old_contents: #!/usr/bin/env python3
  
  import argparse
  import os
  import numpy as np
  
  import matplotlib as mpl
  mpl.use('Agg')
  import matplotlib.pyplot as plt
  plt.style.use('bmh')
  
  def main():
      parser = argparse.ArgumentParser()
      parser.add_argument('expDir', type=str)
      args = parser.parse_args()
  
      trainP = os.path.join(args.expDir, 'train.csv')
      trainData = np.loadtxt(trainP, delimiter=',').reshape(-1, 3)
      testP = os.path.join(args.expDir, 'test.csv')
      testData = np.loadtxt(testP, delimiter=',').reshape(-1, 3)
  
      N = 475  # number of minibatches + 1
  
      trainI, trainLoss, trainErr = np.split(trainData, [1,2], axis=1)
      trainI, trainLoss, trainErr = [x.ravel() for x in
                                     (trainI, trainLoss, trainErr)]
      trainI_, trainLoss_, trainErr_ = rolling(N, trainI, trainLoss, trainErr)
  
      testI, testLoss, testErr = np.split(testData, [1,2], axis=1)
  
      fig, ax = plt.subplots(1, 1, figsize=(6, 5))
      # plt.plot(trainI, trainLoss, label='Train')
      plt.plot(trainI_, trainLoss_, label='Train')
      plt.plot(testI, testLoss, label='Test')
      plt.xlabel('Epoch')
      plt.ylabel('Cross-Entropy Loss')
      plt.legend()
      ax.set_yscale('log')
      loss_fname = os.path.join(args.expDir, 'loss.png')
      plt.savefig(loss_fname)
      print('Created {}'.format(loss_fname))
  
      fig, ax = plt.subplots(1, 1, figsize=(6, 5))
      # plt.plot(trainI, trainErr, label='Train')
      plt.plot(trainI_, trainErr_, label='Train')
      plt.plot(testI, testErr, label='Test')
      plt.xlabel('Epoch')
      plt.ylabel('Error')
      ax.set_yscale('log')
      plt.legend()
      err_fname = os.path.join(args.expDir, 'error.png')
      plt.savefig(err_fname)
      print('Created {}'.format(err_fname))
  
      loss_err_fname = os.path.join(args.expDir, 'loss-error.png')
      os.system('convert +append {} {} {}'.format(loss_fname, err_fname, loss_err_fname))
      print('Created {}'.format(loss_err_fname))
  
  def rolling(N, i, loss, err):
      i_ = i[N-1:]
      K = np.full(N, 1./N)
      loss_ = np.convolve(loss, K, 'valid')
      err_ = np.convolve(err, K, 'valid')
      return i_, loss_, err_
  
  if __name__ == '__main__':
      main()
  
new_contents: #!/usr/bin/env python3
  
  import argparse
  import os
  import numpy as np
  
  import matplotlib as mpl
  mpl.use('Agg')
  import matplotlib.pyplot as plt
  plt.style.use('bmh')
  
  def main():
      parser = argparse.ArgumentParser()
      parser.add_argument('expDir', type=str)
      args = parser.parse_args()
  
      trainP = os.path.join(args.expDir, 'train.csv')
      trainData = np.loadtxt(trainP, delimiter=',').reshape(-1, 3)
      testP = os.path.join(args.expDir, 'test.csv')
      testData = np.loadtxt(testP, delimiter=',').reshape(-1, 3)
  
      N = 203  # number of minibatches 
  
      trainI, trainLoss, trainErr = np.split(trainData, [1,2], axis=1)
      trainI, trainLoss, trainErr = [x.ravel() for x in
                                     (trainI, trainLoss, trainErr)]
      trainI_, trainLoss_, trainErr_ = rolling(N, trainI, trainLoss, trainErr)
  
      testI, testLoss, testErr = np.split(testData, [1,2], axis=1)
  
      fig, ax = plt.subplots(1, 1, figsize=(6, 5))
      # plt.plot(trainI, trainLoss, label='Train')
      plt.plot(trainI_, trainLoss_, label='Train')
      plt.plot(testI, testLoss, label='Test')
      plt.xlabel('Epoch')
      plt.ylabel('Cross-Entropy Loss')
      plt.legend()
      ax.set_yscale('log')
      loss_fname = os.path.join(args.expDir, 'loss.png')
      plt.savefig(loss_fname)
      print('Created {}'.format(loss_fname))
  
      fig, ax = plt.subplots(1, 1, figsize=(6, 5))
      # plt.plot(trainI, trainErr, label='Train')
      plt.plot(trainI_, trainErr_, label='Train')
      plt.plot(testI, testErr, label='Test')
      plt.xlabel('Epoch')
      plt.ylabel('Error')
      ax.set_yscale('log')
      plt.legend()
      err_fname = os.path.join(args.expDir, 'error.png')
      plt.savefig(err_fname)
      print('Created {}'.format(err_fname))
  
      loss_err_fname = os.path.join(args.expDir, 'loss-error.png')
      os.system('convert +append {} {} {}'.format(loss_fname, err_fname, loss_err_fname))
      print('Created {}'.format(loss_err_fname))
  
  def rolling(N, i, loss, err):
      i_ = i[N-1:]
      K = np.full(N, 1./N)
      loss_ = np.convolve(loss, K, 'valid')
      err_ = np.convolve(err, K, 'valid')
      return i_, loss_, err_
  
  if __name__ == '__main__':
      main()
  
subject: update for bigger mini batches