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Derrick Mwiti mwitiderrick

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mwitiderrick / predict.py
Created June 14, 2018 16:46
predictions = classifier.predict(X_test)
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, predictions)
@mwitiderrick
mwitiderrick / 6.py
Created June 14, 2018 16:47
from sklearn.externals import joblib
joblib.dump(tfidfVectorizer, 'tfidfVectorizer.pkl')
joblib.dump(classifier, 'classifier.pkl')
@mwitiderrick
mwitiderrick / 7.py
Created June 14, 2018 16:47
from flask import Flask, render_template, request
from sklearn.externals import joblib
import numpy as np
import re
import nltk
from sklearn.naive_bayes import GaussianNB
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import TfidfVectorizer
nltk.download('stopwords')
nltk.download('wordnet')
@mwitiderrick
mwitiderrick / main.py
Last active June 27, 2018 06:58
def main():
classifier = joblib.load('classifier.pkl')
tfidfVectorizer = joblib.load('tfidfVectorizer.pkl')
if request.method == 'GET':
return render_template('index.html')
if request.method == 'POST':
review = request.form['review']
corpus = []
review = re.sub('[^a-zA-Z]', ' ', review)
@mwitiderrick
mwitiderrick / thesite.html
Created June 14, 2018 16:50
<!DOCTYPE html>
<html>
<head>
<!-- Latest compiled and minified CSS -->
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css" integrity="sha384-BVYiiSIFeK1dGmJRAkycuHAHRg32OmUcww7on3RYdg4Va+PmSTsz/K68vbdEjh4u" crossorigin="anonymous">
<!-- Optional theme -->
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap-theme.min.css" integrity="sha384-rHyoN1iRsVXV4nD0JutlnGaslCJuC7uwjduW9SVrLvRYooPp2bWYgmgJQIXwl/Sp" crossorigin="anonymous">
<!-- Latest compiled and minified JavaScript -->
@mwitiderrick
mwitiderrick / GANs1.py
Created June 28, 2018 12:32
from keras.layers import Input
from keras.models import Model, Sequential
from keras.layers.core import Dense, Dropout
from keras.layers.advanced_activations import LeakyReLU
from keras.datasets import mnist
from keras.optimizers import Adam
from keras import initializers
from tqdm import tqdm
@mwitiderrick
mwitiderrick / GANs0.py
Created June 28, 2018 12:33
import keras
import matplotlib.pyplot as plt
@mwitiderrick
mwitiderrick / GANs2.py
Created June 28, 2018 12:34
import numpy as np
np.random.seed(1000)
Next we set the dimension of a random noise vector.
random_dim = 100
@mwitiderrick
mwitiderrick / GANs3.py
Created June 28, 2018 12:35
def load_minst_data():
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = (x_train.astype(np.float32) - 127.5)/127.5
x_train = x_train.reshape(60000, 784)
return (x_train, y_train, x_test, y_test)
@mwitiderrick
mwitiderrick / GANs4.py
Created June 28, 2018 12:35
optimizer = Adam(lr=0.0002, beta_1=0.5)