model.fit NotImplementedError: Cannot convert a symbolic Tensor to a numpy array.
Epoch 1/100
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
Cell In[4], line 1
----> 1 history = model.fit(x_train, y_train, batch_size=32, epochs=100, callbacks=[cp_callback])
File /home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:66, in enable_multi_worker.<locals>._method_wrapper(self, *args, **kwargs)
64 def _method_wrapper(self, *args, **kwargs):
65 if not self._in_multi_worker_mode(): # pylint: disable=protected-access
---> 66 return method(self, *args, **kwargs)
68 # Running inside `run_distribute_coordinator` already.
69 if dc_context.get_current_worker_context():
File /home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:848, in Model.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)
841 with traceme.TraceMe(
842 'TraceContext',
843 graph_type='train',
844 epoch_num=epoch,
845 step_num=step,
846 batch_size=batch_size):
847 callbacks.on_train_batch_begin(step)
--> 848 tmp_logs = train_function(iterator)
849 # Catch OutOfRangeError for Datasets of unknown size.
850 # This blocks until the batch has finished executing.
851 # TODO(b/150292341): Allow multiple async steps here.
852 if not data_handler.inferred_steps:
File /home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py:580, in Function.__call__(self, *args, **kwds)
578 xla_context.Exit()
579 else:
--> 580 result = self._call(*args, **kwds)
582 if tracing_count == self._get_tracing_count():
583 self._call_counter.called_without_tracing()
File /home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py:627, in Function._call(self, *args, **kwds)
624 try:
625 # This is the first call of __call__, so we have to initialize.
626 initializers = []
--> 627 self._initialize(args, kwds, add_initializers_to=initializers)
628 finally:
629 # At this point we know that the initialization is complete (or less
630 # interestingly an exception was raised) so we no longer need a lock.
631 self._lock.release()
File /home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py:505, in Function._initialize(self, args, kwds, add_initializers_to)
502 self._lifted_initializer_graph = lifted_initializer_graph
503 self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
504 self._concrete_stateful_fn = (
--> 505 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
506 *args, **kwds))
508 def invalid_creator_scope(*unused_args, **unused_kwds):
509 """Disables variable creation."""
File /home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/tensorflow/python/eager/function.py:2446, in Function._get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2444 args, kwargs = None, None
2445 with self._lock:
-> 2446 graph_function, _, _ = self._maybe_define_function(args, kwargs)
2447 return graph_function
File /home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/tensorflow/python/eager/function.py:2777, in Function._maybe_define_function(self, args, kwargs)
2774 return self._define_function_with_shape_relaxation(args, kwargs)
2776 self._function_cache.missed.add(call_context_key)
-> 2777 graph_function = self._create_graph_function(args, kwargs)
2778 self._function_cache.primary[cache_key] = graph_function
2779 return graph_function, args, kwargs
File /home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/tensorflow/python/eager/function.py:2657, in Function._create_graph_function(self, args, kwargs, override_flat_arg_shapes)
2652 missing_arg_names = [
2653 "%s_%d" % (arg, i) for i, arg in enumerate(missing_arg_names)
2654 ]
2655 arg_names = base_arg_names + missing_arg_names
2656 graph_function = ConcreteFunction(
-> 2657 func_graph_module.func_graph_from_py_func(
2658 self._name,
2659 self._python_function,
2660 args,
2661 kwargs,
2662 self.input_signature,
2663 autograph=self._autograph,
2664 autograph_options=self._autograph_options,
2665 arg_names=arg_names,
2666 override_flat_arg_shapes=override_flat_arg_shapes,
2667 capture_by_value=self._capture_by_value),
2668 self._function_attributes,
2669 # Tell the ConcreteFunction to clean up its graph once it goes out of
2670 # scope. This is not the default behavior since it gets used in some
2671 # places (like Keras) where the FuncGraph lives longer than the
2672 # ConcreteFunction.
2673 shared_func_graph=False)
2674 return graph_function
File /home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py:981, 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)
978 else:
979 _, original_func = tf_decorator.unwrap(python_func)
--> 981 func_outputs = python_func(*func_args, **func_kwargs)
983 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
984 # TensorArrays and `None`s.
