How to use the deeprank.utils.convert_embed_2_numpy 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 / dataset.py View on Github 
self.feat_size = self.config['feat_size']
self._PAD_ = len(self.word_dict)
self.word_dict[self._PAD_] = '[PAD]'
self.iword_dict['[PAD]'] = self._PAD_
self.embed_dict[self._PAD_] = np.zeros((50, ), dtype=np.float32)
self.W_init_embed = np.float32(
np.random.uniform(-0.02, 0.02, [len(self.word_dict), 50]))
self.embedding = utils.convert_embed_2_numpy(
self.embed_dict, embed = self.W_init_embed)
self.W_init_idf = np.float32(
np.zeros([len(self.word_dict), 1]))
self.idf_embedding = utils.convert_embed_2_numpy(
self.idf_dict, embed = self.W_init_idf)filename=self.Letor07Path + '/docid_doc.txt')
self.embed_dict = utils.read_embedding(
filename=self.Letor07Path + '/embed_wiki-pdc_d50_norm')
self.idf_dict = utils.read_embedding(
filename=self.Letor07Path + '/embed.idf')
self.feat_size = self.config['feat_size']
self._PAD_ = len(self.word_dict)
self.word_dict[self._PAD_] = '[PAD]'
self.iword_dict['[PAD]'] = self._PAD_
self.embed_dict[self._PAD_] = np.zeros((50, ), dtype=np.float32)
self.W_init_embed = np.float32(
np.random.uniform(-0.02, 0.02, [len(self.word_dict), 50]))
self.embedding = utils.convert_embed_2_numpy(
self.embed_dict, embed = self.W_init_embed)
self.W_init_idf = np.float32(
np.zeros([len(self.word_dict), 1]))
self.idf_embedding = utils.convert_embed_2_numpy(
self.idf_dict, embed = self.W_init_idf)deeprank
Rank Protein-Protein Interactions using Deep Learning
