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How to train a neural network with an array?
For example if your training data have n rows and m features ie shape : (n, m), You have to set the input_shape of the first Dense layer of the model to (m, ) ie the model should expect m features coming in to it. Now, your labels are [ [1,2,3], [4,5,6]]. In normal 3 class classification task this will be one hot vectors with 1 and 0 s.
Is it possible to train a recurrent neural network?
Yes this is possible by treating the audio as a sequence into a Recurrent Neural Network (RNN). You can train a RNN against a target that is correct at the end of a sequence, or even to predict another sequence offset from the input.
When to use a RNN in a data set?
RNNs can work well for sequences of data that are variable length, and where there is a well-defined dimension over which the sequences evolve. But they are less well adapted for variable-sized sets of features where there is no clear order or sequence.
When do you use a RNN in NLP?
Some other things worth noting: RNNs can work well for sequences of data that are variable length, and where there is a well-defined dimension over which the sequences evolve. RNNs can get state-of-the-art results for signal processing, NLP and related tasks, but only when there is a very large amount of training data.
How to identify error in a neural network?
In order to identify the kind of errors our model generates, we split the dev set into two parts — the eyeball set and the blackbox set. The eyeball set is the sample set that we actually evaluate. We can check these records manually, to guess the source of errors.
Where does the data for a neural network come from?
We may extract data from the net or surveys or various other sources. The basic inputs came from humans. And humans are error prone. Thus, we should acknowledge the fact that all our train/dev/test data has some mislabeled records. If our model is well built and trained properly, then it should be able to overcome such errors.
How to train a keras model with an array?
While defining a keras model, you have to provide input shape to the first layer of the model. For example if your training data have n rows and m features ie shape : (n, m), You have to set the input_shape of the first Dense layer of the model to (m, ) ie the model should expect m features coming in to it.