系统信息：

OS平台和发行版（例如Linux Ubuntu 16.04）：Windows 10

从（源或二进制）安装的TensorFlow：已安装pip

TensorFlow版本（使用下面的命令）：v2.0.0-rc2-26-g64c3d382ca 2.0.0

Python版本：3.7.1

错误：

无法将TensorFlow Keras LSTM模型保存为Savedmodel格式以导出到Google Cloud存储桶。

错误消息：

ValueError：尝试保存引用不是简单常数的符号Tensor Tensor（“ dropout / mul_1：0”，shape =（None，1280），dtype = float32）的函数b'__ inference_lstm_2_layer_call_fn_36083'。不支持。

代码：

import tensorflow as tf
import os
import cv2
import numpy as np
import matplotlib.pyplot as plt
import tqdm
import datetime
from sklearn.preprocessing import LabelBinarizer 

model = tf.keras.Sequential([
    tf.keras.layers.Masking(mask_value=0.),tf.keras.layers.LSTM(512,dropout=0.5,recurrent_dropout=0.5),tf.keras.layers.Dense(256,activation='relu'),tf.keras.layers.Dropout(0.5),tf.keras.layers.Dense(len(LABELS),activation='softmax')
])

model.compile(loss='categorical_crossentropy',optimizer='rmsprop',metrics=['accuracy','top_k_categorical_accuracy'])

test_file = 'C:/.../testlist01.txt'
train_file = 'C:/.../trainlist01.txt'

with open(test_file) as f:
    test_list = [row.strip() for row in list(f)]

with open(train_file) as f:
    train_list = [row.strip() for row in list(f)]
    train_list = [row.split(' ')[0] for row in train_list]


def make_generator(file_list):
    def generator():
        np.random.shuffle(file_list)
        for path in file_list:
            full_path = os.path.join(BASE_PATH,path).replace('.avi','.npy')

            label = os.path.basename(os.path.dirname(path))
            features = np.load(full_path)

            padded_sequence = np.zeros((SEQUENCE_LENGTH,1280))
            padded_sequence[0:len(features)] = np.array(features)

            transformed_label = encoder.transform([label])
            yield padded_sequence,transformed_label[0]
    return generator

train_dataset = tf.data.Dataset.from_generator(make_generator(train_list),output_types=(tf.float32,tf.int16),output_shapes=((SEQUENCE_LENGTH,1280),(len(LABELS))))
train_dataset = train_dataset.batch(16).prefetch(tf.data.experimental.AUTOTUNE)

valid_dataset = tf.data.Dataset.from_generator(make_generator(test_list),(len(LABELS))))
valid_dataset = valid_dataset.batch(16).prefetch(tf.data.experimental.AUTOTUNE)

model.fit(train_dataset,epochs=17,validation_data=valid_dataset)

BASE_DIRECTORY = 'C:\\...\\saved_model\\LSTM\\1\\';
tf.saved_model.save(model,BASE_DIRECTORY)