crino.criterion.CrossEntropy:
The cross-entropy criterion is well suited for targets vector
that are normalized between 0 and 1, used along with a final
Sigmoid activation module.
crino.network.PretrainedMLP:
A PretrainedMLP is a specialization of the MLP, where the
layers are pretrained, for input part on the training examples
(x)orforoutputpartonthetraininglabels(:math:mathbf{y}) using a Stacked AutoEncoder strategy.
crino.module.Sigmoid:
A Sigmoid activation module computes its outputs with the
non-linear element-wise sigmoid function, that can be defined as
σ(x) = (1 + tanh(x ⁄ 2)) ⁄ 2 = [1 ⁄ (1 + exp( − xi))]ni = 1,
with x = [x1, x2, …, xn] ∈ ℝn.
crino.module.Softmax:
A Softmax activation module computes its outputs with the
non-linear softmax function, that can be defined as
softmax(x) = [exp(xi) ⁄ ∑ni = 1exp(xi)]ni = 1,
with x = [x1, x2, …, xn] ∈ ℝn.
crino.module.Tanh:
A Tanh activation module computes its outputs with the
non-linear element-wise hyperbolic tangent function, that can be defined as
tanh(x) = [(exp(xi) − exp( − xi))/(exp(xi) + exp( − xi))]ni = 1,
with x = [x1, x2, …, xn] ∈ ℝn.