# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for Softplus and SoftplusGrad."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np

from tensorflow.python.framework import constant_op
from tensorflow.python.ops import gradient_checker
from tensorflow.python.ops import gradients_impl
from tensorflow.python.ops import nn_ops
import tensorflow.python.ops.nn_grad  # pylint: disable=unused-import
from tensorflow.python.platform import test


class SoftplusTest(test.TestCase):

  def _npSoftplus(self, np_features):
    np_features = np.asarray(np_features)
    zero = np.asarray(0).astype(np_features.dtype)
    return np.logaddexp(zero, 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 = nn_ops.softplus(np_features)
      tf_softplus = softplus.eval()
    self.assertAllCloseAccordingToType(np_softplus, tf_softplus)
    self.assertTrue(np.all(tf_softplus > 0))
    self.assertShapeEqual(np_softplus, softplus)

  def testNumbers(self):
    for t in [np.float16, np.float32, np.float64]:
      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)
      log_eps = np.log(np.finfo(t).eps)
      one = t(1)
      ten = t(10)
      self._testSoftplus(
          [
              log_eps, log_eps - one, log_eps + one, log_eps - ten,
              log_eps + ten, -log_eps, -log_eps - one, -log_eps + one,
              -log_eps - ten, -log_eps + ten
          ],
          use_gpu=False)
      self._testSoftplus(
          [
              log_eps, log_eps - one, log_eps + one, log_eps - ten,
              log_eps + ten - log_eps, -log_eps - one, -log_eps + one,
              -log_eps - ten, -log_eps + ten
          ],
          use_gpu=True)

  def testGradient(self):
    with self.cached_session():
      x = constant_op.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 = nn_ops.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 = gradient_checker.compute_gradient_error(
          x, [2, 5], y, [2, 5], x_init_value=x_init)
    print("softplus (float) gradient err = ", err)
    self.assertLess(err, 1e-4)

  def testGradGrad(self):
    with self.cached_session():
      x = constant_op.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 = nn_ops.softplus(x, name="softplus")
      (grad,) = gradients_impl.gradients(y, x)
      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 = gradient_checker.compute_gradient_error(
          x, [2, 5], grad, [2, 5], x_init_value=x_init)
    print("softplus (float) gradient of gradient err = ", err)
    self.assertLess(err, 5e-5)

  def testGradGradGrad(self):
    with self.cached_session():
      x = constant_op.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 = nn_ops.softplus(x, name="softplus")
      (grad,) = gradients_impl.gradients(y, x)
      (grad_grad,) = gradients_impl.gradients(grad, x)
      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 = gradient_checker.compute_gradient_error(
          x, [2, 5], grad_grad, [2, 5], x_init_value=x_init)
    print("softplus (float) third-order gradient err = ", err)
    self.assertLess(err, 5e-5)

  def testNoInts(self):
    with self.cached_session():
      with self.assertRaisesRegexp(
          TypeError,
          "'features' has DataType int32 not in list of allowed values"):
        nn_ops.softplus(constant_op.constant(42)).eval()


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