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"""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()
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