Table Of Contents
Table Of Contents

mx.symbol.FullyConnected

Description

Applies a linear transformation: \(Y = XW^T + b\).

If flatten is set to be true, then the shapes are:

  • data: (batch_size, x1, x2, …, xn)

  • weight: (num_hidden, x1 * x2 * … * xn)

  • bias: (num_hidden,)

  • out: (batch_size, num_hidden)

If flatten is set to be false, then the shapes are:

  • data: (x1, x2, …, xn, input_dim)

  • weight: (num_hidden, input_dim)

  • bias: (num_hidden,)

  • out: (x1, x2, …, xn, num_hidden)

The learnable parameters include both weight and bias.

If no_bias is set to be true, then the bias term is ignored.

Note

The sparse support for FullyConnected is limited to forward evaluation with row_sparse weight and bias, where the length of weight.indices and bias.indices must be equal to num_hidden. This could be useful for model inference with row_sparse weights trained with importance sampling or noise contrastive estimation.

To compute linear transformation with ‘csr’ sparse data, sparse.dot is recommended instead of sparse.FullyConnected.

Usage

mx.symbol.FullyConnected(...)

Arguments

Argument

Description

data

NDArray-or-Symbol.

Input data.

weight

NDArray-or-Symbol.

Weight matrix.

bias

NDArray-or-Symbol.

Bias parameter.

num.hidden

int, required.

Number of hidden nodes of the output.

no.bias

boolean, optional, default=0.

Whether to disable bias parameter.

flatten

boolean, optional, default=1.

Whether to collapse all but the first axis of the input data tensor.

name

string, optional.

Name of the resulting symbol.