NotImplementedError：无法将符号张量 (lstm_4/strided_slice:0) 转换为 numpy 数组

Posted 2023-02-16

【中文标题】NotImplementedError：无法将符号张量 (lstm_4/strided_slice:0) 转换为 numpy 数组

【英文标题】：NotImplementedError: Cannot convert a symbolic Tensor (lstm_4/strided_slice:0) to a numpy array

【发布时间】：2022-01-12 19:02:54

【问题描述】：

Numpy 版本 1.19.2，tensorflow 版本 2.3.0。

我正在尝试创建一个 LSTM 网络：

model=Sequential()
model.add(Embedding(input_dim=vocab_size,output_dim=embed_dim,input_length=max_rev_len,embeddings_initializer=Constant(embed_matrix)))

model.add(LSTM(100))
model.add(Flatten())

model.add(Dense(16,activation='relu'))
model.add(Dropout(0.70))
model.add(Dense(1,activation='sigmoid')) 

当我运行单元格时（我在 Windows 11 上使用 jupyter notebook），我收到错误：

NotImplementedError                       Traceback (most recent call last)
<ipython-input-57-d024ff74859f> in <module>
      5 model.add(Embedding(input_dim=vocab_size,output_dim=embed_dim,input_length=max_rev_len,embeddings_initializer=Constant(embed_matrix)))
      6 
----> 7 model.add(LSTM(100))
      8 model.add(Flatten())
      9 

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\training\tracking\base.py in _method_wrapper(self, *args, **kwargs)
    455     self._self_setattr_tracking = False  # pylint: disable=protected-access
    456     try:
--> 457       result = method(self, *args, **kwargs)
    458     finally:
    459       self._self_setattr_tracking = previous_value  # pylint: disable=protected-access

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\sequential.py in add(self, layer)
    219       # If the model is being built continuously on top of an input layer:
    220       # refresh its output.
--> 221       output_tensor = layer(self.outputs[0])
    222       if len(nest.flatten(output_tensor)) != 1:
    223         raise ValueError(SINGLE_LAYER_OUTPUT_ERROR_MSG)

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\layers\recurrent.py in __call__(self, inputs, initial_state, constants, **kwargs)
    661 
    662     if initial_state is None and constants is None:
--> 663       return super(RNN, self).__call__(inputs, **kwargs)
    664 
    665     # If any of `initial_state` or `constants` are specified and are Keras

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in __call__(self, *args, **kwargs)
    923     # >> model = tf.keras.Model(inputs, outputs)
    924     if _in_functional_construction_mode(self, inputs, args, kwargs, input_list):
--> 925       return self._functional_construction_call(inputs, args, kwargs,
    926                                                 input_list)
    927 

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in _functional_construction_call(self, inputs, args, kwargs, input_list)
   1115           try:
   1116             with ops.enable_auto_cast_variables(self._compute_dtype_object):
-> 1117               outputs = call_fn(cast_inputs, *args, **kwargs)
   1118 
   1119           except errors.OperatorNotAllowedInGraphError as e:

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\layers\recurrent_v2.py in call(self, inputs, mask, training, initial_state)
   1106 
   1107     # LSTM does not support constants. Ignore it during process.
-> 1108     inputs, initial_state, _ = self._process_inputs(inputs, initial_state, None)
   1109 
   1110     if isinstance(mask, list):

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\layers\recurrent.py in _process_inputs(self, inputs, initial_state, constants)
    860         initial_state = self.states
    861     elif initial_state is None:
--> 862       initial_state = self.get_initial_state(inputs)
    863 
    864     if len(initial_state) != len(self.states):

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\layers\recurrent.py in get_initial_state(self, inputs)
    643     dtype = inputs.dtype
    644     if get_initial_state_fn:
--> 645       init_state = get_initial_state_fn(
    646           inputs=None, batch_size=batch_size, dtype=dtype)
    647     else:

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\layers\recurrent.py in get_initial_state(self, inputs, batch_size, dtype)
   2521 
   2522   def get_initial_state(self, inputs=None, batch_size=None, dtype=None):
-> 2523     return list(_generate_zero_filled_state_for_cell(
   2524         self, inputs, batch_size, dtype))
   2525 

