X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=lazy_linear.py;h=97530ef2829bbae5912990adce85c31bdb4d93d9;hb=2d95f238bbaa0e585b50846d39c98df4aae2b7f9;hp=7c9e398a66b16dcc025471b59b6ac874e2a6d5f3;hpb=7443d768bc437889659ba3ed737297f90fe1922e;p=pytorch.git

diff --git a/lazy_linear.py b/lazy_linear.py
index 7c9e398..97530ef 100755
--- a/lazy_linear.py
+++ b/lazy_linear.py
@@ -1,13 +1,18 @@
-#!/usr/bin/env python-for-pytorch
+#!/usr/bin/env python
+
+# Any copyright is dedicated to the Public Domain.
+# https://creativecommons.org/publicdomain/zero/1.0/
+
+# Written by Francois Fleuret <francois@fleuret.org>
 
 from torch import nn, Tensor
 
-##########
+######################################################################
 
 class LazyLinear(nn.Module):
 
     def __init__(self, out_dim, bias = True):
-        super(LazyLinear, self).__init__()
+        super().__init__()
         self.out_dim = out_dim
         self.bias = bias
         self.core = None
@@ -23,16 +28,25 @@ class LazyLinear(nn.Module):
 
         return self.core(x)
 
-##########
+    def named_parameters(self, memo=None, prefix=''):
+        assert self.core is not None, 'Parameters not yet defined'
+        return super().named_parameters(memo, prefix)
+
+######################################################################
+
+if __name__ == "__main__":
+    model = nn.Sequential(nn.Conv2d(3, 8, kernel_size = 5),
+                          nn.ReLU(inplace = True),
+                          LazyLinear(128),
+                          nn.ReLU(inplace = True),
+                          nn.Linear(128, 10))
+
+    # model.eval()
 
-model = nn.Sequential(nn.Conv2d(1, 8, kernel_size = 5),
-                      nn.ReLU(inplace = True),
-                      LazyLinear(128),
-                      nn.ReLU(inplace = True),
-                      nn.Linear(128, 10))
+    input = Tensor(100, 3, 32, 32).normal_()
 
-# model.eval()
+    output = model(input)
 
-input = Tensor(100, 1, 32, 32).normal_()
+    for n, x in model.named_parameters():
+        print(n, x.size())
 
-output = model(input)