llSourcell

#Author - @greentfrapp

def attention(self, query, key, value):
		# Equation 1 in Vaswani et al. (2017)
		# 	Scaled dot product between Query and Keys
		output = tf.matmul(query, key, transpose_b=True) / (tf.cast(tf.shape(query)[2], tf.float32) ** 0.5)
		# 	Softmax to get attention weights
		attention_weights = tf.nn.softmax(output)
		# 	Multiply weights by Values
		weighted_sum = tf.matmul(attention_weights, value)

llSourcell

import torch.nn as nn
import torch.nn.functional as F


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)

llSourcell

from fastText import load_model

classifier = load_model("model_tweet.bin")  
texts = ['Life is good', 'Life is great', 'Life is bad']

labels = classifier.predict(texts)  
print (labels)  

llSourcell

#preprocessing
import nltk
#word2vec mode
import gensim

text_sample="""Renewed fighting has broken out in South Sudan between forces loyal to the president and vice-president. A reporter in the capital, Juba, told the BBC gunfire and large explosions could be heard all over the city; he said heavy artillery was being used. More than 200 people are reported to have died in clashes since Friday. The latest violence came hours after the UN Security Council called on the warring factions to immediately stop the fighting. In a unanimous statement, the council condemned the violence "in the strongest terms" and expressed "particular shock and outrage" at attacks on UN sites. It also called for additional peacekeepers to be sent to South Sudan.
Chinese media say two Chinese UN peacekeepers have now died in Juba. Several other peacekeepers have been injured, as well as a number of civilians who have been caught in crossfire. The latest round of violence erupted when troops loyal to President Salva Kiir and first Vic

llSourcell

def cosine_similarity_ngrams(a, b):
    vec1 = Counter(a)
    vec2 = Counter(b)
    
    intersection = set(vec1.keys()) & set(vec2.keys())
    numerator = sum([vec1[x] * vec2[x] for x in intersection])

    sum1 = sum([vec1[x]**2 for x in vec1.keys()])
    sum2 = sum([vec2[x]**2 for x in vec2.keys()])
    denominator = math.sqrt(sum1) * math.sqrt(sum2)

llSourcell

import dgl
import torch as th

g = dgl.DGLGraph()
g.add_nodes(10)
# A couple edges one-by-one
for i in range(1, 4):
    g.add_edge(i, 0)
# A few more with a paired list
src = list(range(5, 8)); dst = [0]*3

llSourcell

import os
import tensorflow as tf

if __name__ == '__main__':

   #Step 1 - Inspect the test data
   for example in tf.python_io.tf_record_iterator("test_data/before_2011_in_tr/in_tr.tfrecord"):
       print(tf.train.Example.FromString(example))

   #Step 2 - Train the model on the test data

llSourcell

A.) Build
     i. Install NPM Dependencies
     ii. Run ES-Linter
     iii. Run Code-Minifier
B.) Test
     i. Run unit, functional and end-to-end test.
     ii. Run pkg to compile Node.js application
C.) Deploy
     i. Production
        1.) Launch EC2 instance on AWS

llSourcell

import numpy as np
import sys
q=13
A=np.array([[4 ,1, 11, 10],[5, 5 ,9 ,5],[3, 9 ,0 ,10],[1, 3 ,3 ,2],[12, 7 ,3 ,4],[6, 5 ,11 ,4],[3, 3, 5, 0]])
sA = np.array([[6],[9],[11],[11]])
eA = np.array([[0],[-1],[1],[1],[1],[0],[-1]])
bA = np.matmul(A,sA)%q
print bA
bA = np.add(bA,eA)%q
print

llSourcell

# FGSM example code in PyTorch
def fgsm_attack(image, epsilon, data_grad):
    # Collect the element-wise sign of the data gradient
    sign_data_grad = data_grad.sign()
    # Create the perturbed image by adjusting each pixel of the input image
    perturbed_image = image + epsilon*sign_data_grad
    # Adding clipping to maintain [0,1] range
    perturbed_image = torch.clamp(perturbed_image, 0, 1)
    # Return the perturbed image
    return perturbed_image