old_contents: 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):
          x = numpy.random.uniform(-1, 1, (3, 2)).astype(numpy.float32)
          # Avoid values around zero for stability of numerical gradient
          self.x = x + (x > 0) - (x <= 0)
          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__)
  
new_contents: 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)
          # Avoid values around zero and z for stability of numerical gradient
          for i in range(self.x.size):
              if -0.01 < self.x.flat[i] < 0.01 or 0.74 < self.x.flat[i] < 0.76:
                  self.x.flat[i] = 0.5
          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__)
  
subject: FIx test for cliped relu