '
r'Shapes are \[5\] and \[2\]\.)'):
self.evaluate(multiplex_1_op.multiplex(cond, a, b))
def test_multiplex_bad_types(self):
a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0]) # float
b = tf.constant([10, 20, 30, 40, 50]) # int32
cond = tf.constant([True, False, True, False, True], dtype=bool)
with self.assertRaisesRegex(
(errors_impl.InvalidArgumentError, TypeError),
# Eager mode raises InvalidArgumentError with the following message
r'(cannot compute Examples1>MultiplexDense as input #2\(zero-based\) '
r'was expected to be a float tensor but is a int32 tensor '
r'\[Op:Examples1>MultiplexDense\]'
r')|('
# Graph mode raises TypeError with the following message
r"Input 'b_values' of 'Examples1>MultiplexDense' Op has type int32 that "
r"does not match type float32 of argument 'a_values'.)"):
self.evaluate(multiplex_1_op.multiplex(cond, a, b))