Typo Fix (#6017)

examples/cifar10_cnn.py

@@ -91,7 +91,7 @@ else:
         horizontal_flip=True,  # randomly flip images
         vertical_flip=False)  # randomly flip images
 
-    # Compute quantities required for featurewise normalization
+    # Compute quantities required for feature-wise normalization
     # (std, mean, and principal components if ZCA whitening is applied).
     datagen.fit(x_train)
 

examples/mnist_acgan.py

@@ -222,7 +222,7 @@ if __name__ == '__main__':
             noise = np.random.uniform(-1, 1, (2 * batch_size, latent_size))
             sampled_labels = np.random.randint(0, 10, 2 * batch_size)
 
-            # we want to train the genrator to trick the discriminator
+            # we want to train the generator to trick the discriminator
             # For the generator, we want all the {fake, not-fake} labels to say
             # not-fake
             trick = np.ones(2 * batch_size)

examples/mnist_net2net.py

@@ -26,7 +26,7 @@ Notes
 
 Experiments
 - Teacher model: a basic CNN model trained on MNIST for 3 epochs.
-- Net2WiderNet exepriment:
+- Net2WiderNet experiment:
   + Student model has a wider Conv2D layer and a wider FC layer.
   + Comparison of 'random-padding' vs 'net2wider' weight initialization.
   + With both methods, student model should immediately perform as well as

examples/mnist_swwae.py

@@ -35,12 +35,12 @@ applied as a bias because we know the MNIST digits are mapped to [0,1].
 References:
 [3]
 'Deep Residual Learning for Image Recognition'
-Kaiming He, xiangyu Zhang, Shaoqing Ren, Jian Sun
+Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
 https://arxiv.org/abs/1512.03385v1
 
 [4]
 'Identity Mappings in Deep Residual Networks'
-Kaiming He, xiangyu Zhang, Shaoqing Ren, Jian Sun
+Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
 https://arxiv.org/abs/1603.05027v3
 
 '''