python - 模块 'tensorflow' 没有属性 'get_default_graph'

问题描述

对于我的硕士学位，我正在尝试创建一个简单的神经网络。但是我的代码中有一些错误，所以程序停止并且没有创建经过训练的模型。

我无法弄清楚错误消息想要告诉我什么以及我需要在代码中更改什么。因此我需要你的帮助。我用谷歌搜索了这个错误，但既不理解，也无法用其他帖子的建议想法以任何方式解决我的错误。

谁能解释一下为什么 tensorflow 想要创建一个图表，以及框架怎么可能不知道它所需的功能？我只需要为可视化安装一个包吗？是否可以忽略此错误？

我不需要任何图形。但是计算机是否需要它来使用 ml 算法进行分类和计算？

请原谅我糟糕的英语和我对 Tensorflow 的不了解。

提前致谢！

我已经安装了最新的 tensorflow 版本 2.0.0-beta1，以及最新的 keras 版本。

此外，我尝试创建一些图表来显示分类过程。不工作。

我还激活了逐步调试模式来找出我的问题。似乎错误出现在我创建、训练和评估神经元网络的评估模型函数中。

该错误发生在模型创建过程中（model = Sequantial()）。

# -*- 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('>%.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')

此 Traceback 显示在 Spyder 中：

Traceback (most recent call last):

  File "<ipython-input-12-25afb095a816>", line 1, in <module>
    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 <module>
    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'

标签： pythontensorflowmachine-learningkerasneural-network

python - 模块 'tensorflow' 没有属性 'get_default_graph' - 我不想要任何图表

问题描述

解决方案

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