diff options
Diffstat (limited to 'tensorflow/python/kernel_tests/xent_op_test.py')
| -rw-r--r-- | tensorflow/python/kernel_tests/xent_op_test.py | 81 |
1 files changed, 1 insertions, 80 deletions
diff --git a/tensorflow/python/kernel_tests/xent_op_test.py b/tensorflow/python/kernel_tests/xent_op_test.py index 60c726d54c..e3e120a4eb 100644 --- a/tensorflow/python/kernel_tests/xent_op_test.py +++ b/tensorflow/python/kernel_tests/xent_op_test.py @@ -18,16 +18,10 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import itertools -import sys - import numpy as np -from tensorflow.python.client import session from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes -from tensorflow.python.framework import ops -from tensorflow.python.ops import array_ops from tensorflow.python.ops import gen_nn_ops from tensorflow.python.ops import gradient_checker from tensorflow.python.ops import gradients_impl @@ -94,7 +88,7 @@ class XentTest(test.TestCase): 4.]]]).astype(dtype) np_labels = np.array([[[0., 0., 0., 1.]], [[0., .5, .5, 0.]]]).astype(dtype) - self.assertRaisesRegexp(ValueError, "rank 2, but is rank 3", + self.assertRaisesRegexp(ValueError, "must be rank 2", gen_nn_ops.softmax_cross_entropy_with_logits, np_features, np_labels) @@ -134,24 +128,6 @@ class XentTest(test.TestCase): self.assertAllClose( np.array([1.3862, 1.9401]), np_loss, rtol=1.e-3, atol=1.e-3) - def testShapeBroadcast(self): - np_f = np.array([[1., 2., 3., 4.], - [1., 2., 3., 4.]]).astype(np.float32) - np_l = np.array([[0., 0., 0., 1.], - [0., .5, .5, 0.]]).astype(np.float32) - np_loss, np_backprop = self._npXent(np_f, np_l) - tf_f = constant_op.constant( - np.array([[1., 2., 3., 4.]]).astype(np.float32)) - tf_l = constant_op.constant( - np.array([[0., 0., 0., 1.], [0., .5, .5, 0.]]).astype(np.float32)) - for use_gpu in [False, True]: - with self.test_session(use_gpu=use_gpu) as sess: - loss, backprop = gen_nn_ops.softmax_cross_entropy_with_logits( - tf_f, tf_l) - tf_loss, tf_backprop = sess.run([loss, backprop]) - self.assertAllCloseAccordingToType(np_loss, tf_loss) - self.assertAllCloseAccordingToType(np_backprop, tf_backprop) - def testShapeMismatch(self): with self.test_session(): with self.assertRaises(ValueError): @@ -284,60 +260,5 @@ class XentTest(test.TestCase): self.assertAllEqual(np_loss, tf_loss) -class XentBenchmark(test.Benchmark): - - def benchmarkZeroDimension(self): - for (m, n, p, use_gpu) in itertools.product( - [128], - [10, 100, 1000, 10000, 100000], - [0.001, 0.01, 0.5, 0.99, 1.0], - [False]): - k = int(p * n) - if k == 0: - continue - name = "zero_dimension_m_%d_n_%d_k_%g_use_gpu_%s" % (m, n, k, use_gpu) - device = "/%s:0" % ("gpu" if use_gpu else "cpu") - with ops.Graph().as_default(): - with ops.device(device): - labels = array_ops.zeros([0, 2, 4], dtype=dtypes.float32) - logits = array_ops.zeros([0, 2, 4], dtype=dtypes.float32) - op = nn_ops.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - with session.Session() as sess: - r = self.run_op_benchmark(sess, op, min_iters=100, name=name) - gb_processed_input = m * n / 1.0e9 - throughput = gb_processed_input / r["wall_time"] - print("Benchmark: %s \t wall_time: %0.03g s \t " - "Throughput: %0.03g GB/s" % (name, r["wall_time"], throughput)) - sys.stdout.flush() - - def benchmarkSingleClass(self): - for (m, n, p, use_gpu) in itertools.product( - [128], - [10, 100, 1000, 10000, 100000], - [0.001, 0.01, 0.5, 0.99, 1.0], - [False]): - k = int(p * n) - if k == 0: - continue - name = "single_class_m_%d_n_%d_k_%g_use_gpu_%s" % (m, n, k, use_gpu) - device = "/%s:0" % ("gpu" if use_gpu else "cpu") - with ops.Graph().as_default(): - with ops.device(device): - labels = constant_op.constant([[1.], [-1.], [0.]], - dtype=dtypes.float32) - logits = constant_op.constant([[-1.], [0.], [1.]], - dtype=dtypes.float32) - op = nn_ops.softmax_cross_entropy_with_logits( - labels=labels, logits=logits) - with session.Session() as sess: - r = self.run_op_benchmark(sess, op, min_iters=100, name=name) - gb_processed_input = m * n / 1.0e9 - throughput = gb_processed_input / r["wall_time"] - print("Benchmark: %s \t wall_time: %0.03g s \t " - "Throughput: %0.03g GB/s" % (name, r["wall_time"], throughput)) - sys.stdout.flush() - - if __name__ == "__main__": test.main() |