我正在尝试使用LSTM图层训练模型。我正在使用GPU，并且所有需要的库都已加载。

当我这样构建模型时：

model = keras.Sequential()

model.add(layers.LSTM(256, activation="relu", return_sequences=False))  # note the activation function
model.add(layers.Dropout(0.2))

model.add(layers.Dense(256, activation="relu"))
model.add(layers.Dropout(0.2))

model.add(layers.Dense(1))
model.add(layers.Activation(activation="sigmoid"))

model.compile(
    loss=keras.losses.BinaryCrossentropy(),
    optimizer="adam",
    metrics=["accuracy"]
)

这是可行的，但它在LSTM层上使用activation="relu"，所以它不是CuDNNLSTM --如果我没记错的话，当激活函数是tanh（默认值）时，它会自动选择。
所以，它慢得让人痛苦，我想运行更快的CuDNNLSTM。我的代码：

model = keras.Sequential()

model.add(layers.LSTM(256, return_sequences=False))
model.add(layers.Dropout(0.2))

model.add(layers.Dense(256, activation="relu"))
model.add(layers.Dropout(0.2))

model.add(layers.Dense(1))
model.add(layers.Activation(activation="sigmoid"))

model.compile(
    loss=keras.losses.BinaryCrossentropy(),
    optimizer="adam",
    metrics=["accuracy"]
)

基本上是一样的，只是没有提供激活函数，所以会用到tanh，但现在不是训练，输出的结尾是这样的：

2021-04-19 22:41:46.046218: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2021-04-19 22:41:46.046426: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2021-04-19 22:41:46.046642: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2021-04-19 22:41:46.046942: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2021-04-19 22:41:46.047124: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2021-04-19 22:41:46.047312: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll
2021-04-19 22:41:46.047489: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2021-04-19 22:41:46.047663: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2021-04-19 22:41:46.047936: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2021-04-19 22:41:46.665456: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-04-19 22:41:46.665712: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0 
2021-04-19 22:41:46.665876: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N 
2021-04-19 22:41:46.666186: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2982 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
2021-04-19 22:41:46.667505: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-04-19 22:42:07.374456: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)
Epoch 1/50
2021-04-19 22:42:08.922891: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2021-04-19 22:42:09.272264: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2021-04-19 22:42:09.302667: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll

Process finished with exit code -1073740791 (0xC0000409)

它只是开始第一个纪元，然后冻结一分钟，并退出与这个奇怪的退出代码。

• 输入数据的形状：tf.Tensor([50985 29 7], shape=(3,), dtype=int32)个
• 我的GPU：Nvidia GTX 1050 Ti
• CUDA：v11.3
• 操作系统：Windows 10
• 集成开发环境：PyCharm

找到这个问题的解决方案有点困难，因为我没有输出任何错误。我做错了什么吗？有人遇到过类似的问题吗？应该有什么帮助？

//编辑;我尝试过：

• 使用更少的单位（2个单位，而不是256个单位）和更低的batch_size运行此模型
• 使用python 3.7.1将tensorflow降级为2.4.0，将CUDA降级为11.0，将cudnn降级为8.0.1（根据TensorFlow网站上的列表，这应该是正确的组合）
• 重新启动我的电脑：）