Learn Computer Vision with MNIST: Download and Explore the Dataset
Downloading -images-idx3-ubyte.gz to./data/MNIST/raw/train-images-idx3-ubyte.gz--------------------------------------------------------------------------- HTTPError Traceback (most recent calllast) in ()2 train_dataset = torchvision.datasets.MNIST(3 root = './data', train = True,----> 4 transform = transforms_apply, download = True5 )6
The above MNIST wrapper in Pytorch datasets would try many possible places where data is available. After running the code you can see that first it tries to download from Yan Le Cun site but fails to download from there and fall back to other possible options.
download mnist
Note that the data is over 10MB, so the download may take several seconds depending on internet speed. If you plan to load the data more than oncewe recommend you download the data once and read it from disk in the future. See examples.
The corresponding resource file(s) of the dataset is/are expected to be located in the specified directory dir. If dir is omitted the directories in DataDeps.default_loadpath will be searched for an existing MNIST subfolder. In case no such subfolder is found, dir will default to /.julia/datadeps/MNIST. In the case that dir does not yet exist, a download prompt will be triggered. You can also use MNIST.download([dir]) explicitly for pre-downloading (or re-downloading) the dataset. Please take a look at the documentation of the package DataDeps.jl for more detail and configuration options.
mnist_path is a string variable that shows the root of the MNIST fileHowever when I run this, I get a runtime error:RuntimeError: Dataset not found. You can use download=True to download it.
The folder structure sounds correct so are you sure the path is right?Also, what kind of issue were you seeing when downloading the MNIST dataset?In case you were hitting a server error, could you try to update torchvision to the nightly and rerun it, as another download mirror was added?
You can use the function Read.mnist() to read data and arrange them properly from MNIST dataset (the open handwriting digit database ). With this package, you can conveniently get all of necessary informations and then immediately start to check whether your machine learning algorithm works well. It can automatically recognize the type of dataset and returns the informations in corresponding structure.
This package represents a community effort to provide a common interface for accessing common Machine Learning (ML) datasets. In contrast to other data-related Julia packages, the focus of MLDatasets.jl is specifically on downloading, unpacking, and accessing benchmark dataset. Functionality for the purpose of data processing or visualization is only provided to a degree that is special to some dataset.
MLDatasets.jl is build on top of the package DataDeps.jl. To load the data the package looks for the necessary files in various locations (see DataDeps.jl for more information on how to configure such defaults). If the data can't be found in any of those locations, then the package will trigger a download dialog to /.julia/datadeps/. To overwrite this on a case by case basis, it is possible to specify a data directory directly in the dataset constructor (e.g. MNIST(dir = )).
On the other hand, I found that there is an uncompressed file /home/jupyter/.fastai/data/mnist.pkl in the notebook that I could use directly. Since it is already uncompressed, and in a different folder than expected in the lesson notebook, I made the following modifications:
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To speed up this example we can set do_training=False to load some pre-trained parameters. If you have the requests package installed, we will download these automatically. If not, download the following file to the directory containing this notebook.
please note that your browser may uncompress these files without telling you.If the files you downloaded have a larger size than the above, they have beenuncompressed by your browser. Simply rename them to remove the .gz extension.Some people have asked me "my application can't open your image files".These files are not in any standard image format. You have to writeyour own (very simple) program to read them. The file format is describedat the bottom of this page.
The following train_mnist function represents normal training code auser would write for training on MNIST dataset. Python users typicallyuse an argparser to conveniently change default values. The onlyadditional argument you need to add to your existing python function isa reporter object that is used to store performance achieved underdifferent hyperparameter settings.
This is a basic Config file that consists of data, model, storage and archive. All future downloads occur at the paths defined in the config file based on the type of download. For example, all future fastai datasets are downloaded to the data while all pretrained model weights are download to model unless the default download location is updated. The config file directory is defined by enviromental variable FASTAI_HOME if it exists, otherwise it is set to /.fastai.
This is a bit of a late reply but I had this same issue and was able to download the dataset with the below code. Import the OS library so it can read the local working directory where the mnist file will be downloaded. I'm not sure if this will also download the mnist "info" data but it's not needed as you can manually assign test and validation samples later in the lesson.
The design of LeNet contains the essence of CNNs that are still used in larger models such as the ones in ImageNet. In general, it consists of a convolutional layer followed by a pooling layer, another convolution layer followed by a pooling layer, and then two fully connected layers similar to the conventional multilayer perceptrons. We have defined the layers in $CAFFE_ROOT/examples/mnist/lenet_train_test.prototxt.
Specifically, this layer has name mnist, type data, and it reads the data from the given lmdb source. We will use a batch size of 64, and scale the incoming pixels so that they are in the range [0,1). Why 0.00390625? It is 1 divided by 256. And finally, this layer produces two blobs, one is the data blob, and one is the label blob.