fashion-mnist | A MNIST-like fashion product database Benchmark :point_down: | Machine Learning library

 by   zalandoresearch Python Version: Current License: MIT

kandi X-RAY | fashion-mnist Summary

kandi X-RAY | fashion-mnist Summary

fashion-mnist is a Python library typically used in Retail, Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Generative adversarial networks applications. fashion-mnist has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.

A MNIST-like fashion product database. Benchmark :point_down:

            kandi-support Support

              fashion-mnist has a medium active ecosystem.
              It has 10843 star(s) with 2825 fork(s). There are 330 watchers for this library.
              It had no major release in the last 6 months.
              There are 26 open issues and 75 have been closed. On average issues are closed in 51 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of fashion-mnist is current.

            kandi-Quality Quality

              fashion-mnist has 0 bugs and 0 code smells.

            kandi-Security Security

              fashion-mnist has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              fashion-mnist code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              fashion-mnist is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              fashion-mnist releases are not available. You will need to build from source code and install.
              Build file is available. You can build the component from source.
              Installation instructions are not available. Examples and code snippets are available.
              fashion-mnist saves you 241 person hours of effort in developing the same functionality from scratch.
              It has 587 lines of code, 32 functions and 14 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed fashion-mnist and discovered the below as its top functions. This is intended to give you an instant insight into fashion-mnist implemented functionality, and help decide if they suit your requirements.
            • Safely guard memory usage
            • Restart the server
            • Start the workers
            • Close all workers
            • Convert to a sprite image
            • Invert grayscale
            • Convert a mnist to a numpy matrix
            • Create a sprite image
            • Run the worker
            • Get the accuracy of a classifier
            • Return the current epoch as an integer
            • Get a logger
            • Creates base directory if necessary
            • Create a file
            • Parse CLI arguments
            • Parse a parameter value
            • Parse tasks from a json file
            • Parse list
            • Get a json logger
            • Start worker threads
            • Start the S3 thread
            • Parse arguments
            Get all kandi verified functions for this library.

            fashion-mnist Key Features

            No Key Features are available at this moment for fashion-mnist.

            fashion-mnist Examples and Code Snippets

            Offline Development
            Pythondot img1Lines of Code : 54dot img1no licencesLicense : No License
            copy iconCopy
            ├── fashion-mnist
            ├── incubator-mxnet
            └── models
            # install  mxnet prereqs
            sudo apt install -y build-essential git libopenblas-dev liblapack-dev libopencv-dev python-pip python-dev python-s  
            Pythondot img2Lines of Code : 24dot img2License : Permissive (MIT)
            copy iconCopy
            ├── test
            │   ├── 0
            │   ├── 1
            │   ├── 2
            │   ├── 3
            │   ├── 4
            │   ├── 5
            │   ├── 6
            │   ├── 7
            │   ├── 8
            │   └── 9
            └── train
                ├── 0
            Additional Results,Binarized MNIST, Fashion MNIST and Omniglot
            Pythondot img3Lines of Code : 13dot img3License : Permissive (MIT)
            copy iconCopy
            python --exp=sigmoid-belief-network  \
               --keys=dataset,estimator,iw,warmup  \
               --metrics=test:loss/L_k,train:loss/L_k,train:loss/kl_q_p,train:grads/snr  \
            umap - plot fashion mnist example
            Pythondot img4Lines of Code : 50dot img4License : Non-SPDX (BSD 3-Clause "New" or "Revised" License)
            copy iconCopy
            UMAP on the Fashion MNIST Digits dataset using Datashader
            This is a simple example of using UMAP on the Fashion-MNIST
            dataset. The goal of this example is largely to demonstrate
            the use o  

            Community Discussions


            Pytorch: How to transform image patches into matrix of feature vectors?
            Asked 2022-Apr-08 at 00:40

            For use as input in a neural network, I want to obtain a matrix of feature vectors from image patches. I'm using the Fashion-MNIST dataset (28x28 images) and have used Tensor.unfold to obtain patches (16 7x7 patches) by doing:



            Answered 2022-Apr-07 at 13:31

            To close this out, moving the content of the comments to here:



            How to use MSELoss function for Fashion_MNIST in pytorch?
            Asked 2021-May-30 at 12:28

            I want to get through Fashion_Mnist data, I would like to see the output gradient which might be mean squared sum between first and second layer

            My code first below



            Answered 2021-May-30 at 12:28

            The error is caused by the number of samples in the dataset and the batch size.

