Tushar-N

import torch
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence

seqs = ['gigantic_string','tiny_str','medium_str']

# make <pad> idx 0
vocab = ['<pad>'] + sorted(set(''.join(seqs)))

# make model

swyoon

import numpy as np
import tensorflow as tf

__author__ = "Sangwoong Yoon"

def np_to_tfrecords(X, Y, file_path_prefix, verbose=True):
    """
    Converts a Numpy array (or two Numpy arrays) into a tfrecord file.
    For supervised learning, feed training inputs to X and training labels to Y.
    For unsupervised learning, only feed training inputs to X, and feed None to Y.

apaszke

from graphviz import Digraph
import torch
from torch.autograd import Variable, Function

def iter_graph(root, callback):
    queue = [root]
    seen = set()
    while queue:
        fn = queue.pop()
        if fn in seen:

spro

import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.autograd import Variable
from torch import optim
import numpy as np
import math, random

# Generating a noisy multi-sin wave 

reddragon

import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
import torch.optim as optim

from torch.autograd import Variable

Dref360

import operator
import threading
from functools import reduce

import keras
import keras.backend as K
from keras.engine import Model
import numpy as np
import tensorflow as tf
import time

vgoklani

#Source code with the blog post at http://monik.in/a-noobs-guide-to-implementing-rnn-lstm-using-tensorflow/
import numpy as np
import random
from random import shuffle
import tensorflow as tf

from tensorflow.models.rnn import rnn_cell
from tensorflow.models.rnn import rnn

NUM_EXAMPLES = 10000

vgoklani

upstream myapp {
    server 127.0.0.1:8081;
}

limit_req_zone $binary_remote_addr zone=login:10m rate=1r/s;

server {
    listen 443 ssl spdy;
    server_name _;
  

philipperemy

from __future__ import print_function

import numpy as np
from keras.callbacks import Callback
from keras.layers import Dense
from keras.layers import LSTM
from keras.models import Sequential
from numpy.random import choice

from utils import prepare_sequences

infra-0-0

"""
The most simple DNS client written for Python with asyncio:

* Only A record is support (no CNAME, no AAAA, no MX, etc.)
* Almost no error handling
* Doesn't  support fragmented UDP packets (is it possible?)

"""
import asyncio
import logging