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"""Tests for SoftmaxOp."""
import tensorflow.python.platform
import numpy as np
import tensorflow as tf
class SoftmaxTest(tf.test.TestCase):
def _npSoftmax(self, features):
batch_dim = 0
class_dim = 1
batch_size = features.shape[batch_dim]
e = np.exp(features -
np.reshape(np.amax(features, axis=class_dim), [batch_size, 1]))
return e / np.reshape(np.sum(e, axis=class_dim), [batch_size, 1])
def _testSoftmax(self, np_features, use_gpu=False):
np_softmax = self._npSoftmax(np_features)
with self.test_session(use_gpu=use_gpu):
tf_softmax = tf.nn.softmax(np_features)
out = tf_softmax.eval()
self.assertAllClose(np_softmax, out)
self.assertShapeEqual(np_softmax, tf_softmax)
# Bonus check: the softmaxes should add to one in each
# batch element.
self.assertAllClose(np.ones(out.shape[0]),
np.sum(out, axis=1))
def _testAll(self, features):
self._testSoftmax(features, use_gpu=False)
self._testSoftmax(features, use_gpu=True)
def testNpSoftmax(self):
features = [[1., 1., 1., 1.], [1., 2., 3., 4.]]
# Batch 0: All exps are 1. The expected result is
# [0.25, 0.25, 0.25, 0.25]
#
# Batch 1:
# exps = [1., 2.718, 7.389, 20.085]
# sum = 31.192
# Softmaxes = exps / sum = [0.0320586, 0.08714432, 0.23688282, 0.64391426]
np_sm = self._npSoftmax(np.array(features))
self.assertAllClose(
np.array([[0.25, 0.25, 0.25, 0.25],
[0.0320586, 0.08714432, 0.23688282, 0.64391426]]),
np_sm,
rtol=1.e-5, atol=1.e-5)
def testShapeMismatch(self):
with self.assertRaises(ValueError):
tf.nn.softmax([0., 1., 2., 3.])
def testFloat(self):
self._testAll(
np.array([[1., 1., 1., 1.], [1., 2., 3., 4.]]).astype(np.float32))
def testDouble(self):
self._testSoftmax(
np.array([[1., 1., 1., 1.], [1., 2., 3., 4.]]).astype(np.float64),
use_gpu=False)
if __name__ == "__main__":
tf.test.main()
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