Table Of Contents
Table Of Contents

Embedding

class mxnet.gluon.nn.Embedding(input_dim, output_dim, dtype='float32', weight_initializer=None, sparse_grad=False, **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]]

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.
  • sparse_grad (bool) – If True, gradient w.r.t. weight will be a ‘row_sparse’ NDArray.
  • 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, sparse_grad=False, **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.
export(path[, epoch]) Export HybridBlock to json format that can be loaded by SymbolBlock.imports, mxnet.mod.Module or the C++ interface.
forward(x, *args) Defines the forward computation.
hybrid_forward(F, x, weight) Overrides to construct symbolic graph for this Block.
hybridize([active]) Activates or deactivates HybridBlock s recursively.
infer_shape(*args) Infers shape of Parameters from inputs.
infer_type(*args) Infers data type of Parameters from inputs.
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.