A method of automatic text generation based on char-rnn model

Abstract

The invention discloses a method for automatically generating text based on a Char-RNN model, comprising the following steps: S1, acquiring text data meeting feature requirements; S2, modeling the text data, and using a vector matrix to represent letters or Chinese characters, get the training data; S3, input the training data batch by batch into the Char-RNN model for training, get the probability of the next character corresponding to each character, and continuously revise this probability with the increase of the training times to reach the preset After a good number of training times, the training model result will be saved; S4. Use the input keyword as the starting character, use the trained model result to obtain the probability corresponding to the next character and output it, and use this as the next character input , and so on to generate a piece of text. Compared with the common RNN model, the present invention solves the problem of gradient disappearance or gradient explosion that may occur during long sequence data processing.

Description

technical field [0001] The invention belongs to the technical field of neural networks, and in particular relates to a method for automatically generating text based on a Char-RNN model. Background technique [0002] Recurrent neural network (RNN) is a classic neural network and the network of choice for time series data. When it comes to certain sequential machine learning tasks, RNNs can achieve high levels of accuracy that no other algorithm can match. This is due to the fact that traditional neural networks only have a kind of short-term memory, while RNN has the advantage of limited short-term memory. [0003] The main purpose of RNN is to process sequence data. In the traditional neural network model, from the input layer to the hidden layer to the output layer, the layers are fully connected, and the nodes between each layer are unconnected. But this ordinary neural network is powerless for many problems. For example, when you want to predict what the next word in...

AI Technical Summary

Problems solved by technology

[0008] The main purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art, provide a method for automatic text generation based on the Char-RNN model, and solve the problem of gradient disappearance or gradient explosion that will occur when processing long sequence data

Images