fix

Author: peisuke

network.py

@@ -5,24 +5,6 @@
 class Network(nn.Module):
     def __init__(self, input_dim):
         super(Network, self).__init__()
-        self.l1 = nn.Linear(input_dim, 128)
-        self.l2 = nn.Linear(128, 128 - input_dim)
-        self.l3 = nn.Linear(128, 128)
-        self.l4 = nn.Linear(128, 128)
-        self.l5 = nn.Linear(128, 1)
-
-    def forward(self, x):
-        h = F.softplus(self.l1(x), beta=100)
-        h = F.softplus(self.l2(h), beta=100)
-        h = torch.cat((h, x), axis=1)
-        h = F.softplus(self.l3(h), beta=100)
-        h = F.softplus(self.l4(h), beta=100)
-        h = self.l5(h)
-        return h
-
-class NetworkLarge(nn.Module):
-    def __init__(self, input_dim):
-        super(NetworkLarge, self).__init__()
         self.l1 = nn.Linear(input_dim, 512)
         self.l2 = nn.Linear(512, 512)
         self.l3 = nn.Linear(512, 512)

utils.py

@@ -4,7 +4,7 @@
 import torch.autograd as autograd
 import torch.nn as nn
 
-from network import NetworkLarge as Network
+from network import Network
 
 def sample_fake(pts, noise=0.3):
     sampled = pts + torch.normal(0, 1, pts.shape) * noise.unsqueeze(1)