mx.nd.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.

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.

Value¶

out The result mx.ndarray