Py36 fix

1_linear_regression.py

@@ -41,7 +41,7 @@ def main():
 
     for i in range(100):
         cost = 0.
-        num_batches = len(X) / batch_size
+        num_batches = len(X) // batch_size
         for k in range(num_batches):
             start, end = k * batch_size, (k + 1) * batch_size
             cost += train(model, loss, optimizer, X[start:end], Y[start:end])

2_logistic_regression.py

@@ -8,9 +8,11 @@
 
 
 def build_model(input_dim, output_dim):
-    # We don't need the softmax layer here since CrossEntropyLoss already uses it internally.
+    # We don't need the softmax layer here since CrossEntropyLoss already
+    # uses it internally.
     model = torch.nn.Sequential()
-    model.add_module("linear", torch.nn.Linear(input_dim, output_dim, bias=False))
+    model.add_module("linear",
+                     torch.nn.Linear(input_dim, output_dim, bias=False))
     return model
 
 
@@ -56,10 +58,11 @@ def main():
 
     for i in range(100):
         cost = 0.
-        num_batches = n_examples / batch_size
+        num_batches = n_examples // batch_size
         for k in range(num_batches):
             start, end = k * batch_size, (k + 1) * batch_size
-            cost += train(model, loss, optimizer, trX[start:end], trY[start:end])
+            cost += train(model, loss, optimizer,
+                          trX[start:end], trY[start:end])
         predY = predict(model, teX)
         print("Epoch %d, cost = %f, acc = %.2f%%"
               % (i + 1, cost / num_batches, 100. * np.mean(predY == teY)))

3_neural_net.py

@@ -57,7 +57,7 @@ def main():
 
     for i in range(100):
         cost = 0.
-        num_batches = n_examples / batch_size
+        num_batches = n_examples // batch_size
         for k in range(num_batches):
             start, end = k * batch_size, (k + 1) * batch_size
             cost += train(model, loss, optimizer, trX[start:end], trY[start:end])

4_modern_neural_net.py

@@ -61,7 +61,7 @@ def main():
 
     for i in range(100):
         cost = 0.
-        num_batches = n_examples / batch_size
+        num_batches = n_examples // batch_size
         for k in range(num_batches):
             start, end = k * batch_size, (k + 1) * batch_size
             cost += train(model, loss, optimizer, trX[start:end], trY[start:end])

5_convolutional_net.py

@@ -78,7 +78,7 @@ def main():
 
     for i in range(20):
         cost = 0.
-        num_batches = n_examples / batch_size
+        num_batches = n_examples // batch_size
         for k in range(num_batches):
             start, end = k * batch_size, (k + 1) * batch_size
             cost += train(model, loss, optimizer, trX[start:end], trY[start:end])

data_util.py

@@ -1,11 +1,20 @@
 import gzip
 import os
 from os import path
-import urllib
 import numpy as np
 
+import sys
+if sys.version_info.major < 3:
+    import urllib
+else:
+    import urllib.request as request
+
+
 DATASET_DIR = 'datasets/'
 
+MNIST_FILES = ["train-images-idx3-ubyte.gz", "train-labels-idx1-ubyte.gz",
+               "t10k-images-idx3-ubyte.gz", "t10k-labels-idx1-ubyte.gz"]
+
 
 def download_file(url, local_path):
     dir_path = path.dirname(local_path)
@@ -14,13 +23,15 @@ def download_file(url, local_path):
         os.makedirs(dir_path)
 
     print("Downloading from '%s' ..." % url)
-    urllib.URLopener().retrieve(url, local_path)
+    if sys.version_info.major < 3:
+        urllib.URLopener().retrieve(url, local_path)
+    else:
+        request.urlretrieve(url, local_path)
 
 
 def download_mnist(local_path):
     url_root = "http://yann.lecun.com/exdb/mnist/"
-    for f_name in ["train-images-idx3-ubyte.gz", "train-labels-idx1-ubyte.gz",
-                   "t10k-images-idx3-ubyte.gz", "t10k-labels-idx1-ubyte.gz"]:
+    for f_name in MNIST_FILES:
         f_path = os.path.join(local_path, f_name)
         if not path.exists(f_path):
             download_file(url_root + f_name, f_path)
@@ -39,6 +50,11 @@ def load_mnist(ntrain=60000, ntest=10000, onehot=True):
     data_dir = os.path.join(DATASET_DIR, 'mnist/')
     if not path.exists(data_dir):
         download_mnist(data_dir)
+    else:
+        # check all files
+        checks = [path.exists(os.path.join(data_dir, f)) for f in MNIST_FILES]
+        if not np.all(checks):
+            download_mnist(data_dir)
 
     with gzip.open(os.path.join(data_dir, 'train-images-idx3-ubyte.gz')) as fd:
         buf = fd.read()