Multi-Layer Perceptron using scikit-chainer

mlp.py

__author__ = 'du'

from chainer import FunctionSet, functions as F, optimizers
from skchainer import ChainerClassifier


class MultiLayerPerceptron(ChainerClassifier):
    def _setup_network(self, **params):
        print(params)
        network = FunctionSet(
            l1=F.Linear(params["input_dim"], params["hidden_dim"]),
            l2=F.Linear(params["hidden_dim"], params["n_classes"])
        )

        return network

    def forward(self, x):
        h = F.relu(self.network.l1(x))
        y = self.network.l2(h)
        return y

    def loss_func(self, y, t):
        return F.softmax_cross_entropy(y, t)

    def output_func(self, h):
        return F.softmax(h)


if __name__ == "__main__":
    import numpy as np
    from sklearn import datasets

    iris = datasets.load_iris()
    x = iris.data.astype(np.float32)
    y = iris.target.astype(np.int32)

    input_dim = x.shape[1]
    n_classes = len(set(y))

    model = MultiLayerPerceptron(optimizer=optimizers.AdaDelta(rho=0.5),
                                 input_dim=input_dim, hidden_dim=10, n_classes=n_classes, report=100).fit(x, y)
    print(model.score(x, y))