在keras上使用gpu的方法
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参考技术A 80Yes you can run keras models on GPU. Few things you will have to check first.
your system has GPU (Nvidia. As AMD doesn't work yet)
You have installed the GPU version of tensorflow
You have installed CUDA installation instructions
Verify that tensorflow is running with GPU check if GPU is working
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
OR
from tensorflow.python.client import device_libprint(device_lib.list_local_devices())
output will be something like this:
[ name: "/cpu:0"device_type: "CPU", name: "/gpu:0"device_type: "GPU"]
Once all this is done your model will run on GPU:
To Check if keras(>=2.1.1) is using GPU:
from keras import backend as K
K.tensorflow_backend._get_available_gpus()
All the best.
在 LSTM 网络的输入上使用 Masking 时，Keras（TensorFlow 后端）多 GPU 模型（4gpus）失败
【中文标题】在 LSTM 网络的输入上使用 Masking 时，Keras（TensorFlow 后端）多 GPU 模型（4gpus）失败【英文标题】：Keras(TensorFlow backend) multi-gpu model(4gpus) is failing when using Masking on input of LSTM network 【发布时间】：2020-01-03 18:02:31 【问题描述】：在 LSTM 中屏蔽输入层并尝试使用 TensorFlow 背景在 Keras 的 fi_genrator 的多 GPU 模型上运行会引发错误。
为 LSTM 创建了一个 fit_generator，代码在多 GPU 模型上成功运行（我看到所有 GPU 都在 watch -n0.5 nvidia-smi 上使用）。因为我的代码有 8 个时间戳，并且并非所有时间戳都始终可用。所以，我想对输入使用掩码。但是，当我屏蔽输入并运行代码时，会出现错误。
class quickdrawSequence(Sequence):
def __init__(self, batch_size=128,shuffle=True,total_combinations = total_combinations,listFils=listFiles):
self.batch_size = batch_size
self.shuffle = shuffle
self.total_combinations = total_combinations
self.listFiles=listFils
self.on_epoch_end()
def on_epoch_end(self):
#'Updates indexes after each epoch'
if self.shuffle == True:
np.random.shuffle(self.listFiles)
def __len__(self):
# number of batches for each epoch
return self.total_combinations//self.batch_size
def __getitem__(self, idx): # this idx comes from the call of genrator by fit_generator since we inherited sequence
#print("index from __getitem__ is : "+ str(idx))
curnt_batchFile = listFiles[idx]
#print(idx, curnt_batchFile)
x,y = self.__data_generation(curnt_batchFile)
return x, y
def __data_generation(self,curnt_batchFile):
curnt_batchFile_x = curnt_batchFile
curnt_batchFile_y = curnt_batchFile.replace("_x.npy","_y.npy")
x_val = np.load(curnt_batchFile_x)
y_val = np.load(curnt_batchFile_y)
return x_val,y_val
training_generator = quickdrawSequence(batch_size=Batch_size,shuffle=True,total_combinations = total_combinations,listFils=listFiles)
validation_generator = quickdrawSequence(batch_size=Batch_size,shuffle=True,total_combinations = total_combinations,listFils=listFiles)
with tf.device('/cpu:0'):
lstm_model = Sequential()
#lstm_model.add(LSTM(units=100,input_shape=(Time_Steps,Num_Features),return_sequences=True))
lstm_model.add(Masking(mask_value=-5,input_shape=(Time_Steps,Num_Features)))
lstm_model.add(LSTM(units=100,return_sequences=True))
lstm_model.add(Dropout(0.2))
lstm_model.add(LSTM(units=100, return_sequences=True))
#lstm_model.add(BatchNormalization())
lstm_model.add(Dropout(0.2))
lstm_model.add(TimeDistributed(Dense(25,activation='relu')))
lstm_model.add(TimeDistributed(Dense(1,activation='sigmoid')))
lstm_model.summary()
parallel_model = multi_gpu_model(lstm_model, gpus=4)
parallel_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
parallel_model.fit_generator(
generator=training_generator,
steps_per_epoch=Bbatches_afterGpuDiv,
epochs=Epochs,
verbose=1,
validation_data=validation_generator,
validation_steps=2,
use_multiprocessing=True,
workers=8,
max_queue_size=8
)
output:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
masking (Masking) (None, 8, 9) 0
_________________________________________________________________
lstm (LSTM) (None, 8, 100) 44000
_________________________________________________________________
dropout (Dropout) (None, 8, 100) 0
