python - Tensorflow 的 name_scope 在 Tensorboard 中不起作用

Question

Settings:
Mac OS 10.14.6
Python 3.7.4
Tensorflow 2.0.0

我在 name_scope 设置方面遇到问题。我在代码中编写 name_scope() ，但 Tensorboard 中没有 name_scope，如 this image in Tensorboard 。我打算在此图像中为 Flatten、Dense 等创建一个名称范围。

我卸载并重新安装 Tensorflow，但得到了相同的结果。作为附加信息，我在终端中没有收到任何错误。有人对这种情况有任何想法吗？

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

这个问题我自己解决了。可能是因为 pip 和 anaconda 的碰撞。所以我做了下面的事情。

1. 卸载所有 pip 软件包。

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

2. 卸载所有 anaconda 软件包。

卸载软件包。

    $ 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_profile中的内容

    $ sudo nano .bash_profile

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

重新运行张量板。

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