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# CTC vanilla and CTC via crossentropy are equal, and their gradients as well. In this reformulation it's easier to experiment with modifications of CTC.
# References on CTC regularization:
#  "A Novel Re-weighting Method for Connectionist Temporal Classification", Li et al, https://arxiv.org/abs/1904.10619
#  "Focal CTC Loss for Chinese Optical Character Recognition on Unbalanced Datasets", Feng et al, https://www.hindawi.com/journals/complexity/2019/9345861/
#  "Improved training for online end-to-end speech recognition systems", Kim et al, https://arxiv.org/abs/1711.02212

import torch
import torch.nn.functional as F

## generate example data

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" Append this to your vimrc file
"""""""""""""""""""""""""""""""""
" auto-update the timestamp right before saving a file
" The Timestamp format is : Sat 07 Dec 2013 12:51:00 AM CST
" Within the 20 first lines, the matched lines are :
" Last [Cc]hange(d)
" Changed
" Last [Mm]odified
" Modified
" Last [Uu]pdate(d)

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from typing import Optional

import numpy as np
import torch
from torch.utils.data import DataLoader, TensorDataset

from baal.active import ActiveLearningDataset


class AlternateIterator:

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#!/bin/bash
#
# Install specific version of a Homebrew formula
#
# Usage: brewv.sh formula_name desired_version
#
# Notes:
# - this will unshallow your brew repo copy. It might take some time the first time 
#   you call this script
# - it will uninstall (instead of unlink) all your other versions of the formula.

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

# Do this to display pytorch version.
# The version used in this gist is 0.3.0.post4.
print(torch.__version__)

# There are three steps to demonstrate multi head network
# 1. build the network

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def plot_timings(loader, n_batches, model_time=0.2, max_time=2.5):
    
    fig, ax = plt.subplots()
    ax.set_axisbelow(True)
    ax.yaxis.grid(which="major", color='black', linewidth=1)
    
    zero_time = time.time()
    
    worker_ids = {}
    worker_count = count()

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def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')):
    """ Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
        Args:
            logits: logits distribution shape (..., vocabulary size)
            top_k >0: keep only top k tokens with highest probability (top-k filtering).
            top_p >0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
    """
    top_k = min(top_k, logits.size(-1))  # Safety check
    if top_k > 0:
        # Remove all tokens with a probability less than the last token of the top-k

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import onnx
import struct
import torch
import torch.nn as nn
import torchvision as tv
import warnings


# enum DataType {
#     UNDEFINED = 0;

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Sample Data

Get cat.bin

Xcode Resources Add cat.bin

Playground

import CoreML

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