How to use the deeprank.rank_module.RankNet function in deeprank
To help you get started, we’ve selected a few deeprank examples, based on popular ways it is used in public projects.
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pl8787 / DeepRank_PyTorch / deeprank / rank_module / deeprank_net.py View on Github 
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils.rnn import pad_sequence
from deeprank import rank_module
class DeepRankNet(rank_module.RankNet):
def __init__(self, config):
super().__init__(config)
self.input_type = 'LL'
self.qw_embedding = nn.Embedding(
config['vocab_size'],
config['dim_weight'],
padding_idx=config['pad_value']
)
self.embedding = nn.Embedding(
config['vocab_size'],
config['embed_dim'],
padding_idx=config['pad_value']
)
self.embedding.weight.requires_grad = config['finetune_embed']from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
from deeprank import rank_module
class MatchPyramidNet(rank_module.RankNet):
def __init__(self, config):
super().__init__(config)
self.input_type = 'S'
self.embedding = nn.Embedding(
config['vocab_size'],
config['embed_dim'],
padding_idx=config['pad_value']
)
self.embedding.weight.requires_grad = config['finetune_embed']
cin = config['simmat_channel']
self.conv_layers = []
for cout, h, w in config['conv_params']:deeprank
Rank Protein-Protein Interactions using Deep Learning
