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)
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)
conv3 = cc_layers.Conv2DNoBiasLayer(
pool2,
n_filters=256,
filter_size=5,
weights_std=winit3,
nonlinearity=nonlinearity,
pad=2)
pool3 = cc_layers.Pooling2DLayer(conv3, 12, stride=12)
winitD1 = k/numpy.sqrt(numpy.prod(pool3.get_output_shape()))
winitD2 = k/numpy.sqrt(512)
pool3_shuffle = cc_layers.ShuffleC01BToBC01Layer(pool3)
fc4 = layers.DenseLayer(
pool3_shuffle,
n_outputs=512,
weights_std=winitD1,
init_bias_value=1.0,
nonlinearity=layers.rectify,
dropout=0.5)
output = layers.DenseLayer(
fc4,
n_outputs=10,
weights_std=winitD2,
init_bias_value=0.0,
nonlinearity=layers.softmax)
class CAELayer1Model(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)
binit = 0.0
def trec(x):
return x * (x > 0.0)
nonlinearity = trec
conv1 = cc_layers.Conv2DNoBiasLayer(input,
n_filters=96,
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 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)
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)
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)
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)