WIP

Author: christianversloot

extra_keras_datasets/__init__.py

@@ -1,4 +1,5 @@
 from __future__ import absolute_import
 
 from . import emnist
-from . import kmnist
+from . import kmnist
+from . import svhn

extra_keras_datasets/svhn.py

@@ -0,0 +1,59 @@
+'''
+  Import the SVHN dataset
+  Source: http://ufldl.stanford.edu/housenumbers/
+  Description: Street View House Numbers
+
+  ~~~ Important note ~~~
+  Please cite the following paper when using or referencing the dataset:
+  Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, Andrew Y. Ng Reading Digits in Natural Images with Unsupervised Feature Learning NIPS Workshop on Deep Learning and Unsupervised Feature Learning 2011. Retrieved from http://ufldl.stanford.edu/housenumbers/nips2011_housenumbers.pdf
+
+'''
+
+from keras.utils.data_utils import get_file
+import numpy as np
+from zipfile import ZipFile
+from scipy import io as sio
+import os
+
+def load_data(path='svhn_matlab.npz', type='normal'):
+  """Loads the SVHN dataset.
+  # Arguments
+      path: path where to cache the dataset locally
+          (relative to ~/.keras/datasets).
+      type: any of normal, extra (extra appends ~530K extra images for training)
+  # Returns
+      Tuple of Numpy arrays: `(input_train, target_train), (input_test, target_test)`.
+  """
+  path_train = get_file(f'{path}_train',
+                    origin='http://ufldl.stanford.edu/housenumbers/train_32x32.mat')
+  path_test = get_file(f'{path}_test',
+                    origin='http://ufldl.stanford.edu/housenumbers/test_32x32.mat')
+
+  # Load data from Matlab file.
+  # Source: https://stackoverflow.com/a/53547262
+  mat_train = sio.loadmat(path_train)
+  mat_test = sio.loadmat(path_test)
+  
+  # Prepare training data
+  input_train = mat_train['X']
+  input_train = np.rollaxis(input_train, 3, 0)
+  target_train = mat_train['y'].flatten()
+
+  # Prepare testing data
+  input_test = mat_test['X']
+  input_test = np.rollaxis(input_test, 3, 0)
+  target_test = mat_test['y'].flatten()
+
+  # Append extra data, if required
+  if type == 'extra':
+    path_extra = get_file(f'{path}_extra',
+                  origin='http://ufldl.stanford.edu/housenumbers/extra_32x32.mat')
+    mat_extra = sio.loadmat(path_extra)
+    input_extra = mat_extra['X']
+    input_extra = np.rollaxis(input_extra, 3, 0)
+    target_extra = mat_extra['y'].flatten()
+    input_train = np.insert(input_extra, input_train)
+    target_train = np.concatenate(target_extra, target_train)
+
+  # Return data
+  return (input_train, target_train), (input_test, target_test)