Sha256: d2fb4734b1cc1985a7d53913f5eefef7b3962d3bf0276427cd02b0d478352650
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Contents
# ported from https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/sparse.py
module Torch
module NN
class Embedding < Module
def initialize(num_embeddings, embedding_dim, padding_idx: nil, max_norm: nil,
norm_type: 2.0, scale_grad_by_freq: false, sparse: false, _weight: nil)
super()
@num_embeddings = num_embeddings
@embedding_dim = embedding_dim
if padding_idx
if padding_idx > 0
raise ArgumentError, "Padding_idx must be within num_embeddings" unless padding_idx < @num_embeddings
elsif padding_idx < 0
raise ArgumentError, "Padding_idx must be within num_embeddings" unless padding_idx >= -@num_embeddings
padding_idx = @num_embeddings + padding_idx
end
end
@padding_idx = padding_idx
@max_norm = max_norm
@norm_type = norm_type
@scale_grad_by_freq = scale_grad_by_freq
if _weight.nil?
@weight = Parameter.new(Tensor.new(num_embeddings, embedding_dim))
reset_parameters
else
raise ArgumentError, "Shape of weight does not match num_embeddings and embedding_dim" unless _weight.shape == [num_embeddings, embedding_dim]
@weight = Parameter.new(_weight)
end
@sparse = sparse
end
def reset_parameters
Init.normal!(@weight)
if @padding_idx
Torch.no_grad do
@weight[@padding_idx].fill!(0)
end
end
end
def forward(input)
F.embedding(input, @weight, padding_idx: @padding_idx, max_norm: @max_norm, norm_type: @norm_type, scale_grad_by_freq: @scale_grad_by_freq, sparse: @sparse)
end
def inspect
"Embedding(#{@num_embeddings}, #{@embedding_dim})"
end
end
end
end
Version data entries
57 entries across 57 versions & 1 rubygems