X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=agtree2dot.git;a=blobdiff_plain;f=README.md;h=69806721794f3085c45668ec9d828f369addf76a;hp=4c219b713b8e2f3199a66ead5e6dd46082153076;hb=HEAD;hpb=63f04303f0320d25d36e6a4f9f535e62cdb139e1

diff --git a/README.md b/README.md
index 4c219b7..6980672 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,8 @@
 # Introduction #
 
-This package provides a function that generates a dot file from the
-auto-grad graph.
+This package provides a function that generates a
+[dot file](https://en.wikipedia.org/wiki/DOT_(graph_description_language))
+from a [PyTorch](http://pytorch.org) autograd graph.
 
 # Usage #
 
@@ -9,19 +10,19 @@ auto-grad graph.
 
 ### agtree2dot.save_dot(variable, variable_labels, result_file) ###
 
-Saves into `result_file` a dot file corresponding to the auto-grad graph for `variable`, which can be either a single `Variable` or a set of `Variable`s. The dictionary `variable_labels` associates strings to some variables, which will be used in the resulting graph.
+Saves into `result_file` a dot file corresponding to the autograd
+graph for the `Variable` `variable`. The dictionary `variable_labels`
+associates strings to some variables, which will be used in the
+resulting graph.
 
 ## Example ##
 
-A typical use would be:
+A typical use is provided in [mlp.py](https://fleuret.org/git-extract/agtree2dot/mlp.py):
 
 ```python
-import torch
-
 from torch import nn
 from torch.nn import functional as fn
 from torch import Tensor
-from torch.autograd import Variable
 from torch.nn import Module
 
 import agtree2dot
@@ -39,21 +40,36 @@ class MLP(Module):
         return x
 
 mlp = MLP(10, 20, 1)
-input = Variable(Tensor(100, 10).normal_())
-target = Variable(Tensor(100).normal_())
+input = Tensor(100, 10).normal_()
+target = Tensor(100, 1).normal_()
 output = mlp(input)
 criterion = nn.MSELoss()
 loss = criterion(output, target)
 
 agtree2dot.save_dot(loss,
-                    { input: 'input', loss: 'loss' },
+                    {
+                        input: 'input',
+                        target: 'target',
+                        loss: 'loss',
+                        mlp.fc1.weight: 'weight1',
+                        mlp.fc1.bias: 'bias1',
+                        mlp.fc2.weight: 'weight2',
+                        mlp.fc2.bias: 'bias2',
+                    },
                     open('./mlp.dot', 'w'))
-```
 
-which would generate a file mlp.dot, which can then be translated to pdf with
+print('Generated mlp.dot')
 
-```
-dot mlp.dot -Lg -T pdf -o mlp.pdf
+try:
+    subprocess.check_call(['dot', 'mlp.dot', '-Lg', '-T', 'pdf', '-o', 'mlp.pdf' ])
+
+except subprocess.CalledProcessError:
+    print('Calling the dot command failed. Is Graphviz installed?')
+    sys.exit(1)
+
+print('Generated mlp.pdf')
 ```
 
-to produce [mlp.pdf](https://fleuret.org/git-extract/agtree2dot/mlp.pdf).
+which would generate a file mlp.dot and try to generate
+[mlp.pdf](https://fleuret.org/git-extract/agtree2dot/mlp.pdf) from it
+with [Graphviz tools.](http://www.graphviz.org/)