title: module 'tensorflow' has no attribute 'get_default_graph' - i don't want any graph
body: <p>for my master degree, I'm trying to create a simple neuronal network.
  But there are some errors in my code, so the programm stops and doesn't create a trained model.</p>
  
  <p>I couldn't figure out what the error message wants to tell me and what I need to change in my code. Hence I need your help. I googled the error, but neither understood, nor could I solve my error in any way with the proposed ideas of other posts.</p>
  
  <p>Can anyone explain me why tensorflow wants to create a graph and how it is possible that the framework doesn't know the needed function for it? Do I just have to install a package for the visualisation? Is it Possible to ignore this error? </p>
  
  <p>I don't need any graphics. But does the computer need it for the classification and calculation with a ml-algorithm?</p>
  
  <p>Please excuse my poor English and my unawareness of Tensorflow either.</p>
  
  <p>Thanks in advance!</p>
  
  <p>I've installed the newest tensorflow version 2.0.0-beta1, as well as the latest keras version.</p>
  
  <p>Moreover, I've tried to create some graphs to show the classification process. Doesn't work.</p>
  
  <p>I also activated the step-by-step debugging mode to find out my problem.
  It seems the error appears inside the evaluate_model function in which I create, train and evaluate a neuronal network.</p>
  
  <p>The error occurs during the model creation process (model = Sequantial()).</p>
  
  <pre><code># -*- coding: utf-8 -*-
  """
  Created on Wed Apr  3 16:26:14 2019
  
  @author: mattdoe
  """
  
  from data_preprocessor_db import data_storage # validation data
  from sklearn.model_selection import StratifiedKFold
  from sklearn.metrics import confusion_matrix
  from keras.utils import to_categorical
  from keras.models import Sequential
  from keras.layers import Dense
  from keras.utils import normalize
  from numpy import mean
  from numpy import std
  from numpy import array
  
  
  
  # create and evaluate a single multi-layer-perzeptron
  def evaluate_model(Train, Test, Target_Train, Target_Test):
      # define model
      model = Sequential()
      # input layer automatically created
      model.add(Dense(9, input_dim=9, kernel_initializer='normal', activation='relu')) # 1st hidden layer
      model.add(Dense(9, kernel_initializer='normal', activation='relu')) # 2nd hidden layer
      model.add(Dense(9, activation='softmax')) #output layer
  
      # create model
      model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
  
      # fit model
      model.fit(Train, to_categorical(Target_Train), epochs=50, verbose=0)
  
      # evaluate the model
      test_loss, test_acc = model.evaluate(Test, to_categorical(Target_Test), verbose=0)
  
      # as well: create a confussion matrix
      predicted = model.predict(Test)
      conf_mat = confusion_matrix(Target_Test, predicted)
  
      return model, test_acc, conf_mat
  
  
  
  # for seperation of data_storage
  # Link_ID = []
  Input, Output = list(), list()
  
  # list all results of k-fold cross-validation
  scores, members, matrix = list(), list(), list()
  
  # seperate data_storage in Input and Output data
  for items in data_storage:
      # Link_ID = items[0] # identifier not needed
      Input.append([items[1], items[2], items[3], items[4], items[5], items[6], items[7], items[8], items[9]]) # Input: all characteristics
      Output.append(items[10]) # Output: scenario_class 1 to 8
  
  # change to numpy_array (scalar index array)
  Input = array(Input)
  Output = array(Output)
  
  # normalize Data
  Input = normalize(Input)
  # Output = normalize(Output) not needed; categorical number
  
  # prepare k-fold cross-validation
  kfold = StratifiedKFold(n_splits=15, random_state=1, shuffle=True)
  
  for train_ix, test_ix in kfold.split(Input, Output):
      # select samples
      Train, Target_Train = Input[train_ix], Output[train_ix]
      Test, Target_Test = Input[test_ix], Output[test_ix]
  
      # evaluate model
      model, test_acc, conf_mat = evaluate_model(Train, Test, Target_Train, Target_Test)
  
      # display each evalution result
      print('&gt;%.3f' % test_acc)
  
      # add result to list
      scores.append(test_acc)
      members.append(model)
      matrix.append(conf_mat)
  
  # summarize expected performance
  print('Estimated Accuracy %.3f (%.3f)' % (mean(scores), std(scores)))
  # as well in confursion_matrix
  print ('Confussion Matrix %' %(mean(matrix)))
  
  
  
  # save model // trained neuronal network
  model.save('neuronal_network_1.h5')
  </code></pre>
  
  <p>This Traceback is shown in Spyder:</p>
  
  <pre><code>Traceback (most recent call last):
  
    File "&lt;ipython-input-12-25afb095a816&gt;", line 1, in &lt;module&gt;
      runfile('C:/Workspace/Master-Thesis/Programm/MapValidationML/ml_neuronal_network_1.py', wdir='C:/Workspace/Master-Thesis/Programm/MapValidationML')
  
    File "C:\ProgramData\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 786, in runfile
      execfile(filename, namespace)
  
    File "C:\ProgramData\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 110, in execfile
      exec(compile(f.read(), filename, 'exec'), namespace)
  
    File "C:/Workspace/Master-Thesis/Programm/MapValidationML/ml_neuronal_network_1.py", line 77, in &lt;module&gt;
      model, test_acc, conf_mat = evaluate_model(Train, Test, Target_Train, Target_Test)
  
    File "C:/Workspace/Master-Thesis/Programm/MapValidationML/ml_neuronal_network_1.py", line 24, in evaluate_model
      model = Sequential()
  
    File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\sequential.py", line 87, in __init__
      super(Sequential, self).__init__(name=name)
  
    File "C:\ProgramData\Anaconda3\lib\site-packages\keras\legacy\interfaces.py", line 91, in wrapper
      return func(*args, **kwargs)
  
    File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\network.py", line 96, in __init__
      self._init_subclassed_network(**kwargs)
  
    File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\network.py", line 294, in _init_subclassed_network
      self._base_init(name=name)
  
    File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\network.py", line 109, in _base_init
      name = prefix + '_' + str(K.get_uid(prefix))
  
    File "C:\ProgramData\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py", line 74, in get_uid
      graph = tf.get_default_graph()
  
  AttributeError: module 'tensorflow' has no attribute 'get_default_graph'
  </code></pre>