Add fix_values option

MaestroGraph · Apr 26, 2018 · 044530d · 044530d
1 parent 8ae1dda
commit 044530d
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Showing 2 changed files with 14 additions and 5 deletions.
diff --git a/g1vae.experiment.py b/g1vae.experiment.py
@@ -136,7 +136,7 @@ def generate_er(n=128, m=512, num=64):
 SIZE = 60000
 PLOT = True
 
-def go(nodes=128, links=512, batch=64, epochs=350, k=750, kpe=7, additional=512, modelname='baseline', cuda=False, seed=1, bias=True, lr=0.001, lambd=0.01, subsample=None):
+def go(nodes=128, links=512, batch=64, epochs=350, k=750, kpe=7, additional=512, modelname='baseline', cuda=False, seed=1, bias=True, lr=0.001, lambd=0.01, subsample=None, fix_values=False):
 
     FT = torch.cuda.FloatTensor if cuda else torch.FloatTensor
 
@@ -154,9 +154,10 @@ def go(nodes=128, links=512, batch=64, epochs=350, k=750, kpe=7, additional=512,
 
         zsize = 256
 
-        encoder = GraphASHLayer(nodes, (zsize * 2, ), k=kpe, additional=additional, subsample=subsample)
+        encoder = GraphASHLayer(nodes, (zsize * 2, ), k=kpe, additional=additional, subsample=subsample, fix_values=fix_values)
 
-        decoder = gaussian.CASHLayer((1, zsize), SHAPE, poolsize=1, k=k, additional=additional, has_bias=bias, has_channels=True, adaptive_bias=False, subsample=subsample)
+        decoder = gaussian.CASHLayer((1, zsize), SHAPE, poolsize=1, k=k, additional=additional, has_bias=bias,
+                                     has_channels=True, adaptive_bias=False, subsample=subsample, fix_values=fix_values)
 
         if cuda:
             encoder.cuda()
@@ -292,11 +293,15 @@ def go(nodes=128, links=512, batch=64, epochs=350, k=750, kpe=7, additional=512,
                         help="Sample a subset of the indices to estimate gradients for",
                         default=None, type=float)
 
+    parser.add_argument("-F", "--fix-values", dest="fix_values",
+                        help="Whather to force the values to be 1",
+                        action="store_true")
+
     options = parser.parse_args()
 
     print('OPTIONS ', options)
     LOG.info('OPTIONS ' + str(options))
 
     go(batch=options.batch_size, nodes=options.nodes, links=options.links, k=options.k, kpe=options.kpe, bias=options.bias,
         additional=options.additional, modelname=options.model, cuda=options.cuda,
-        lr=options.lr, lambd=options.lambd, subsample=options.subsample)
+        lr=options.lr, lambd=options.lambd, subsample=options.subsample, fix_values=options.fix_values)
diff --git a/gaussian.py b/gaussian.py
@@ -904,7 +904,7 @@ class CASHLayer(HyperLayer):
     """
     def __init__(self, in_shape, out_shape, k,
                  additional=0, poolsize=4, deconvs=2, ksize=2, sigma_scale=0.1, has_bias=True,
-                 has_channels=False, adaptive_bias=False, subsample=None, min_sigma=0.0):
+                 has_channels=False, adaptive_bias=False, subsample=None, min_sigma=0.0, fix_values=False):
         """
         :param in_shape:
         :param out_shape:
@@ -930,6 +930,7 @@ def forward(self, input):
         self.sigma_scale = sigma_scale
         self.has_channels = has_channels
         self.adaptive_bias = adaptive_bias
+        self.fix_values = fix_values
 
         self.w_rank = len(in_shape) + len(out_shape)
 
@@ -1009,6 +1010,9 @@ def hyper(self, input):
         means, sigmas, values = self.split_out(res, input.size()[1:], self.out_shape)
         sigmas = sigmas * self.sigma_scale
 
+        if self.fix_values:
+            values = values * 0.0 + 1.0
+
         if not self.has_bias:
             return means, sigmas, values
         if self.adaptive_bias: