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