My project is to forecast the wti crude oil price using ann. I already have the dataset and I divided it into 70% training data and 30% testing data. That's the only basic thing I know and I did for my project. Now I dunno what to do next since I don't have any tutorial or guidance I can refer to. Can anyone tell me what to do next?
Consider that you have TrainData, TargetTrain, TestData and TargetTest.
TrainData and TestData samples are in row and features are in column.
TargetTrain and TargetTest are two classes and are 0 or 1
InputNum=size(TrainData,2);
OutputNum=2; % two class problem
Xtr=TrainData;
Ytr=full(ind2vec(double(TargetTrain+1)));
Xts=TestData;
Yts=full(ind2vec(double(TargetTest+1)));
%% Network Structure
net = feedforwardnet(11);
%% Training
net.trainParam.showWindow=1;
net.trainParam.max_fail=7;
net = train(net,Xtr',Ytr);
For evaluation you can test:
out_train=net(Xtr');
out_test=net(Xts');
This code create ANN with 11 hidden nets.
Related
I wanna make in Matlab a Naive Bayes model with the carsmall data.
This is my code:
load carsmall
car = table(Model_Year, Weight);
naive_model = fitcnb(car, Origin)
But I get this error and I don't know why. Can anybody say where the error is?
Error using ClassificationNaiveBayes/findNoDataCombos (line 256)
A normal distribution cannot be fit for the combination of class Italy and predictor Model_Year. The data has zero variance.
Since the case of "Italy" appears only once there is no variance, and the normal distribution is pointless for this case. This causes the error for fitcnb, removing this element works OK.
I suggest you to organize more your code, maybe you where going to do this later but, is a good practice. So, here is the new code with a bit more of detail.
clear all
load carsmall
X = [Model_Year Weight];
Y = cellstr(Origin);
%The next line helps to see how many classnames you have
tabulate(Y);
Y(36)=[]; %removing the only case of italy
X (36,:)=[];%removing the only case of italy
%Train a naive Bayes classifier. It is good practice to specify the class order.
naive_model = fitcnb(X, Y,'ClassNames',{'USA','France','Japan','Germany','Sweden'});
I just created a model that does a binary classification and has a dense layer of 1 unit at the end. I used Sigmoid activation. However, I get this error now when I wanna convert it to CoreML.
I tried to change the number of units to 2 and activation to softmax but still didn't work.
import coremltools as ct
#1. define input size
image_input = ct.ImageType(scale=1/255)
#2. give classifier
classifier_config = coremltools.ClassifierConfig(class_labels=[0, 1]) #ERROR here
#3. convert the model
coreml_model = coremltools.convert("mask_detection_model_surgical_mask.h5",
inputs=[image_input], classifier_config=classifier_config)
#4. load and resize an example image
example_image = Image.open("Unknown3.jpg").resize((256, 256))
# Make a prediction using Core ML
out_dict = coreml_model.predict({mymodel.input_names[0]: example_image})
print(out_dict["classLabels"])
# save to disk
#coreml_model.save("FINALLY.mlmodel")
I found the answer to my question.
Use Softmax activation and 2 Dense units as the final layer with either loss='binary_crossentropy' or `loss='categorical_crossentropy'
Good luck to hundreds of people who posted a similar question but received no answer.
I'm trying to come up with a MLP model for timeseries prediction following this blog post. I have 138 timeseries with a lookback_window=28 (splitted as 50127 timeseries for traing and 24255 timeseries for validation). I need to predict the next value (timesteps=28, n_features=1). I started from a 3 layer network but it didn't train well. I tried to make the network deeper by adding more layers/more hunits, but it doesn't improve. In the picture, you can see the result of prediction of the following model Here is my model code:
inp = Input(batch_shape=(batch_size, lookback_window))
first_layer = Dense(1000, input_dim=28, activation='relu')(inp)
snd_layer = Dense(500)(first_layer)
thirs_layer = Dense(250)(snd_layer)
tmp = Dense(100)(thirs_layer)
tmp2 = Dense(50)(tmp)
tmp3 = Dense(25)(tmp2)
out = Dense(1)(tmp3)
model = Model(inp, out)
model.compile(loss='mean_squared_error', optimizer='adam')
history = model.fit(train_data, train_y,
epochs=1000,
batch_size=539,
validation_data=(validation_data, validation_y),
verbose=1,
shuffle=False)
What am I missing? How can I improve it?
The main thing I noticed is that you are not using non-linearities in your layers. I would use relus for the hidden layers and linear layer for the final layer in case you want values larger than 1 / -1 to be possible. If you do not want them to be possible use tanh. By increasing the data you make the problem harder and therefore your mostly linear model is underfitting severely.
