(self, input_shape)
return (batch_size, self.num_filters, out_dim[0], out_dim[1], out_dim[2])
input is bct01
input is bct01
input is bct01
def get_output_shape_for(self, input_shape):
        """ input is bct01
        """
        batch_size = input_shape[0]
        volume_shape = np.asarray(input_shape[-3:]).astype(np.float32)
        filter_size = np.asarray(self.filter_size).astype(np.float32)
        pad = np.asarray(self.pad).astype(np.float32)
        strides = np.asarray(self.strides).astype(np.float32)
        # TODO check this is right. also it depends on border_mode
        # this assumes strides = 1
        #out_dim = (video_shape-(2*np.floor(kernel_shape/2.))).astype(np.int32)
        #out_dim = ( (volume_shape-filter_size) + 1).astype(np.int32)
        out_dim = ( (volume_shape + 2*pad - filter_size) // strides + 1 ).astype(np.int32)
        return (batch_size, self.num_filters, out_dim[0], out_dim[1], out_dim[2])
def get_output_shape_for ( self , input_shape ) : batch_size = input_shape [ 0 ] volume_shape = np . asarray ( input_shape [ - 3 : ] ) . astype ( np . float32 ) filter_size = np . asarray ( self . filter_size ) . astype ( np . float32 ) pad = np . asarray ( self . pad ) . astype ( np . float32 ) strides = np . asarray ( self . strides ) . astype ( np . float32 ) # TODO check this is right. also it depends on border_mode # this assumes strides = 1 #out_dim = (video_shape-(2*np.floor(kernel_shape/2.))).astype(np.int32) #out_dim = ( (volume_shape-filter_size) + 1).astype(np.int32) out_dim = ( ( volume_shape + 2 * pad - filter_size ) // strides + 1 ) . astype ( np . int32 ) return ( batch_size , self . num_filters , out_dim [ 0 ] , out_dim [ 1 ] , out_dim [ 2 ] )