How to pass a vector(ndarray) using iterator without including it as input data for the neural network?

I’m using the Mxnet Resnet implementation as shown in

Apart from the image rec iterator and label iterators for training and validation data … i need one more ndarray to be passed at each batch(array size = batch size*10). The values of this iterator will not be used as input to the network rather it will be used to perform symbolic matrix operations on the last layer of the network. So how do I achieve this ?

My iterator which returns array
var1=,np.zeros(n),batch_size=500,shuffle = False,)
var2 =, np.zeros(n), batch_size = batch_size,shuffle = False,)
global var_iter
var_iter = MultiIter([var1,var2])

related issues:

If its static data (does not change from batch to batch) just pass it directly to your last layer.
Otherwise, give it as input to the network and adjust all hybrid_forward functions to take another parameter (your data) and return a tuple instead of only one output.

You might want to write your own Sequential Block for this.

It’s hard to tell what exactly you are trying to achieve without any code.
However, I work with a network that needs to propagate feature maps and a certain error through the graph, so it might be a bit similiar. Here are some code snippets:

class HybridDualSequential(nn.HybridSequential):
    """Stacks HybridBlocks with 2 outputs and inputs sequentially."""

    def __init__(self, prefix=None, params=None):
        super(HybridDualSequential, self).__init__(prefix=prefix, params=params)

    def hybrid_forward(self, F, x, stacked_w=None):
        y = stacked_w
        error_sum = F.zeros(shape=(1,))
        for block in self._children.values():
            x,  y = block.hybrid_forward(F, x, stacked_w=y)
            error_sum = error_sum + y
        return x, error_sum

This is similiar to SequentialBlock as used in the ResNet code, only difference is it passes two objects on. Now you can build the network with hybrid_forward functions that take these as inputs, like so:

class BasicBlockV1(HybridBlock):

        def __init__(self, channels, stride, downsample=False, in_channels=0, clip_threshold=1.0,
                     prefix='', **kwargs):
            super(BasicBlockV1, self).__init__(prefix=prefix, **kwargs)
            with self.name_scope():
                self.conv = nn.Conv2D(channels[0], kernel_size=7, strides=2, padding=3, use_bias=False)
                self.fixed_data = nd.array([0,0,1])
                if downsample:
                    self.downsample = nn.HybridSequential(prefix=prefix)
                    self.downsample.add(nn.AvgPool2D(pool_size=2, strides=2, padding=0))
                        nn.Conv2D(channels, kernel_size=1, strides=1, in_channels=in_channels,
                                  use_bias=False, prefix="sc_qconv_"))
                    self.downsample = None

        def hybrid_forward(self, F, x, stacked_w):
            # Now you can use x and stacked_w here, if you only need them in the last layer, just pass stacked_w on until you have it in the layer where you need to do something with it
           # or you use the class variables like self.fixed_data, if your data does not change with the batch size
            return self.conv(x), stacked_w + self.fixed_data

Then you can create a model by adding these blocks to the HybridDualSequential:

seq = HybridDualSequential()
for i in range(10):
    seq.add(BasicBlockV1(64, 2)

Then you can call seq with your data seq(a) or seq(a,b)

Hope that helps.