githubharald

"""
Compute score for decoded text in a CTC-trained neural network using TensorFlow:
1. decode text with best path decoding (or some other decoder)
2. feed decoded text into loss function
3. loss is negative logarithm of probability

Example data: two time-steps, 2 labels (0, 1) and the blank label (2). 
Decoding results in [0] (i.e. string containing one entry for label 0).
The probability is the sum over all paths yielding [0], these are: [0, 0], [0, 2], [2, 0]
with probability

kunev

import asyncio

@asyncio.coroutine
def open_file(name):
    print("opening {}".format(name))
    return open(name)

@asyncio.coroutine
def close_file(file):
    print("closing {}".format(file.name))

ceteri

// load error messages from a log into memory
// then interactively search for various patterns

// base RDD
val lines = sc.textFile("log.txt")

// transformed RDDs
val errors = lines.filter(_.startsWith("ERROR"))
val messages = errors.map(_.split("\t")).map(r => r(1))
messages.cache()

simonw

import json, datetime

class RoundTripEncoder(json.JSONEncoder):
    DATE_FORMAT = "%Y-%m-%d"
    TIME_FORMAT = "%H:%M:%S"
    def default(self, obj):
        if isinstance(obj, datetime.datetime):
            return {
                "_type": "datetime",
                "value": obj.strftime("%s %s" % (

abhinav-upadhyay

#!/usr/bin/env python

# An example of decoding/encoding datetime values in JSON data in Python.
# Code adapted from: http://broadcast.oreilly.com/2009/05/pymotw-json.html

# Copyright (c) 2023, Abhinav Upadhyay
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met: