def graph_fn():
tf_x = tf.constant([0., 0.5, 1.])
tf_y = tf.constant([0.6, 0.7, 1.0])
new_x = tf.constant([0., 0.25, 0.5, 0.75, 1.])
tf_interpolate_outputs = calibration_builder._tf_linear_interp1d(
new_x, tf_x, tf_y)
return tf_interpolate_outputs
def _get_tf_interp1d(self, new_x, x, y):
"""Helper performing 1d linear interpolation using Tensorflow."""
with self.test_session() as sess:
tf_interp_outputs = calibration_builder._tf_linear_interp1d(
tf.convert_to_tensor(new_x, dtype=tf.float32),
tf.convert_to_tensor(x, dtype=tf.float32),
tf.convert_to_tensor(y, dtype=tf.float32))
np_tf_interp_outputs = sess.run(tf_interp_outputs)
return np_tf_interp_outputs
def test_tf_linear_interp1d_map(self):
"""Tests TF linear interpolation mapping to a single number."""
with self.test_session() as sess:
tf_x = tf.constant([0., 0.5, 1.])
tf_y = tf.constant([0.5, 0.5, 0.5])
new_x = tf.constant([0., 0.25, 0.5, 0.75, 1.])
tf_map_outputs = calibration_builder._tf_linear_interp1d(
new_x, tf_x, tf_y)
tf_map_outputs_np = sess.run([tf_map_outputs])
self.assertAllClose(tf_map_outputs_np, [[0.5, 0.5, 0.5, 0.5, 0.5]])
def graph_fn():
tf_interp_outputs = calibration_builder._tf_linear_interp1d(
tf.convert_to_tensor(new_x, dtype=tf.float32),
tf.convert_to_tensor(x, dtype=tf.float32),
tf.convert_to_tensor(y, dtype=tf.float32))
return tf_interp_outputs
