我正在尝试理解和调试我的代码。我尝试使用在 GPU 上在 tf2.0/tf.keras 下开发的 CNN 模型进行预测，但得到了那些错误消息。有人可以帮我解决吗？
这是我的环境配置
enviroments:
python 3.6.8
tensorflow-gpu 2.0.0-rc0
nvidia 418.x
CUDA 10.0
cuDNN 7.6+**
和日志文件，
2019-09-28 13:10:59.833892: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0
2019-09-28 13:11:00.228025: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2019-09-28 13:11:00.957534: E tensorflow/stream_executor/cuda/cuda_dnn.cc:329] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR
2019-09-28 13:11:00.963310: E tensorflow/stream_executor/cuda/cuda_dnn.cc:329] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR
2019-09-28 13:11:00.963416: W tensorflow/core/common_runtime/base_collective_executor.cc:216] BaseCollectiveExecutor::StartAbort Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
[[{{node mobilenetv2_1.00_192/Conv1/Conv2D}}]]
mobilenetv2_1.00_192/block_15_expand_BN/cond/then/_630/Const: (Const): /job:localhost/replica:0/task:0/device:GPU:0=====>GPU Available: True
=====> 4 Physical GPUs, 1 Logical GPUs
mobilenetv2_1.00_192/block_15_expand_BN/cond/then/_630/Const_1: (Const): /job:localhost/replica:0/task:0/device:GPU:0
mobilenetv2_1.00_192/block_15_depthwise_BN/cond/then/_644/Const: (Const): /job:localhost/replica:0/task:0/device:GPU:0
mobilenetv2_1.00_192/block_15_depthwise_BN/cond/then/_644/Const_1: (Const): /job:localhost/replica:0/task:0/device:GPU:0
mobilenetv2_1.00_192/block_15_project_BN/cond/then/_658/Const: (Const): /job:localhost/replica:0/task:0/device:GPU:0
mobilenetv2_1.00_192/block_15_project_BN/cond/then/_658/Const_1: (Const): /job:localhost/replica:0/task:0/device:GPU:0
mobilenetv2_1.00_192/block_16_expand_BN/cond/then/_672/Const: (Const): /job:localhost/replica:0/task:0/device:GPU:0
mobilenetv2_1.00_192/block_16_expand_BN/cond/then/_672/Const_1: (Const): /job:localhost/replica:0/task:0/device:GPU:0
mobilenetv2_1.00_192/block_16_depthwise_BN/cond/then/_686/Const: (Const): /job:localhost/replica:0/task:0/device:GPU:0
mobilenetv2_1.00_192/block_16_depthwise_BN/cond/then/_686/Const_1: (Const): /job:localhost/replica:0/task:0/device:GPU:0
mobilenetv2_1.00_192/block_16_project_BN/cond/then/_700/Const: (Const): /job:localhost/replica:0/task:0/device:GPU:0
mobilenetv2_1.00_192/block_16_project_BN/cond/then/_700/Const_1: (Const): /job:localhost/replica:0/task:0/device:GPU:0
mobilenetv2_1.00_192/Conv_1_bn/cond/then/_714/Const: (Const): /job:localhost/replica:0/task:0/device:GPU:0
mobilenetv2_1.00_192/Conv_1_bn/cond/then/_714/Const_1: (Const): /job:localhost/replica:0/task:0/device:GPU:0
Traceback (most recent call last):
File "NSFW_Server.py", line 162, in <module>
model.predict(initial_tensor)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training.py", line 915, in predict
use_multiprocessing=use_multiprocessing)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training_arrays.py", line 722, in predict
callbacks=callbacks)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training_arrays.py", line 393, in model_iteration
batch_outs = f(ins_batch)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/backend.py", line 3625, in __call__
outputs = self._graph_fn(*converted_inputs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/eager/function.py", line 1081, in __call__
return self._call_impl(args, kwargs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/eager/function.py", line 1121, in _call_impl
return self._call_flat(args, self.captured_inputs, cancellation_manager)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/eager/function.py", line 1224, in _call_flat
ctx, args, cancellation_manager=cancellation_manager)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/eager/function.py", line 511, in call
ctx=ctx)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/eager/execute.py", line 67, in quick_execute
six.raise_from(core._status_to_exception(e.code, message), None)
File "<string>", line 3, in raise_from
tensorflow.python.framework.errors_impl.UnknownError: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
[[node mobilenetv2_1.00_192/Conv1/Conv2D (defined at /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py:1751) ]] [Op:__inference_keras_scratch_graph_10727]
Function call stack:
keras_scratch_graph
编码
if __name__ == "__main__":
print("=====>GPU Available: ", tf.test.is_gpu_available())
tf.debugging.set_log_device_placement(True)
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
try:
# Currently, memory growth needs to be the same across GPUs
tf.config.experimental.set_visible_devices(gpus[0], 'GPU')
tf.config.experimental.set_memory_growth(gpus[0], True)
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print("=====>", len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
except RuntimeError as e:
# Memory growth must be set before GPUs have been initialized
print(e)
paras_path = "./paras/{}".format(int(2011))
model = tf.keras.experimental.load_from_saved_model(paras_path)
initial_tensor = np.zeros((1, INPUT_SHAPE, INPUT_SHAPE, 3))
model.predict(initial_tensor)