_________________________________________________________________
lstm_1 (LSTM) (None, 8, 100) 80400
_________________________________________________________________
dropout_1 (Dropout) (None, 8, 100) 0
_________________________________________________________________
time_distributed (TimeDistri (None, 8, 25) 2525
_________________________________________________________________
time_distributed_1 (TimeDist (None, 8, 1) 26
=================================================================
Total params: 126,951
Trainable params: 126,951
Non-trainable params: 0
_________________________________________________________________
Epoch 1/10
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-11-d4fa72c44cd9> in <module>()
40 use_multiprocessing=True,
41 workers=8,
---> 42 max_queue_size=8
43 )
/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/tensorflow/python/keras/engine/training.pyc in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
2175 use_multiprocessing=use_multiprocessing,
2176 shuffle=shuffle,
-> 2177 initial_epoch=initial_epoch)
2178
2179 def evaluate_generator(self,
/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/tensorflow/python/keras/engine/training_generator.pyc in fit_generator(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
174
175 outs = model.train_on_batch(
--> 176 x, y, sample_weight=sample_weight, class_weight=class_weight)
177
178 if not isinstance(outs, list):
/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/tensorflow/python/keras/engine/training.pyc in train_on_batch(self, x, y, sample_weight, class_weight)
1938
1939 self._make_train_function()
-> 1940 outputs = self.train_function(ins)
1941
1942 if len(outputs) == 1:
/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/tensorflow/python/keras/backend.pyc in __call__(self, inputs)
2945 raise TypeError('`inputs` should be a list or tuple.')
2946
-> 2947 session = get_session()
2948 feed_arrays = []
2949 array_vals = []
/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/tensorflow/python/keras/backend.pyc in get_session()
467 if not _MANUAL_VAR_INIT:
468 with session.graph.as_default():
--> 469 _initialize_variables(session)
470 return session
471
/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/tensorflow/python/keras/backend.pyc in _initialize_variables(session)
729 # marked as initialized.
730 is_initialized = session.run(
--> 731 [variables_module.is_variable_initialized(v) for v in candidate_vars])
732 uninitialized_vars = []
733 for flag, v in zip(is_initialized, candidate_vars):
/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in run(self, fetches, feed_dict, options, run_metadata)
927 try:
928 result = self._run(None, fetches, feed_dict, options_ptr,
--> 929 run_metadata_ptr)
930 if run_metadata:
931 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _run(self, handle, fetches, feed_dict, options, run_metadata)
1150 if final_fetches or final_targets or (handle and feed_dict_tensor):
1151 results = self._do_run(handle, final_targets, final_fetches,
-> 1152 feed_dict_tensor, options, run_metadata)
1153 else:
1154 results = []
/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1326 if handle is None:
1327 return self._do_call(_run_fn, feeds, fetches, targets, options,
-> 1328 run_metadata)
1329 else:
1330 return self._do_call(_prun_fn, handle, feeds, fetches)
/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _do_call(self, fn, *args)
1346 pass
1347 message = error_interpolation.interpolate(message, self._graph)
-> 1348 raise type(e)(node_def, op, message)
1349
1350 def _extend_graph(self):
InvalidArgumentError: Cannot assign a device for operation replica_0/sequential/lstm/transpose_1: Could not satisfy explicit device specification '/device:GPU:0' because no supported kernel for GPU devices is available.
