What error for neural network?

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During backpropagation, the network uses something called error responsibility through which it calculates how much it should change connection weights and biases. About your 1.5 error: this is fairly high! Normally, your error should be between 0 and 1 . A 'good' error is anywhere between 0 and 0.05 .

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There is a lot more to developing a neural network model than just instantiating a Python object. What should I do when I realize my model is not as accurate as I want it to be?

During backpropagation, the network uses something called error responsibility through which it calculates how much it should change connection weights and biases. About your 1.5 error: this is fairly high! Normally, your error should be between 0 and 1. A 'good' error is anywhere between 0 and 0.05.

What we’ve seen so far suggests that a neural network needs non-zero bias vectors if: There’s a systematic error in the predictions performed by an unbiased neural network The null input to the network implies a non-null output of the same network The decision surface of a neural network isn’t a subspace of the network space

m e a n = 1 + ( − 1) + 0 3 = 0. So your error is 0. It's wrong (as solution you can use absolute value of error and then take a mean). But in real algorithm you will probably use cross entropy or square error there no this problem. Simple difference you will use only for simple algorithms like Perceptron.

Based on this errors, the neural network will propagate this values to the previous layer and modify the weights according to these errors to get better results, this is the concept of backpropagation. Well, you know how to train the NN, but you don’t know if the network is working well or if will predict the results in a correct way.

It is the process of shifting the error backwards layer by layer and attributing the correct amount of error to each neuron in the neural network. The error attributable to a particular neuron is a good approximation for how changing that neuron’s weights (from the connections leading into the neuron) and bias will affect the cost function.

Cross-entropy and mean squared error are the two main types of loss functions to use when training neural network models. Do you have any questions? Ask your questions in the comments below and I will do my best to answer.

In the case of neural networks, the complexity can be varied by changing the number of adaptive parameters in the network. This is called structural stabilization. The second principal approach to controlling the complexity of a model is through the use of regularization which involves the addition of a penalty term to the error function.

Neural network initialization means initialized the values of the parameters i.e, weights and biases. Biases can be initialized to zero but we can’t initialize weights with zero. Weight initialization is one of the crucial factors in neural networks since bad weight initialization can prevent a neural network from learning the patterns.

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