From 74beea9aa5f3583fc15271650da6419b38aac253 Mon Sep 17 00:00:00 2001
From: Schneider Leo <leo.schneider@etu.ec-lyon.fr>
Date: Mon, 21 Oct 2024 14:03:24 +0200
Subject: [PATCH] datasets
---
model_custom.py | 7 +------
1 file changed, 1 insertion(+), 6 deletions(-)
diff --git a/model_custom.py b/model_custom.py
index 30fb32f..abb8685 100644
--- a/model_custom.py
+++ b/model_custom.py
@@ -42,7 +42,7 @@ class Model_Common_Transformer(nn.Module):
def __init__(self, drop_rate=0.1, embedding_dim=128, nb_aa=23,
regressor_layer_size_rt=512, regressor_layer_size_int=512, decoder_rt_ff=512, decoder_int_ff=512,
n_head=1, seq_length=25,
- charge_max=4, charge_frag_max=3, encoder_ff=512, encoder_num_layer=1, decoder_rt_num_layer=1,
+ charge_max=5, charge_frag_max=3, encoder_ff=512, encoder_num_layer=1, decoder_rt_num_layer=1,
decoder_int_num_layer=1, acti='relu', norm=False):
self.charge_max = charge_max
self.seq_length = seq_length
@@ -101,14 +101,9 @@ class Model_Common_Transformer(nn.Module):
d_model=self.embedding_dim)
def forward(self, seq, charge):
- print(seq.size(),charge.size())
- print(seq, charge)
print(torch.cuda.mem_get_info())
meta_ohe = torch.nn.functional.one_hot(charge - 1, self.charge_max).float()
seq_emb = torch.nn.functional.one_hot(seq, self.nb_aa).float()
- print(seq_emb.shape)
- print(self.nb_aa, self.embedding_dim)
- print(seq_emb)
emb = self.pos_embedding(self.emb(seq_emb))
meta_enc = self.meta_enc(meta_ohe)
--
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