def __init__(self, use_sigmoid=False):
"""Args:
num_outputs (int): The numer of outputs (i.e. target classes)."""
super(net_fmnist_2c2d, self).__init__()
self.add_module(
'conv1',
tfconv2d(in_channels=1,
out_channels=32,
kernel_size=5,
tf_padding_type='same'))
self.add_module('relu1', nn.ReLU())
self.add_module(
'max_pool1',
tfmaxpool2d(kernel_size=2, stride=2, tf_padding_type='same'))
self.add_module(
'conv2',
tfconv2d(in_channels=32,
out_channels=64,
kernel_size=5,
tf_padding_type='same'))
self.add_module('relu2', nn.ReLU())
self.add_module(
'max_pool2',
tfmaxpool2d(kernel_size=2, stride=2, tf_padding_type='same'))
self.add_module('flatten', flatten())
self.add_module('dense1',
nn.Linear(in_features=7 * 7 * 64, out_features=1024))
if use_sigmoid:
self.add_module('relu3', nn.Sigmoid())
else:
self.add_module('relu3', nn.ReLU())
self.add_module('dense2', nn.Linear(in_features=1024, out_features=10))
# init the layers
for module in self.modules():
if isinstance(module, nn.Conv2d):
nn.init.constant_(module.bias, 0.05)
module.weight.data = _truncated_normal_init(module.weight.data,
mean=0,
stddev=0.05)
if isinstance(module, nn.Linear):
nn.init.constant_(module.bias, 0.05)
module.weight.data = _truncated_normal_init(module.weight.data,
mean=0,
stddev=0.05)
def __init__(self, use_sigmoid=False):
super(net_cifar10_3c3d, self).__init__()
self.add_module(
'conv1', tfconv2d(in_channels=3, out_channels=64, kernel_size=5))
self.add_module('relu1', nn.ReLU())
self.add_module(
'maxpool1',
tfmaxpool2d(kernel_size=3, stride=2, tf_padding_type='same'))
self.add_module(
'conv2', tfconv2d(in_channels=64, out_channels=96, kernel_size=3))
self.add_module('relu2', nn.ReLU())
self.add_module(
'maxpool2',
tfmaxpool2d(kernel_size=3, stride=2, tf_padding_type='same'))
self.add_module(
'conv3',
tfconv2d(in_channels=96,
out_channels=128,
kernel_size=3,
tf_padding_type='same'))
self.add_module('relu3', nn.ReLU())
self.add_module(
'maxpool3',
tfmaxpool2d(kernel_size=3, stride=2, tf_padding_type='same'))
self.add_module('flatten', flatten())
self.add_module('dense1',
nn.Linear(in_features=3 * 3 * 128, out_features=512))
self.add_module('relu4', nn.ReLU())
self.add_module('dense2', nn.Linear(in_features=512, out_features=256))
if use_sigmoid:
self.add_module('relu5', nn.Sigmoid())
else:
self.add_module('relu5', nn.ReLU())
self.add_module('dense3', nn.Linear(in_features=256, out_features=10))
# init the layers
for module in self.modules():
if isinstance(module, nn.Conv2d):
nn.init.constant_(module.bias, 0.0)
nn.init.xavier_normal_(module.weight)
if isinstance(module, nn.Linear):
nn.init.constant_(module.bias, 0.0)
nn.init.xavier_uniform_(module.weight)
def __init__(self, use_sigmoid=False):
super(net_cifar100_allcnnc, self).__init__()
self.add_module('dropout1', nn.Dropout(p=0.2))
self.add_module(
'conv1',
tfconv2d(in_channels=3,
out_channels=96,
kernel_size=3,
tf_padding_type='same'))
self.add_module('relu1', nn.ReLU())
self.add_module(
'conv2',
tfconv2d(in_channels=96,
out_channels=96,
kernel_size=3,
tf_padding_type='same'))
self.add_module('relu2', nn.ReLU())
self.add_module(
'conv3',
tfconv2d(in_channels=96,
out_channels=96,
kernel_size=3,
stride=(2, 2),
tf_padding_type='same'))
self.add_module('relu3', nn.ReLU())
self.add_module('dropout2', nn.Dropout(p=0.5))
self.add_module(
'conv4',
tfconv2d(in_channels=96,
out_channels=192,
kernel_size=3,
tf_padding_type='same'))
self.add_module('relu4', nn.ReLU())
self.add_module(
'conv5',
tfconv2d(in_channels=192,
out_channels=192,
kernel_size=3,
tf_padding_type='same'))
self.add_module('relu5', nn.ReLU())
self.add_module(
'conv6',
tfconv2d(in_channels=192,
out_channels=192,
kernel_size=3,
stride=(2, 2),
tf_padding_type='same'))
self.add_module('relu6', nn.ReLU())
self.add_module('dropout3', nn.Dropout(p=0.5))
self.add_module(
'conv7', tfconv2d(in_channels=192, out_channels=192,
kernel_size=3))
self.add_module('relu7', nn.ReLU())
self.add_module(
'conv8',
tfconv2d(in_channels=192,
out_channels=192,
kernel_size=1,
tf_padding_type='same'))
self.add_module('relu8', nn.ReLU())
self.add_module(
'conv9',
tfconv2d(in_channels=192,
out_channels=100,
kernel_size=1,
tf_padding_type='same'))
if use_sigmoid:
self.add_module('sigmoid9', nn.Sigmoid())
else:
self.add_module('relu9', nn.ReLU())
self.add_module('mean', mean_allcnnc())
# init the layers
for module in self.modules():
if isinstance(module, nn.Conv2d):
nn.init.constant_(module.bias, 0.1)
nn.init.xavier_normal_(module.weight)