Colocation Debug Info:
Colocation group had the following types and devices:
TensorArrayScatterV3: CPU XLA_CPU XLA_GPU
TensorArrayReadV3: CPU XLA_CPU XLA_GPU
Enter: GPU CPU XLA_CPU XLA_GPU
TensorArrayV3: CPU XLA_CPU XLA_GPU
Transpose: GPU CPU XLA_CPU XLA_GPU
Colocation members and user-requested devices:
replica_0/sequential/lstm/transpose_1 (Transpose) /device:GPU:0
replica_0/sequential/lstm/TensorArray_2 (TensorArrayV3)
replica_0/sequential/lstm/TensorArrayUnstack_1/TensorArrayScatter/TensorArrayScatterV3 (TensorArrayScatterV3) /device:GPU:0
replica_0/sequential/lstm/while/TensorArrayReadV3_1/Enter (Enter) /device:GPU:0
replica_0/sequential/lstm/while/TensorArrayReadV3_1 (TensorArrayReadV3) /device:GPU:0
Registered kernels:
device='XLA_CPU'; Tperm in [DT_INT32, DT_INT64]; T in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT8, DT_COMPLEX64, DT_INT64, DT_BOOL, DT_QINT8, DT_QUINT8, DT_QINT32, DT_HALF, DT_UINT32, DT_UINT64]
device='XLA_GPU'; Tperm in [DT_INT32, DT_INT64]; T in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT8, ..., DT_QINT32, DT_BFLOAT16, DT_HALF, DT_UINT32, DT_UINT64]
device='XLA_CPU_JIT'; Tperm in [DT_INT32, DT_INT64]; T in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT8, DT_COMPLEX64, DT_INT64, DT_BOOL, DT_QINT8, DT_QUINT8, DT_QINT32, DT_HALF, DT_UINT32, DT_UINT64]
device='XLA_GPU_JIT'; Tperm in [DT_INT32, DT_INT64]; T in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT8, ..., DT_QINT32, DT_BFLOAT16, DT_HALF, DT_UINT32, DT_UINT64]
device='GPU'; T in [DT_BOOL]
device='GPU'; T in [DT_COMPLEX128]
device='GPU'; T in [DT_COMPLEX64]
device='GPU'; T in [DT_DOUBLE]
device='GPU'; T in [DT_FLOAT]
device='GPU'; T in [DT_BFLOAT16]
device='GPU'; T in [DT_HALF]
device='GPU'; T in [DT_INT8]
device='GPU'; T in [DT_UINT8]
device='GPU'; T in [DT_INT16]
device='GPU'; T in [DT_UINT16]
device='GPU'; T in [DT_INT32]
device='GPU'; T in [DT_INT64]
device='CPU'; T in [DT_VARIANT]
device='CPU'; T in [DT_RESOURCE]
device='CPU'; T in [DT_STRING]
device='CPU'; T in [DT_BOOL]
device='CPU'; T in [DT_COMPLEX128]
device='CPU'; T in [DT_COMPLEX64]
device='CPU'; T in [DT_DOUBLE]
device='CPU'; T in [DT_FLOAT]
device='CPU'; T in [DT_BFLOAT16]
device='CPU'; T in [DT_HALF]
device='CPU'; T in [DT_INT8]
device='CPU'; T in [DT_UINT8]
device='CPU'; T in [DT_INT16]
device='CPU'; T in [DT_UINT16]
device='CPU'; T in [DT_INT32]
device='CPU'; T in [DT_INT64]
[[node replica_0/sequential/lstm/transpose_1 (defined at <ipython-input-11-d4fa72c44cd9>:29) = Transpose[T=DT_BOOL, Tperm=DT_INT32, _device="/device:GPU:0"](replica_0/sequential/lstm/ExpandDims, replica_0/sequential/lstm/transpose_1/perm)]]
Caused by op u'replica_0/sequential/lstm/transpose_1', defined at:
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/runpy.py", line 174, in _run_module_as_main
"__main__", fname, loader, pkg_name)
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/runpy.py", line 72, in _run_code
exec code in run_globals
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/ipykernel_launcher.py", line 16, in <module>
app.launch_new_instance()
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/traitlets/config/application.py", line 658, in launch_instance
app.start()
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/ipykernel/kernelapp.py", line 499, in start
self.io_loop.start()
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/tornado/ioloop.py", line 1017, in start
self._run_callback(self._callbacks.popleft())
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/tornado/ioloop.py", line 758, in _run_callback
ret = callback()
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/tornado/stack_context.py", line 300, in null_wrapper
return fn(*args, **kwargs)
