removed_trailing_whitespace

dsm/datasets.py

@@ -197,24 +197,24 @@ def _load_support_dataset():
 def _load_mnist():
   """Helper function to load and preprocess the MNIST dataset.
 
-  The MNIST database of handwritten digits, available from this page, has a 
-  training set of 60,000 examples, and a test set of 10,000 examples. 
-  It is a good database for people who want to try learning techniques and 
-  pattern recognition methods on real-world data while spending minimal 
+  The MNIST database of handwritten digits, available from this page, has a
+  training set of 60,000 examples, and a test set of 10,000 examples.
+  It is a good database for people who want to try learning techniques and
+  pattern recognition methods on real-world data while spending minimal
   efforts on preprocessing and formatting [1].
 
   Please refer to http://yann.lecun.com/exdb/mnist/.
   for the original datasource.
 
   References
   ----------
-  [1]: LeCun, Y. (1998). The MNIST database of handwritten digits. 
+  [1]: LeCun, Y. (1998). The MNIST database of handwritten digits.
   http://yann.lecun.com/exdb/mnist/.
 
   """
 
 
-  train = torchvision.datasets.MNIST(root='datasets/', 
+  train = torchvision.datasets.MNIST(root='datasets/',
                                      train=True, download=True)
   x = train.data.numpy()
   x = np.expand_dims(x, 1).astype(float)

dsm/dsm_torch.py

@@ -35,12 +35,11 @@
 
 import torch.nn as nn
 import torch
-import torchvision
 import numpy as np
 
 __pdoc__ = {}
 
-for clsn in ['DeepSurvivalMachinesTorch', 
+for clsn in ['DeepSurvivalMachinesTorch',
              'DeepRecurrentSurvivalMachinesTorch']:
   for membr in ['training', 'dump_patches']:
 
@@ -368,7 +367,7 @@ def create_conv_representation(inputdim, hidden, typ='ConvNet'):
 
   if typ == 'ConvNet':
     inputdim = np.squeeze(inputdim)
-    linear_dim = ((((inputdim-2) // 2) - 2) // 2) ** 2 
+    linear_dim = ((((inputdim-2) // 2) - 2) // 2) ** 2
     linear_dim *= 16
     embedding = nn.Sequential(
       nn.Conv2d(1, 6, 3),
@@ -474,8 +473,8 @@ def __init__(self, inputdim, k, typ='ConvNet',
         nn.Linear(hidden, k, bias=True)
         ) for r in range(self.risks)})
 
-    self.embedding = create_conv_representation(inputdim=inputdim, 
-                                                hidden=hidden, 
+    self.embedding = create_conv_representation(inputdim=inputdim,
+                                                hidden=hidden,
                                                 typ='ConvNet')
 
   def forward(self, x, risk='1'):