I am trying to learn Neural Networks using scikit-neuralnetwork framework and I know basics about Neural Networks and now trying to implement it with scikit-learn. but I am confused on 2 points.
1- what is the structure of this NN given below? Somehow, in some examples felt to me, some people don't put input layer as a layer. Otherwise, I am thinking this as a 2 layer NN has input layer with 100 nodes and 1 node at the ouput layer.
from sknn.mlp import Classifier, Layer nn = Classifier( layers=[ Layer("Maxout", units=100, pieces=2), Layer("Softmax")], learning_rate=0.001, n_iter=25) nn.fit(X_train, y_train)
2- Does scikit-neuralnetwork do back propagation within the code that I put above?