Python 无法将符号张量（顺序/lstm/stripped_切片：0）转换为numpy数组。在MacBook Pro M1上使用Tensorflow时出现问题

在尝试使用LSTM层运行这个简单的神经网络程序来预测股票价格之后，我遇到了以下错误

---------------------------------------------------------------------------
NotImplementedError                       Traceback (most recent call last)
<ipython-input-15-47761426ea46> in <module>
      1 # Fitting the RNN to the Training set
----> 2 model.fit(X_train, Y_train, epochs=1)

/opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
   1098                 _r=1):
   1099               callbacks.on_train_batch_begin(step)
-> 1100               tmp_logs = self.train_function(iterator)
   1101               if data_handler.should_sync:
   1102                 context.async_wait()

/opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
    826     tracing_count = self.experimental_get_tracing_count()
    827     with trace.Trace(self._name) as tm:
--> 828       result = self._call(*args, **kwds)
    829       compiler = "xla" if self._experimental_compile else "nonXla"
    830       new_tracing_count = self.experimental_get_tracing_count()

/opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
    869       # This is the first call of __call__, so we have to initialize.
    870       initializers = []
--> 871       self._initialize(args, kwds, add_initializers_to=initializers)
    872     finally:
    873       # At this point we know that the initialization is complete (or less

/opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
    723     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
    724     self._concrete_stateful_fn = (
--> 725         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
    726             *args, **kwds))
    727 

/opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
   2967       args, kwargs = None, None
   2968     with self._lock:
-> 2969       graph_function, _ = self._maybe_define_function(args, kwargs)
   2970     return graph_function
   2971 

/opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
   3359 
   3360           self._function_cache.missed.add(call_context_key)
-> 3361           graph_function = self._create_graph_function(args, kwargs)
   3362           self._function_cache.primary[cache_key] = graph_function
   3363 

/opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
   3194     arg_names = base_arg_names + missing_arg_names
   3195     graph_function = ConcreteFunction(
-> 3196         func_graph_module.func_graph_from_py_func(
   3197             self._name,
   3198             self._python_function,

/opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
    988         _, original_func = tf_decorator.unwrap(python_func)
    989 
--> 990       func_outputs = python_func(*func_args, **func_kwargs)
    991 
    992       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

/opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
    632             xla_context.Exit()
    633         else:
--> 634           out = weak_wrapped_fn().__wrapped__(*args, **kwds)
    635         return out
    636 

/opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
    975           except Exception as e:  # pylint:disable=broad-except
    976             if hasattr(e, "ag_error_metadata"):
--> 977               raise e.ag_error_metadata.to_exception(e)
    978             else:
    979               raise

NotImplementedError: in user code:

    /opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:805 train_function  *
        return step_function(self, iterator)
    /opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:795 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    /opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:1259 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2730 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:3417 _call_for_each_replica
        return fn(*args, **kwargs)
    /opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:788 run_step  **
        outputs = model.train_step(data)
    /opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:754 train_step
        y_pred = self(x, training=True)
    /opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:1007 __call__
        outputs = call_fn(inputs, *args, **kwargs)
    /opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/keras/engine/sequential.py:389 call
        outputs = layer(inputs, **kwargs)
    /opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py:660 __call__
        return super(RNN, self).__call__(inputs, **kwargs)
    /opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:1007 __call__
        outputs = call_fn(inputs, *args, **kwargs)
    /opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent_v2.py:1163 call
        inputs, initial_state, _ = self._process_inputs(inputs, initial_state, None)
    /opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py:859 _process_inputs
        initial_state = self.get_initial_state(inputs)
    /opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py:642 get_initial_state
        init_state = get_initial_state_fn(
    /opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py:2506 get_initial_state
        return list(_generate_zero_filled_state_for_cell(
    /opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py:2987 _generate_zero_filled_state_for_cell
        return _generate_zero_filled_state(batch_size, cell.state_size, dtype)
    /opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py:3003 _generate_zero_filled_state
        return nest.map_structure(create_zeros, state_size)
    /opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/util/nest.py:659 map_structure
        structure[0], [func(*x) for x in entries],
    /opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/util/nest.py:659 <listcomp>
        structure[0], [func(*x) for x in entries],
    /opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py:3000 create_zeros
        return array_ops.zeros(init_state_size, dtype=dtype)
    /opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:201 wrapper
        return target(*args, **kwargs)
    /opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/ops/array_ops.py:2819 wrapped
        tensor = fun(*args, **kwargs)
    /opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/ops/array_ops.py:2868 zeros
        output = _constant_if_small(zero, shape, dtype, name)
    /opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/ops/array_ops.py:2804 _constant_if_small
        if np.prod(shape) < 1000:
    <__array_function__ internals>:5 prod
        
