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"""Tests for tensorflow.ops.argmax_op."""
import tensorflow.python.platform
import numpy as np
import tensorflow as tf
class ArgMaxTest(tf.test.TestCase):
def _testArg(self, method, x, dimension,
expected_values, use_gpu=False, expected_err_re=None):
with self.test_session(use_gpu=use_gpu):
ans = method(x, dimension=dimension)
if expected_err_re is None:
tf_ans = ans.eval()
self.assertAllEqual(tf_ans, expected_values)
self.assertShapeEqual(expected_values, ans)
else:
with self.assertRaisesOpError(expected_err_re):
ans.eval()
def _testBothArg(self, method, x, dimension,
expected_values, expected_err_re=None):
self._testArg(method, x, dimension,
expected_values, True, expected_err_re)
self._testArg(method, x, dimension,
expected_values, False, expected_err_re)
def _testBasic(self, dtype):
x = np.asarray(100*np.random.randn(200), dtype=dtype)
# Check that argmin and argmax match numpy along the primary
# dimension
self._testBothArg(tf.argmax, x, 0, x.argmax())
self._testBothArg(tf.argmin, x, 0, x.argmin())
def _testDim(self, dtype):
x = np.asarray(100*np.random.randn(3, 2, 4, 5, 6), dtype=dtype)
# Check that argmin and argmax match numpy along all dimensions
for dim in range(5):
self._testBothArg(tf.argmax, x, dim, x.argmax(dim))
self._testBothArg(tf.argmin, x, dim, x.argmin(dim))
def testFloat(self):
self._testBasic(np.float32)
self._testDim(np.float32)
def testDouble(self):
self._testBasic(np.float64)
self._testDim(np.float64)
def testInt32(self):
self._testBasic(np.int32)
self._testDim(np.int32)
def testInt64(self):
self._testBasic(np.int64)
self._testDim(np.int64)
if __name__ == "__main__":
tf.test.main()
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