Added weight_variable_glorot

.gitignore

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+__pycache__
+

decagon_pytorch/weight.py

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+import torch
+import numpy as np
+
+
+def weight_variable_glorot(input_dim, output_dim):
+    """Create a weight variable with Glorot & Bengio (AISTATS 2010)
+    initialization.
+    """
+    init_range = np.sqrt(6.0 / (input_dim + output_dim))
+    initial = -init_range + 2 * init_range * \
+        torch.rand(( input_dim, output_dim ), dtype=torch.float32)
+    initial = initial.requires_grad_(True)
+    return initial