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z`=ibu-Qa2*3Q_upfX^Cy+qzjM1=T7KcRzgw*1zoH-s{$$_pjJTRL5JLh?3kAOOi(M z#9Nblf{;}2(z6F5wGSTNPK zP3>JiXgJuJ+W+esyR3t~tE{P=HPnle@&D9O{#yvsqo%_Gb5nlc;^w8~|He6LSMvY=c$fhVB|H}g%#S7qfsdOLMoQ~L!N<+>&&BX?a#HegaZqv#a8dGc zbN|DDIr%Ah1vp_fv>|YOFen`vOoNW-zY##h$-%+RNy+i=qqL_){cpg*3FZ@kA=1+U zU>?5z%n9)F3j8O>DZu~Ft^U6=xH$jW{~sKfpO5!{a^U~bE5OCW|8E`m{}v^{#n15{ h&HsoO_)meH99&#HuyuOK`+pEWKPnxajH)c^e*wp?j%)w` diff --git a/mlp.py b/mlp.py index 21d166b..4bf5841 100755 --- a/mlp.py +++ b/mlp.py @@ -19,30 +19,32 @@ import subprocess +import torch 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): - 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 = Tensor(100, 10).normal_() -target = Tensor(100, 1).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, -- 2.39.5