import numpy

from chainer import cuda
from chainer import cudnn
from chainer import function


if cudnn.available:
    from chainer.cudnn import libcudnn
    _mode = libcudnn.cudnnActivationMode['CUDNN_ACTIVATION_RELU']


class ReLU(function.Function):

    """Rectified Linear Unit."""
    # TODO(beam2d): Implement in-place version.

    def __init__(self, use_cudnn=True):
        self.use_cudnn = use_cudnn

    def forward_cpu(self, x):
        return numpy.maximum(0, x[0]),

    def forward_gpu(self, x):
        y = cuda.empty_like(x[0])
        if cudnn.enabled and self.use_cudnn:
            handle = cudnn.get_default_handle()
            desc = cudnn.get_tensor_desc(x[0], 1, 1)
            libcudnn.cudnnActivationForward(
                handle, _mode, 1, desc.value, cudnn.get_ptr(x[0]),
                0, desc.value, cudnn.get_ptr(y))
            self.y = y
        else:
            cuda.elementwise(
                'float* y, const float* x', 'y[i] = max(0.f, x[i])',
                'relu_fwd')(y, x[0])
        return y,

    def backward_cpu(self, x, gy):
        return gy[0] * (x[0] > 0),

    def backward_gpu(self, x, gy):
        gx = cuda.empty_like(x[0])
        if cudnn.enabled and self.use_cudnn:
            handle = cudnn.get_default_handle()
            desc = cudnn.get_tensor_desc(self.y, 1, 1)
            libcudnn.cudnnActivationBackward(
                handle, _mode, 1, desc.value, cudnn.get_ptr(self.y),
                desc.value, cudnn.get_ptr(
                    gy[0]), desc.value, cudnn.get_ptr(x[0]),
                0, desc.value, cudnn.get_ptr(gx))
        else:
            cuda.elementwise(
                'float* gx, const float* x, const float* gy',
                'gx[i] = x[i] > 0 ? gy[i] : 0',
                'relu_bwd')(gx, x[0], gy[0])
        return gx,


def relu(x, use_cudnn=True):
    """Rectified Linear Unit function :math:`f(x)=\\max(0, x)`.

    Args:
        x (~chainer.Variable): Input variable.
        use_cudnn (bool): If True and CuDNN is enabled, then this function uses
            CuDNN as the core implementation.

    Returns:
        ~chainer.Variable: Output variable.

    """
    return ReLU(use_cudnn)(x)

@@ -87,16 +87,53 @@
 unction%0A
+from chainer.utils import type_check%0A
 %0A%0Aif cud
@@ -443,16 +443,179 @@
 _cudnn%0A%0A
+    def check_type_forward(self, in_types):%0A        type_check.expect(%0A            in_types.size() == 1,%0A            in_types%5B0%5D.dtype == numpy.float32%0A        )%0A%0A
     def 

Add forward typecheck to ReLU