"""Tests for Softplus and SoftplusGrad."""
import tensorflow.python.platform

import numpy as np
import tensorflow as tf

from tensorflow.python.kernel_tests import gradient_checker as gc


class SoftplusTest(tf.test.TestCase):

  def _npSoftplus(self, np_features):
    return np.log(1 + np.exp(np_features))

  def _testSoftplus(self, np_features, use_gpu=False):
    np_softplus = self._npSoftplus(np_features)
    with self.test_session(use_gpu=use_gpu):
      softplus = tf.nn.softplus(np_features)
      tf_softplus = softplus.eval()
    self.assertAllClose(np_softplus, tf_softplus)
    self.assertShapeEqual(np_softplus, softplus)

  def testNumbers(self):
    for t in [np.float, np.double]:
      self._testSoftplus(
          np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
          use_gpu=False)
      self._testSoftplus(
          np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
          use_gpu=True)

  def testGradient(self):
    with self.test_session():
      x = tf.constant(
          [-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
          shape=[2, 5], name="x")
      y = tf.nn.softplus(x, name="softplus")
      x_init = np.asarray(
          [[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
          dtype=np.float32, order="F")
      err = gc.ComputeGradientError(x, [2, 5], y, [2, 5], x_init_value=x_init)
    print "softplus (float) gradient err = ", err
    self.assertLess(err, 1e-4)


if __name__ == "__main__":
  tf.test.main()