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# 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 tensorflow.kernels.logging_ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import ops
from tensorflow.python.framework import test_util
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import gradients_impl
from tensorflow.python.ops import logging_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import test
class LoggingOpsTest(test.TestCase):
def testAssertDivideByZero(self):
with self.test_session() as sess:
epsilon = ops.convert_to_tensor(1e-20)
x = ops.convert_to_tensor(0.0)
y = ops.convert_to_tensor(1.0)
z = ops.convert_to_tensor(2.0)
# assert(epsilon < y)
# z / y
with sess.graph.control_dependencies([
control_flow_ops.Assert(
math_ops.less(epsilon, y), ["Divide-by-zero"])
]):
out = math_ops.div(z, y)
self.assertAllEqual(2.0, out.eval())
# assert(epsilon < x)
# z / x
#
# This tests printing out multiple tensors
with sess.graph.control_dependencies([
control_flow_ops.Assert(
math_ops.less(epsilon, x), ["Divide-by-zero", "less than x"])
]):
out = math_ops.div(z, x)
with self.assertRaisesOpError("less than x"):
out.eval()
class PrintGradientTest(test.TestCase):
@test_util.run_in_graph_and_eager_modes
def testPrintShape(self):
inp = constant_op.constant(2.0, shape=[100, 32])
inp_printed = logging_ops.Print(inp, [inp])
self.assertEqual(inp.get_shape(), inp_printed.get_shape())
def testPrintGradient(self):
with self.test_session():
inp = constant_op.constant(2.0, shape=[100, 32], name="in")
w = constant_op.constant(4.0, shape=[10, 100], name="w")
wx = math_ops.matmul(w, inp, name="wx")
wx_print = logging_ops.Print(wx, [w, w, w])
wx_grad = gradients_impl.gradients(wx, w)[0]
wx_print_grad = gradients_impl.gradients(wx_print, w)[0]
wxg = wx_grad.eval()
wxpg = wx_print_grad.eval()
self.assertAllEqual(wxg, wxpg)
if __name__ == "__main__":
test.main()
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