From 66deb59c2d4e76c97db41a82cd410924cd062a50 Mon Sep 17 00:00:00 2001
From: Franck Dary <franck.dary@lis-lab.fr>
Date: Wed, 7 Jul 2021 13:52:28 +0200
Subject: [PATCH] Added big neural network
---
Networks.py | 11 +++++++----
1 file changed, 7 insertions(+), 4 deletions(-)
diff --git a/Networks.py b/Networks.py
index d2441b5..b1da146 100644
--- a/Networks.py
+++ b/Networks.py
@@ -10,10 +10,13 @@ def createNetwork(name, dicts, outputSizes, incremental) :
historyNb = 5
suffixSize = 4
prefixSize = 4
+ hiddenSize = 1600
columns = ["UPOS", "FORM"]
if name == "base" :
- return BaseNet(dicts, outputSizes, incremental, featureFunctionAll, historyNb, suffixSize, prefixSize, columns)
+ return BaseNet(dicts, outputSizes, incremental, featureFunctionAll, historyNb, suffixSize, prefixSize, columns, hiddenSize)
+ elif name == "big" :
+ return BaseNet(dicts, outputSizes, incremental, featureFunctionAll, historyNb, suffixSize, prefixSize, columns, hiddenSize*2)
elif name == "lstm" :
return LSTMNet(dicts, outputSizes, incremental)
elif name == "separated" :
@@ -26,7 +29,7 @@ def createNetwork(name, dicts, outputSizes, incremental) :
################################################################################
class BaseNet(nn.Module):
- def __init__(self, dicts, outputSizes, incremental, featureFunction, historyNb, suffixSize, prefixSize, columns) :
+ def __init__(self, dicts, outputSizes, incremental, featureFunction, historyNb, suffixSize, prefixSize, columns, hiddenSize) :
super().__init__()
self.dummyParam = nn.Parameter(torch.empty(0), requires_grad=False)
@@ -44,9 +47,9 @@ class BaseNet(nn.Module):
self.outputSizes = outputSizes
for name in dicts.dicts :
self.add_module("emb_"+name, nn.Embedding(len(dicts.dicts[name]), self.embSize))
- self.fc1 = nn.Linear(self.inputSize * self.embSize, 1600)
+ self.fc1 = nn.Linear(self.inputSize * self.embSize, hiddenSize)
for i in range(len(outputSizes)) :
- self.add_module("output_"+str(i), nn.Linear(1600, outputSizes[i]))
+ self.add_module("output_"+str(i), nn.Linear(hiddenSize, outputSizes[i]))
self.dropout = nn.Dropout(0.3)
self.apply(self.initWeights)
--
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