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 542, in <lambda>
self.io_loop.add_callback(lambda : self._handle_events(self.socket, 0))
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 456, in _handle_events
self._handle_recv()
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 486, in _handle_recv
self._run_callback(callback, msg)
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 438, in _run_callback
callback(*args, **kwargs)
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/tornado/stack_context.py", line 300, in null_wrapper
return fn(*args, **kwargs)
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 283, in dispatcher
return self.dispatch_shell(stream, msg)
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 233, in dispatch_shell
handler(stream, idents, msg)
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 399, in execute_request
user_expressions, allow_stdin)
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/ipykernel/ipkernel.py", line 208, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/ipykernel/zmqshell.py", line 537, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2714, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2818, in run_ast_nodes
if self.run_code(code, result):
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2878, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-11-d4fa72c44cd9>", line 29, in <module>
parallel_model = multi_gpu_model(lstm_model, gpus=4)
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/tensorflow/python/keras/utils/multi_gpu_utils.py", line 239, in multi_gpu_model
outputs = model(inputs)
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/tensorflow/python/keras/engine/base_layer.py", line 757, in __call__
outputs = self.call(inputs, *args, **kwargs)
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/tensorflow/python/keras/engine/sequential.py", line 229, in call
return super(Sequential, self).call(inputs, training=training, mask=mask)
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/tensorflow/python/keras/engine/network.py", line 845, in call
mask=masks)
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/tensorflow/python/keras/engine/network.py", line 1031, in _run_internal_graph
output_tensors = layer.call(computed_tensor, **kwargs)
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/tensorflow/python/keras/layers/recurrent.py", line 2237, in call
inputs, mask=mask, training=training, initial_state=initial_state)
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/tensorflow/python/keras/layers/recurrent.py", line 750, in call
input_length=timesteps)
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/tensorflow/python/keras/backend.py", line 3119, in rnn
mask = array_ops.transpose(mask, axes)
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/tensorflow/python/ops/array_ops.py", line 1420, in transpose
ret = transpose_fn(a, perm, name=name)
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/tensorflow/python/ops/gen_array_ops.py", line 8927, in transpose
"Transpose", x=x, perm=perm, name=name)
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/tensorflow/python/util/deprecation.py", line 488, in new_func
return func(*args, **kwargs)
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3274, in create_op
op_def=op_def)
File "/home/sa/anaconda/envs/tf112_cu9_py27_pycuda_2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1770, in __init__
self._traceback = tf_stack.extract_stack()
InvalidArgumentError (see above for traceback): Cannot assign a device for operation replica_0/sequential/lstm/transpose_1: Could not satisfy explicit device specification '/device:GPU:0' because no supported kernel for GPU devices is available.
