I trained ANN models for regression problems.
It's quit often that the models fail to predict at low value range of certain output feature as show in the following figure.
Parity plot
Could anyone help me?
What's wrong with the model? and how can I fix it?
Thanks in advance.^ ^
Related
I am trying to pre-train Byt5 model for text classification task.
But Byt5 always predict 1 specific label regardless of how much hyper-parameter tuning is done.
Same data for mt5 is giving decent result.
Can anyone please tell me what I should do?
I have the following LSTM network(Fig 1) for predicting the Bitcoin Price. The input is every hour close price of Bitcoin. I am facing some issues and any advice is appreciated.
Earlier on the same network, my RMSE on testing and training set was 6.71 and 7.41 RMSE. I recompiled the whole code and there was an abrupt increase to 233.51 for the training set and 345.56 for the testing set. Can anyone help me with finding out the reason behind this?
Also, How to improve the accuracy of my network as it very low in every iteration?
How should I decide the parameters for my LSTM network. (units, epochs, batch_size, time_steps to input)
Thank you in advance for any help extended.
Your question requires a lot more information. For example, data size, timesteps lookback, data preprocessing procedure, etc. But I would recommend you to debug your problem with the following method. First, check whether your input/output data are processed properly or not. Then, try to train the simpler model apart from LSTM as it could result in overfitting. But sometimes, if the input signal is too random, it is normal that your model results would highly fluctuate as there's no correlation in data.
PS. never use the Machine Learning model to predict stock price. It never works.
I am trying to make a face recognition model with PyTorch. My model performs well with both loss scores for training and validation close to 0. The problem is when I tested it using same input, it gives me different results like below:
and below
The left side is the true label and the right one is the prediction output. I set random seed and ran model.eval() before feeding the input to the model.
Does anyone know how to solve this?
Please advise
I am new to neural networks so I tried my first neural network which is pretty close to one at keras learn page,given below:
https://github.com/aakarsh1011/Neural-Network/blob/master/MNSIT%20classification.ipynb
Kindlly look at the ending where I red a random image and tried to predict it which comes out as a bag, and when trained at epocs=5 it predicted it as a sandal.
Is something wrong with my code or labeling.
UPDATE - Being new to the field I didn't know the importance of epochs so I asked this question, I was afraid that I don't over-fit the model or train train too much. But there is no definite way to do this, it's all try and error. GOOD LUCK!
First of all, as far as I can see, your code is correct. Your model predicting the wrong item can be caused by the model not being trained for long enough. I would highly recommend you to set epochs=100 and you will be able to see the model's accuracy rise. You should generally always try to give your model as many epochs as possible for training. It will simply take some time. Try out some different numbers of epochs to find the one not taking too long, but still giving an acceptable result.
I am trying to develop an Artificial Neural Network using PyBrain to model biological data. My ANN compiles and runs, but its accuracy value is very low, never surpassing ~62%. From a coding perspective, how can I improve the ANN's accuracy? Something I noticed was that each time, the outputs of the ANN are not the same, either, even though the test data set doesn't change--is there a reason the ANN is acting to unstably, and how can I improve this?
Thank you! :)
If you creating new network each time you run your script then it is normal that outputs are different.
Each time you create ANN pybrain initialize weights of connections with random values (range 0 to 1).
You can save your ANN with NetworkWriter and read it with NetworkReader in pybrain.tools.customxml (see code documentation for reference, pybrain API is missing few things).
You can adjust training process with learning rate and momentum. Also you could apply more training epoch to your network.
If you provide your code I could say more.