old_contents: import csv
  import tensorflow as tf
  from src.autoencoder import autoencoder
  
  # Read in CSV file
  brca = []
  count = 0
  
  # brca is 192 long
  with open("brca_toronto_collab_mutect_123_030617.csv") as csvFile:
      reader = csv.reader(csvFile)
      for row in reader:
          row.pop()
          if count != 0:
              floatRow = [float(i) for i in row]
              brca.append(floatRow)
          count += 1
  csvFile.close()
  
  lenBRCA = len(brca[0])
  
  # Initialize weight and adjustment vectors
  b = tf.Variable(tf.zeros([lenBRCA]))
  W = tf.get_variable('W', shape=[lenBRCA, lenBRCA], initializer = tf.contrib.layers.xavier_initializer())
  print(b.get_shape())
  print(W.get_shape())
  
  # Start tensorflow session
  sess = tf.InteractiveSession()
  tf.global_variables_initializer().run()
  autoencoder.printTensor(b)
  autoencoder.printTensor(W)
  
new_contents: import csv
  import tensorflow as tf
  from src.autoencoder import autoencoder
  
  # Read in CSV file
  brca = []
  count = 0
  
  # brca is 192 long
  with open("brca_toronto_collab_mutect_123_030617.csv") as csvFile:
      reader = csv.reader(csvFile)
      for row in reader:
          row.pop()
          if count != 0:
              floatRow = [float(i) for i in row]
              brca.append(floatRow)
          count += 1
  csvFile.close()
  
  lenBRCA = len(brca[0])
  
  # Initialize weight and adjustment vectors
  b = tf.Variable(tf.zeros([lenBRCA]))
  W = tf.get_variable('W', shape=[lenBRCA, lenBRCA], initializer = tf.contrib.layers.xavier_initializer())
  print(b.get_shape())
  print(W.get_shape())
  
  # Start tensorflow session
  sess = tf.InteractiveSession()
  tf.global_variables_initializer().run()
  print(b)
  print(W)
  autoencoder.printTensor(b)
  autoencoder.printTensor(W)
  
subject: test printing tensor