The block construct is similar to the architecture construct in that it requires input, output, and neuralflow blocks. However, it also contains a
params block, where the user can define parameters to pass to the layer they are defining. Consider the following example, which defines a residual identity block (one of the components of ResNet):
block: residual_identity_block input: in ; output: out ; params: k, nbf1, nbf2, nbf3 ; in -> Conv2D: [nbf1, 1] -> BatchNormalization:  -> Activation: ['relu'] -> x x -> Conv2D: [nbf2, k, padding='same'] -> BatchNormalization:  -> Activation: ['relu'] -> x x -> Conv2D: [nbf3, 1] -> BatchNormalization:  -> x [x, inp] -> Sum:  -> Activation: ['relu'] -> out ;
The input and output block just define variables, representing the input and output to the
The params block defines four parameters that must be specified when calling this block:
params: k, nbf1, nbf2, nbf3;
The neuralflow is defined in the same way as in the architecture construct.
This block can then be saved in an .nml script, and imported into a training script using the
import keyword followed by relative path of the defined nml file:
This layer then be used in an architecture neuralflow, specifying values for the parameters.
-> residual_identity_block[3, 64, 256, 128] ->