From c32d9da5676cf35cd5316e4552ffe220a482f224 Mon Sep 17 00:00:00 2001
From: Alex Moreo <alejandro.moreo@isti.cnr.it>
Date: Wed, 8 Jul 2020 12:13:35 +0200
Subject: [PATCH] activating every layer
---
src/model/classifiers.py | 1 -
src/model/transformations.py | 17 ++++++++++-------
2 files changed, 10 insertions(+), 8 deletions(-)
diff --git a/src/model/classifiers.py b/src/model/classifiers.py
index b2c0515..3e757f9 100644
--- a/src/model/classifiers.py
+++ b/src/model/classifiers.py
@@ -5,7 +5,6 @@ from sklearn.metrics import accuracy_score, f1_score
from tqdm import tqdm
import math
from sklearn.model_selection import train_test_split
-
from model.early_stop import EarlyStop
from model.transformations import FFProjection
diff --git a/src/model/transformations.py b/src/model/transformations.py
index 4380213..6d0911e 100644
--- a/src/model/transformations.py
+++ b/src/model/transformations.py
@@ -19,7 +19,8 @@ class CNNProjection(nn.Module):
hidden_sizes=[1024],
output_size=out_size,
activation=nn.functional.relu,
- dropout=dropout)
+ dropout=dropout,
+ activate_last=True)
self.output_size = out_size
def convolve(self, x):
@@ -42,8 +43,6 @@ class CNNProjection(nn.Module):
x = self.embed(x) # (N, W, D)
x = self.convolve(x) # (N, len(Ks)*Co]
x = self.fc(x)
- #x = F.relu(self.fc1(x)) # (N, C)
- # x = self.dropout(x)
x = self.l2norm(x)
return x
@@ -52,7 +51,8 @@ class CNNProjection(nn.Module):
class FFProjection(nn.Module):
- def __init__(self, input_size, hidden_sizes, output_size, activation=nn.functional.relu, dropout=0.5):
+ def __init__(self, input_size, hidden_sizes, output_size, activation=nn.functional.relu, dropout=0.5,
+ activate_last=False):
super(FFProjection, self).__init__()
sizes = [input_size] + hidden_sizes + [output_size]
self.ff = nn.ModuleList([
@@ -60,11 +60,14 @@ class FFProjection(nn.Module):
])
self.activation = activation
self.dropout = nn.Dropout(p=dropout)
+ self.activate_last = activate_last
def forward(self, x):
- for linear in self.ff[:-1]:
- x = self.dropout(self.activation(linear(x)))
- x = self.ff[-1](x)
+ last_layer_idx = len(self.ff)-1
+ for i,linear in enumerate(self.ff):
+ x = linear(x)
+ if i < last_layer_idx or self.activate_last:
+ x = self.dropout(self.activation(x))
return x