awni

import numpy as np

def fleiss_kappa(ratings):
    """
    Args:
        ratings: An N x R numpy array. N is the number of
            samples and R is the number of reviewers. Each
            entry (n, r) is the category assigned to example
            n by reviewer r.
    Returns:

awni

"""
Author: Awni Hannun

This is an example CTC decoder written in Python. The code is
intended to be a simple example and is not designed to be
especially efficient.

The algorithm is a prefix beam search for a model trained
with the CTC loss function.

awni

import time
import torch
import torch.nn as nn
from torch.autograd import Variable

def attend_bmm(eh, dhx):
    dhx = dhx.unsqueeze(1)
    pax = torch.bmm(eh, dhx.transpose(1,2)).squeeze(dim=2)
    ax = nn.functional.softmax(pax)

awni

import torch
import torch.autograd as autograd
import numpy as np

np.random.seed(11)

for size in range(1, 2000, 1):
    a = np.random.randint(0, 2, size).astype(np.uint8)
    av = autograd.Variable(torch.ByteTensor(a))