enumaris
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Is there a way to incorporate new data into an already trained neural network without retraining on all my data in Keras?
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19 votes

In keras, you can save your model using model.save and then load that model using model.load. If you call .fit again on the model that you've loaded, it will continue training from the save point and ...

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Are "artificial neural network" and "neural network" the same thing?
4 votes

As the name suggests, an "artificial neural network" is a neural network that is artificial - i.e. not a biological one. The addition of the word "artificial" is there to emphasize the non-biological ...

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What will a neural network respond to a never-seen feature?
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4 votes

If I understand your question correctly, you want to "shut off" one of the input neurons of a ANN at training time and then turn it back on at inference time correct? Well, this question can be ...

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How can RNN handle variable sized inputs?
1 votes

The point of the RNN is that, in your terminology, w1...w10 are actually all the same sets of weights. The weights are not different for different time steps. So really you have w1=w2=w3=...=w10=w. ...

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how does the addition of 1×1 layers reduce from the complexity and solve sparse structure in inception module?
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1 votes

Imagine you have an input tensor of size (32,32,512) where the dimensions correspond to (Height,Width,Channels) and imagine you have to apply a 128 different 5x5 convolutions to this tensor. This ...

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How to change a weight/bias with gradient
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1 votes

After you've found the gradients, which we'll call $dW,db$ as shorthand for the gradients of the cost function with respect to the weights and biases respectively, you can do a variety of things to ...

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Getting ValueError while implementing LSTM in keras
0 votes

Your input_shape shouldn't include your batch_size dimension. If you want to perform batch optimization, then you should put input_shape=(1,) and specify in addition batch_size=156060 - but this won't ...

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Can a neural network work with support datawhich was not there while training?
0 votes

The whole point of neural networks is to be able to generalize. In other words, neural networks would be useless if they could only ever be used on your training data and will completely fail on test (...

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Losing Order with Word Embeddings?
0 votes

The embedding layer that you add at the start of your neural network in solution #2 has to be a time-distributed layer. This means that the layer must process each time slice independently and then ...

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Calculate mean from cumulative percentage table
0 votes

So, given a probability distribution p(x), the mean is the integral of x*p(x) over the relevant domain (i.e. where p(x) is non-zero). What you have there isn't a probability distribution, though, it's ...

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