/
mnist.py
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/
mnist.py
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import sys
import random
import numpy as np
import chainer
import chainer.links as L
import chainer.functions as F
import chainer.initializers as I
from chainer import Variable, optimizers, link, Chain
from load_mnist import load
import draw
def norm(x):
"""
ベクトルの正規化
x -> x/|x|
"""
s = F.sum(x**2, axis=1) ** 0.5 # [a,b]^T
height = s.data.shape[0]
a = Variable(np.ones((1, height), dtype=np.float32)) # [1,1]
eye = Variable(np.eye(height, dtype=np.float32))
b = F.inv(F.matmul(s, a) * eye) # [1/a, 0; 0, 1/b]
return F.matmul(b, x)
def cos(a, b):
"""
cos-similarity
"""
height = a.data.shape[0]
c = F.matmul(norm(a), norm(b), transb=True)
eye = Variable(np.eye(height, dtype=np.float32))
return F.sum(c * eye, axis=0)
class Encoder(Chain):
def __init__(self):
super().__init__(
lin1=L.Linear(784, 100),
lin2=L.Linear(100, 100),
lin3=L.Linear(100, 100),
)
def __call__(self, x):
h1 = F.dropout(self.lin1(x), train=True, ratio=0.2)
h2 = F.relu(self.lin2(h1))
y = norm(self.lin3(h2))
return y
class Network(Chain):
def __init__(self):
self.enc = Encoder()
self.enc.zerograds()
self.opt = optimizers.SGD()
self.opt.setup(self.enc)
def sim(self, pairs):
"""
pairs = [(a1, b1), (a2, b2), ...]
sims = [s1, s2, ...]
return sims
"""
c1 = []
c2 = []
for a, b in pairs:
c1.append(a)
c2.append(b)
c1 = Variable(np.array(c1, dtype=np.float32))
c2 = Variable(np.array(c2, dtype=np.float32))
x1 = self.enc(c1)
x2 = self.enc(c2)
ys = cos(x1, x2).data.tolist()
return ys
def train(self, pairs, sims):
"""
pairs = [(a1, b1), (a2, b2), ...]
sims = [s1, s2, ...]
"""
c1 = []
c2 = []
for a, b in pairs:
c1.append(a)
c2.append(b)
c1 = Variable(np.array(c1, dtype=np.float32))
c2 = Variable(np.array(c2, dtype=np.float32))
x1 = self.enc(c1)
x2 = self.enc(c2)
ys = cos(x1, x2)
sims = Variable(np.array(sims, dtype=np.float32))
loss = F.mean_squared_error(ys, sims)
self.enc.zerograds()
loss.backward()
self.opt.update()
return loss
net = Network()
data, test_data = load(100, 100)
for _ in range(1000):
pairs = []
sims = []
for __ in range(70): # batch
idx1 = random.randrange(10)
idx2 = (idx1 + random.randrange(9)) % 10
i1 = random.randrange(len(data[idx1]))
i2 = random.randrange(len(data[idx1]))
i3 = random.randrange(len(data[idx2]))
pairs.append((data[idx1][i1], data[idx1][i2]))
sims.append(1.0)
pairs.append((data[idx1][i1], data[idx2][i3]))
sims.append(0.0)
loss = net.train(pairs, sims)
sys.stderr.write("\r# {} loss = {}".format(_, loss.data))
sys.stderr.flush()
# test
for i in range(10):
for j in range(len(test_data[i])):
x = test_data[i][j]
pairs = []
for i2 in range(10):
for j2 in range(len(test_data[i])):
x2 = test_data[i2][j2]
pairs.append((x, x2))
sims = net.sim(pairs)
print(' '.join(list(map(str, sims))))