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# These are all the modules we'll be using later. Make sure you can import them | |
# before proceeding further. | |
from __future__ import print_function | |
import matplotlib.pyplot as plt | |
import numpy as np | |
import os | |
import random | |
import hashlib | |
import json | |
import sys |
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import tweepy | |
from textblob import TextBlob | |
import csv | |
def Tweep(): | |
consumer_key = "" | |
consumer_secret = "" | |
access_token = "" |
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from __future__ import print_function | |
import tensorflow as tf | |
hello = tf.constant('Hello, Tensorflow!') | |
sess = tf.session() | |
print(sess.run(hello)) | |
sess.close() | |
wp_embed_register_handler( 'gist', '/https?:\/\/gist\.github\.com\/([a-z0-9]+)(\?file=.*)?/i', 'bhww_embed_handler_gist' ); |
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from __future__ import print_function | |
import tensorflow as tf | |
a= tf.constant(2) | |
b = tf.constant(3) | |
with tf.Session() as sess: | |
print("a=2, b=3") | |
print("Addition with constants: %i" % sess.run(a+b)) | |
print("Multiplication with constants: %i" % sess.run(a*b)) |
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from __future__ import print_function | |
import tensorflow as tf | |
import numpy | |
import matplotlib.pyplot as plt | |
import random | |
#parameters | |
learning_rate = 0.01 | |
training_epochs = 1000 | |
display_step = 50 |
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from __future__ import print_function | |
import tensorflow as tf | |
# Import MNIST data | |
from tensorflow.examples.tutorials.mnist import input_data | |
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) | |
# Parameters | |
learning_rate = 0.01 |
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from __future__ import print_function | |
import numpy as np | |
import tensorflow as tf | |
# Import MNIST data | |
from tensorflow.examples.tutorials.mnist import input_data | |
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) | |
# In this example, we limit mnist data |
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""" | |
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
BSD License | |
""" | |
import numpy as np | |
# data I/O | |
data = open('input.txt', 'r').read() # should be simple plain text file | |
chars = list(set(data)) | |
data_size, vocab_size = len(data), len(chars) |
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#!/bin/bash | |
sudo systemctl start docker | |
echo "Docker is running!" | |
sudo make dev.checkout | |
sudo make dev.clone | |
sudo make dev.provision | |
echo "Updating devstack, sit back and enjoy!" |
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#!/bin/sh | |
# Aeneas (python3.6+) installation that works only for Debian distros (including WSL with Ubuntu) | |
echo "Updating OS..." | |
sudo apt-get update | |
sudo apt-get upgrade | |
echo "Cloning py3-aeneas..." | |
git clone https://github.com/akki2825/aeneas |
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