基于长短期神经网络lstm的电子密度预测（代码完整，数据齐全）

function [trainedNet, info] = trainNetwork(varargin) % trainNetwork Train a neural network % % trainedNet = trainNetwork(ds, layers, options) trains and returns a % network trainedNet for a classification problem. ds is an % imageDatastore with categorical labels or a MiniBatchable Datastore % with responses, layers is an array of network layers or a LayerGraph % and options is a set of training options. % % trainedNet = trainNetwork(X, Y, layers, options) trains and returns a % network, trainedNet. The format for X depends on the input layer. For % an image input layer, X is a numeric array of images arranged so that % the first three dimensions are the width, height and channels, and the % last dimension indexes the individual images. In a classification % problem, Y specifies the labels for the images as a categorical vector. % In a regression problem, Y contains the responses arranged as a matrix % of size number of observations by number of responses, or a four % dimensional numeric array, where the last dimension corresponds to the % number of observations. % % trainedNet = trainNetwork(C, Y, layers, options) trains an LSTM network % for classifcation and regression problems for sequence or time-series % data. layers must define an LSTM network. It must begin with a sequence % input layer. C is a cell array containing sequence or time-series % predictors. The entries of C are D-by-S matrices where D is the number % of values per timestep, and S is the length of the sequence. For % sequence-to-label classification problems, Y is a categorical vector of % labels. For sequence-to-sequence classification problems, Y is a cell % array of categorical sequences. For sequence-to-one regression % problems, Y is a matrix of targets. For sequence-to-sequence regression % problems, Y is a cell array of numeric sequences. For % sequence-to-sequence problems, the number of time steps of the % sequences in Y must be identical to the corresponding predictor % sequences in C. For sequence-to-sequence problems with one observation, % C can be a matrix, and Y must be a categorical sequence of labels or a % matrix of responses. % % trainedNet = trainNetwork(tbl, layers, options) trains and returns a % network, trainedNet. For networks with an image input layer, tbl is a % table containing predictors in the first column as either absolute or % relative image paths or images. Responses must be in the second column % as categorical labels for the images. In a regression problem, % responses must be in the second column as either vectors or cell arrays % containing 3-D arrays or in multiple columns as scalars. For networks % with a sequence input layer, tbl is a table containing absolute or % relative MAT file paths of predictors in the first column. For a % sequence-to-label classification problem, the second column must be a % categorical vector of labels. For a sequence-to-one regression problem, % the second column must be a numeric array of responses or in multiple % columns as scalars. For a sequence-to-sequence classification problem, % the second column must be an absolute or relative file path to a MAT % file with a categorical sequence. For a sequence-to-sequence regression % problem, the second column must be an absolute or relative file path to % a MAT file with a numeric response sequence. % % trainedNet = trainNetwork(tbl, responseNames, ...) trains and returns a % network, trainedNet. responseNames is a character vector, a string % array, or a cell array of character vectors specifying the names of the % variables in tbl that contain the responses. % % [trainedNet, info] = trainNetwork(...) trains and returns a network, % trainedNet. info contains information on training progress. % % Example 1: % Train a convolutional neural network on some synthetic images % of handwritten digits. Then run the trained network on a test % set, and calculate the accuracy. % % [XTrain, YTrain] = digitTrain4DArrayData; % % layers = [ ... % imageInputLayer([28 28 1]) % convolution2dLayer(5,20) % reluLayer % maxPooling2dLayer(2,'Stride',2) % fullyConnectedLayer(10) % softmaxLayer % classificationLayer]; % options = trainingOptions('sgdm', 'Plots', 'training-progress'); % net = trainNetwork(XTrain, YTrain, layers, options); % % [XTest, YTest] = digitTest4DArrayData; % % YPred = classify(net, XTest); % accuracy = sum(YTest == YPred)/numel(YTest) % % Example 2: % Train a long short-term memory network to classify speakers of a % spoken vowel sounds on preprocessed speech data. Then make % predictions using a test set, and calculate the accuracy. % % [XTrain, YTrain] = japaneseVowelsTrainData; % % layers = [ ... % sequenceInputLayer(12) % lstmLayer(100, 'OutputMode', 'last') % fullyConnectedLayer(9) % softmaxLayer % classificationLayer]; % options = trainingOptions('adam', 'Plots', 'training-progress'); % net = trainNetwork(XTrain, YTrain, layers, options); % % [XTest, YTest] = japaneseVowelsTestData; % % YPred = classify(net, XTest); % accuracy = sum(YTest == YPred)/numel(YTest) % % Example 3: % Train a network on synthetic digit data, and measure its % accuracy: % % [XTrain, YTrain] = digitTrain4DArrayData; % % layers = [ % imageInputLayer([28 28 1], 'Name', 'input') % convolution2dLayer(5, 20, 'Name', 'conv_1') % reluLayer('Name', 'relu_1') % convolution2dLayer(3, 20, 'Padding', 1, 'Name', 'conv_2') % reluLayer('Name', 'relu_2') % convolution2dLayer(3, 20, 'Padding', 1, 'Name', 'conv_3') % reluLayer('Name', 'relu_3') % additionLayer(2,'Name', 'add') % fullyConnectedLayer(10, 'Name', 'fc') % softmaxLayer('Name', 'softmax') % classificationLayer('Name', 'classoutput')]; % % lgraph = layerGraph(layers); % % lgraph = connectLayers(lgraph, 'relu_1', 'add/in2'); % % plot(lgraph); % % options = trainingOptions('sgdm', 'Plots', 'training-progress'); % [net,info] = trainNetwork(XTrain, YTrain, lgraph, options); % % [XTest, YTest] = digitTest4DArrayData; % YPred = classify(net, XTest); % accuracy = sum(YTest == YPred)/numel(YTest) % % See also nnet.cnn.layer, trainingOptions, SeriesNetwork, DAGNetwork, LayerGraph. % Copyright 2015-2018 The MathWorks, Inc. narginchk(3,4); try [layersOrGraph, opts, X, Y] = iParseInputArguments(varargin{:}); [trainedNet, info] = doTrainNetwork(layersOrGraph, opts, X, Y); catch e iThrowCNNException( e ); end end function [trainedNet, info] = doTrainNetwork(layersOrGraph, opts, X, Y) haveDAGNetwork = iHaveDAGNetwork(layersOrGraph); analyzedLayers = iInferParameters(layersOrGraph); layers = analyzedLayers.ExternalLayers; internalLayers = analyzedLayers.InternalLayers; % Validate training data iValidateTrainingDataForProblem( X, Y, layers ); % Set desired precision precision = nnet.internal.cnn.util.Precision('single'); % Set up and validate parallel training isRNN = nnet.internal.cnn.util.isRNN( internalLayers ); executionSettings = nnet.internal.cnn.assembler.setupExecutionEnvironment(... opts, isRNN, X, precision ); % Create a training dispatcher trainingDispatcher = iCreateTrainingDataDispatcher(X, Y, opts, ... executionSettings, layers); % Create a validation dispatcher if validation data was passed in validationDispatcher = iValidationDispatcher( opts, executionSettings, ... layers ); % Assert that trai