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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 <string>
#include <vector>
namespace fdeep {
namespace internal {
class category_encoding_layer : public layer {
public:
explicit category_encoding_layer(const std::string& name,
const std::size_t& num_tokens,
const std::string& output_mode)
: layer(name)
, num_tokens_(num_tokens)
, output_mode_(output_mode)
{
assertion(output_mode_ == "one_hot" || output_mode_ == "multi_hot" || output_mode_ == "count",
"Unsupported output mode (" + output_mode_ + ").");
}
protected:
tensors apply_impl(const tensors& inputs) const override
{
assertion(inputs.size() == 1, "need exactly one input");
const auto input = inputs[0];
assertion(input.shape().rank() == 1, "Tensor of rank 1 required, but shape is '" + show_tensor_shape(input.shape()) + "'");
if (output_mode_ == "one_hot") {
assertion(input.shape().depth_ == 1, "Tensor of depth 1 required, but is: " + fplus::show(input.shape().depth_));
tensor out(tensor_shape(num_tokens_), float_type(0));
const std::size_t idx = fplus::floor<float_type, std::size_t>(input.get_ignore_rank(tensor_pos(0)));
assertion(idx <= num_tokens_, "Invalid input value (> num_tokens).");
out.set_ignore_rank(tensor_pos(idx), 1);
return { out };
} else {
tensor out(tensor_shape(num_tokens_), float_type(0));
for (const auto& x : *(input.as_vector())) {
const std::size_t idx = fplus::floor<float_type, std::size_t>(x);
assertion(idx <= num_tokens_, "Invalid input value (> num_tokens).");
if (output_mode_ == "multi_hot") {
out.set_ignore_rank(tensor_pos(idx), 1);
} else if (output_mode_ == "count") {
out.set_ignore_rank(tensor_pos(idx), out.get_ignore_rank(tensor_pos(idx)) + 1);
}
}
return { out };
}
}
std::size_t num_tokens_;
std::string output_mode_;
};
}
}
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