def testWordCount(self):
with tf.compat.v1.Graph().as_default():
string_tensor = tf.constant(['abc', 'def', 'fghijklm', 'z', ''])
tokenized_tensor = tf.compat.v1.string_split(string_tensor, delimiter='')
output_tensor = mappers.word_count(tokenized_tensor)
output_3d_tensor = mappers.word_count(
tf.sparse.expand_dims(
tf.sparse.expand_dims(tokenized_tensor, axis=1), axis=1))
with tf.compat.v1.Session():
output = output_tensor.eval()
self.assertEqual(5, len(output))
self.assertEqual(15, sum(output))
self.assertAllEqual(output, [3, 3, 8, 1, 0])
self.assertAllEqual(output, output_3d_tensor.eval())
def testWordCountEmpty(self):
output_tensor = mappers.word_count(
tf.compat.v1.string_split(tf.constant([''])))
with tf.compat.v1.Session():
output = output_tensor.eval()
self.assertEqual(1, len(output))
self.assertEqual(0, sum(output))
def testWordCount(self):
string_tensor = tf.constant(['abc', 'def', 'fghijklm', 'z', ''])
tokenized_tensor = tf.compat.v1.string_split(string_tensor, delimiter='')
output_tensor = mappers.word_count(tokenized_tensor)
with tf.compat.v1.Session():
output = output_tensor.eval()
self.assertEqual(5, len(output))
self.assertEqual(15, sum(output))
self.assertAllEqual(output, [3, 3, 8, 1, 0])