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diff --git a/media/libaom/src/av1/encoder/ml.h b/media/libaom/src/av1/encoder/ml.h
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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_