TARGET DATA FEATURE EXTRACTION METHOD AND DEVICE

1 . A target data feature extraction method, comprising:

extracting a feature vector of target data;

determining initial unit data and initial hidden data of a predetermined neural network, inputting the feature vector, the initial unit data and the initial hidden data to the predetermined neural network for processing so as to update unit data and hidden data of the predetermined neural network, and storing updated hidden data;

inputting again the updated unit data and the hidden data to the predetermined neural network for processing so as to update again the unit data and the hidden data, performing recursive processing of the update for predetermined processing times, and storing hidden data after the update each time; and

merging and outputting a plurality of sets of hidden data stored after the predetermined processing times, as a target data feature.

2 . The target data feature extraction method according to claim 1 , wherein the predetermined neural network comprises a fully connected layer and a long short-term memory (LSTM) network unit; the step of inputting the feature vector, the initial unit data and the initial hidden data to the predetermined neural network comprises:

merging and inputting the feature vector and the initial hidden data to the fully connected layer to obtain a fully connected layer processing result; and

inputting the fully connected layer processing result and the initial unit data to the LSTM network unit for processing.

3 . The target data feature extraction method according to claim 2 , wherein the step of merging and inputting the feature vector and the initial hidden data to the fully connected layer to obtain the fully connected layer processing result comprises:

merging and inputting the feature vector and the initial hidden data to the fully connected layer for processing to generate a convolutional feature vector; and

equally dividing the convolutional feature vector into a plurality of sub vectors, and processing each of the sub vectors by a sigmoid function to obtain a processing result.

4 . The target data feature extraction method according to claim 1 , applied to a neural network device, the neural network device comprising a long short-term memory (LSTM) network unit, the method using the LSTM network unit to perform the recursive processing on the unit data and the hidden data for the predetermined processing times.

5 . The target data feature extraction method according to claim 2 , wherein the LSTM network unit comprises a forget gate, an input gate and an output gate sequentially connected; the forget gate is for determining, in the inputted unit data, the unit data preserved up to the current moment; the input gate is for determining a quantity of sets of information inputted to a current LSTM network unit, and updating data of the current LSTM network unit; and the output gate is for determining the unit data and the hidden data needing to be outputted by the current LSTM network unit.

6 . The target data feature extraction method according to claim 5 , wherein the forget gate, the input gate and the output gate comprise a plurality of functions, which are a sigmoid function, a tanh function, an addition function and a multiplication function, and the plurality of functions are used to perform operations by operators in a neural network processor.

7 . The target data feature extraction method according to claim 1 , wherein the step of extracting the target data comprises:

performing preprocessing the target data; and

inputting the preprocessed target data to the predetermined neural network for processing to extract the feature vector of the target data.

8 . A target data feature extraction device, comprising:

an extraction unit, for extracting a feature vector of target data;

a processing unit, for determining initial unit data and initial hidden data of a predetermined neural network, inputting the feature vector, the initial unit data and the initial hidden data to the predetermined neural network for processing so as to update unit data and hidden data of the predetermined neural network, and storing updated hidden data;

an update unit, for inputting again the updated unit data and the hidden data to the predetermined neural network for processing so as to update again the unit data and the hidden data, performing recursive processing of the update for predetermined processing times, and storing hidden data after the update each time; and

an output unit, merging and outputting a plurality of sets of hidden data stored after the predetermined processing times, as a target data feature.

9 . The target data feature extraction device according to claim 8 , wherein the processing unit comprises:

a first processing sub unit, merging and inputting the feature vector and the initial hidden data to the fully connected layer for processing to generate a processing result; and

a second processing sub unit, inputting the processing result and the initial unit data to the LSTM network unit for processing.

10 . The target data feature extraction device according to claim 8 , wherein the extraction unit comprises:

a preprocessing sub unit, for performing preprocessing of the target data; and

an extraction sub unit, for inputting the preprocessed target data to a convolutional neural network for processing to extract the feature vector of the target data.