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Attributeerror directoryiterator object has no attribute next


Finally, I got some time to create a complete project tutorial on cifar-10 image classification. This was a project that I have done in my college. I will try to teach you how to do this project so that you can also do the same.

So, today we will create an image classifier using the keras library and the cifar-10 dataset. We will be using deep convolutional neural networks to do this project.

Even if you don’t know a lot about deep learning and machine learning, you can just follow along with me and I will try to explain every line of code that I write.

Our Action Plan for Today

Let’s do a quick overview of what we are going to do. We are going to create an image classifier that classifies every object in the cifar-10 dataset. For this project, we will be using the keras library, which is the easiest python library for doing deep learning projects.

We will create a convolutional neural network model and train it. Finally, we will test the output and evaluate the results that we get. Without any further ado, let’s do it. 

Cifar 10 dataset

Cifar-10 is a standard computer vision dataset used for image recognition. It is a subset of the 80 million tiny images dataset and cons

multiprocessing — Process-based parallelism

Python 3.7.3

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Source code:Lib/multiprocessing/

Introduction

is a package that supports spawning processes using an API similar to the module. The package offers both local and remote concurrency, effectively side-stepping the Global Interpreter Lock by using subprocesses instead of threads. Due to this, the module allows the programmer to fully leverage multiple processors on a given machine. It runs on both Unix and Windows.

The module also introduces APIs which do not have analogs in the module. A prime example of this is the object which offers a convenient means of parallelizing the execution of a function across multiple input values, distributing the input data across processes (data parallelism). The following example demonstrates the common practice of defining such functions in a module so that child processes can successfully import that module. This basic example of data parallelism using ,

frommultiprocessingimportPooldeff(x):returnx*xif__name__=='__main__':withPool(5)asp:print(p.map(f,[1,2,3]))

will print to standard output

The class

In , processes are spawned by creating a object a

Python Enhancement Proposals

Author:
Ben Hoyt <benhoyt at gmail.com>
BDFL-Delegate:
Victor Stinner <vstinner at python.org>
Status:
Final
Type:
Standards Track
Created:
30-May-2014
Python-Version:
3.5
Post-History:
27-Jun-2014, 08-Jul-2014, 14-Jul-2014

Abstract

This PEP proposes including a new directory iteration function, , in the standard library. This new function adds useful functionality and increases the speed of by 2-20 times (depending on the platform and file system) by avoiding calls to in most cases.

Rationale

Python’s built-in is significantly slower than it needs to be, because – in addition to calling on each directory – it executes the system call or on each file to resolve whether the entry is a directory or not.

But the underlying system calls – / on Windows and on POSIX systems – already tell you whether the files returned are directories or not, so no further system calls are needed. Further, the Windows system calls come back all the information for a object on the directory entry, such as file size and last modification time.

In short, you can reduce the number of system calls required for a tree fun

Changelog

  • bpo-37280: Use threadpool for reading from file for sendfile fallback mode.

  • bpo-37279: Fix asyncio sendfile support when sendfile sends extra data in fallback mode.

  • bpo-19865: now also supports non-BMP characters on platforms with 16-bit (for example, Windows and AIX).

  • bpo-35922: Fix and to return rather than raise when no relevant rule is defined in the robots.txt file. Patch by Rémi Lapeyre.

  • bpo-36607: Eliminate raised by if internal tasks weak set is changed by another thread during iteration.

  • bpo-36402: Fix a race condition at Python shutdown when waiting for threads. Wait until the Python thread state of all non-daemon threads get deleted (join all non-daemon threads), rather than just wait until non-daemon Python threads complete.

  • bpo-34886: Fix an unintended ValueError from when checking for conflicting and or and or args when they were explicitly provided but with values within a passed in dict rather than as passed directly by name. Patch contributed by Rémi Lapeyre.

  • bpo-37173: The exception message for now correctly reports the passed class rather than the builtins module.

  • bpo-12639: no longer fails if keyfile is not .

  • bpo-36 attributeerror directoryiterator object has no attribute next

    AttributeError: 'DirectoryIterator' object has no attribute 'nb_class'

    Hi,

    I am trying to get the first model to work, which is the vgg16 model, everything looks to load fine and I did not change anything in the vgg16.py file only changed something in the first 6 sentences of the notebook.

    This is the code i am currently using:

    import tensorflow.contrib.keras as keras
    import vgg16; reload(vgg16)
    from vgg16 import Vgg16
    vgg = Vgg16()
    path = ‘/home/gertjan/Documents/data/’
    batches = vgg.get_batches(path + ‘train’, batch_size=64)
    val_batches = vgg.get_batches(path + ‘validation’, batch_size=64)
    vgg.finetune(batches)
    vgg.fit(batches, val_batches, nb_epoch=2)

    When i run this i get the following error:

    AttributeError: ‘DirectoryIterator’ object has no attribute ‘nb_class’

    I found the nb_class being called like this : batches.nb_class in the finetune function and I have tried some things but can not figure out why it is not working, any help would be greatly appreciated.

    Thanks in advance.

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