def test_reduce_batch_count_mean_and_var_per_key_elementwise(self):
x = tf.constant([[1, 2], [3, 4], [1, 2]], dtype=tf.float32)
key = tf.constant(['a', 'a', 'b'], dtype=tf.string)
key_vocab, count, mean, var = (
tf_utils.reduce_batch_count_mean_and_var_per_key(x, key, False))
with tf.compat.v1.Session():
self.assertAllEqual(key_vocab.eval(), tf.constant(['a', 'b']))
self.assertAllEqual(count.eval(), [[2., 2.], [1., 1.]])
self.assertAllEqual(mean.eval(), [[2., 3.], [1., 2.]])
self.assertAllEqual(var.eval(), [[1., 1.], [0., 0.]])
def test_reduce_batch_count_mean_and_var_per_key(self):
x = tf.constant([[1], [2], [3], [4], [4]], dtype=tf.float32)
key = tf.constant(['a', 'a', 'a', 'b', 'a'], dtype=tf.string)
key_vocab, count, mean, var = (
tf_utils.reduce_batch_count_mean_and_var_per_key(x, key, True))
with tf.compat.v1.Session():
self.assertAllEqual(key_vocab.eval(), tf.constant(['a', 'b']))
self.assertAllEqual(count.eval(), tf.constant([4, 1]))
self.assertAllEqual(mean.eval(), tf.constant([2.5, 4]))
self.assertAllEqual(var.eval(), tf.constant([1.25, 0]))
def test_reduce_batch_count_mean_and_var_per_key_sparse(self):
x = tf.SparseTensor(indices=[[0, 0], [0, 2], [1, 1], [1, 2], [2, 3]],
values=[1., 2., 3., 4., 4.],
dense_shape=[3, 4])
key = tf.SparseTensor(indices=[[0, 0], [0, 2], [1, 1], [1, 2], [2, 3]],
values=['a', 'a', 'a', 'a', 'b'],
dense_shape=[3, 4])
key_vocab, count, mean, var = (
tf_utils.reduce_batch_count_mean_and_var_per_key(x, key, True))
with tf.compat.v1.Session():
self.assertAllEqual(key_vocab.eval(), tf.constant(['a', 'b']))
self.assertAllEqual(count.eval(), tf.constant([4, 1]))
self.assertAllEqual(mean.eval(), tf.constant([2.5, 4]))
self.assertAllEqual(var.eval(), tf.constant([1.25, 0]))
def _reduce_batch_count_mean_and_var_per_key(x, key):
return tf_utils.reduce_batch_count_mean_and_var_per_key(
x, key, reduce_instance_dims=reduce_instance_dims)