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Update.
[agtree2dot.git]
/
mlp.py
diff --git
a/mlp.py
b/mlp.py
index
21d166b
..
4bf5841
100755
(executable)
--- a/
mlp.py
+++ b/
mlp.py
@@
-19,30
+19,32
@@
import subprocess
import subprocess
+import torch
from torch import nn
from torch import nn
-from torch.nn import functional as fn
-from torch import Tensor
from torch.nn import Module
import agtree2dot
class MLP(Module):
def __init__(self, input_dim, hidden_dim, output_dim):
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)
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)
x = self.fc2(x)
return x
mlp = MLP(10, 20, 1)
-input = Tensor(100, 10).normal_()
-target = Tensor(100, 1).normal_()
-output = mlp(input)
criterion = nn.MSELoss()
criterion = nn.MSELoss()
+
+input = torch.randn(100, 10)
+target = torch.randn(100, 1)
+
+output = mlp(input)
+
loss = criterion(output, target)
agtree2dot.save_dot(loss,
loss = criterion(output, target)
agtree2dot.save_dot(loss,