NNgrad_test function
A function to test gradient evaluation of a neural network by comparing it with central finite differencing.
NNgrad_test(net, loss = Qloss(), eps = 1e-05)
net |
an object of class network, see ?network |
loss |
a loss function to compute, see ?Qloss, ?multinomial |
eps |
small value used in the computation of the finite differencing. Default value is 0.00001 |
the exact (computed via backpropagation) and approximate (via central finite differencing) gradients and also a plot of one against the other.
Ian Goodfellow, Yoshua Bengio, Aaron Courville, Francis Bach. Deep Learning. (2016)
Terrence J. Sejnowski. The Deep Learning Revolution (The MIT Press). (2018)
Neural Networks YouTube playlist by 3brown1blue: https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi
http://neuralnetworksanddeeplearning.com/
net <- network( dims = c(5,10,2),
activ=list(ReLU(),softmax()))
NNgrad_test(net)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.