Update README

README.md

@@ -5,10 +5,12 @@ An implementation in Chainer of the neural network visualization by Zeiler and F
 ## Run
 
 ### Preparing a Trained Model
-Download the pretrained VGG Chainer model following the README in [this](https://github.com/mitmul/chainer-imagenet-vgg) repository.
+
+Download a pretrained VGG Chainer model following the README in [this](https://github.com/mitmul/chainer-imagenet-vgg) repository.
 
 ### Visualizing the Activations
-Then, run the `visualize.py` script as follows. The VGG model will be feeded with an image and the activations in each of the five convolutional layer will be projected back to the input space, i.e. the space of the original image of size (3, 224, 224). The projections will be stored in the specified output directory.
+
+Run the visualization script as follows. The VGG model will be feeded with an image and the activations in each of the five convolutional layer will be projected back to the input space, i.e. the space of the original image of size (3, 224, 224). The projections will be stored in the specified output directory.
 
 ```bash
 python visualize.py --image-filename images/cat.jpg --model-filename VGG.model --out-dirname results --gpu 0