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\layers\recurrent.py in _generate_zero_filled_state_for_cell(cell, inputs, batch_size, dtype)
   2966     batch_size = array_ops.shape(inputs)[0]
   2967     dtype = inputs.dtype
-> 2968   return _generate_zero_filled_state(batch_size, cell.state_size, dtype)
   2969 
   2970 

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\layers\recurrent.py in _generate_zero_filled_state(batch_size_tensor, state_size, dtype)
   2982 
   2983   if nest.is_sequence(state_size):
-> 2984     return nest.map_structure(create_zeros, state_size)
   2985   else:
   2986     return create_zeros(state_size)

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\util\nest.py in map_structure(func, *structure, **kwargs)
    633 
    634   return pack_sequence_as(
--> 635       structure[0], [func(*x) for x in entries],
    636       expand_composites=expand_composites)
    637 

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\util\nest.py in <listcomp>(.0)
    633 
    634   return pack_sequence_as(
--> 635       structure[0], [func(*x) for x in entries],
    636       expand_composites=expand_composites)
    637 

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\layers\recurrent.py in create_zeros(unnested_state_size)
   2979     flat_dims = tensor_shape.as_shape(unnested_state_size).as_list()
   2980     init_state_size = [batch_size_tensor] + flat_dims
-> 2981     return array_ops.zeros(init_state_size, dtype=dtype)
   2982 
   2983   if nest.is_sequence(state_size):

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\util\dispatch.py in wrapper(*args, **kwargs)
    199     """Call target, and fall back on dispatchers if there is a TypeError."""
    200     try:
--> 201       return target(*args, **kwargs)
    202     except (TypeError, ValueError):
    203       # Note: convert_to_eager_tensor currently raises a ValueError, not a

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\ops\array_ops.py in wrapped(*args, **kwargs)
   2745 
   2746   def wrapped(*args, **kwargs):
-> 2747     tensor = fun(*args, **kwargs)
   2748     tensor._is_zeros_tensor = True
   2749     return tensor

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\ops\array_ops.py in zeros(shape, dtype, name)
   2792           # Create a constant if it won't be very big. Otherwise create a fill
   2793           # op to prevent serialized GraphDefs from becoming too large.
-> 2794           output = _constant_if_small(zero, shape, dtype, name)
   2795           if output is not None:
   2796             return output

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\ops\array_ops.py in _constant_if_small(value, shape, dtype, name)
   2730 def _constant_if_small(value, shape, dtype, name):
   2731   try:
-> 2732     if np.prod(shape) < 1000:
   2733       return constant(value, shape=shape, dtype=dtype, name=name)
   2734   except TypeError:

<__array_function__ internals> in prod(*args, **kwargs)

C:\ProgramData\Anaconda3\lib\site-packages\numpy\core\fromnumeric.py in prod(a, axis, dtype, out, keepdims, initial, where)
   3049     ...               # total product 1*2*3 = 6
   3050     array([1, 2, 6])
-> 3051     >>> a = np.array([[1, 2, 3], [4, 5, 6]])
   3052     >>> np.cumprod(a, dtype=float) # specify type of output
   3053     array([   1.,    2.,    6.,   24.,  120.,  720.])

C:\ProgramData\Anaconda3\lib\site-packages\numpy\core\fromnumeric.py in _wrapreduction(obj, ufunc, method, axis, dtype, out, **kwargs)
     84             else:
     85                 return reduction(axis=axis, out=out, **passkwargs)
---> 86 
     87     return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
     88 

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py in __array__(self)
    843 
    844   def __array__(self):
--> 845     raise NotImplementedError(
    846         "Cannot convert a symbolic Tensor () to a numpy array."
    847         " This error may indicate that you're trying to pass a Tensor to"

NotImplementedError: Cannot convert a symbolic Tensor (lstm_4/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

我尝试了here 提到的解决方案，但即使降级 numpy 也无济于事。我不确定这是 tensorflow 错误还是我做错了什么。请指教！

【参考方案1】：

我相信这是一个错误。我设法通过升级到 tensorflow-gpu==2.7 来解决它。 我也有 python 3.9.7 和最新的 numpy。

最好的