    /opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3030 prod
        return _wrapreduction(a, np.multiply, 'prod', axis, dtype, out,
    /opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/numpy/core/fromnumeric.py:87 _wrapreduction
        return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
    /opt/homebrew/Caskroom/miniforge/base/envs/tensorflowenv/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:852 __array__
        raise NotImplementedError(

    NotImplementedError: Cannot convert a symbolic Tensor (sequential/lstm/strided_slice:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported

其中错误发生在model.fit命令之后。我一直在研究这个错误，到目前为止，我发现这可能是我使用的numpy版本的一个问题，但是我不确定情况是否如此，也不确定如何更改它。我已经使用新阶段的视频从GitHub存储库为apple M1安装了tensorflow软件包，链接如下：

由于有很多关于如何在Mac M1上安装tensorflow的在线教程，而且我也尝试了很多，但迄今为止都没有多大成功，因此我不能完全相信安装过程是否成功/正确。有人能给我指出解决这个问题的正确方向吗

#!/usr/bin/env python
# coding: utf-8

# In[1]:


# Import General Libraries

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd


# In[2]:


# Import the Training DataSet
training_dataset = pd.read_csv('/Users/frisodekruiff/Downloads/Archive/AAPL.csv')
training_set = training_dataset.iloc[:,1:-1].values


# In[3]:


# Importing sklearn
from sklearn.preprocessing import MinMaxScaler

# Feature Scaling
sc = MinMaxScaler(feature_range = (0, 1))
training_set_scaled = sc.fit_transform(training_set)
print(training_set_scaled)


# In[4]:


# Creating Data Structure with 252 Data Points and 1 answer
X_train = []
Y_train = []

for i in range(252,len(training_set)):
    X_train.append(training_set_scaled[i - 252:i,0])
    Y_train.append(training_set_scaled[i,0])

X_train, Y_train = np.array(X_train), np.array(Y_train)


# In[5]:


# Reshaping
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))


# In[6]:


# Importing Tensorflow and Keras

import tensorflow as tf
from tensorflow import keras


# In[7]:


# Importing Keras Libraries

from keras import models
from keras import layers


# In[8]:


# Initializing the RNN
model = keras.Sequential()


# In[9]:


# Adding the First Layer
model.add(layers.LSTM(128, return_sequences = True))

# Adding First Dropout
model.add(layers.Dropout(0.2))


# In[10]:


# Adding the Second Layer
model.add(layers.LSTM(64))

# Adding Second Dropout
model.add(layers.Dropout(0.2))


# In[11]:


# Adding the Third Layer
model.add(layers.LSTM(32))

# Adding Third Dropout
model.add(layers.Dropout(0.2))


# In[12]:


# Adding the Fourth Layer
model.add(layers.LSTM(16))

# Adding Fourth Dropout
model.add(layers.Dropout(0.2))


# In[13]:


# Adding Output Layer
model.add(layers.Dense(units = 10))


# In[14]:


# Compiling the RNN
model.compile(optimizer = 'adam', loss = 'mean_squared_error')


# In[15]:


# Fitting the RNN to the Training set
model.fit(X_train, Y_train, epochs=1)


# In[ ]:


model.summary()