NAME

    AI::MXNet::Gluon::Contrib::NN::BasicLayers - An additional collection of Gluon's building blocks.

NAME

    AI::MXNet::Gluon::NN::Concurrent - Lays Blocks concurrently.

DESCRIPTION

    Lays Blocks concurrently.

    This block feeds its input to all children blocks, and
    produces the output by concatenating all the children blocks' outputs
    on the specified axis.

    Example:

        $net = nn->Concurrent();
        # use net's name_scope to give children blocks appropriate names.
        $net->name_scope(sub {
            $net->add(nn->Dense(10, activation=>'relu'));
            $net->add(nn->Dense(20));
            $net->add(nn->Identity());
        });

    Parameters
    ----------
    axis : int, default -1
        The axis on which to concatenate the outputs.

NAME

    AI::MXNet::Gluon::NN::HybridConcurrent - Lays HubridBlocks concurrently.

DESCRIPTION

    Lays HybridBlocks concurrently.

    This block feeds its input to all children blocks, and
    produces the output by concatenating all the children blocks' outputs
    on the specified axis.

    Example:

        $net = nn->HybridConcurrent();
        # use net's name_scope to give children blocks appropriate names.
        $net->name_scope(sub {
            $net->add(nn->Dense(10, activation=>'relu'));
            $net->add(nn->Dense(20));
            $net->add(nn->Identity());
        });

    Parameters
    ----------
    axis : int, default -1
        The axis on which to concatenate the outputs.

NAME

    AI::MXNet::Gluon::NN::Identity - Block that passes through the input directly.

DESCRIPTION

    Block that passes through the input directly.

    This block can be used in conjunction with HybridConcurrent
    block for residual connection.

    Example:

        $net = nn->HybridConcurrent();
        # use net's name_scope to give child Blocks appropriate names.
        $net->name_scope(sub {
            $net->add(nn->Dense(10, activation=>'relu'));
            $net->add(nn->Dense(20));
            $net->add(nn->Identity());
        });

NAME

    AI::MXNet::Gluon::NN::SparseEmbedding - Turns non-negative integers (indexes/tokens) into dense vectors.

DESCRIPTION

    Turns non-negative integers (indexes/tokens) into dense vectors
    of fixed size. eg. [4, 20] -> [[0.25, 0.1], [0.6, -0.2]]

    This SparseBlock is designed for distributed training with extremely large
    input dimension. Both weight and gradient w.r.t. weight are AI::MXNet::NDArray::RowSparse.

    Parameters
    ----------
    input_dim : int
        Size of the vocabulary, i.e. maximum integer index + 1.
    output_dim : int
        Dimension of the dense embedding.
    dtype : Dtype, default 'float32'
        Data type of output embeddings.
    weight_initializer : Initializer
        Initializer for the embeddings matrix.