old_contents: from keras.models import model_from_json
  from dspp_cnn import X, Y, weights
  from dsppkeras.datasets import dspp
  
  # load YAML and create model
  with open('model.json', 'r') as fp:
      model = model_from_json(fp.read())
  # load weights into new model
  model.load_weights("model.h5")
  print("Loaded model from disk")
  
  prediction = model.predict(X[::100])
  
  import matplotlib
  matplotlib.use('Agg')
  import matplotlib.pyplot as plt
  
  fig = plt.figure(figsize=(9*3, 9*3))
  for i, (exp, w, pred) in enumerate(zip(Y[::100], weights[::100], prediction)):
      ax = fig.add_subplot(9,9,i+1)
      ax.plot(exp, label="ncSPC")
      ax.plot(pred, label="predicion")
      ax.plot(w, label="weights")
      ax.legend()
      ax.set_xlim(0,250)
  fig.tight_layout()
  fig.savefig("model/plot.png")
  
new_contents: from keras.models import model_from_json
  from dspp_train import X, Y, weights
  from dsppkeras.datasets import dspp
  
  # load YAML and create model
  with open('model.json', 'r') as fp:
      model = model_from_json(fp.read())
  # load weights into new model
  model.load_weights("model.h5")
  print("Loaded model from disk")
  
  prediction = model.predict(X[::100])
  
  import matplotlib
  matplotlib.use('Agg')
  import matplotlib.pyplot as plt
  
  fig = plt.figure(figsize=(9*3, 9*3))
  for i, (exp, w, pred) in enumerate(zip(Y[::100], weights[::100], prediction)):
      ax = fig.add_subplot(9,9,i+1)
      ax.plot(exp, label="ncSPC")
      ax.plot(pred, label="predicion")
      ax.plot(w, label="weights")
      ax.legend()
      ax.set_xlim(0,250)
  fig.tight_layout()
  fig.savefig("model/plot.png")
  
subject: Fix the import dependency