code: def load_dataset():
      # We first define a download function, supporting both Python 2 and 3.
      if sys.version_info[0] == 2:
          from urllib import urlretrieve
      else:
          from urllib.request import urlretrieve
  
      def download(
          filename, source="http://yann.lecun.com/exdb/mnist/"
      ):
          print("Downloading %s" % filename)
          urlretrieve(source + filename, filename)
  
      # We then define functions for loading MNIST images and labels.
      # For convenience, they also download the requested files if needed.
      import gzip
  
      def load_mnist_images(filename):
          if not os.path.exists(filename):
              download(filename)
          # Read the inputs in Yann LeCun's binary format.
          with gzip.open(filename, "rb") as f:
              data = np.frombuffer(f.read(), np.uint8, offset=16)
          # The inputs are vectors now, we reshape them to monochrome 2D images,
          # following the shape convention: (examples, channels, rows, columns)
          data = data.reshape(-1, 1, 28, 28)
          # The inputs come as bytes, we convert them to float32 in range [0,1].
          # (Actually to range [0, 255/256], for compatibility to the version
          # provided at http://deeplearning.net/data/mnist/mnist.pkl.gz.)
          return data / np.float32(256)
  
      def load_mnist_labels(filename):
          if not os.path.exists(filename):
              download(filename)
          # Read the labels in Yann LeCun's binary format.
          with gzip.open(filename, "rb") as f:
              data = np.frombuffer(f.read(), np.uint8, offset=8)
          # The labels are vectors of integers now, that's exactly what we want.
          return data
  
      # We can now download and read the training and test set images and labels.
      X_train = load_mnist_images("train-images-idx3-ubyte.gz")
      y_train = load_mnist_labels("train-labels-idx1-ubyte.gz")
      X_test = load_mnist_images("t10k-images-idx3-ubyte.gz")
      y_test = load_mnist_labels("t10k-labels-idx1-ubyte.gz")
  
      # We reserve the last 10000 training examples for validation.
      X_train, X_val = X_train[:-10000], X_train[-10000:]
      y_train, y_val = y_train[:-10000], y_train[-10000:]
  
      # We just return all the arrays in order, as expected in main().
      # (It doesn't matter how we do this as long as we can read them again.)
      return X_train, y_train, X_val, y_val, X_test, y_test
  
file_path: Lasagne/examples/mnist.py
imports: from __future__ import print_function
  import sys
  import os
  import time
  import numpy
  import theano
  import theano.tensor
  import lasagne
label_desc: Use image node_id 1 for calling a global function with example usage: load_dataset() and returns: X_train,  y_train,  X_val,  y_val,  X_test,  y_test, unknown, data