Colocation Debug Info:
Colocation group had the following types and devices:
TensorArrayScatterV3: CPU XLA_CPU XLA_GPU
TensorArrayReadV3: CPU XLA_CPU XLA_GPU
Enter: GPU CPU XLA_CPU XLA_GPU
TensorArrayV3: CPU XLA_CPU XLA_GPU
Transpose: GPU CPU XLA_CPU XLA_GPU
Colocation members and user-requested devices:
replica_0/sequential/lstm/transpose_1 (Transpose) /device:GPU:0
replica_0/sequential/lstm/TensorArray_2 (TensorArrayV3)
replica_0/sequential/lstm/TensorArrayUnstack_1/TensorArrayScatter/TensorArrayScatterV3 (TensorArrayScatterV3) /device:GPU:0
replica_0/sequential/lstm/while/TensorArrayReadV3_1/Enter (Enter) /device:GPU:0
replica_0/sequential/lstm/while/TensorArrayReadV3_1 (TensorArrayReadV3) /device:GPU:0
Registered kernels:
device='XLA_CPU'; Tperm in [DT_INT32, DT_INT64]; T in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT8, DT_COMPLEX64, DT_INT64, DT_BOOL, DT_QINT8, DT_QUINT8, DT_QINT32, DT_HALF, DT_UINT32, DT_UINT64]
device='XLA_GPU'; Tperm in [DT_INT32, DT_INT64]; T in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT8, ..., DT_QINT32, DT_BFLOAT16, DT_HALF, DT_UINT32, DT_UINT64]
device='XLA_CPU_JIT'; Tperm in [DT_INT32, DT_INT64]; T in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT8, DT_COMPLEX64, DT_INT64, DT_BOOL, DT_QINT8, DT_QUINT8, DT_QINT32, DT_HALF, DT_UINT32, DT_UINT64]
device='XLA_GPU_JIT'; Tperm in [DT_INT32, DT_INT64]; T in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT8, ..., DT_QINT32, DT_BFLOAT16, DT_HALF, DT_UINT32, DT_UINT64]
device='GPU'; T in [DT_BOOL]
device='GPU'; T in [DT_COMPLEX128]
device='GPU'; T in [DT_COMPLEX64]
device='GPU'; T in [DT_DOUBLE]
device='GPU'; T in [DT_FLOAT]
device='GPU'; T in [DT_BFLOAT16]
device='GPU'; T in [DT_HALF]
device='GPU'; T in [DT_INT8]
device='GPU'; T in [DT_UINT8]
device='GPU'; T in [DT_INT16]
device='GPU'; T in [DT_UINT16]
device='GPU'; T in [DT_INT32]
device='GPU'; T in [DT_INT64]
device='CPU'; T in [DT_VARIANT]
device='CPU'; T in [DT_RESOURCE]
device='CPU'; T in [DT_STRING]
device='CPU'; T in [DT_BOOL]
device='CPU'; T in [DT_COMPLEX128]
device='CPU'; T in [DT_COMPLEX64]
device='CPU'; T in [DT_DOUBLE]
device='CPU'; T in [DT_FLOAT]
device='CPU'; T in [DT_BFLOAT16]
device='CPU'; T in [DT_HALF]
device='CPU'; T in [DT_INT8]
device='CPU'; T in [DT_UINT8]
device='CPU'; T in [DT_INT16]
device='CPU'; T in [DT_UINT16]
device='CPU'; T in [DT_INT32]
device='CPU'; T in [DT_INT64]
[[node replica_0/sequential/lstm/transpose_1 (defined at <ipython-input-11-d4fa72c44cd9>:29) = Transpose[T=DT_BOOL, Tperm=DT_INT32, _device="/device:GPU:0"](replica_0/sequential/lstm/ExpandDims, replica_0/sequential/lstm/transpose_1/perm)]]
【问题讨论】：
【参考方案1】：在阅读了一些文档后，我意识到上述错误试图说明什么。它说一个操作没有 gpu 实现。如果我们将soft_placement设置为True，这些没有gpu实现的操作会被TF放到cpu上。
// 是否允许软放置。如果 allow_soft_placement 为真， // 一个 op 将被放置在 CPU 上，如果 // 1. OP 没有 GPU 实现 // 要么 // 2. 没有已知或注册的 GPU 设备 // 要么 // 3. 需要与来自 CPU 的 reftype 输入共同定位。
所以在上面的代码中添加，config.allow_soft_placement = True
它有效。
【讨论】：
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