mrmaheshrajput

try:
    import faiss
except:
    !pip install faiss-cpu -q
    import faiss
    
    
# Build the index
d = embeddings.shape[1]        # Dimension
index = faiss.IndexFlatL2(d)

rmdort

class AttentionWithContext(Layer):
    """
        Attention operation, with a context/query vector, for temporal data.
        Supports Masking.
        Follows the work of Yang et al. [https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf]
        "Hierarchical Attention Networks for Document Classification"
        by using a context vector to assist the attention
        # Input shape
            3D tensor with shape: `(samples, steps, features)`.
        # Output shape

cbaziotis

from keras import backend as K, initializers, regularizers, constraints
from keras.engine.topology import Layer


def dot_product(x, kernel):
    """
    Wrapper for dot product operation, in order to be compatible with both
    Theano and Tensorflow
    Args:

frozeman

/*
Basically "web3" comes from Mist,
but "Web3" CAN come from  the dapp.

A Dapp has 3 ways to use web3.
2. and 3. would work when in Mist and outside.
*/

// 1. simply use, web3 comes already defined

KonradIT

#HeroProApp Screenshots

##Windows 7 HTML5 Browser App

##Windows 7 Native App

##iOS HTML5/Native (pinned to start screen) Click here to see the screenshots

shlomibabluki

# coding=UTF-8
from __future__ import division
import re

# This is a naive text summarization algorithm
# Created by Shlomi Babluki
# April, 2013


class SummaryTool(object):