# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license" file accompanying this file. This file is distributed
# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
# express or implied. See the License for the specific language governing
# permissions and limitations under the License.
from typing import List, Optional, Tuple
from mxnet.gluon import nn
from gluonts.core.component import validated
from gluonts.mx import Tensor
from .representation import Representation
[docs]class Embedding(Representation):
"""
A class representing an embedding operation on top of a given binning. Note
that this representation is intended to applied on top of
categorical/binned data.
Parameters
----------
num_bins
The number of categories/bins of the data on which this representation
is applied.
size
The desired embedding size. By default, the following heuristic is
used:
https://developers.googleblog.com/2017/11/introducing-tensorflow-feature-columns.html
(default: round(num_bins**(1/4)))
"""
@validated()
def __init__(
self, num_bins: int, size: Optional[int] = None, *args, **kwargs
):
super().__init__(*args, **kwargs)
self.num_bins = num_bins
if size is None:
self.size = round(self.num_bins ** (1 / 4))
else:
self.size = size
self.embedding = nn.Embedding(
input_dim=self.num_bins, output_dim=self.size
)
[docs] def hybrid_forward(
self,
F,
data: Tensor,
observed_indicator: Tensor,
scale: Optional[Tensor],
rep_params: List[Tensor],
**kwargs,
) -> Tuple[Tensor, Tensor, List[Tensor]]:
data = self.embedding(data)
return data, scale, rep_params