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.