Update readme to improve spacing

project/README.md

@@ -5,19 +5,29 @@ This project is based around the topic of variational autoencoders, or VAES. VAE
 This project was built using Keras 2.2.4-tf with Tensorflow running underneath. All code is contained within report.ipynb; it contains a total of five separate VAE examples.
 
 Version 1. F. Chollet's VAE from Chapter 8.4 of Deep Learning with Python Notebooks on the mnist dataset.
+
 Version 2. My VAE on the mnist dataset.
+
 Version 3. F. Chollet's VAE on the fashion_mnist dataset.
+
 Version 4. My VAE on the fashion_mnist dataset.
+
 Version 5. My VAE on the cifar10 dataset.
 
 These are the libraries that must be imported to create the VAE. Keras 2.2.4-tf must be installed within Tensorflow 2 (https://www.tensorflow.org/install) beforehand with access to the NumPy library (https://numpy.org/).
 
 from tensorflow import keras
+
 from tensorflow.keras import backend as K
+
 import tensorflow.keras
+
 from tensorflow.keras import layers
+
 from tensorflow.keras import backend as K
+
 from tensorflow.keras.models import Model
+
 import numpy as np
 
 All datasets imported from here:
@@ -27,5 +37,7 @@ from tensorflow.keras.datasets import *
 These are the libraries that must be imported to view results of the VAE trained on various datasets. In order to view, matplotlib (https://matplotlib.org/), scipy (https://www.scipy.org/), and pandas (https://pandas.pydata.org/) must be installed.
 
 import matplotlib.pyplot as plt
+
 from scipy.stats import norm
-import pandas as pd
+
+import pandas as pd