How to use the anna.layers.cc_layers.Unpooling2DLayer function in anna
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ifp-uiuc / an-analysis-of-unsupervised-pre-training-iclr-2015 / stl10 / models.py View on Github 
input,
n_filters=64,
filter_size=5,
weights_std=winit1,
nonlinearity=nonlinearity,
pad=2)
pool1 = cc_layers.Pooling2DLayer(conv1, 2, stride=2)
conv2 = cc_layers.Conv2DNoBiasLayer(
pool1,
n_filters=128,
filter_size=5,
weights_std=winit2,
nonlinearity=nonlinearity,
pad=2)
pool2 = cc_layers.Pooling2DLayer(conv2, 2, stride=2)
unpool3 = cc_layers.Unpooling2DLayer(pool2, pool2)
deconv3 = cc_layers.Deconv2DNoBiasLayer(
unpool3, conv2, nonlinearity=layers.identity)
unpool4 = cc_layers.Unpooling2DLayer(deconv3, pool1)
output = cc_layers.Deconv2DNoBiasLayer(
unpool4, conv1, nonlinearity=layers.identity)
class CAELayer3Model(anna.models.UnsupervisedModel):
batch = 128
input = cc_layers.Input2DLayer(batch, 3, 96, 96)
k = float(numpy.random.rand()*1+0.2)
print '## k = %.3f' % k
winit1 = k/numpy.sqrt(5*5*3)
winit2 = k/numpy.sqrt(5*5*64)
winit3 = k/numpy.sqrt(5*5*128)n_filters=144,
filter_size=5,
weights_std=winit2,
nonlinearity=nonlinearity,
pad=2)
pool2 = cc_layers.Pooling2DLayer(conv2, 2, stride=2)
conv3 = cc_layers.Conv2DNoBiasLayer(
pool2,
n_filters=192,
filter_size=3,
weights_std=winit3,
nonlinearity=nonlinearity,
pad=1)
deconv4 = cc_layers.Deconv2DNoBiasLayer(
conv3, conv3, nonlinearity=layers.identity)
unpool5 = cc_layers.Unpooling2DLayer(deconv4, pool2)
deconv5 = cc_layers.Deconv2DNoBiasLayer(
unpool5, conv2, nonlinearity=layers.identity)
unpool6 = cc_layers.Unpooling2DLayer(deconv5, pool1)
output = cc_layers.Deconv2DNoBiasLayer(
unpool6, conv1, nonlinearity=layers.identity)
class CNNModel(anna.models.SupervisedModel):
batch = 128
input = cc_layers.Input2DLayer(batch, 3, 32, 32)
k = float(numpy.random.rand()*1+0.2)
print '## k = %.3f' % k
winit1 = k/numpy.sqrt(5*5*3)
winit2 = k/numpy.sqrt(5*5*96)
winit3 = k/numpy.sqrt(5*5*144)nonlinearity=nonlinearity,
pad=2)
pool2 = cc_layers.Pooling2DLayer(conv2, 2, stride=2)
conv3 = cc_layers.Conv2DNoBiasLayer(
pool2,
n_filters=256,
filter_size=5,
weights_std=winit3,
nonlinearity=nonlinearity,
pad=2)
deconv4 = cc_layers.Deconv2DNoBiasLayer(
conv3, conv3, nonlinearity=layers.identity)
unpool5 = cc_layers.Unpooling2DLayer(deconv4, pool2)
deconv5 = cc_layers.Deconv2DNoBiasLayer(
unpool5, conv2, nonlinearity=layers.identity)
unpool6 = cc_layers.Unpooling2DLayer(deconv5, pool1)
output = cc_layers.Deconv2DNoBiasLayer(
unpool6, conv1, nonlinearity=layers.identity)
class CNNModel(anna.models.SupervisedModel):
batch = 128
input = cc_layers.Input2DLayer(batch, 3, 96, 96)
k = float(numpy.random.rand()*1+0.2)
print '## k = %.3f' % k
winit1 = k/numpy.sqrt(5*5*3)
winit2 = k/numpy.sqrt(5*5*64)
winit3 = k/numpy.sqrt(5*5*128)
binit = 0.0
def trec(x):nonlinearity=nonlinearity,
pad=2)
pool2 = cc_layers.Pooling2DLayer(conv2, 2, stride=2)
conv3 = cc_layers.Conv2DNoBiasLayer(
pool2,
n_filters=256,
filter_size=5,
weights_std=winit3,
nonlinearity=nonlinearity,
pad=2)
deconv3 = cc_layers.Deconv2DNoBiasLayer(
conv3, conv3, nonlinearity=layers.identity)
unpool4 = cc_layers.Unpooling2DLayer(deconv3, pool2)
