I am working in Keras and would like to produce a plot like Figure 3 in this paper. In the caption of Figure 3 in the paper, its says 10 iterations correspond to 1 epoch. I assume this is due to the batch size used for training per epoch.If anyone has further insights to confirm this would be appreciated.
Epoch - One pass through the data, or how many times you have seen each record.
Iteration - Update of the models parameters.
Basically they are saying that they updated the model parameters ten times per each pass through the data.
from https://deeplearning4j.org/glossary
Related
cross_val_score : what does it return ? The score for training / test set ?! I have a model with 5 fold.what does the cross_val_score correspond to ?can someone explain in layman's terms?
Great question! Cross-validation splits your training dataset into 5 parts (aka folds). It then rotates which part of the dataset gets used for testing.
It's basically like this:
You have your training data. You have no clue what machine learning algorithm to use, but you have a hunch that it might be either deep learning or linear regression.
So you take your training data and you divide it up into 5 different equal sections (randomize first tho). You use 4 of those sections to train your deep learning model. Then you test it by comparing the answers it gives you to the answers you know to be true, from the 5th section (the test/validation section). You do this another 4 times, rotating which part gets to be the test part. Then you take the average score of all 5 times, and that is your cross-validation score.
You repeat the process for linear regression. Whichever algorithm gives you the best cross-validation score is the one you will pick, because it's the best for that problem.
Imagine you are picking between two cars: a Honda and a Toyota. You don't want to just test-drive a car once before buying it. It's a big decision. So for the Honda, you test-drive it 5 times and you average your experience over those 5 times. Same for the Toyota, you test-drive it 5 different times and average your experience so you can make an informed decision.
Cross-validation is basically taking a machine learning algorithm (or its hyperparameters, etc.) for a test-drive and seeing how it does.
I'm working on a project to predict demand for a product based on past historical data for multiple stores. I have data from multiple stores over a 5 year period. I split the 5-year time series into overlapping subsequences and use the last 18 months to predict the next 3 and I'm able to make predictions. However, I've run into a problem in choosing a cross-validation method.
I want to have a holdout test split, and use some sort of cross-validation for training my model and tuning parameters. However, the last year of the data was a recession where almost all demand suffered. When I use the last 20% (time-wise) of the data as a holdout set, my test score is very low compared to my OOF cross-validation scores, even though I am using a timeseriessplit CV. This is very likely to be caused by this recession being new behavior, and the model can't predict these strong downswings since it has never seen them before.
The solution I'm thinking of is using a random 20% of the data as a holdout, and a shuffled Kfold as cross-validation. Since I am not feeding any information about when the sequence started into the model except the starting month (1 to 12) of the sequence (to help the model explain seasonality), my theory is that the model should not overfit this data based on that. If all types of economy are present in the data, the results of the model should extrapolate to new data too.
I would like a second opinion on this, do you think my assumptions are correct? Is there a different way to solve this problem?
Your overall assumption is correct in that you can probably take random chunks of time to form your training and testing set. However, when doing it this way, you need to be careful. Rather than predicting the raw values of the next 3 months from the prior 18 months, I would predict the relative increase/decrease of sales in the next 3 months vs. the mean of the past 18 months.
(see here)
http://people.stern.nyu.edu/churvich/Forecasting/Handouts/CourantTalk2.pdf
Otherwise, the correlation between the next 3 months with your prior 18 months data might give you a misleading impression about the accuracy of your model
I am using Jason Brownlee's tutorial (mirror) to apply LSTM network on some syslog/network log data. He's a master!
I have syslog data(a specific event) for each day for last 1 year and so I am using LSTM network for time series analysis. I am using LSTM from Keras deep learning library.
As I understand -
About Batch_size
A batch of data is a fixed-sized number of rows from the training
dataset that defines how many patterns to process before updating
the weights of the network. Based on the batch_size the Model
takes random samples from the data for the analysis. For time series
this is not desirable, hence the batch_size should always be 1.
About setting value for shuffle value
By default, the samples within an epoch are shuffled prior to being exposed to the network. This is undesirable for the LSTM
because we want the network to build up state as it learns across
the sequence of observations. We can disable the shuffling of
samples by setting “shuffle” to “False“.
Scenario1 -
Using above two rules/guidelines - I ran several trials with different number of neurons, epoch size and different layers and got better results from the baseline model(persistence model).
