fully_connected¶

fully_connected(data=None, weight=None, bias=None, num_hidden=_Null, no_bias=_Null, flatten=_Null, out=None, name=None, **kwargs)¶

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.

Defined in src/operator/nn/fully_connected.cc:L291

Parameters:

data : ndarray: Input data.
weight : ndarray: Weight matrix.
bias : ndarray: 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.
out : ndarray, optional: The output ndarray to hold the result.

Returns:

out : ndarray or list of ndarrays: The output of this function.