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TencentOS-tiny/components/ai/nnom/inc/layers/nnom_simple_cell.h
2021-09-08 23:47:15 +08:00

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2.0 KiB
C

/*
* Copyright (c) 2018-2020
* Jianjia Ma
* majianjia@live.com
*
* SPDX-License-Identifier: Apache-2.0
*
* Change Logs:
* Date Author Notes
* 2020-08-20 Jianjia Ma The first version
*/
#ifndef __NNOM_SIMPLE_CELL_H__
#define __NNOM_SIMPLE_CELL_H__
#ifdef __cplusplus
extern "C" {
#endif
#include "nnom_rnn.h"
#include "nnom_activation.h"
// This Simple Cell replicate the Keras's SimpleCell as blow
/*
def call(self, inputs, states, training=None):
prev_output = states[0] if nest.is_sequence(states) else states
h = K.dot(inputs, self.kernel)
h = K.bias_add(h, self.bias)
output = h + K.dot(prev_output, self.recurrent_kernel)
output = self.activation(output)
new_state = [output] if nest.is_sequence(states) else output
return output, new_state
*/
// a machine interface for configuration
typedef struct _nnom_simple_cell_config_t
{
nnom_layer_config_t super;
nnom_tensor_t *weights;
nnom_tensor_t* recurrent_weights;
nnom_tensor_t *bias;
nnom_qformat_param_t q_dec_iw, q_dec_hw, q_dec_h;
nnom_activation_type_t act_type; // type of the activation
uint16_t units;
} nnom_simple_cell_config_t;
typedef struct _nnom_simple_cell_t
{
nnom_rnn_cell_t super;
nnom_activation_type_t act_type;
nnom_tensor_t* weights;
nnom_tensor_t* recurrent_weights;
nnom_tensor_t* bias;
// experimental,
// iw: input x weight
// hw: hidden state x recurrent weight
// h: hidden state
nnom_qformat_param_t q_dec_iw, q_dec_hw, q_dec_h;
nnom_qformat_param_t oshift_iw, oshift_hw, bias_shift;
} nnom_simple_cell_t;
// RNN cells
// The shape for RNN input is (batch, timestamp, feature), where batch is always 1.
//
// SimpleCell
nnom_rnn_cell_t *simple_cell_s(const nnom_simple_cell_config_t* config);
nnom_status_t simple_cell_free(nnom_rnn_cell_t* cell);
nnom_status_t simple_cell_build(nnom_rnn_cell_t* cell);
nnom_status_t simple_cell_run(nnom_rnn_cell_t* cell);
#ifdef __cplusplus
}
#endif
#endif /* __NNOM_SIMPLE_CELL_H__ */