Created
September 16, 2015 12:12
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Stacked denoising(deep) Autoencoder (with libDNN)
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import chainer | |
import chainer.functions as F | |
import chainer.optimizers as Opt | |
import numpy | |
from sklearn.datasets import fetch_mldata | |
from libdnn import StackedAutoEncoder | |
model = chainer.FunctionSet( | |
enc1=F.Linear(28 ** 2, 200), | |
enc2=F.Linear(200, 30), | |
dec2=F.Linear(30, 200), | |
dec1=F.Linear(200, 28 ** 2) | |
) | |
def encode(self, x, layer, train): | |
if train: | |
x = F.dropout(x, ratio=0.2, train=train) | |
if layer == 0: | |
return x | |
x = F.sigmoid(self.model.enc1(x)) | |
if layer == 1: | |
return x | |
x = F.sigmoid(self.model.enc2(x)) | |
if layer == 2: | |
return x | |
return x | |
def decode(self, x, layer=None, train=False): | |
if not train or layer == 2: | |
x = F.sigmoid(self.model.dec2(x)) | |
if not train or layer == 1: | |
x = F.sigmoid(self.model.dec1(x)) | |
return x | |
sda = StackedAutoEncoder(model, gpu=0) | |
sda.set_order(('enc1', 'enc2'), ('dec2', 'dec1')) | |
sda.set_optimizer(Opt.AdaDelta) | |
sda.set_encode(encode) | |
sda.set_decode(decode) | |
mnist = fetch_mldata('MNIST original', data_home='.') | |
perm = numpy.random.permutation(len(mnist.data)) | |
mnist.data = mnist.data.astype(numpy.float32) / 255 | |
train_data = mnist.data[perm][:60000] | |
test_data = mnist.data[perm][60000:] | |
for epoch in range(10): | |
print('epoch : %d' % (epoch + 1)) | |
err = sda.train(train_data, batchsize=200) | |
print(err) | |
perm = numpy.random.permutation(len(test_data)) | |
terr = sda.test(test_data[perm][:100]) | |
print(terr) | |
with open('sda.log', mode='a') as f: | |
f.write("%d %f %f %f\n" % (epoch + 1, err[0], err[1], terr)) | |
sda.save_param('sda.param.npy') |
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