Chainerを使った多層パーセプトロン：関数フィッティング

import numpy as np
import matplotlib.pyplot as plt
import chainer
from chainer import cuda, Function, gradient_check, Variable, optimizers, serializers, utils
from chainer import Link, Chain, ChainList
import chainer.functions as F
import chainer.links as L

class MyChain(Chain):
    def __init__(self):
        super(MyChain, self).__init__(
            l1 = L.Linear(1, 10),
            l2 = L.Linear(10, 1),
        )

    def __call__(self, x):
        h = F.sigmoid(self.l1(x))
        out = self.l2(h)
        return out

class MyModel_fitting(Chain):
    def __init__(self, predictor):
        super(MyModel_fitting, self).__init__(predictor=predictor)

    def __call__(self, x, t):
        y = self.predictor(x)
        loss = F.mean_squared_error(y,t)*0.5
#        loss = (y-t)*(y-t)*0.5
        return loss

    def predict(self, x):
        y = self.predictor(x)
        return y

if __name__ == "__main__":
    #---Loading training data---#
    train_x = np.linspace(0.0, 1.0, num=1000, dtype=np.float32)
    train_y = train_x*train_x
    n_epoch = 1000
    n_batch = 100

    model = MyModel_fitting(MyChain())
#    serializers.load_hdf5('MyChain.model', model)
    optimizer = optimizers.Adam()
    optimizer.setup(model)

    for epoch in range(n_epoch):
        print 'epoch : ', epoch
        indexes = np.random.permutation(np.size(train_x))
        for i in range(n_batch):
            model.zerograds()
            x = Variable(np.array([[train_x[indexes[i]]]], dtype=np.float32))
            t = Variable(np.array([[train_y[indexes[i]]]], dtype=np.float32))
            loss = model(x, t)
            loss.backward()
            optimizer.update()
        print 'loss : ', loss.data

    learned_y = np.zeros_like(train_y)
    for i in range(np.size(learned_y)):
        x = Variable(np.array([[train_x[i]]], dtype=np.float32))
        learned_y[i] = model.predict(x).data[0,0]

    plt.plot(train_x, train_y, 'o')
    plt.plot(train_x, learned_y)
    plt.show()
    serializers.save_hdf5('MyChain.model', model)