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Paddle/python/paddle/fluid/tests/unittests/test_selu_op.py

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# Copyright (c) 2018 PaddlePaddle 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.
from __future__ import print_function
import unittest
import numpy as np
import six
from op_test import OpTest
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard
class SeluTest(OpTest):
def setUp(self):
self.op_type = "selu"
self.x_shape = [3, 5, 5, 10]
self.dtype = np.float64
self.init_x_shape()
self.init_dtype()
alpha = 1.6732632423543772848170429916717
scale = 1.0507009873554804934193349852946
x = np.random.normal(size=self.x_shape).astype(self.dtype)
# Since zero point in selu is not differentiable, avoid randomize
# zero.
x[np.abs(x) < 0.005] = 0.02
x_flat = x.flatten()
for i in range(x_flat.size):
if x_flat[i] < 0:
x_flat[i] = alpha * np.exp(x_flat[i]) - alpha
x_flat[i] = scale * x_flat[i]
out_np = x_flat.reshape(self.x_shape)
self.inputs = {'X': x}
self.outputs = {'Out': out_np}
self.attrs = {
'alpha': alpha,
'scale': scale,
}
def init_x_shape(self):
pass
def init_dtype(self):
pass
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
class TestSeluOpError(unittest.TestCase):
def test_errors(self):
with program_guard(Program()):
# The input type must be Variable.
self.assertRaises(TypeError, fluid.layers.selu, 1)
# The input dtype must be float16, float32, float64.
x_int32 = fluid.data(name='x_int32', shape=[12, 10], dtype='int32')
self.assertRaises(TypeError, fluid.layers.selu, x_int32)
# support the input dtype is float32
x_fp32 = fluid.data(name='x_fp32', shape=[12, 10], dtype='float32')
fluid.layers.selu(x_fp32)
if __name__ == "__main__":
unittest.main()