模型无法使用未知张量的张量流数据管道上

Question

我有一个用于视频数据的数据加载程序管道。尽管我指定了管道的输出，但在调用型号fit时仍会遇到以下错误。 “ valueerror：as_list（）未在未知张量上定义”。我搜索了错误，大多数人说这是因为tf.numpy_function返回（tensorflow Pipeline）未知形状。在该功能之后指定形状应解决问题。但是，不是。

AUTOTUNE = tf.data.experimental.AUTOTUNE

#get list of numpy files in directory
train_ds = tf.data.Dataset.list_files("dir") 

#load numpy files (video with shape 40,160,160,3), get corresponding label and output both
#video and label
def get_label(file_path):
    label = tf.strings.split(file_path, os.path.sep)
    return label [-2]

def process_image(file_path):
    label = get_label(file_path)
    video= np.load(file_path, allow_pickle=True)
    video= tf.convert_to_tensor(video/255, dtype=tf.float32) 
    return video, np.float32(label)

train_ds = train_ds.map(lambda item: tf.numpy_function(
          process_image, [item], (tf.float32, tf.float32)),num_parallel_calls = AUTOTUNE ) 

#Convert video to tf object
def set_shape(video, label):
  video = tf.reshape(video, (40,160,160,3))
  #video = tf.ensure_shape(video, (40,160,160,3)) #also does not work
  #video = tf.convert_to_tensor(video, dtype=tf.float32) #also does not work
  return video, label

train_ds = train_ds.map(set_shape)

#batching
train_ds = train_ds.batch(batch_size =5)

#optimazation
train_ds = train_ds.prefetch(AUTOTUNE)


train_ds.take(1) 

尽管该代码的其余部分看起来不错（当我手动输入数据时确实有效），但如果不是，我会粘贴它。

def create_LRCN_model():
    '''
    This function will construct the required LRCN model.
    Returns:
        model: It is the required constructed LRCN model.
    '''
 
    # We will use a Sequential model for model construction.
    model = Sequential()
    
    # Define the Model Architecture.

    ########################################################################################
    
    model.add(TimeDistributed(Conv2D(128, (3, 3), padding='same',activation = 'relu'),
                              input_shape = (40, 160, 160, 3)))
    
    model.add(TimeDistributed(MaxPooling2D((4, 4)))) 
    model.add(TimeDistributed(Dropout(0.25)))
    
    model.add(TimeDistributed(Conv2D(256, (3, 3), padding='same',activation = 'relu')))
    model.add(TimeDistributed(MaxPooling2D((4, 4))))
    model.add(TimeDistributed(Dropout(0.25)))
    
    model.add(TimeDistributed(Conv2D(128, (3, 3), padding='same',activation = 'relu')))
    model.add(TimeDistributed(MaxPooling2D((2, 2))))
    model.add(TimeDistributed(Dropout(0.25)))
    
    model.add(TimeDistributed(Conv2D(64, (3, 3), padding='same',activation = 'relu')))
    model.add(TimeDistributed(MaxPooling2D((2, 2))))
    #model.add(TimeDistributed(Dropout(0.25)))
                                      
    model.add(TimeDistributed(Flatten()))
                                      
    model.add(LSTM(32))
                                      
    model.add(Dense(1, activation = 'sigmoid'))
 
    ########################################################################################
    # Display the models summary.
    model.summary()
    
    # Return the constructed LRCN model.
    return model

LRCN_model = create_LRCN_model()
early_stopping_callback = EarlyStopping(monitor = 'val_loss', patience = 15, mode = 'min', restore_best_weights = True)
LRCN_model.compile(loss='binary_crossentropy', optimizer = 'Adam', metrics = ["accuracy"])
LRCN_model_training_history = LRCN_model.fit(train_ds, validation_data= val_ds, epochs = 70,   callbacks = [early_stopping_callback])

Answer 1

好吧，我找到了另一个解决方案。我不完全知道为什么它有效，只是调用以下功能来完成工作。

def set_shape(video, label):

  video.set_shape((40,160,160, 3))
  label.set_shape([])

  return video, label

Answer 2

知道了！您只需要在模型编译中将“准确性”更改为“ binary_accuracy”即可。它使用您的代码以及一些虚拟视频和标签输入数据为我工作。