mxnet.gluon.contrib.nn.SparseEmbedding¶

class mxnet.gluon.contrib.nn.SparseEmbedding(input_dim, output_dim, dtype='float32', weight_initializer=None, **kwargs)[source]¶

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 RowSparseNDArray.

Note: if sparse_grad is set to True, the gradient w.r.t weight will be sparse. Only a subset of optimizers support sparse gradients, including SGD, AdaGrad and Adam. By default lazy updates is turned on, which may perform differently from standard updates. For more details, please check the Optimization API at: https://mxnet.incubator.apache.org/api/python/optimization/optimization.html

Parameters

input_dim (int) – Size of the vocabulary, i.e. maximum integer index + 1.
output_dim (int) – Dimension of the dense embedding.
dtype (str or np.dtype, default 'float32') – Data type of output embeddings.
weight_initializer (Initializer) – Initializer for the embeddings matrix.
Inputs –
- data: (N-1)-D tensor with shape: (x1, x2, …, xN-1).
Output –
- out: N-D tensor with shape: (x1, x2, …, xN-1, output_dim).

__init__(input_dim, output_dim, dtype='float32', weight_initializer=None, **kwargs)[source]¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`(input_dim, output_dim[, dtype, …])	Initialize self.
`apply`(fn)	Applies `fn` recursively to every child block as well as self.
`cast`(dtype)	Cast this Block to use another data type.
`collect_params`([select])	Returns a `ParameterDict` containing this `Block` and all of its children’s Parameters(default), also can returns the select `ParameterDict` which match some given regular expressions.
`forward`(x)	Overrides to implement forward computation using `NDArray`.
`hybridize`([active])	Activates or deactivates `HybridBlock` s recursively.
`initialize`([init, ctx, verbose, force_reinit])	Initializes `Parameter` s of this `Block` and its children.
`load_parameters`(filename[, ctx, …])	Load parameters from file previously saved by save_parameters.
`load_params`(filename[, ctx, allow_missing, …])	[Deprecated] Please use load_parameters.
`name_scope`()	Returns a name space object managing a child `Block` and parameter names.
`register_child`(block[, name])	Registers block as a child of self.
`register_forward_hook`(hook)	Registers a forward hook on the block.
`register_forward_pre_hook`(hook)	Registers a forward pre-hook on the block.
`save_parameters`(filename)	Save parameters to file.
`save_params`(filename)	[Deprecated] Please use save_parameters.
`summary`(*inputs)	Print the summary of the model’s output and parameters.

Attributes

`name`	Name of this `Block`, without ‘_’ in the end.
`params`	Returns this `Block`’s parameter dictionary (does not include its children’s parameters).
`prefix`	Prefix of this `Block`.