985 func_outputs = nest.map_structure(convert, func_outputs,
986 expand_composites=True)
File /home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py:441, in Function._defun_with_scope.<locals>.wrapped_fn(*args, **kwds)
426 # We register a variable creator with reduced priority. If an outer
427 # variable creator is just modifying keyword arguments to the variable
428 # constructor, this will work harmoniously. Since the `scope` registered
(...)
436 # better than the alternative, tracing the initialization graph but giving
437 # the user a variable type they didn't want.
438 with ops.get_default_graph()._variable_creator_scope(scope, priority=50): # pylint: disable=protected-access
439 # __wrapped__ allows AutoGraph to swap in a converted function. We give
440 # the function a weak reference to itself to avoid a reference cycle.
--> 441 return weak_wrapped_fn().__wrapped__(*args, **kwds)
File /home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py:968, in func_graph_from_py_func.<locals>.wrapper(*args, **kwargs)
966 except Exception as e: # pylint:disable=broad-except
967 if hasattr(e, "ag_error_metadata"):
--> 968 raise e.ag_error_metadata.to_exception(e)
969 else:
970 raise
NotImplementedError: in user code:
/home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:571 train_function *
outputs = self.distribute_strategy.run(
/home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:951 run **
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2290 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2649 _call_for_each_replica
return fn(*args, **kwargs)
/home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:531 train_step **
y_pred = self(x, training=True)
/home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:927 __call__
outputs = call_fn(cast_inputs, *args, **kwargs)
/home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/tensorflow/python/keras/engine/sequential.py:291 call
outputs = layer(inputs, **kwargs)
/home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py:654 __call__
return super(RNN, self).__call__(inputs, **kwargs)
/home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:927 __call__
outputs = call_fn(cast_inputs, *args, **kwargs)
/home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py:1530 call
return super(SimpleRNN, self).call(
/home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py:721 call
inputs, initial_state, constants = self._process_inputs(
/home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py:848 _process_inputs
initial_state = self.get_initial_state(inputs)
/home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py:636 get_initial_state
init_state = get_initial_state_fn(
/home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py:1343 get_initial_state
return _generate_zero_filled_state_for_cell(self, inputs, batch_size, dtype)
/home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py:2926 _generate_zero_filled_state_for_cell
return _generate_zero_filled_state(batch_size, cell.state_size, dtype)
/home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py:2944 _generate_zero_filled_state
return create_zeros(state_size)
/home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py:2939 create_zeros
return array_ops.zeros(init_state_size, dtype=dtype)
/home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/tensorflow/python/ops/array_ops.py:2677 wrapped
tensor = fun(*args, **kwargs)
/home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/tensorflow/python/ops/array_ops.py:2721 zeros
output = _constant_if_small(zero, shape, dtype, name)
/home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/tensorflow/python/ops/array_ops.py:2662 _constant_if_small
if np.prod(shape) < 1000:
<__array_function__ internals>:180 prod
/home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3045 prod
return _wrapreduction(a, np.multiply, 'prod', axis, dtype, out,
/home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/numpy/core/fromnumeric.py:86 _wrapreduction
return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
/home/software/anaconda3/envs/mydlenv/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:748 __array__
raise NotImplementedError("Cannot convert a symbolic Tensor ({}) to a numpy"
NotImplementedError: Cannot convert a symbolic Tensor (sequential_1/simple_rnn_1/strided_slice:0) to a numpy array.
问题原因及解决方法：
numpy版本 过高，应降低其版本。（ TensorFlow ， numpy， pandas ， python 版本要兼容)
以下版本搭配不行： mydlenv
tensorflow 2.2.0
pandas 1.5.3
numpy 1.23.4
以下版本搭配可以：
这是numpy版本与tensorflow版本不兼容导致的 tensorflow2.2 + numpy1.19.2 表示没问题。
numpy 1.18.5
tensorflow 2.3.0pandas 1.5.3 requires numpy>=1.20.3
pip uninstall numpy
pip install numpy==1.19.2 -i https://pypi.douban.com/simple