Use back propogation to adjust the weight matrices 'w' and 'v' to make the predictions more accurate.
Use back propogation to adjust the weight matrices 'w' and 'v' to make the predictions more accurate. First adjust the 'v' weights (hidden to output layer) and then move back to adjust the 'w' weights (input to hidden layer).
http://ufldl.stanford.edu/wiki/index.php/Backpropagation_Algorithm
http://www4.rgu.ac.uk/files/chapter3%20-%20bp.pdf
Return the fit (weigth matrix 'w' and weigth matrix 'v').
Show the flaw by printing the error message.
Show the flaw by printing the error message.
the method where the error occurred
the error message
Minimize the error in the prediction by adjusting the weight vector 'w'.
Minimize the error in the prediction by adjusting the weight vector 'w'. The error 'eo' is simply the difference between the target value 'yi' and the predicted value 'zo'. Mininize 1/2 of the dot product of error with itself using gradient-descent.
the effective input layer training data/matrix
the effective output layer training data/matrix
the weights between these two layers
Given several input vectors 'zi', predict the output/response vector 'zo(0)'.
Given an input vector 'zi', predict the output/response scalar 'zo(0)'.
Given a new discrete data vector z, predict the y-value of f(z).
Given a new discrete data vector z, predict the y-value of f(z).
the vector to use for prediction
Given several input vectors 'zi', predict the output/response vectors 'zo'.
Given several input vectors 'zi', predict the output/response vectors 'zo'.
the new input vectors (stored as rows in a matrix)
Given an input vector 'zi', predict the output/response vector 'zo'.
Given an input vector 'zi', predict the output/response vector 'zo'. For the hidden to output layer bias, prepend the hidden values with a one (_11).
the new input vector
Set the initial weight matrices 'w' and 'v' randomly with a value in (0, 1) before training.
Set the initial weight matrices 'w' and 'v' randomly with a value in (0, 1) before training.
the random number stream to use
Set the initial weight matrices 'w and 'v' manually before training.
Set the initial weight matrices 'w and 'v' manually before training.
the initial weights for w
the initial weights for v
Given training data 'x' and 'y', fit the weight matrices 'w' and 'v'.
The
NeuralNetclass supports basic 3-layer (input, hidden and output) Neural Networks. Given several input and output vectors (training data), fit the weights connecting the layers, so that for a new input vector 'zi', the net can predict the output vector 'zo' ('zh' is the itermediate value at the hidden layer), i.e.,zi --> zh = f (w * zi) --> zo = g (v * zh)
Note, w_0 and v_0 are treated as biases, so zi_0 and zh_0 must be 1.0.