            In more detail, the training MNIST dataset includes 60,000 samples, your current batch_size is 128 and you will need 60000/128=468.75 loops to finish training on one epoch. So the problem comes from here, for 468 loops, your data will have 128 samples but the last loop just contains 60000 - 468*128 = 96 samples.

            To solve this problem, I think you need to find the suitable batch_size and the number of neural in your model as well.

            I think it should work for computing loss



            How to check the output gradient by each layer in pytorch in my code?
            Asked 2021-May-29 at 11:31

            I am working on the pytorch to learn.

            And There is a question how to check the output gradient by each layer in my code.

            My code is below



            Answered 2021-May-29 at 11:31

            Well, this is a good question if you need to know the inner computation within your model. Let me explain to you!

            So firstly when you print the model variable you'll get this output:



            How to use Tensorboard in AWS Sagemaker
            Asked 2020-Dec-11 at 11:40


            Answered 2020-Dec-11 at 11:40

            Your tensorboard logdir is not logs/fit.. but there is the current date appended. Try using a logs/fit as log_dir and see if it's working.


            If you want to use tensorboard locally you have to send tensorboard logs to S3 and read from there. In order to do this you have to do what your third linked example does, so include sagemaker debugger:

            from sagemaker.debugger import TensorBoardOutputConfig

            tensorboard_output_config = TensorBoardOutputConfig( s3_output_path='s3://path/for/tensorboard/data/emission', container_local_output_path='/local/path/for/tensorboard/data/emission' )

            then your tensorboard command will be something like:

            AWS_REGION= AWS_LOG_LEVEL=3 tensorboard --logdir s3://path/for/tensorboard/data/emission

            Alternatively if you want to use tensorboard in the notebook you have to do what the second linked example does, so simply install in a cell and run tensorboard with something like:




            How could you use imshow to show a greyscale image from a row in a dataframe?
            Asked 2020-Nov-27 at 11:53

            So I have this fashion-mnist dataset in which each label is binary (representing two different clothes items) and the feature labels are called pixel1, pixel2, pixel3 etc. The features values are the number of pixels at that feature. The dataset has been imported and converted to a data frame with pandas.

            What I'm trying to do here is to take one row and use imshow to display the clothes item as a greyscale image. Does anyone know how to do this?



            Answered 2020-Nov-27 at 11:53

            you can try like this:



            How to expand the dimension of each batch in a tensorflow dataset
            Asked 2020-Oct-30 at 19:20

            I created a dataset, however, I keep on running into this error when trying to fit my Sequential CNN model with it.



            Answered 2020-Oct-28 at 08:15

            Well you can do a simple expand dims:



            Pytorch: Dimensions for cross entropy is correct but somehow wrong for MSE?
            Asked 2020-Jun-17 at 09:24

            I was creating a program that would take in as input the Fashion MNIST set and I was tweaking around with my model to see how different parameters would change the accuracy.

            One of the tweaks I made to my model was to change my model's loss function from cross entropy to MSE.



            Answered 2020-Jun-17 at 06:39

            nn.CrossEntropyLoss and nn.MSELoss are completely different loss functions with fundamentally different rationale behind them.

            nn.CrossEntropyLoss is a loss function for discrete labeling tasks. Therefore it expects as inputs a prediction of label probabilities and targets as ground-truth discrete labels: x shape is nxc (where c is the number of labels) and y is of shape n of type integer, each target takes values in the range {0,...,c-1}.

            In contrast, nn.MSELoss is a loss function for regression tasks. Therefore it expects both predictions and targets to be of the same shape and data type. That is, if your prediction is of shape nxc the target should also be of shape nxc (and not just n as in the cross-entropy case).

            If you are insisting on using MSE loss instead of cross entropy, you will need to convert the target integer labels you currently have (of shape n) into 1-hot vectors of shape nxc and only then compute the MSE loss between your predictions and the generated one-hot targets.


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install fashion-mnist

            You can download it from GitHub.
            You can use fashion-mnist like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.


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