I managed to get better results by the following changes:
Using RMSprop instead of Adam with lr=0.001, and as #TommasoPasini mentioned added them to all Dense layers (expect the last one). It improves the results a lot!
epochs= 3000 instead of 1000.
But now I think it is overfitting. Here are the plots of the results and the validation and train loss:
I have been reading the documentation: here and here but it's really unclear for me and I don't see how to use pratically crossval to do a leave one out cross-validation.
vals = crossval(fun,X)
vals = crossval(fun,X,Y,...)
mse = crossval('mse',X,y,'Predfun',predfun)
mcr = crossval('mcr',X,y,'Predfun',predfun)
val = crossval(criterion,X1,X2,...,y,'Predfun',predfun)
vals = crossval(...,'name',value)
I really don't understand the funpart.
I have estimatimate chlorophyll rate with different index. Then I have done a linear regression between those index and the field taken chlorophyll rate. Now I want to validate them, one of my estimation is a column with 22 entries, so I want to use 21 of them as trainee and 1 as a test, and do 22 loops so that all the data have been used as test.
But I don't where should I put the regression model? If my regression is Y=aX+b,
do I re-use the a and b calculated before for the train part, or do I do a new linear regression with the train part then see what's the test will be with that?
I am not sure I totally understood how to make a leave one out model.
Then I want to know the result of the test by calculating the RMSE (and maybe the R²).
How do I code that using crossval?
I saw the answer to the question here but I don't have access to the crossvalind fonction with my license.
Well I finaly figure it out: so this is my script:
First I charged my data and the linear regression fonction
X=indicesCha_without_Cloud(:,3);
y=Cha_g_m2t_without_Cloud(:,3);
testval=#(XTRAIN,ytrain,XTEST)Linear_regression_indices( XTRAIN,ytrain,XTEST);
where in my case fun(in the Mathwork help) is testvaland Linear_regression_indices is a very simple fonction:
function [ Linear_regression_indices ] = Linear_regression_indices(XTRAIN,ytrain,XTEST )
Linear_regression_indices=(polyval(polyfit(XTRAIN,ytrain,1),XTEST));
end
There is 2 ways to do it and they both give the same result:
one by using simply the crossval fonction
cvMse = crossval('mse',X,y,'predfun',testval,'leaveout',1);
this will do as many fold as the data size, using each time one of the data as Xtest
the second one is using cvpartition
c = cvpartition(n,'LeaveOut') creates a random partition for leave-one-out cross validation on n observations. Leave-one-out is a special case of 'KFold', in which the number of folds equals the number of observations. link
c = cvpartition(y,'LeaveOut');
cvMse2=crossval('mse',X,y,'predfun',testval,'partition',c);
then the RMSE can be easily calculated
RMSE=sqrt(cvMse);
RMSE2=sqrt(cvMse2);
then I simply get my answer, in my case RMSE=0,3548
I am working on a project thumb recognition. following is code I am reading the 118 images of order 42 X 25 and storing them in training matrix.
training=zeros(118, 1050);
imagefiles = dir('*.png');
nfiles = length(imagefiles);
for ii=1:nfiles
currentfilename = imagefiles(ii).name;
I = imread(currentfilename);
BW=im2bw(I,graythresh(I));
temp = reshape(BW,1,1050);
training(ii,:)=temp;
end
Now I am creating a matrix of labelData to assign labels to images.
labelData = zeros(118,1);
labelData(1:50,:) = 0;
labelData(51:83,:) = 1;
labelData(84:118,:) = 2;
Here i am training my system by giving training data and label data.
options=optimset('MaxIter',5000);
SVMStruct = svmtrain(training,labelData,'Kernel_Function','linear','QuadProg_Opts',options);
BUT when I run this code it is giving me an error like
Error 1 : SVMTRAIN only supports classification into two groups. GROUP contains 3 groups.
Error 2 : SVMStruct = svmtrain(training,labelData,'Kernel_Function','linear','QuadProg_Opts',options);
Kindly help me what is the problem I used it before it was working fine but now I dont know what is going on. Thanks in advance.
Error 1 tells you what the problem is - the MATLAB built-in SVM only supports binary classification. You are assigning 3 classes.
Your options are:
Construct three classifiers: 0 vs. 1,2 then 1 vs. 0,2 then 2 vs. 0,1 and look at the output of each.
Construct 0 vs. not 0 and then 1 vs. 2
Use a multi-class SVM trainer from LIBSVM or svmlight or other such packages.
The error message is pretty clear. MATLAB's svmtrain does not support multiclass classification, that is only two classes are allowed.
So, you have two options: 1) write your own multiclass classifier as a wrapper around svmtrain. You can implement one-vs-all or one-vs-one strategies. 2) use a svm implementation that already supports multiclass classification such as libsvm.
Your problem is in the labelData vector ceck it and find the eror, yoy shoild OAA architector if hthe number of classes is more then .