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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github pl8787 / DeepRank_PyTorch / deeprank / rank_module / deeprank_net.py View on Github external
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']
github pl8787 / DeepRank_PyTorch / deeprank / rank_module / matchpyramid_net.py View on Github external
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

Apache-2.0
Latest version published 3 years ago

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