Multilayer Perceptron with Chainer: Function Fitting

For the purpose of studying Chainer, implement the matters described in Apocalypse on artificial intelligence_Function approximation by multi-layer perceptron using Chainer. Saw.

fitting_with_MLP.py

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)

The regression problem is solved by returning the squared error as an error function. Again, the caveat is how to pass the value to Variable. Chainer seems to be implemented on the premise of mini-batch learning, and mini-batch learning is performed by entering [[input vector 1], [input vector 2], ...]. When learning online, you need to add one more parenthesis, such as [[Input Vector 1]].