(self, input, *args, **kwargs)
return self.nonlinearity(out_bct01)
input is bct01
        based on
        https://github.com/lpigou/Theano-3D-ConvNet/blob/master/convnet3d/convnet3d.py
        released as public domain.
input is bct01
        based on
        https://github.com/lpigou/Theano-3D-ConvNet/blob/master/convnet3d/convnet3d.py
        released as public domain.
input is bct01 based on https : // github . com / lpigou / Theano - 3D - ConvNet / blob / master / convnet3d / convnet3d . py released as public domain .
def get_output_for(self, input, *args, **kwargs):
        """ input is bct01
        based on
        https://github.com/lpigou/Theano-3D-ConvNet/blob/master/convnet3d/convnet3d.py
        released as public domain.
        """
        input_shape = self.input_layer.get_output_shape()
        t, h, w = input_shape[2], input_shape[3], input_shape[4]
        input_c = input_shape[1]
        batch_size = input_shape[0]
        filter_t, filter_h, filter_w = self.filter_size
        input_btc01 = input.dimshuffle([0,2,1,3,4]) # bct01 -> btc01
        out_btc01 = conv3d2d.conv3d(signals=btc01, filters=self.W,
                signals_shape=(batch_size, t, input_c, h, w),
                filters_shape=(self.num_filters, filter_t, input_c, filter_h, filter_w),
                border_mode='valid')
        out_bct01 = out_btc01.dimshuffle([0,2,1,3,4]) # btc01 -> bct01
        if self.b is not None:
            out_bct01 = out_bct01 + self.b.dimshuffle('x',0,'x','x','x')
        return self.nonlinearity(out_bct01)
def get_output_for ( self , input , * args , * * kwargs ) : input_shape = self . input_layer . get_output_shape ( ) t , h , w = input_shape [ 2 ] , input_shape [ 3 ] , input_shape [ 4 ] input_c = input_shape [ 1 ] batch_size = input_shape [ 0 ] filter_t , filter_h , filter_w = self . filter_size input_btc01 = input . dimshuffle ( [ 0 , 2 , 1 , 3 , 4 ] ) # bct01 -> btc01 out_btc01 = conv3d2d . conv3d ( signals = btc01 , filters = self . W , signals_shape = ( batch_size , t , input_c , h , w ) , filters_shape = ( self . num_filters , filter_t , input_c , filter_h , filter_w ) , border_mode = 'valid' ) out_bct01 = out_btc01 . dimshuffle ( [ 0 , 2 , 1 , 3 , 4 ] ) # btc01 -> bct01 if self . b is not None : out_bct01 = out_bct01 + self . b . dimshuffle ( 'x' , 0 , 'x' , 'x' , 'x' ) return self . nonlinearity ( out_bct01 )