Scenario2-
Without using above guidelines/rules - I ran several trials with different number of neurons, epoch size and different layers and got even better results than Scenario 1.
Query - Setting shuffle to True and Batch_size values to 1 for time series. Is this a rule or a guideline?
It seems logical reading the tutorial that the data for time series should not be shuffled as we do not want to change the sequence of data, but for my data the results are better if I let the data be shuffled.
At the end what I think, what matters is how I get better predictions with my runs.
I think I should try and put away "theory" over concrete evidence, such as metrics, elbows, RMSEs,etc.
Kindly enlighten.
It depends a lot on the size of your data, also in the number of variables, decreasing batch size in my experience gives better results since the update is more frequent but in huge datasets it is very expensive. And you have to play with this trade-off (training time vs result).
About your shuffle it may be the case that your data is not that correlated with the past, if that is the case shuffling the data helps the network to learn and be able to generalize (like ordered by label) check reason 7 of the following 37 reasons your neural network not working
Batch size the larger the difficult it is to generalize (reason 11). When data clearly depends on the past you can declare your LSTM in Keras to stateful, this means: "that the states computed for the samples in one batch will be reused as initial states for the samples in the next batch" according to Keras API. Hope this helps.
I am trying to implement a Neural Network. I am currently working on the backpropagation part. I don't need help with the coding. I have wrote the feedForward part so far with great success. But my question more related to the dataset I am using.
this is my data set:
http://archive.ics.uci.edu/ml/machine-learning-databases/cpu-performance/machine.data
I have to use a 5-fold cross validation to backpropagate and stop training until error threshold is .1, .01, .001 meaning I have to do 3 trials. Ignore the first 2 point for each data point. I have already normalized the set. The architecture of the network is 7 neuron in input layer, 3 neurons in 1 hidden layer and 1 output. Very basic implementation.
So my question is,
I have to break the set up in 5 smaller subset right? keep one for testing and rest for validation and training right?
how long do I train? say I use 1 fold (about 42 data sets per fold) and i reach my desired error threshold. Do I stop? and use the test data? else I load up the next set and keep training? what if I run out of data set before I reach my desired error threshold?
also should I follow something like this?
use input values
feed forward, then compare error
backpropagate and adjust weights
repeat process a-c until i reach error threshold and finish all data in the current fold?
thank you for your time and response. I am really trying to understand how to use the dataset. I will update you guys once I have written the code.
while I'm reading in how to build ANN in pybrain, they say:
Train the network for some epochs. Usually you would set something
like 5 here,
trainer.trainEpochs( 1 )
I looked for what is that mean , then I conclude that we use an epoch of data to update weights, If I choose to train the data with 5 epochs as pybrain advice, the dataset will be divided into 5 subsets, and the wights will update 5 times as maximum.
I'm familiar with online training where the wights are updated after each sample data or feature vector, My question is how to be sure that 5 epochs will be enough to build a model and setting the weights probably? what is the advantage of this way on online training? Also the term "epoch" is used on online training, does it mean one feature vector?
One epoch consists of one full training cycle on the training set. Once every sample in the set is seen, you start again - marking the beginning of the 2nd epoch.
This has nothing to do with batch or online training per se. Batch means that you update once at the end of the epoch (after every sample is seen, i.e. #epoch updates) and online that you update after each sample (#samples * #epoch updates).
You can't be sure if 5 epochs or 500 is enough for convergence since it will vary from data to data. You can stop training when the error converges or gets lower than a certain threshold. This also goes into the territory of preventing overfitting. You can read up on early stopping and cross-validation regarding that.
sorry for reactivating this thread.
im new to neural nets and im investigating the impact of 'mini-batch' training.
so far, as i understand it, an epoch (as runDOSrun is saying) is a through use of all in the TrainingSet (not DataSet. because DataSet = TrainingSet + ValidationSet). in mini batch training, you can sub divide the TrainingSet into small Sets and update weights inside an epoch. 'hopefully' this would make the network 'converge' faster.
some definitions of neural networks are outdated and, i guess, must be redefined.
The number of epochs is a hyperparameter that defines the number times that the learning algorithm will work through the entire training dataset. One epoch means that each sample in the training dataset has had an opportunity to update the internal model parameters.