nlpmodels/models/word2vec.py
def _init_weights(self):
        """
        Initializes the weights of the embeddings vectors.

        Here we set the embeddings to be U(a,b) distribution.
        """

        self._input_embedding.weight.data.uniform_(-0.5 / self._embedding_size,
                                                   0.5 / self._embedding_size)

        self._output_embedding.weight.data.uniform_(-0.5 / self._embedding_size,
                                                    0.5 / self._embedding_size)

        Initializes the weights of the embeddings vectors.

        Here we set the embeddings to be U(a,b) distribution.
        
Initializes the weights of the embeddings vectors.
Here we set the embeddings to be U(a,b) distribution.
Initializes the weights of the embeddings vectors . Here we set the embeddings to be U ( a b ) distribution .
def _init_weights(self):
        self._input_embedding.weight.data.uniform_(-0.5 / self._embedding_size,
                                                   0.5 / self._embedding_size)
        self._output_embedding.weight.data.uniform_(-0.5 / self._embedding_size,
                                                    0.5 / self._embedding_size)
def _init_weights ( self ) : self . _input_embedding . weight . data . uniform_ ( - 0.5 / self . _embedding_size , 0.5 / self . _embedding_size ) self . _output_embedding . weight . data . uniform_ ( - 0.5 / self . _embedding_size , 0.5 / self . _embedding_size )
Initializes the weights of the embeddings vectors.
Initializes the weights of the embeddings vectors .
"""
        Initializes the weights of the embeddings vectors.

        Here we set the embeddings to be U(a,b) distribution.
        """
self