def __init__(self,
num_in=0,
num_h1=0,
num_h2=0,
num_out=0,
dropout=1.0,
weight_scale=1e-2):
super(ThreeLayerNet, self).__init__()
self.fc1 = nn.FC(num_in, num_h1, weight_scale=weight_scale)
self.fc2 = nn.FC(num_h1, num_h2, weight_scale=weight_scale)
self.fc3 = nn.FC(num_h2, num_out, weight_scale=weight_scale)
self.relu = nn.ReLU()
self.z1_cache = None
self.z1 = None
self.z2_cache = None
self.z2 = None
self.z3_cache = None
self.use_dropout = dropout != 1.0
self.dropout_param = {}
if self.use_dropout:
self.dropout_param['mode'] = 'train'
self.dropout_param['p'] = dropout
self.dropout = nn.Dropout()
self.U1 = None
self.U2 = None
self.params = self._get_params()
def __init__(self,
in_channels=1,
out_channels=10,
dropout=1.0,
weight_scale=1e-2):
super(LeNet5, self).__init__()
self.conv1 = nn.Conv2d2(in_channels,
5,
5,
6,
stride=1,
padding=0,
weight_scale=weight_scale)
self.conv2 = nn.Conv2d2(6,
5,
5,
16,
stride=1,
padding=0,
weight_scale=weight_scale)
self.conv3 = nn.Conv2d2(16, 5, 5, 120, stride=1, padding=0)
self.maxPool1 = nn.MaxPool2(2, 2, 6, stride=2)
self.maxPool2 = nn.MaxPool2(2, 2, 16, stride=2)
self.fc1 = nn.FC(120, 84)
self.fc2 = nn.FC(84, out_channels)
self.relu = nn.ReLU()
self.z1 = None
self.z1_cache = None
self.z2 = None
self.z2_cache = None
self.z3 = None
self.z3_cache = None
self.z4 = None
self.z4_cache = None
self.z5 = None
self.z5_cache = None
self.z6 = None
self.z6_cache = None
# self.z7 = None
self.z7_cache = None
self.use_dropout = dropout != 1.0
self.dropout_param = {}
if self.use_dropout:
self.dropout_param['mode'] = 'train'
self.dropout_param['p'] = dropout
self.dropout = nn.Dropout()
self.U1 = None
self.params = self._get_params()
def _get_fcs(self):
fcs = list()
if self.hidden_dims is None:
fcs.append(
nn.FC(self.input_dim,
self.num_classes,
weight_scale=self.weight_scale))
else:
for i in range(self.num_layers):
if i == 0:
num_in = self.input_dim
else:
num_in = self.hidden_dims[i - 1]
if i == (self.num_layers - 1):
num_out = self.num_classes
else:
num_out = self.hidden_dims[i]
fcs.append(nn.FC(num_in, num_out))
return fcs
def __init__(self, in_channels, out_channels, momentum=0, nesterov=False):
super(AlexNet, self).__init__()
self.conv1 = nn.Conv2d(in_channels,
11,
11,
96,
stride=4,
padding=0,
momentum=momentum,
nesterov=nesterov)
self.conv2 = nn.Conv2d(96,
5,
5,
256,
stride=1,
padding=2,
momentum=momentum,
nesterov=nesterov)
self.conv3 = nn.Conv2d(256,
3,
3,
384,
stride=1,
padding=1,
momentum=momentum,
nesterov=nesterov)
self.conv4 = nn.Conv2d(384,
3,
3,
384,
stride=1,
padding=1,
momentum=momentum,
nesterov=nesterov)
self.conv5 = nn.Conv2d(384,
3,
3,
256,
stride=1,
padding=1,
momentum=momentum,
nesterov=nesterov)
self.maxPool1 = nn.MaxPool(3, 3, 96, stride=2)
self.maxPool2 = nn.MaxPool(3, 3, 256, stride=2)
self.maxPool3 = nn.MaxPool(3, 3, 256, stride=2)
self.fc1 = nn.FC(9216, 4096, momentum=momentum, nesterov=nesterov)
self.fc2 = nn.FC(4096, 4096, momentum=momentum, nesterov=nesterov)
self.fc3 = nn.FC(4096,
out_channels,
momentum=momentum,
nesterov=nesterov)
self.relu1 = nn.ReLU()
self.relu2 = nn.ReLU()
self.relu3 = nn.ReLU()
self.relu4 = nn.ReLU()
self.relu5 = nn.ReLU()
self.relu6 = nn.ReLU()
self.relu7 = nn.ReLU()
self.dropout = nn.Dropout()
self.U1 = None
self.U2 = None