Embedding¶

class Embedding(num_embeddings, embedding_dim, padding_idx=None, max_norm=None, norm_type=None, initial_weight=None, freeze=False, **kwargs)[source]¶

A simple lookup table that stores embeddings of a fixed dictionary and size.

This module is often used to store word embeddings and retrieve them using indices. The input to the module is a list of indices, and the output is the corresponding word embeddings. The indices should less than num_embeddings.

Parameters

num_embeddings (int) – size of embedding dictionary.
embedding_dim (int) – size of each embedding vector.
padding_idx (Optional[int]) – should be set to None, not supportted now.
max_norm (Optional[float]) – should be set to None, not supportted now.
norm_type (Optional[float]) – should be set to None, not supportted now.
initial_weight (Optional[Parameter]) – the learnable weights of the module of shape (num_embeddings, embedding_dim).

Examples

>>> import numpy as np
>>> weight = mge.tensor(np.array([(1.2,2.3,3.4,4.5,5.6)], dtype=np.float32))
>>> data = mge.tensor(np.array([(0,0)], dtype=np.int32))
>>> embedding = M.Embedding(1, 5, initial_weight=weight)
>>> output = embedding(data)
>>> with np.printoptions(precision=6):
...     print(output.numpy())
[[[1.2 2.3 3.4 4.5 5.6]
  [1.2 2.3 3.4 4.5 5.6]]]

classmethod from_pretrained(embeddings, freeze=True, padding_idx=None, max_norm=None, norm_type=None)[source]¶

Creates Embedding instance from given 2-dimensional FloatTensor.

Parameters

embeddings (Parameter) – tensor contained weight for the embedding.
freeze (Optional[bool]) – if True, the weight does not get updated during the learning process. Default: True.
padding_idx (Optional[int]) – should be set to None, not support Now.
max_norm (Optional[float]) – should be set to None, not support Now.
norm_type (Optional[float]) – should be set to None, not support Now.

Examples

>>> import numpy as np
>>> weight = mge.tensor(np.array([(1.2,2.3,3.4,4.5,5.6)], dtype=np.float32))
>>> data = mge.tensor(np.array([(0,0)], dtype=np.int32))
>>> embedding = M.Embedding.from_pretrained(weight, freeze=False)
>>> output = embedding(data)
>>> output.numpy()
array([[[1.2, 2.3, 3.4, 4.5, 5.6],
        [1.2, 2.3, 3.4, 4.5, 5.6]]], dtype=float32)

Dropout

PixelShuffle