名称\u Tensorflow的作用域在Tensorboard中不起作用

Question

Settings:
Mac OS 10.14.6
Python 3.7.4
Tensorflow 2.0.0

我有一个问题，当谈到名称的范围设置。 我在代码中编写name_scope()，但在Tensorboard中它没有像this image in Tensorboard那样的名称作用域。 我打算在这个图像中为Flatten、Dense等命名范围

我卸载并重新安装了Tensorflow，但得到了相同的结果。 作为补充信息，我没有得到任何终端错误。 有人知道这种情况吗

def _model_B0001(self):
    with tf.name_scope('1stLayer'):
        inputs = Input(self.shape)
        x = Flatten()(inputs)
        x = Dense(512)(x)
        output1 = Dense(self.nb_classes1, activation='softmax', name='output1')(x)
        output2 = Dense(self.nb_classes2, activation='softmax', name='output2')(x)
        predictions = [output1, output2]
    model = Model(inputs, predictions)
    model.compile(loss={'output1': 'categorical_crossentropy',
                        'output2': 'categorical_crossentropy'},
                  optimizer='adam',
                  metrics=['accuracy'])
    model.summary()
    with open(self.summary_txt, "w") as fp:
        model.summary(print_fn=lambda x: fp.write(x + "\r\n"))
    return model

Answer 1

我自己解决了这个问题。 可能是因为皮普和水蟒的碰撞。 所以我在下面做了

1. 卸载所有pip包

    $ pip freeze > piplist.txt
    $ sudo pip uninstall -r piplist.txt
   

2. 卸载所有anaconda包

卸载包

    $ conda install anaconda-clean
    $ anaconda-clean

删除包后删除空文件夹

    $ rm -fr ~/.anaconda_backup
    $ rm -fr /anaconda3

删除.bash\u配置文件中的内容

    $ sudo nano .bash_profile

1. 重新安装anaconda。从https://www.anaconda.com/distribution/下载

2. 重新运行张力板

   def _model_A0001(self):
    with tf.name_scope('1stLayer') as scope:
       # print(scope)
       # sys.exit()
       inputs = Input(self.shape)
       x = Conv2D(32, (3, 3), border_mode='same', name='conv2d')(inputs)
       x = Activation('relu', name='relu')(x)
       x = MaxPooling2D(pool_size=(2, 2), name='max_pooling')(x)
       x = Dropout(0.25, name='dropout')(x)
   
    with tf.name_scope('2ndLayer'):
       x = Conv2D(64, (3, 3), border_mode='same')(x)
       x = Activation('relu')(x)
       x = Conv2D(64, (3, 3))(x)
       x = MaxPooling2D(pool_size=(2, 2))(x)
       x = Dropout(0.25)(x)
   
    with tf.name_scope('DenseLayer'):
       x = Flatten()(x)
       x = Dense(512)(x)
       x = Activation('relu')(x)
       x = Dropout(0.5)(x)
       output1 = Dense(self.nb_classes1, activation='softmax', name='output1')(x)
       output2 = Dense(self.nb_classes2, activation='softmax', name='output2')(x)
       predictions = [output1, output2]
       # inputs = [inputs, inputs]
       model = Model(inputs, outputs=predictions)
       model.compile(loss={'output1': 'categorical_crossentropy',
                           'output2': 'categorical_crossentropy'},
                     optimizer='adam',
                     metrics=['accuracy'])
    model.summary()
    with open(self.summary_txt, "w") as fp:
       model.summary(print_fn=lambda x: fp.write(x + "\r\n"))
    return model