Mac M1Pro芯片，在找TB30元解决了下载安装问题之后，import tensorflow可以正常运行，但是在keras库下，
出现错误代码如下：
history = ann.fit(X_train, y_train, # 指定训练集
epochs=30, # 指定训练的轮次
batch_size=64, # 指定数据批量
validation_data=(X_test, y_test)) #指定验证集,这里为了简化模型，直接用测试集数据进行验证
报错 信息：
Epoch 1/30
2023-01-09 21:09:03.019104: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
2023-01-09 21:09:04.182418: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:418 : NOT_FOUND: could not find registered platform with id: 0x153714350
2023-01-09 21:09:04.182452: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:418 : NOT_FOUND: could not find registered platform with id: 0x153714350
2023-01-09 21:09:04.529939: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:418 : NOT_FOUND: could not find registered platform with id: 0x153714350
2023-01-09 21:09:04.529967: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:418 : NOT_FOUND: could not find registered platform with id: 0x153714350
2023-01-09 21:09:04.617016: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:418 : NOT_FOUND: could not find registered platform with id: 0x153714350
2023-01-09 21:09:04.617046: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:418 : NOT_FOUND: could not find registered platform with id: 0x153714350
---------------------------------------------------------------------------
NotFoundError Traceback (most recent call last)
Cell In[17], line 1
----> 1 history = ann.fit(X_train, y_train, # 指定训练集
2 epochs=30, # 指定训练的轮次
3 batch_size=64, # 指定数据批量
4 validation_data=(X_test, y_test))
File ~/opt/anaconda3/envs/tf/lib/python3.9/site-packages/keras/utils/traceback_utils.py:70, in filter_traceback..error_handler(*args, **kwargs)
67 filtered_tb = _process_traceback_frames(e.__traceback__)
68 # To get the full stack trace, call:
69 # `tf.debugging.disable_traceback_filtering()`
---> 70 raise e.with_traceback(filtered_tb) from None
71 finally:
72 del filtered_tb
File ~/opt/anaconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/eager/execute.py:52, in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
50 try:
51 ctx.ensure_initialized()
---> 52 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
53 inputs, attrs, num_outputs)
54 except core._NotOkStatusException as e:
55 if name is not None:
NotFoundError: Graph execution error:
Detected at node 'StatefulPartitionedCall_4' defined at (most recent call last):
File "/Users/atongmu/opt/anaconda3/envs/tf/lib/python3.9/runpy.py", line 197, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/Users/atongmu/opt/anaconda3/envs/tf/lib/python3.9/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/Users/atongmu/opt/anaconda3/envs/tf/lib/python3.9/site-packages/ipykernel_launcher.py", line 17, in
app.launch_new_instance()
File "/Users/atongmu/opt/anaconda3/envs/tf/lib/python3.9/site-packages/traitlets/config/application.py", line 992, in launch_instance
app.start()
File "/Users/atongmu/opt/anaconda3/envs/tf/lib/python3.9/site-packages/ipykernel/kernelapp.py", line 711, in start
self.io_loop.start()
File "/Users/atongmu/opt/anaconda3/envs/tf/lib/python3.9/site-packages/tornado/platform/asyncio.py", line 215, in start
self.asyncio_loop.run_forever()
File "/Users/atongmu/opt/anaconda3/envs/tf/lib/python3.9/asyncio/base_events.py", line 601, in run_forever
self._run_once()
File "/Users/atongmu/opt/anaconda3/envs/tf/lib/python3.9/asyncio/base_events.py", line 1905, in _run_once
handle._run()
File "/Users/atongmu/opt/anaconda3/envs/tf/lib/python3.9/asyncio/events.py", line 80, in _run
self._context.run(self._callback, *self._args)
File "/Users/atongmu/opt/anaconda3/envs/tf/lib/python3.9/site-packages/ipykernel/kernelbase.py", line 510, in dispatch_queue
await self.process_one()
File "/Users/atongmu/opt/anaconda3/envs/tf/lib/python3.9/site-packages/ipykernel/kernelbase.py", line 499, in process_one
await dispatch(*args)
File "/Users/atongmu/opt/anaconda3/envs/tf/lib/python3.9/site-packages/ipykernel/kernelbase.py", line 406, in dispatch_shell
await result
File "/Users/atongmu/opt/anaconda3/envs/tf/lib/python3.9/site-packages/ipykernel/kernelbase.py", line 729, in execute_request
reply_content = await reply_content
File "/Users/atongmu/opt/anaconda3/envs/tf/lib/python3.9/site-packages/ipykernel/ipkernel.py", line 411, in do_execute
res = shell.run_cell(
File "/Users/atongmu/opt/anaconda3/envs/tf/lib/python3.9/site-packages/ipykernel/zmqshell.py", line 531, in run_cell
return super().run_cell(*args, **kwargs)
File "/Users/atongmu/opt/anaconda3/envs/tf/lib/python3.9/site-packages/IPython/core/interactiveshell.py", line 2940, in run_cell
result = self._run_cell(
File "/Users/atongmu/opt/anaconda3/envs/tf/lib/python3.9/site-packages/IPython/core/interactiveshell.py", line 2995, in _run_cell
