File size: 2,237 Bytes
7fdc7b5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 |
import torch
import torch.nn as nn
from tape.models import modeling_bert
config=modeling_bert.ProteinBertConfig(hidden_size=400)
class ClassificationModel1(torch.nn.Module):
def __init__(self):
super(ClassificationModel1, self).__init__()
self.dense_layer1 = nn.Linear(1024, 2048)
self.dense_layer2 = nn.Linear(2048, 512)
self.output_layer = nn.Linear(512, 45)
self.dropout1 = nn.Dropout(0.2)
self.dropout2 = nn.Dropout(0.3)
def forward(self, protein_sequence):
hidden_layer_output1 = torch.relu(self.dense_layer1(protein_sequence))
#hidden_layer_output1 = self.dropout1(hidden_layer_output1)
hidden_layer_output2 = torch.relu(self.dense_layer2(hidden_layer_output1))
#hidden_layer_output2 = self.dropout2(hidden_layer_output2)
output = self.output_layer(hidden_layer_output2)
return output
class ClassificationModel2(nn.Module):
def __init__(self):
super(ClassificationModel2,self).__init__()
self.attention_layer=nn.Linear(400,1)
self.hidden_layer=nn.Linear(400,1024)
self.output_layer=nn.Linear(1024,45)
self.relu=nn.ReLU()
self.dropout=nn.Dropout(0.1)
self.layernorm1=nn.LayerNorm(400)
self.layernorm2=nn.LayerNorm(1024)
self.attention_selfoutput= modeling_bert.ProteinBertSelfOutput(config)
def forward(self,sequence):
sequence=self.layernorm1(sequence)
attention_values=self.attention_layer(sequence)
attention_values=self.dropout(attention_values)
attention_weights=torch.softmax(attention_values,dim=1)
weighted_embeddings=sequence*attention_weights
attention_embeddings=torch.sum(weighted_embeddings,dim=1)
attention_embeddings=self.attention_selfoutput(attention_embeddings,attention_embeddings)
attention_embeddings=self.dropout(attention_embeddings)
attention_embeddings = self.attention_selfoutput(attention_embeddings, attention_embeddings)
hidden_output=self.dropout(self.relu(self.hidden_layer(attention_embeddings)))
hidden_output=self.layernorm2(hidden_output)
output=self.output_layer(hidden_output)
return output |