deconv4 = cc_layers.Deconv2DNoBiasLayer(
unpool4, conv2, nonlinearity=layers.identity)
unpool5 = cc_layers.Unpooling2DLayer(deconv4, pool1)
output = cc_layers.Deconv2DNoBiasLayer(
unpool5, conv1, nonlinearity=layers.identity)
class SupervisedModel(anna.models.SupervisedModel):
batch = 128
input = cc_layers.Input2DLayer(batch, 3, 96, 96)
k = float(numpy.random.rand()*1+0.2)
print '## k = %.3f' % k
winit1 = k/numpy.sqrt(5*5*3)
winit2 = k/numpy.sqrt(5*5*64)
winit3 = k/numpy.sqrt(5*5*128)
binit = 0.0
def trec(x):n_filters=128,
filter_size=5,
weights_std=winit2,
nonlinearity=nonlinearity,
pad=2)
pool2 = cc_layers.Pooling2DLayer(conv2, 2, stride=2)
conv3 = cc_layers.Conv2DNoBiasLayer(
pool2,
n_filters=256,
filter_size=5,
weights_std=winit3,
nonlinearity=nonlinearity,
pad=2)
deconv3 = cc_layers.Deconv2DNoBiasLayer(
conv3, conv3, nonlinearity=layers.identity)
unpool4 = cc_layers.Unpooling2DLayer(deconv3, pool2)
deconv4 = cc_layers.Deconv2DNoBiasLayer(
unpool4, conv2, nonlinearity=layers.identity)
unpool5 = cc_layers.Unpooling2DLayer(deconv4, pool1)
output = cc_layers.Deconv2DNoBiasLayer(
unpool5, conv1, nonlinearity=layers.identity)
class SupervisedModel(anna.models.SupervisedModel):
batch = 128
input = cc_layers.Input2DLayer(batch, 3, 96, 96)
k = float(numpy.random.rand()*1+0.2)
print '## k = %.3f' % k
winit1 = k/numpy.sqrt(5*5*3)
winit2 = k/numpy.sqrt(5*5*64)
winit3 = k/numpy.sqrt(5*5*128)input,
n_filters=96,
filter_size=5,
weights_std=winit1,
nonlinearity=nonlinearity,
pad=2)
pool1 = cc_layers.Pooling2DLayer(conv1, 2, stride=2)
conv2 = cc_layers.Conv2DNoBiasLayer(
pool1,
n_filters=144,
filter_size=5,
weights_std=winit2,
nonlinearity=nonlinearity,
pad=2)
pool2 = cc_layers.Pooling2DLayer(conv2, 2, stride=2)
unpool3 = cc_layers.Unpooling2DLayer(pool2, pool2)
deconv3 = cc_layers.Deconv2DNoBiasLayer(
unpool3, conv2, nonlinearity=layers.identity)
unpool4 = cc_layers.Unpooling2DLayer(deconv3, pool1)
output = cc_layers.Deconv2DNoBiasLayer(
unpool4, conv1, nonlinearity=layers.identity)
class CAELayer3Model(anna.models.UnsupervisedModel):
batch = 128
input = cc_layers.Input2DLayer(batch, 3, 32, 32)
k = float(numpy.random.rand()*1+0.2)
print '## k = %.3f' % k
winit1 = k/numpy.sqrt(5*5*3)
winit2 = k/numpy.sqrt(5*5*96)
winit3 = k/numpy.sqrt(5*5*144)nonlinearity=nonlinearity,
pad=2)
pool2 = cc_layers.Pooling2DLayer(conv2, 2, stride=2)
conv3 = cc_layers.Conv2DNoBiasLayer(
pool2,
n_filters=192,
filter_size=3,
weights_std=winit3,
nonlinearity=nonlinearity,
pad=1)
deconv4 = cc_layers.Deconv2DNoBiasLayer(
conv3, conv3, nonlinearity=layers.identity)
unpool5 = cc_layers.Unpooling2DLayer(deconv4, pool2)
deconv5 = cc_layers.Deconv2DNoBiasLayer(
unpool5, conv2, nonlinearity=layers.identity)
unpool6 = cc_layers.Unpooling2DLayer(deconv5, pool1)
output = cc_layers.Deconv2DNoBiasLayer(
unpool6, conv1, nonlinearity=layers.identity)
class CNNModel(anna.models.SupervisedModel):
batch = 128
input = cc_layers.Input2DLayer(batch, 3, 32, 32)
k = float(numpy.random.rand()*1+0.2)
print '## k = %.3f' % k
winit1 = k/numpy.sqrt(5*5*3)
winit2 = k/numpy.sqrt(5*5*96)
winit3 = k/numpy.sqrt(5*5*144)
binit = 0.0
def trec(x):n_filters=128,
filter_size=5,
