Added dropout_sparse()

decagon_pytorch/__init__.py

@@ -0,0 +1,3 @@
+from .weights import *
+from .convolve import *
+from .model import *

decagon_pytorch/dropout.py

@@ -0,0 +1,18 @@
+import torch
+
+
+def dropout_sparse(x, keep_prob):
+    """Dropout for sparse tensors.
+    """
+    x = x.coalesce()
+    i = x._indices()
+    v = x._values()
+    size = x.size()
+
+    n = keep_prob + torch.rand(len(v))
+    n = torch.floor(n).to(torch.bool)
+    i = i[:,n]
+    v = v[n]
+    x = torch.sparse_coo_tensor(i, v, size=size)
+
+    return x * (1./keep_prob)

decagon_pytorch/weight.py → decagon_pytorch/weights.py

@@ -2,7 +2,7 @@ import torch
 import numpy as np
 
 
-def weight_variable_glorot(input_dim, output_dim):
+def init_glorot(input_dim, output_dim):
     """Create a weight variable with Glorot & Bengio (AISTATS 2010)
     initialization.
     """