model = chainer.FunctionSet(
fh1=F.Linear(28 ** 2, 100),
fh3=F.Linear(100, 28 ** 2),
)
def forward(self, x, train):
if train:
x = F.dropout(x, ratio=0.4)
h = F.dropout(F.sigmoid(self.model.fh1(x)), train=train)
h = F.dropout(self.model.fh3(h), train=train)
return h
ae = AutoEncoder(model, gpu=-1)
ae.set_forward(forward)
ae.set_optimizer(Opt.Adam)
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 = ae.train(train_data, batchsize=200)
print(err)
perm = numpy.random.permutation(len(test_data))
terr = ae.test(test_data[perm][:100])
model = chainer.FunctionSet(enc_conv=F.Convolution2D(1, 64, 5, pad=2),
dec_conv=F.Convolution2D(64, 1, 5, pad=2))
def forward(self, x, train):
if train:
x = F.dropout(x, ratio=0.4)
h = F.sigmoid(self.model.enc_conv(x))
h = self.model.dec_conv(h)
return h
ae = AutoEncoder(model, gpu=-1)
ae.set_forward(forward)
ae.load_param('./cae.param.npy')
imager = V.Visualizer(ae)
imager.plot_filters('enc_conv')
imager.plot_filters('dec_conv')
mnist = fetch_mldata('MNIST original', data_home='.')
mnist.data = mnist.data.astype(numpy.float32) / 255
def output(self, x, layer):
h = self.model.enc_conv(x)
if layer == 1:
return h
h = F.sigmoid(h)
model = chainer.FunctionSet(
enc_conv=F.Convolution2D(1, 64, 5, pad=2),
dec_conv=F.Convolution2D(64, 1, 5, pad=2)
)
def forward(self, x, train):
if train:
x = F.dropout(x, ratio=0.4)
h = F.sigmoid(self.model.enc_conv(x))
h = self.model.dec_conv(h)
return h
ae = AutoEncoder(model, gpu=-1)
ae.set_forward(forward)
ae.load_param('./cae.param.npy')
imager = V.Visualizer(ae)
imager.plot_filters('enc_conv')
imager.plot_filters('dec_conv')
mnist = fetch_mldata('MNIST original', data_home='.')
mnist.data = mnist.data.astype(numpy.float32) / 255
def output(self, x, layer):
h = self.model.enc_conv(x)
if layer == 1:
return h
h = F.sigmoid(h)
fh1=F.Linear(28**2, 100),
fh2=F.Linear(100, 28**2),
)
def forward(self, x, train):
if train:
F.dropout(x, ratio=0.3)
h = F.dropout(F.sigmoid(self.model.fh1(x)), train=train)
h = F.dropout(self.model.fh2(h), train=train)
return h
ae = AutoEncoder(model, gpu=-1)
ae.set_forward(forward)
ae.load_param('./ae.param.npy')
imager = V.Visualizer(ae)
imager.plot_filters('fh1', shape=(28, 28))
imager.plot_filters('fh2', shape=(28, 28), T=True)
mnist = fetch_mldata('MNIST original', data_home='.')
perm = numpy.random.permutation(len(mnist.data))
train_data = mnist.data[perm][:60000].astype(numpy.float32) / 255
test_data = mnist.data[perm][60000:].astype(numpy.float32) / 255
for i in range(20):
y = ae.forward(train_data[i * 20:i * 20 + 25], train=False)
y = chainer.cuda.to_cpu(y.data)
for n in range(25):
model = chainer.FunctionSet(
fh1=F.Linear(28 ** 2, 100),
fh2=F.Linear(100, 28 ** 2),
)
def forward(self, x, train):
if train:
F.dropout(x, ratio=0.3)
h = F.dropout(F.sigmoid(self.model.fh1(x)), train=train)
h = F.dropout(self.model.fh2(h), train=train)
return h
ae = AutoEncoder(model, gpu=-1)
ae.set_forward(forward)
ae.load_param('./ae.param.npy')
imager = V.Visualizer(ae)
imager.plot_filters('fh1', shape=(28, 28))
imager.plot_filters('fh2', shape=(28, 28), T=True)
mnist = fetch_mldata('MNIST original', data_home='.')
perm = numpy.random.permutation(len(mnist.data))
train_data = mnist.data[perm][:60000].astype(numpy.float32) / 255
test_data = mnist.data[perm][60000:].astype(numpy.float32) / 255
for i in range(20):
y = ae.forward(train_data[i * 20: i * 20 + 25], train=False)
y = chainer.cuda.to_cpu(y.data)
for n in range(25):
model = chainer.FunctionSet(enc_conv=F.Convolution2D(1, 64, 5, pad=2),
dec_conv=F.Convolution2D(64, 1, 5, pad=2))
def forward(self, x, train):
if train:
x = F.dropout(x, ratio=0.4, train=train)
h = F.sigmoid(self.model.enc_conv(x))
h = self.model.dec_conv(h)
return h
ae = AutoEncoder(model, gpu=-1)
ae.set_forward(forward)
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].reshape(60000, 1, 28, 28)
test_data = mnist.data[perm][60000:].reshape(10000, 1, 28, 28)
for epoch in range(40):
print('epoch : %d' % epoch)
err = ae.train(train_data, batchsize=200)
print(err)
perm = numpy.random.permutation(len(test_data))
terr = ae.test(test_data[perm][:100])
print(err)
model = chainer.FunctionSet(
enc_conv=F.Convolution2D(1, 64, 5, pad=2),
dec_conv=F.Convolution2D(64, 1, 5, pad=2)
)
def forward(self, x, train):
if train:
x = F.dropout(x, ratio=0.4, train=train)
h = F.sigmoid(self.model.enc_conv(x))
h = self.model.dec_conv(h)
return h
ae = AutoEncoder(model, gpu=-1)
ae.set_forward(forward)
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].reshape(60000, 1, 28, 28)
test_data = mnist.data[perm][60000:].reshape(10000, 1, 28, 28)
for epoch in range(40):
print('epoch : %d' % epoch)
err = ae.train(train_data, batchsize=200)
print(err)
perm = numpy.random.permutation(len(test_data))
terr = ae.test(test_data[perm][:100])
print(err)