weights_std=winit2,
nonlinearity=nonlinearity,
pad=2)
pool2 = cc_layers.Pooling2DLayer(conv2, 2, stride=2)
conv3 = cc_layers.Conv2DNoBiasLayer(
pool2,
n_filters=256,
filter_size=5,
weights_std=winit3,
nonlinearity=nonlinearity,
pad=2)
deconv4 = cc_layers.Deconv2DNoBiasLayer(
conv3, conv3, nonlinearity=layers.identity)
unpool5 = cc_layers.Unpooling2DLayer(deconv4, pool2)
deconv5 = cc_layers.Deconv2DNoBiasLayer(
unpool5, conv2, nonlinearity=layers.identity)
unpool6 = cc_layers.Unpooling2DLayer(deconv5, pool1)
output = cc_layers.Deconv2DNoBiasLayer(
unpool6, conv1, nonlinearity=layers.identity)
class CNNModel(anna.models.SupervisedModel):
batch = 128
input = cc_layers.Input2DLayer(batch, 3, 96, 96)
k = float(numpy.random.rand()*1+0.2)
print '## k = %.3f' % k
winit1 = k/numpy.sqrt(5*5*3)
winit2 = k/numpy.sqrt(5*5*64)
winit3 = k/numpy.sqrt(5*5*128)binit = 0.0
def trec(x):
return x*(x > 0.0)
nonlinearity = trec
conv1 = cc_layers.Conv2DNoBiasLayer(
input,
n_filters=64,
filter_size=5,
weights_std=winit1,
nonlinearity=nonlinearity,
pad=2)
pool1 = cc_layers.Pooling2DLayer(conv1, 2, stride=2)
unpool2 = cc_layers.Unpooling2DLayer(pool1, pool1)
output = cc_layers.Deconv2DNoBiasLayer(
unpool2, conv1, nonlinearity=layers.identity)
class CAELayer2Model(anna.models.UnsupervisedModel):
batch = 128
input = cc_layers.Input2DLayer(batch, 3, 96, 96)
k = float(numpy.random.rand()*1+0.2)
print '## k = %.3f' % k
winit1 = k/numpy.sqrt(5*5*3)
winit2 = k/numpy.sqrt(5*5*64)
binit = 0.0
def trec(x):
return x*(x > 0.0)weights_std=winit1,
nonlinearity=nonlinearity,
pad=2)
pool1 = cc_layers.Pooling2DLayer(conv1, 2, stride=2)
conv2 = cc_layers.Conv2DNoBiasLayer(
pool1,
n_filters=144,
filter_size=5,
weights_std=winit2,
nonlinearity=nonlinearity,
pad=2)
pool2 = cc_layers.Pooling2DLayer(conv2, 2, stride=2)
unpool3 = cc_layers.Unpooling2DLayer(pool2, pool2)
deconv3 = cc_layers.Deconv2DNoBiasLayer(
unpool3, conv2, nonlinearity=layers.identity)
unpool4 = cc_layers.Unpooling2DLayer(deconv3, pool1)
output = cc_layers.Deconv2DNoBiasLayer(
unpool4, conv1, nonlinearity=layers.identity)
class CAELayer3Model(anna.models.UnsupervisedModel):
batch = 128
input = cc_layers.Input2DLayer(batch, 3, 32, 32)
k = float(numpy.random.rand()*1+0.2)
print '## k = %.3f' % k
winit1 = k/numpy.sqrt(5*5*3)
winit2 = k/numpy.sqrt(5*5*96)
winit3 = k/numpy.sqrt(5*5*144)
binit = 0.0
def trec(x):anna
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48 / 100Popular anna functions
- anna.datasets.supervised_data_loader.SupervisedDataContainer
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- anna.datasets.supervised_dataset.SupervisedDataset
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- anna.datasets.unsupervised_dataset.UnsupervisedDataset
- anna.layers.cc_layers
- anna.layers.cc_layers.Conv2DNoBiasLayer
- anna.layers.cc_layers.Deconv2DNoBiasLayer
- anna.layers.cc_layers.Pooling2DLayer
- anna.layers.cc_layers.Unpooling2DLayer
- anna.layers.layers
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- anna.util
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- anna.util.Normer3
- anna.util.Preprocessor
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