Keras和TensorBoard AttributeError:“Sequential”对象没有属性“\u get\u distribution\u strategy”

Question

我正在使用keras并尝试使用tensorboard绘制日志。下面您可以找到我得到的错误，以及我正在使用的软件包版本列表。我不能理解它给我的错误是“顺序”对象没有属性“获取分布策略”

包装： Keras 2.3.1 Keras应用程序1.0.8 Keras预处理1.1.0 张力板2.1.0 tensorflow 2.1.0 张量流估计器2.1.0

型号：

model = Sequential()
    model.add(Embedding(MAX_NB_WORDS, EMBEDDING_DIM, input_shape=(X.shape[1],)))
    model.add(GlobalAveragePooling1D())
    #model.add(Dense(10, activation='sigmoid'))
    model.add(Dense(len(CATEGORIES), activation='softmax'))
    model.summary()
    #opt = 'adam'       # Here we can choose a certain optimizer for our model
    opt = 'rmsprop'
    model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])                  # Here we choose the loss function, input our optimizer choice, and set our metrics.

    # Create a TensorBoard instance with the path to the logs directory
    tensorboard = TensorBoard(log_dir='logs/{}'.format(time()),
                    histogram_freq = 1,
                    embeddings_freq = 1,
                    embeddings_data = X)

    history = model.fit(X, Y, epochs=epochs, batch_size=batch_size, validation_split=0.1, callbacks=[tensorboard])

错误：

C:\Users\Bruno\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\callbacks\tensorboard_v2.py:102: UserWarning: The TensorBoard callback does not support embeddings display when using TensorFlow 2.0. Embeddings-related arguments are ignored.
  warnings.warn('The TensorBoard callback does not support '
C:\Users\Bruno\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\framework\indexed_slices.py:433: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.
  "Converting sparse IndexedSlices to a dense Tensor of unknown shape. "
Train on 1123 samples, validate on 125 samples
Traceback (most recent call last):
  File ".\NN_Training.py", line 128, in <module>
    history = model.fit(X, Y, epochs=epochs, batch_size=batch_size, validation_split=0.1, callbacks=[tensorboard])    # Feed in the train
set for X and y and run the model!!!
  File "C:\Users\Bruno\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training.py", line 1239, in fit
    validation_freq=validation_freq)
  File "C:\Users\Bruno\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training_arrays.py", line 119, in fit_loop
    callbacks.set_model(callback_model)
  File "C:\Users\Bruno\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\callbacks\callbacks.py", line 68, in set_model
    callback.set_model(model)
  File "C:\Users\Bruno\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\callbacks\tensorboard_v2.py", line 116, in set_model
    super(TensorBoard, self).set_model(model)
  File "C:\Users\Bruno\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\keras\callbacks.py", line 1532, in
set_model
    self.log_dir, self.model._get_distribution_strategy())  # pylint: disable=protected-access
AttributeError: 'Sequential' object has no attribute '_get_distribution_strategy'```

Answer 1

您的python环境似乎混合了来自keras和tensorflow.keras的导入。试着像这样使用顺序模块：

model = tensorflow.keras.Sequential()

或者将导入内容更改为类似的内容

import tensorflow
layers = tensorflow.keras.layers
BatchNormalization = tensorflow.keras.layers.BatchNormalization
Conv2D = tensorflow.keras.layers.Conv2D
Flatten = tensorflow.keras.layers.Flatten
TensorBoard = tensorflow.keras.callbacks.TensorBoard
ModelCheckpoint = tensorflow.keras.callbacks.ModelCheckpoint

…等等