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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_