How to unpack pkl file?

Question

I have a pkl file from MNIST dataset, which consists of handwritten digit images.

I\'d like to take a look at each of those digit images, so I need to unpack the pkl fil

Answer 1

The pickle (and gzip if the file is compressed) module need to be used

NOTE: These are already in the standard Python library. No need to install anything new

Answer 2

Generally

Your pkl file is, in fact, a serialized pickle file, which means it has been dumped using Python's pickle module.

To un-pickle the data you can:

import pickle


with open('serialized.pkl', 'rb') as f:
    data = pickle.load(f)

For the MNIST data set

Note gzip is only needed if the file is compressed:

import gzip
import pickle


with gzip.open('mnist.pkl.gz', 'rb') as f:
    train_set, valid_set, test_set = pickle.load(f)

Where each set can be further divided (i.e. for the training set):

train_x, train_y = train_set

Those would be the inputs (digits) and outputs (labels) of your sets.

If you want to display the digits:

import matplotlib.cm as cm
import matplotlib.pyplot as plt


plt.imshow(train_x[0].reshape((28, 28)), cmap=cm.Greys_r)
plt.show()

The other alternative would be to look at the original data:

http://yann.lecun.com/exdb/mnist/

But that will be harder, as you'll need to create a program to read the binary data in those files. So I recommend you to use Python, and load the data with pickle. As you've seen, it's very easy. ;-)

Answer 3

Handy one-liner

pkl() (
  python -c 'import pickle,sys;d=pickle.load(open(sys.argv[1],"rb"));print(d)' "$1"
)
pkl my.pkl

Will print __str__ for the pickled object.

The generic problem of visualizing an object is of course undefined, so if __str__ is not enough, you will need a custom script.

Answer 4

In case you want to work with the original MNIST files, here is how you can deserialize them.

If you haven't downloaded the files yet, do that first by running the following in the terminal:

wget http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
wget http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
wget http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
wget http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz

Then save the following as deserialize.py and run it.

import numpy as np
import gzip

IMG_DIM = 28

def decode_image_file(fname):
    result = []
    n_bytes_per_img = IMG_DIM*IMG_DIM

    with gzip.open(fname, 'rb') as f:
        bytes_ = f.read()
        data = bytes_[16:]

        if len(data) % n_bytes_per_img != 0:
            raise Exception('Something wrong with the file')

        result = np.frombuffer(data, dtype=np.uint8).reshape(
            len(bytes_)//n_bytes_per_img, n_bytes_per_img)

    return result

def decode_label_file(fname):
    result = []

    with gzip.open(fname, 'rb') as f:
        bytes_ = f.read()
        data = bytes_[8:]

        result = np.frombuffer(data, dtype=np.uint8)

    return result

train_images = decode_image_file('train-images-idx3-ubyte.gz')
train_labels = decode_label_file('train-labels-idx1-ubyte.gz')

test_images = decode_image_file('t10k-images-idx3-ubyte.gz')
test_labels = decode_label_file('t10k-labels-idx1-ubyte.gz')

The script doesn't normalize the pixel values like in the pickled file. To do that, all you have to do is

train_images = train_images/255
test_images = test_images/255