delete LCAE, adjust training

models/ae.py

@@ -3,7 +3,7 @@ import torch.nn as nn
 import torch.functional as F
 
 class AE(nn.Module):
-    def __init__(self, in_dim=320):
+    def __init__(self, in_dim=400):
         super(AE, self).__init__()
         self.net = nn.Sequential(
             nn.Linear(in_dim, 64),
@@ -16,69 +16,13 @@ class AE(nn.Module):
             nn.ReLU(),
             nn.Linear(64, 64),
             nn.ReLU(),
-            nn.Linear(64, 320),
+            nn.Linear(64, in_dim),
             nn.ReLU(),
         )
 
     def forward(self, data):
-        return self.net(data)
-
-    def init_weights(self):
-        def _weight_init(m):
-            if hasattr(m, 'weight'):
-                if isinstance(m.weight, torch.Tensor):
-                    torch.nn.init.xavier_uniform_(m.weight,
-                                                  gain=nn.init.calculate_gain('relu'))
-            if hasattr(m, 'bias'):
-                if isinstance(m.bias, torch.Tensor):
-                    m.bias.data.fill_(0.01)
-
-        self.apply(_weight_init)
-
-
-
-class LCAE(nn.Module):
-    def __init__(self, in_dim=320):
-        super(LCAE, self).__init__()
-        num_mem = 10
-        mem_size= 8
-        self.num_mem = num_mem
-        self.encode = nn.Sequential(
-            nn.Linear(in_dim, 64),
-            nn.ReLU(),
-            nn.Linear(64, 64),
-            nn.ReLU(),
-            nn.Linear(64, num_mem),
-            nn.Softmax(-1)
-        )
-
-        self.decode = nn.Sequential(
-            nn.Linear(mem_size, 64),
-            nn.ReLU(),
-            nn.Linear(64, 64),
-            nn.ReLU(),
-            nn.Linear(64, 320),
-            nn.ReLU(),
-        )
-
-        self.M = nn.Parameter(
-            torch.randn(num_mem, mem_size)
-        )
-
-    def forward(self, data):
-        alphas = self.encode(data).unsqueeze(-1)
-        entropy_alphas = (alphas * -alphas.log()).sum(1)
-        M = self.M.expand(data.shape[0], *self.M.shape)
-        #print(M.shape, alphas.shape)  # torch.Size([128, 4, 8]) torch.Size([128, 4, 1])
-        elu = nn.ELU()
-        weighted = alphas * (1+elu(M+1e-13))
-        #print(weighted.shape)
-        summed = weighted.sum(1)
-        #print(summed.shape)
-        decoded = self.decode(summed)
-        diversity = (alphas.sum(dim=0)/data.shape[0]).max()
-        #print(alphas[0])
-        return decoded, entropy_alphas, diversity
+        x = data.view(data.shape[0], -1)
+        return self.net(x), x
 
     def init_weights(self):
         def _weight_init(m):

models/layers.py

@@ -21,8 +21,8 @@ class Subspectrogram(object):
 
 if __name__ == '__main__':
     import numpy as np
-    sub_spec_tnfm = Subspectrogram(20, 10)
-    X = np.random.rand(1, 60, 40)
+    sub_spec_tnfm = Subspectrogram(20, 20)
+    X = np.random.rand(1, 80, 40)
     Y = sub_spec_tnfm(X)
-    print(f'\t Sub-Spectrogram transformation from shape {X.shape} to {Y.shape}')
-    print('Done ...')
+    print(f'\tSub-Spectrogram transformation from shape {X.shape} to {Y.shape}')
+    print('\tDone ...')