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/*
* Copyright (c) 2016, Alliance for Open Media. All rights reserved
*
* This source code is subject to the terms of the BSD 2 Clause License and
* the Alliance for Open Media Patent License 1.0. If the BSD 2 Clause License
* was not distributed with this source code in the LICENSE file, you can
* obtain it at www.aomedia.org/license/software. If the Alliance for Open
* Media Patent License 1.0 was not distributed with this source code in the
* PATENTS file, you can obtain it at www.aomedia.org/license/patent.
*/
#ifndef AOM_AV1_ENCODER_ML_H_
#define AOM_AV1_ENCODER_ML_H_
#ifdef __cplusplus
extern "C" {
#endif
#define NN_MAX_HIDDEN_LAYERS 10
#define NN_MAX_NODES_PER_LAYER 128
typedef struct {
int num_inputs; // Number of input nodes, i.e. features.
int num_outputs; // Number of output nodes.
int num_hidden_layers; // Number of hidden layers, maximum 10.
// Number of nodes for each hidden layer.
int num_hidden_nodes[NN_MAX_HIDDEN_LAYERS];
// Weight parameters, indexed by layer.
const float *weights[NN_MAX_HIDDEN_LAYERS + 1];
// Bias parameters, indexed by layer.
const float *bias[NN_MAX_HIDDEN_LAYERS + 1];
} NN_CONFIG;
// Calculate prediction based on the given input features and neural net config.
// Assume there are no more than NN_MAX_NODES_PER_LAYER nodes in each hidden
// layer.
void av1_nn_predict(const float *features, const NN_CONFIG *nn_config,
float *output);
// Applies the softmax normalization function to the input
// to get a valid probability distribution in the output:
// output[i] = exp(input[i]) / sum_{k \in [0,n)}(exp(input[k]))
void av1_nn_softmax(const float *input, float *output, int n);
#ifdef __cplusplus
} // extern "C"
#endif
#endif // AOM_AV1_ENCODER_ML_H_
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