oliverfoggin

//Create the rect you would like your text to be inside of...
CGRect maxTextRect = CGRectMake(0, 0, 200, 60);

//Create the attributed string
NSAttributedString *theString = //... do all the setup.

//Find the rect that the string will draw into **inside the maxTextRect**
CGRect actualRect = [theString boundingRectWithSize:maxTextRect.size options:NSStringDrawingUsesLineFragmentOrigin context:nil];

//Offset the actual rect inside the maxTextRect

niw

/*

Why not simply use textField:shouldChangeCharactersInRange:replacementString:?
===

If you google how to limit a length of text field, you'll see many implementations simply using textField:shouldChangeCharactersInRange:replacementString: like

  - (BOOL)textField:(UITextField *)textField shouldChangeCharactersInRange:(NSRange)range replacementString:(NSString *)string
  {
      return [[textField.text stringByReplacingCharactersInRange:range withString:string] length] <= MAX_LENGTH;

monikkinom

#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

xlvector

# pylint: disable=C0111,too-many-arguments,too-many-instance-attributes,too-many-locals,redefined-outer-name,fixme
# pylint: disable=superfluous-parens, no-member, invalid-name
import sys
sys.path.insert(0, "../../python")
import mxnet as mx
import numpy as np
import cv2, random

from io import BytesIO
from captcha.image import ImageCaptcha

dhpollack

import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.optim import lr_scheduler
import torch.utils.data as data
from torch.nn.utils.rnn import pack_padded_sequence as pack, pad_packed_sequence as unpack
import torchaudio
import torchaudio.transforms as tat

basaundi

#!/usr/bin/env python
# Ander Martinez Sanchez

from __future__ import division, print_function
from math import exp, log
from collections import Counter


def ngram_count(words, n):
    if n <= len(words):

lampts

# required tensorflow 0.12
# required gensim 0.13.3+ for new api model.wv.index2word or just use model.index2word

from gensim.models import Word2Vec
import tensorflow as tf
from tensorflow.contrib.tensorboard.plugins import projector

# loading your gensim
model = Word2Vec.load("YOUR-MODEL")

j314erre

import tensorflow as tf
import numpy as np


class TextCNN(object):
    """
    A CNN for text classification.
    Uses an embedding layer, followed by a convolutional, max-pooling and softmax layer.
    """
    def __init__(

y0ast

Now available here: https://github.com/y0ast/pytorch-snippets/tree/main/fast_mnist

p2

#!/usr/bin/python
#
#  Read a CSV/TSV with a header row (!) and put it into a new sqlite table

import sys
import csv
import sqlite3


class Importer (object):