obd-python-gan-chapter2.2

chapter2-2.py

import chainer.optimizers as Opt
import chainer.functions as F
import chainer.links as L
from chainer import Variable, Chain, config

import numpy as np
#import matplotlib.pyplot as plt

### Generate data
np.random.seed(123)
D=100
N=2

xdata = np.random.randn(D*N).reshape(D,N).astype(np.float32)

def f(x):
    y=x*x
    return y

tdata = (xdata[:,1] > f(xdata[:,0])).astype(np.int32)

### Neural network
C=2
#NN_ver1 = Chain(l1=L.Linear(N,C))
NN = Chain(l1=L.Linear(N,4), l2=L.Linear(4,C))

"""
def model_ver1(x):
    y=NN.l1(x)
    return y
"""

def model(x):
    h=NN.l1(x)
    h = F.sigmoid(h)
    y = NN.l2(h)
    return y

optNN = Opt.SGD()
optNN.setup(NN)

loss_series = []
acc_series  = []

T = 20000
for time in range(T):
    config.train = True
    optNN.target.zerograds()
    ydata = model(xdata)
    loss = F.softmax_cross_entropy(ydata, tdata)
    acc = F.accuracy(ydata, tdata)
    loss.backward()
    optNN.update()
    loss_series.append(loss.data)
    acc_series.append(acc.data)

for time in range(0,T,100):
    print(time, loss_series[time], acc_series[time])

Result (20 seconds on obd)

0 0.6227948 0.77
100 0.5632122 0.77
200 0.54191923 0.77
300 0.5280521 0.77
400 0.51627535 0.77
...
19600 0.15089221 0.95
19700 0.15005276 0.95
19800 0.1492205 0.95
19900 0.14839555 0.95