nigeljyng

from keras import backend as K
from keras.engine import InputSpec
from keras.engine.topology import Layer
import numpy as np


class TemporalMaxPooling(Layer):
    """
    This pooling layer accepts the temporal sequence output by a recurrent layer
    and performs temporal pooling, looking at only the non-masked portion of the sequence.

BrikerMan

from gensim.models import KeyedVectors

# Load gensim word2vec
w2v_path = '<Gensim File Path>'
w2v = KeyedVectors.load_word2vec_format(w2v_path)

import io

# Vector file, `\t` seperated the vectors and `\n` seperate the words
"""

cbaziotis

class SelfAttention(nn.Module):
    def __init__(self, attention_size, batch_first=False, non_linearity="tanh"):
        super(SelfAttention, self).__init__()

        self.batch_first = batch_first
        self.attention_weights = Parameter(torch.FloatTensor(attention_size))
        self.softmax = nn.Softmax(dim=-1)

        if non_linearity == "relu":
            self.non_linearity = nn.ReLU()