fashion-mnist | A MNIST-like fashion product database Benchmark :point_down: | Machine Learning library
kandi X-RAY | fashion-mnist Summary
kandi X-RAY | fashion-mnist Summary
Top functions reviewed by kandi - BETA
- 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
fashion-mnist Key Features
fashion-mnist Examples and Code Snippets
├── fashion-mnist ├── incubator-mxnet ├── m1.1.py ├── m1.2.py ├── m2.1.py ├── m3.1.py ├── m4.1.py └── models # install mxnet prereqs sudo apt install -y build-essential git libopenblas-dev liblapack-dev libopencv-dev python-pip python-dev python-s
unzip data.zip data ├── test │ ├── 0 │ ├── 1 │ ├── 2 │ ├── 3 │ ├── 4 │ ├── 5 │ ├── 6 │ ├── 7 │ ├── 8 │ └── 9 └── train ├── 0
python report.py --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 \ --detailed_metrics=test:loss/L_k,train:loss/L_k,train:loss/kl_q_p,train:loss/k
""" 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
Trending Discussions on fashion-mnist
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:...
ANSWERAnswered 2022-Apr-07 at 13:31
To close this out, moving the content of the comments to here:
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...
ANSWERAnswered 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
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...
ANSWERAnswered 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:
I am referring to the links below to use Tensorboard in Sagemaker Script Mode method.
Below is my tensorboard callback in my training script which is a .py file...
ANSWERAnswered 2020-Dec-11 at 11:40
logdir is not
logs/fit.. but there is the current date appended. Try using a
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:
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?...
ANSWERAnswered 2020-Nov-27 at 11:53
you can try like this:
I created a tf.data dataset, however, I keep on running into this error when trying to fit my Sequential CNN model with it....
ANSWERAnswered 2020-Oct-28 at 08:15
Well you can do a simple expand dims:
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....
ANSWERAnswered 2020-Jun-17 at 06:39
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
c is the number of labels) and
y is of shape
n of type integer, each target takes values in the range
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
c the target should also be of shape
c (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
c and only then compute the MSE loss between your predictions and the generated one-hot targets.
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No vulnerabilities reported
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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