return runner(coro)
File "/Users/atongmu/opt/anaconda3/envs/tf/lib/python3.9/site-packages/IPython/core/async_helpers.py", line 129, in _pseudo_sync_runner
coro.send(None)
File "/Users/atongmu/opt/anaconda3/envs/tf/lib/python3.9/site-packages/IPython/core/interactiveshell.py", line 3194, in run_cell_async
has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
File "/Users/atongmu/opt/anaconda3/envs/tf/lib/python3.9/site-packages/IPython/core/interactiveshell.py", line 3373, in run_ast_nodes
if await self.run_code(code, result, async_=asy):
File "/Users/atongmu/opt/anaconda3/envs/tf/lib/python3.9/site-packages/IPython/core/interactiveshell.py", line 3433, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "/var/folders/9s/w9b7v1sj66d3j12lzwmm59xr0000gn/T/ipykernel_64050/3685881096.py", line 1, in
history = ann.fit(X_train, y_train, # 指定训练集
File "/Users/atongmu/opt/anaconda3/envs/tf/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "/Users/atongmu/opt/anaconda3/envs/tf/lib/python3.9/site-packages/keras/engine/training.py", line 1650, in fit
tmp_logs = self.train_function(iterator)
File "/Users/atongmu/opt/anaconda3/envs/tf/lib/python3.9/site-packages/keras/engine/training.py", line 1249, in train_function
return step_function(self, iterator)
File "/Users/atongmu/opt/anaconda3/envs/tf/lib/python3.9/site-packages/keras/engine/training.py", line 1233, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/Users/atongmu/opt/anaconda3/envs/tf/lib/python3.9/site-packages/keras/engine/training.py", line 1222, in run_step
outputs = model.train_step(data)
File "/Users/atongmu/opt/anaconda3/envs/tf/lib/python3.9/site-packages/keras/engine/training.py", line 1027, in train_step
self.optimizer.minimize(loss, self.trainable_variables, tape=tape)
File "/Users/atongmu/opt/anaconda3/envs/tf/lib/python3.9/site-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 527, in minimize
self.apply_gradients(grads_and_vars)
File "/Users/atongmu/opt/anaconda3/envs/tf/lib/python3.9/site-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 1140, in apply_gradients
return super().apply_gradients(grads_and_vars, name=name)
File "/Users/atongmu/opt/anaconda3/envs/tf/lib/python3.9/site-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 634, in apply_gradients
iteration = self._internal_apply_gradients(grads_and_vars)
File "/Users/atongmu/opt/anaconda3/envs/tf/lib/python3.9/site-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 1166, in _internal_apply_gradients
return tf.__internal__.distribute.interim.maybe_merge_call(
File "/Users/atongmu/opt/anaconda3/envs/tf/lib/python3.9/site-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 1216, in _distributed_apply_gradients_fn
distribution.extended.update(
File "/Users/atongmu/opt/anaconda3/envs/tf/lib/python3.9/site-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 1211, in apply_grad_to_update_var
return self._update_step_xla(grad, var, id(self._var_key(var)))
Node: 'StatefulPartitionedCall_4'
could not find registered platform with id: 0x153714350
[[{{node StatefulPartitionedCall_4}}]] [Op:__inference_train_function_1200]
望有人能帮忙解决！不胜感激！
版本不匹配要降级
pip install tensorflow-metal==0.5.0
pip install tensorflow-macos==2.9.0
报错主要意思：
NotFoundError: Graph execution error:
NotFoundError:图形执行错误：
Node: 'StatefulPartitionedCall_4'
could not find registered platform with id: 0x153714350
节点：'StatefulPartitionedCall_4'
找不到id为0x153714350的注册平台
看到这个错误，我们可以分析一下：OP_REQUIRES failed at xla_ops.cc:418 : NOT_FOUND: could not find registered platform with id: 0x153714350。这个错误似乎和TensorFlow的XLA (Accelerated Linear Algebra)有关，XLA是TensorFlow中的一个优化器，它可以使用高性能线性代数库来优化TensorFlow代码。
那么，为什么会出现这个错误呢？这个错误的原因可能是在Mac上安装TensorFlow时使用了错误的版本，或者你使用的设备没有安装对应的线性代数库。
建议你可以尝试以下方法：
安装支持Mac M1Pro芯片的TensorFlow版本，可以参考官方文档，试着重新安装TensorFlow
如果你使用的是CPU版本的TensorFlow，可以尝试使用GPU版本。可以参考官方文档，安装支持M1芯片的GPU版本。
这个错误是由于TensorFlow找不到一个注册的平台造成的。
这个错误的原因可能是系统环境中缺少了必要的库或者依赖项，或者是使用了不支持的硬件。
可以试试以下步骤来解决这个问题：
1、确保已经正确地安装了TensorFlow和Keras，并且系统中没有其他版本的TensorFlow或Keras。
2、确保系统中安装了所有必要的库和依赖项。TensorFlow和Keras可能需要其他库来正常工作，比如NumPy和SciPy。
3、确保使用的是支持TensorFlow的硬件。TensorFlow可能不支持某些硬件，所以应该检查一下TensorFlow的文档，确保硬件是受支持的。
4、还可以试试在代码中禁用XLA优化。在代码中加入以下行可以禁用XLA优化：
import os
os.environ['TF_XLA_FLAGS'] = '--tf_xla_cpu_global_jit'
5、如果以上步骤都不能解决问题，可以试试在TensorFlow的GitHub issue跟踪器中搜索这个错误，看看有没有人遇到过类似的问题。
仅供参考，望采纳，谢谢。
望采纳！！！点击回答右侧采纳即可！！
这个错误是由于在 TensorFlow 中无法找到合法的平台，导致无法执行操作。
可能的原因有：
TensorFlow 不支持 M1 芯片，导致无法使用 GPU 加速。
在初始化 TensorFlow 时没有设置正确的 GPU 参数，导致无法找到合法的平台。
在运行 Keras 模型时出现了其他错误，导致无法执行操作。
为了解决这个问题，建议您尝试以下操作：
检查 TensorFlow 是否支持 M1 芯片。如果 TensorFlow 不支持 M1 芯片，可能需要使用 CPU 运行模型。
检查是否在初始化 TensorFlow 时设置了正确的 GPU 参数。例如，在使用 GPU 运行模型时，需要设