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_2_op.multiplex(cond, a, b)
self.assertAllEqual(result, expect)
def test_multiplex_int(self):
a = tf.constant([1, 2, 3, 4, 5], dtype=tf.int64)
b = tf.constant([10, 20, 30, 40, 50], dtype=tf.int64)
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_2_op.multiplex(cond, a, b)
self.assertAllEqual(result, expect)
def test_multiplex_2d(self):
a = tf.constant([[1, 2, 3], [4, 5, 6]], dtype=tf.int64)
b = tf.constant([[10, 20, 30], [40, 50, 60]], dtype=tf.int64)
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_2_op.multiplex(cond, a, b)
self.assertAllEqual(result, expect)
def test_sparse_op_different(self):
cond = tf.SparseTensor(indices=[[1], [3], [6]],
values=[True, False, True],
dense_shape=[7])
a = tf.SparseTensor(indices=[[1], [3], [5]],
values=['a0', 'a1', 'a2'],
dense_shape=[6])
b = tf.SparseTensor(indices=[[0], [2], [3], [6]],
values=['b0', 'b1', 'b2', 'b3'],
dense_shape=[7])
result = self.evaluate(multiplex_2_op.multiplex(cond, a, b))
self.assertAllEqual([7], result.dense_shape)
self.assertAllEqual([[0], [1], [2], [3]], result.indices)
self.assertAllEqual([b'b0', b'a0', b'b1', b'b2'], result.values)
def test_multiplex_bad_size(self):
a = tf.constant([1, 2, 3, 4, 5], dtype=tf.int64) # longer than b
b = tf.constant([10, 20], dtype=tf.int64) # 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 and b must have the same shape. '
r'a shape: \[5\] b shape: \[2\](?s).* '
r'\[Op:Examples>MultiplexDense\]'
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_2_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], dtype=tf.int64)
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 Examples>MultiplexDense as input #2\(zero-based\) '
r'was expected to be a float tensor but is a int64 tensor '
r'\[Op:Examples>MultiplexDense\]'
r')|('
# Graph mode raises TypeError with the following message
r"Input 'b' of 'Examples>MultiplexDense' Op has type int64 that "
r"does not match type float32 of argument 'a'.)"):
self.evaluate(multiplex_2_op.multiplex(cond, a, b))
def test_multiplex_bad_shape(self):
a = tf.constant([[1, 2, 3], [4, 5, 6]], dtype=tf.int64) # shape (2,3)
b = tf.constant([[10, 20], [30, 40], [50, 60]],
dtype=tf.int64) # 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 and b must have the same shape.'
r' a shape: \[2,3\] b 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_2_op.multiplex(cond, a, b))