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3

The Flatten layer is used for collapsing an ND tensor into a 1D tensor. In your case, the inputs appear to be $28\times28$ images, so Flatten will convert that into a tensor with shape $1\times768$. Note that no information is lost. Flatten layers are usually used where you have a convolutional layer with dimensions $N\times M \times C$ (where $N$,$M$ are ...


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If data collection is expensive, it is better to first try to improve your model. You say your accuracy is bad, but have you using tried better performance metrics? A confusion matrix could help. Another potential problem is that your data may be imbalanced. What if your model is performing badly because, for example, there aren't enough samples from class 2....


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You have not described exactly what the tasks will be, but there are some open source libraries for real time pose tracking. For example, OpenPose is one that can be configured to track the body, the hands and the face. However, this is only going to give you predicted pose information for each frame. If the subjects are meant to be doing specific tasks, e.g....


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You can use stratified cross validation combined with an imbalanced learning technique applied to the training data. Stratification ensures that when you split your data into train and test, the ratio of frequencies between the classes will stay the same and therefore the test data will always be "realistic". However, when training a model (using ...


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You have implicitly assumed that supervised learning is being used, given the assumption that labels are needed. But this might lead to the following potential problems: Log file data tends to be huge, and it may be infeasible to label due to the time/expertise required; Then there's the class imbalance problem, in that attack examples are far far rarer ...


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Accuracy can sometimes be a very coarse metric. When it is applied to three class problems, people often take the class label with maximum predicted probability and predict that. The probabilities of the individual labels are ignored. I'd recommend that as well as accuracy you calculate sensitivity and specificity for each class and the area under the ROC ...


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According to wikipedia of backpropagation: In fitting a neural network, backpropagation computes the gradient of the loss function during supervised learning with respect to the weights of the network for a single input–output example, and does so efficiently, unlike a naive direct computation of the gradient with respect to each weight individually. ...


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Do I start off with the epsilon value at the end of the previous session, currently I reinitialize that as well? You should probably re-start with $\epsilon$ at the value you left off at. Using high values of epsilon may cause the neural network to forget some of what it learned from close-to-optimal policies in favour of learning possibly useless values of ...


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