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// Copyright 2016, Tobias Hermann.
// https://github.com/Dobiasd/frugally-deep
// Distributed under the MIT License.
// (See accompanying LICENSE file or at
// https://opensource.org/licenses/MIT)
#pragma once
#include "fdeep/layers/layer.hpp"
#include <functional>
#include <string>
namespace fdeep {
namespace internal {
class embedding_layer : public layer {
public:
explicit embedding_layer(const std::string& name,
std::size_t input_dim,
std::size_t output_dim,
const float_vec& weights)
: layer(name)
, input_dim_(input_dim)
, output_dim_(output_dim)
, weights_(weights)
{
}
protected:
tensors apply_impl(const tensors& inputs) const override final
{
const auto input_shapes = fplus::transform(fplus_c_mem_fn_t(tensor, shape, tensor_shape), inputs);
// ensure that tensor shape is (1, 1, 1, 1, seq_len)
assertion(inputs.front().shape().size_dim_5_ == 1
&& inputs.front().shape().size_dim_4_ == 1
&& inputs.front().shape().height_ == 1
&& inputs.front().shape().width_ == 1,
"size_dim_5, size_dim_4, height and width dimension must be 1, but shape is '" + show_tensor_shapes(input_shapes) + "'");
tensors results;
for (auto&& input : inputs) {
const std::size_t sequence_len = input.shape().depth_;
float_vec output_vec(sequence_len * output_dim_);
auto&& it = output_vec.begin();
for (std::size_t i = 0; i < sequence_len; ++i) {
std::size_t index = static_cast<std::size_t>(input.get(tensor_pos(i)));
assertion(index < input_dim_, "vocabulary item indices must all be strictly less than the value of input_dim");
it = std::copy_n(weights_.cbegin() + static_cast<float_vec::const_iterator::difference_type>(index * output_dim_), output_dim_, it);
}
results.push_back(tensor(tensor_shape(sequence_len, output_dim_), std::move(output_vec)));
}
return results;
}
const std::size_t input_dim_;
const std::size_t output_dim_;
const float_vec weights_;
};
}
}
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