import unittest

import numpy

import chainer
from chainer import cuda
from chainer import functions
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr


class TestClippedReLU(unittest.TestCase):

    def setUp(self):
        self.x = numpy.random.uniform(-1, 1, (3, 2)).astype(numpy.float32)
        self.gy = numpy.random.uniform(-1, 1, (3, 2)).astype(numpy.float32)
        self.z = 0.75

    def check_forward(self, x_data):
        x = chainer.Variable(x_data)
        y = functions.clipped_relu(x, self.z)
        self.assertEqual(y.data.dtype, numpy.float32)

        y_expect = self.x.copy()
        for i in numpy.ndindex(self.x.shape):
            if self.x[i] < 0:
                y_expect[i] = 0
            elif self.x[i] > self.z:
                y_expect[i] = self.z

        gradient_check.assert_allclose(y_expect, y.data)

    def test_forward_cpu(self):
        self.check_forward(self.x)

    @attr.gpu
    def test_forward_gpu(self):
        self.check_forward(cuda.to_gpu(self.x))

    def check_backward(self, x_data, y_grad):
        x = chainer.Variable(x_data)
        y = functions.clipped_relu(x, self.z)
        self.assertEqual(y.data.dtype, numpy.float32)
        y.grad = y_grad
        y.backward()

        func = y.creator
        f = lambda: func.forward((x.data,))
        gx, = gradient_check.numerical_grad(f, (x.data,), (y.grad,))

        gradient_check.assert_allclose(gx, x.grad)

    def test_backward_cpu(self):
        self.check_backward(self.x, self.gy)

    @attr.gpu
    def test_backward_gpu(self):
        self.check_backward(cuda.to_gpu(self.x), cuda.to_gpu(self.gy))


class TestClippedReLUZeroDim(TestClippedReLU):

    def setUp(self):
        self.x = numpy.random.uniform(-1, 1, ()).astype(numpy.float32)
        self.gy = numpy.random.uniform(-1, 1, ()).astype(numpy.float32)
        self.z = 0.75

testing.run_module(__name__, __file__)

@@ -256,37 +256,32 @@
 (self):%0A        
-self.
 x = numpy.random
@@ -318,32 +318,143 @@
 (numpy.float32)%0A
+        # Avoid values around zero for stability of numerical gradient%0A        self.x = x + (x %3E 0) - (x %3C= 0)%0A
         self.gy 

Avoid values around zero