def __call__(self, x): """Applies N-dimensional convolution layer. Args: x (~chainer.Variable): Input image. Returns: ~chainer.Variable: Output of convolution. """ return convolution_nd.convolution_nd(x, self.W_bar, self.b, self.stride, self.pad, cover_all=self.cover_all)
def __call__(self, x): """Applies N-dimensional convolution layer. Args: x (~chainer.Variable): Input image. Returns: ~chainer.Variable: Output of convolution. """ return convolution_nd.convolution_nd( x, self.W, self.b, self.stride, self.pad, cover_all=self.cover_all)
def forward(self, x): """Applies N-dimensional convolution layer. Args: x (~chainer.Variable): Input image. Returns: ~chainer.Variable: Output of convolution. """ return convolution_nd.convolution_nd( x, self.W, self.b, self.stride, self.pad, cover_all=self.cover_all, dilate=self.dilate, groups=self.groups)
def forward(self, x): """Applies N-dimensional convolution layer. Args: x (~chainer.Variable): Input image. Returns: ~chainer.Variable: Output of convolution. """ if self.W.array is None: self._initialize_params(x.shape[1]) return convolution_nd.convolution_nd( x, self.W, self.b, self.stride, self.pad, cover_all=self.cover_all, dilate=self.dilate, groups=self.groups)
def forward(self, x): """Applies N-dimensional convolution layer. Args: x (~chainer.Variable): Input image. Returns: ~chainer.Variable: Output of convolution. """ return convolution_nd.convolution_nd(x, self.W, self.b, self.stride, self.pad, cover_all=self.cover_all, dilate=self.dilate, groups=self.groups)