def test_reduce_batch_minus_min_and_max_per_key(self, placeholder_fn,
value, expected_result):
x = placeholder_fn()
key = tf.constant(['a', 'a', 'a', 'b'])
batch_keys, batch_minus_min, batch_max = (
tf_utils.reduce_batch_minus_min_and_max_per_key(x, key))
with tf.compat.v1.Session() as sess:
result = sess.run([batch_keys, batch_minus_min, batch_max],
feed_dict={x: value})
self.assertAllEqual(result[0], expected_result[0])
self.assertAllEqual(result[1:], expected_result[1:])
def test_sparse_indices(self):
exception_cls = tf.errors.InvalidArgumentError
error_string = 'Condition x == y did not hold element-wise:'
value = tf.compat.v1.SparseTensorValue(
indices=[[0, 0], [1, 1], [2, 2], [3, 1]],
values=[3, 2, -1, 3],
dense_shape=[4, 5])
key_value = tf.compat.v1.SparseTensorValue(
indices=[[0, 0], [1, 2], [2, 2], [3, 1]],
values=['a', 'a', 'a', 'b'],
dense_shape=[4, 5])
with tf.compat.v1.Graph().as_default():
x = tf.compat.v1.sparse_placeholder(tf.int64, shape=[None, None])
key = tf.compat.v1.sparse_placeholder(tf.string, shape=[None, None])
with tf.compat.v1.Session() as sess:
with self.assertRaisesRegexp(exception_cls, error_string):
sess.run(tf_utils.reduce_batch_minus_min_and_max_per_key(x, key),
feed_dict={x: value, key: key_value})
def _reduce_batch_minus_min_and_max_per_key(x, key):
return tf_utils.reduce_batch_minus_min_and_max_per_key(x, key)
