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some definitions of neural networks are outdated and, i guess, must be redefined.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.
Related
For PyTorch's tutorial on performing transfer learning for computer vision (https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html), we can see that there is a higher validation accuracy than training accuracy. Applying the same steps to my own dataset, I see similar results. Why is this the case? Does it have something to do with ResNet 18's architecture?
Assuming there aren't bugs in your code and the train and validation data are in the same domain, then there are a couple reasons why this may occur.
Training loss/acc is computed as the average across an entire training epoch. The network begins the epoch with one set of weights and ends the epoch with a different (hopefully better!) set of weights. During validation you're evaluating everything using only the most recent weights. This means that the comparison between validation and train accuracy is misleading since training accuracy/loss was computed with samples from potentially much worse states of your model. This is usually most noticeable at the start of training or right after the learning rate is adjusted since the network often starts the epoch in a much worse state than it ends. It's also often noticeable when the training data is relatively small (as is the case in your example).
Another difference is the data augmentations used during training that aren't used during validation. During training you randomly crop and flip the training images. While these random augmentations are useful for increasing the ability of your network to generalize they aren't performed during validation because they would diminish performance.
If you were really motivated and didn't mind spending the extra computational power you could get a more meaningful comparison by running the training data back through your network at the end of each epoch using the same data transforms used for validation.
The short answer is that train and validation data are from different distributions, and it's "easier" for model to predict target in validation data then it is for training.
The likely reason for this particular case, as indicated by this answer, is data augmentation during training. This is a way to regularize your model by increasing variability in the training data.
Other architectures can use Dropout (or its modifications), which are deliberately "hurting" training performance, reducing the potential of overfitting.
Notice, that you're using pretrained model, which already contains some information about how to solve classification problem. If your domain is not that different from the data it was trained on, you can expect good performance off-the-shelf.
I am training a deep residual network with 10 hidden layers with game data.
Does anyone have an idea why I don't get any overfitting here?
Training and test loss still decreasing after 100 epochs of training.
https://imgur.com/Tf3DIZL
Just a couple of advice:
for deep learning is recommended to do even 90/10 or 95/5 splitting (Andrew Ng)
this small difference between curves means that your learning_rate is not tuned; try to increase it (and, probably, number of epochs if you will implement some kind of 'smart' lr-reduce)
it is also reasonable for DNN to try to overfit with the small amount of data (10-100 rows) and an enormous number of iterations
check for data leakage in the set: weights analysis inside each layer may help you in this
Im new to the world of Deep Learning and i would like to clarify something on my 1st Deep learning code, the MNIST example. Maybe also i'm completely wrong BTW so please take it easy :)
I have split the training data to batches, each one with a size of 50 and max epochs to 15 (or until the validation loss variable starts increasing).
I am getting 93% accuracy just on the 1st epoch, how is that possible if (as far as i know) on 1st epoch it has forward and backpropogate the complete training set just 1 time, so the training set have only abjust its weights and biases only once?
I thought i would get a fine accuracy after many epochs not just on 1st abjustance of the weights
Yes..you can get a good accuracy in the first epoch as well. It depends more on the complexity of the data and the model you build. sometimes if the learning rate is too high, than also it could so happen you get a higher training accuracy.
Also, coming to the adjusting weights and biases part, it could be a mini-batch training and for every mini-batch, the model updates the weights. So weights could have updated many times which is equal to number of training data images/ sample size
From what I know about convolutional neural networks, you must feed the same training examples each epoch, but shuffled (so the network won't remember some particular order while training).
However, in this article, they're feeding the network 64000 random samples each epoch (so only some of the training examples were "seen" before):
Each training instance was a uniformly sampled set of 3 images, 2 of
which are of the same class (x and x+), and the third (x−) of a
different class. Each training epoch consisted of 640000 such
instances (randomly chosen each epoch), and a fixed set of 64000
instances used for test.
So, do I have to use the same training examples each epoch, and why?
Experimental results are poor when I use random samples - the accuracy varies a lot. But I want to know why.
Most of the time you might want to use as much data as you can. However, in the paper you cite they train a triplet loss, which uses triples of images, and there could be billions of such triples.
You might wonder, why introduce the idea of epoch in the first place if we're likely to obtain different training sets each time. The answer is technical: we'd like to evaluate the network on the validation data once in a while, also you might want to do learning rate decay based on the number of completed epochs.
I implement the ResNet for the cifar 10 in accordance with this document https://arxiv.org/pdf/1512.03385.pdf
But my accuracy is significantly different from the accuracy obtained in the document
My - 86%
Pcs daughter - 94%
What's my mistake?
https://github.com/slavaglaps/ResNet_cifar10
Your question is a little bit too generic, my opinion is that the network is over fitting to the training data set, as you can see the training loss is quite low, but after the epoch 50 the validation loss is not improving anymore.
I didn't read the paper in deep so I don't know how did they solved the problem but increasing regularization might help. The following link will point you in the right direction http://cs231n.github.io/neural-networks-3/
below I copied the summary of the text:
Summary
To train a Neural Network:
Gradient check your implementation with a small batch of data and be aware of the pitfalls.
As a sanity check, make sure your initial loss is reasonable, and that you can achieve 100% training accuracy on a very small portion of
the data
During training, monitor the loss, the training/validation accuracy, and if you’re feeling fancier, the magnitude of updates in relation to
parameter values (it should be ~1e-3), and when dealing with ConvNets,
the first-layer weights.
The two recommended updates to use are either SGD+Nesterov Momentum or Adam.
Decay your learning rate over the period of the training. For example, halve the learning rate after a fixed number of epochs, or
whenever the validation accuracy tops off.
Search for good hyperparameters with random search (not grid search). Stage your search from coarse (wide hyperparameter ranges,
training only for 1-5 epochs), to fine (narrower rangers, training for
many more epochs)
Form model ensembles for extra performance
I would argue that the difference in data pre processing makes the difference in performance. He is using padding and random crops, which in essence increases the amount of training samples and decreases the generalization error. Also as the previous poster said you are missing regularization features, such as the weight decay.
You should take another look at the paper and make sure you implement everything like they did.