"Mini tutorial" how to use the time distribution layer correctly under LSTM network

The time distributed layer is a sequence rather than a single value that needs to be returned by the LSTM layer in the Keras interface. What is the time distributed layer The added complexity is the TimeDistributed layer (and the previous TimeDistributedDense layer), which is mysteriously described as a layer wrapper that allows us to app ...

Added by jeffrydell on Sun, 30 Jan 2022 06:16:46 +0200

4_ Making RNN s more effective

1, Stacked RNN from keras.models import Sequential from keras.layers import LSTM,Embedding,Dense # Set super parameters vocabulary = 10000 # There are 10000 words in the dictionary embedding_dim=32 # shape(x)=32, and the dimension of word vector x is 32 word_num = 500 # Each film review has 500 words. If it exceeds 500 words ...

Added by sicKo on Tue, 25 Jan 2022 09:20:14 +0200

Battery SOH simulation series - battery SOH estimation method based on LSTM neural network

be based on Estimation of battery SOH based on LSTM neural network    unlike BP neural network, RNN network not only considers the input of the previous time, but also gives the network the ability to remember the information of the previous time. Although RNN network has high accuracy, it has the problem of gradient disappe ...

Added by antisback on Sat, 22 Jan 2022 01:27:42 +0200

TensorFlow realizes multiple input sources and multiple output

TensorFlow implements multiple input multiple output model Sometimes we have more than one input data, and there will be multiple input sources and multiple output sources. In this case, we obviously can't use Sequential, because Sequential can only build linear topology model, which is more suitable for pipeline model. If it is nonlinear to ...

Added by hillbilly928 on Wed, 05 Jan 2022 19:44:23 +0200

Introduction to deep learning series 20: LSTM recurrent neural network to solve the problem of international air passenger prediction

Hello, my technician Howzit, this is the 20th part of the introduction series of in-depth learning. Welcome to communicate with us! Introduction to deep learning series 1: overview of multi-layer perceptron Introduction to deep learning series 2: build your first neural network with TensorFlow Introduction to deep learning series 3: performanc ...

Added by LTJason on Wed, 17 Nov 2021 05:13:51 +0200

lstm token classification model code parsing (pass batch data directly, test pad and pack_padded, pad_packed three functions)

lstm token classification model code parsing Code reference to teacher Che Wanxiang's <plm-nlp-code/chp4/lstm_postag.py > You need to copy the entire folder to install nltk code to run properly import nltk nltk.download() #Select to install all 1. Loading data import torch from torch import nn, optim from torch.nn import functio ...

Added by jwagner on Tue, 09 Nov 2021 19:01:14 +0200

[RNN architecture analysis] LSTM model

preface Understand the internal structure and calculation formula of LSTMMaster the use of LSTM tools in pytochUnderstand the advantages and disadvantages of LSTM LSTM (long short term memory), also known as long short term memory structure, is a variant of traditional RNN. Compared with classical RNN, it can effectively capture the seman ...

Added by tonga on Thu, 07 Oct 2021 08:00:49 +0300