abaybektursun

use pyo3::prelude::*;
use pyo3::buffer::PyBuffer;

use std::thread;
use std::sync::mpsc;
use std::sync::Mutex;
use std::sync::Arc;

use std::net::{TcpListener, TcpStream};
use std::io::prelude::*;

abaybektursun

def forward(sentence):
    # Tokenize characters and words
    word_ids = [vocab.word_to_id(w) for w in sentence.split()]
    char_ids = [vocab.word_to_char_ids(w) for w in sentence.split()]

    if sentence.find('<S>') != 0:
        sentence = '<S> ' + sentence

    for i in xrange(len(word_ids)):
        inputs[0, 0] = word_ids[i]

abaybektursun

# For saving demo resources, use batch size 1 and step 1.
BATCH_SIZE = 1
NUM_TIMESTEPS = 1
MAX_WORD_LEN = 50

# File Paths
vocab_file = "language_model/data/vocab-2016-09-10.txt"
save_dir   = "language_model/output"
pbtxt      = "language_model/data/graph-2016-09-10.pbtxt"
ckpt       = "language_model/data/ckpt-*"

abaybektursun

import os
import sys
import numpy as np
import tensorflow as tf

# From lm_1b
import language_model.lm_1b.data_utils as data_utils

from six.moves       import xrange
from google.protobuf import text_format

abaybektursun

#!/bin/bash

# Assume you are in my_project directory

newdir="language_model"
if [ ! -d "$newdir" ]; then
    mkdir "$newdir"
fi

cd "$newdir"

abaybektursun

if (object.edible == true){
  dog.eat(object);
}
else{
  dog.tearToShreds(object);
}

abaybektursun

# Updated to work with TF 1.4
# Working example for my blog post at:
# http://danijar.com/variable-sequence-lengths-in-tensorflow/
import functools
import sets
import tensorflow as tf
from tensorflow import nn


def lazy_property(function):