# Contact <francois.fleuret@idiap.ch> for comments & bug reports #
#########################################################################
+import subprocess
+
+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
class MLP(Module):
def __init__(self, input_dim, hidden_dim, output_dim):
- super(MLP, self).__init__()
+ super().__init__()
self.fc1 = nn.Linear(input_dim, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
x = self.fc1(x)
- x = fn.tanh(x)
+ x = torch.tanh(x)
x = self.fc2(x)
return x
mlp = MLP(10, 20, 1)
-input = Variable(Tensor(100, 10).normal_())
-target = Variable(Tensor(100).normal_())
-output = mlp(input)
criterion = nn.MSELoss()
+
+input = torch.randn(100, 10)
+target = torch.randn(100, 1)
+
+output = mlp(input)
+
loss = criterion(output, target)
agtree2dot.save_dot(loss,
- { input: 'input', target: 'target', 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'))
-print('Generated mlp.dot. You can convert it to pdf with')
-print('> dot mlp.dot -Lg -T pdf -o mlp.pdf')
+print('Generated mlp.dot')
+
+try:
+
+ fontname = 'Computer Modern'
+ fontsize = 12
+ subprocess.check_call(['dot', 'mlp.dot',
+ '-Lg',
+ '-T', 'pdf',
+ '-Efontname=' + fontname, '-Efontsize=' + str(fontsize),
+ '-Nfontname=' + fontname, '-Nfontsize=' + str(fontsize),
+ '-o', 'mlp.pdf' ])
+
+except subprocess.CalledProcessError:
+
+ print('Calling the dot command failed. Is Graphviz installed?')
+ sys.exit(1)
+
+print('Generated mlp.pdf')