#HeroProApp Screenshots
##iOS HTML5/Native (pinned to start screen) Click here to see the screenshots
try: | |
import faiss | |
except: | |
!pip install faiss-cpu -q | |
import faiss | |
# Build the index | |
d = embeddings.shape[1] # Dimension | |
index = faiss.IndexFlatL2(d) |
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 |
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: |
/* | |
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 |
#HeroProApp Screenshots
##iOS HTML5/Native (pinned to start screen) Click here to see the screenshots
# 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): |