在进行model fit的时候遇到报错ValueError: Layer sequentia expects 1 inputs, but it received 2 input tensors.
我用的代码是model.fit(X_train,Y_train,batch_size=600,epochs=1,verbose=1,validation_data=[X_test,Y_test])
老师给的代码是
明明一模一样哇。这是问什么呀
具体的错误代码为：
100/100 [==============================] - ETA: 0s - loss: 0.1040 - accuracy: 0.9690
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-23-960c89dbb231> in <module>
----> 1 model.fit(X_train,Y_train,batch_size=600,epochs=1,verbose=1,
2 validation_data=[X_test,Y_test])
E:\Software\Anaconda\envs\Env-CV\lib\site-packages\tensorflow\python\keras\engine\training.py in _method_wrapper(self, *args, **kwargs)
106 def _method_wrapper(self, *args, **kwargs):
107 if not self._in_multi_worker_mode(): # pylint: disable=protected-access
--> 108 return method(self, *args, **kwargs)
109
110 # Running inside `run_distribute_coordinator` already.
E:\Software\Anaconda\envs\Env-CV\lib\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)
1121 model=self,
1122 steps_per_execution=self._steps_per_execution)
-> 1123 val_logs = self.evaluate(
1124 x=val_x,
1125 y=val_y,
E:\Software\Anaconda\envs\Env-CV\lib\site-packages\tensorflow\python\keras\engine\training.py in _method_wrapper(self, *args, **kwargs)
106 def _method_wrapper(self, *args, **kwargs):
107 if not self._in_multi_worker_mode(): # pylint: disable=protected-access
--> 108 return method(self, *args, **kwargs)
109
110 # Running inside `run_distribute_coordinator` already.
E:\Software\Anaconda\envs\Env-CV\lib\site-packages\tensorflow\python\keras\engine\training.py in evaluate(self, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, return_dict)
1377 with trace.Trace('TraceContext', graph_type='test', step_num=step):
1378 callbacks.on_test_batch_begin(step)
-> 1379 tmp_logs = test_function(iterator)
1380 if data_handler.should_sync:
1381 context.async_wait()
E:\Software\Anaconda\envs\Env-CV\lib\site-packages\tensorflow\python\eager\def_function.py in __call__(self, *args, **kwds)
778 else:
779 compiler = "nonXla"
--> 780 result = self._call(*args, **kwds)
781
782 new_tracing_count = self._get_tracing_count()
E:\Software\Anaconda\envs\Env-CV\lib\site-packages\tensorflow\python\eager\def_function.py in _call(self, *args, **kwds)
821 # This is the first call of __call__, so we have to initialize.
822 initializers = []
--> 823 self._initialize(args, kwds, add_initializers_to=initializers)
824 finally:
825 # At this point we know that the initialization is complete (or less
E:\Software\Anaconda\envs\Env-CV\lib\site-packages\tensorflow\python\eager\def_function.py in _initialize(self, args, kwds, add_initializers_to)
694 self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
695 self._concrete_stateful_fn = (
--> 696 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
697 *args, **kwds))
698
E:\Software\Anaconda\envs\Env-CV\lib\site-packages\tensorflow\python\eager\function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2853 args, kwargs = None, None
2854 with self._lock:
-> 2855 graph_function, _, _ = self._maybe_define_function(args, kwargs)
2856 return graph_function
2857
E:\Software\Anaconda\envs\Env-CV\lib\site-packages\tensorflow\python\eager\function.py in _maybe_define_function(self, args, kwargs)
3211
3212 self._function_cache.missed.add(call_context_key)
-> 3213 graph_function = self._create_graph_function(args, kwargs)
3214 self._function_cache.primary[cache_key] = graph_function
3215 return graph_function, args, kwargs
E:\Software\Anaconda\envs\Env-CV\lib\site-packages\tensorflow\python\eager\function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3063 arg_names = base_arg_names + missing_arg_names
3064 graph_function = ConcreteFunction(
-> 3065 func_graph_module.func_graph_from_py_func(
3066 self._name,
3067 self._python_function,
E:\Software\Anaconda\envs\Env-CV\lib\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)
984 _, original_func = tf_decorator.unwrap(python_func)
985
--> 986 func_outputs = python_func(*func_args, **func_kwargs)
987
988 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
E:\Software\Anaconda\envs\Env-CV\lib\site-packages\tensorflow\python\eager\def_function.py in wrapped_fn(*args, **kwds)
598 # __wrapped__ allows AutoGraph to swap in a converted function. We give
599 # the function a weak reference to itself to avoid a reference cycle.
--> 600 return weak_wrapped_fn().__wrapped__(*args, **kwds)
601 weak_wrapped_fn = weakref.ref(wrapped_fn)
602
E:\Software\Anaconda\envs\Env-CV\lib\site-packages\tensorflow\python\framework\func_graph.py in wrapper(*args, **kwargs)
971 except Exception as e: # pylint:disable=broad-except
972 if hasattr(e, "ag_error_metadata"):
--> 973 raise e.ag_error_metadata.to_exception(e)
974 else:
975 raise
ValueError: in user code:
E:\Software\Anaconda\envs\Env-CV\lib\site-packages\tensorflow\python\keras\engine\training.py:1224 test_function *
return step_function(self, iterator)
E:\Software\Anaconda\envs\Env-CV\lib\site-packages\tensorflow\python\keras\engine\training.py:1215 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
E:\Software\Anaconda\envs\Env-CV\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:1211 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
E:\Software\Anaconda\envs\Env-CV\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2585 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
E:\Software\Anaconda\envs\Env-CV\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2945 _call_for_each_replica
return fn(*args, **kwargs)
E:\Software\Anaconda\envs\Env-CV\lib\site-packages\tensorflow\python\keras\engine\training.py:1208 run_step **
outputs = model.test_step(data)
E:\Software\Anaconda\envs\Env-CV\lib\site-packages\tensorflow\python\keras\engine\training.py:1174 test_step
y_pred = self(x, training=False)
E:\Software\Anaconda\envs\Env-CV\lib\site-packages\tensorflow\python\keras\engine\base_layer.py:975 __call__
input_spec.assert_input_compatibility(self.input_spec, inputs,
E:\Software\Anaconda\envs\Env-CV\lib\site-packages\tensorflow\python\keras\engine\input_spec.py:155 assert_input_compatibility
raise ValueError('Layer ' + layer_name + ' expects ' +
ValueError: Layer sequential_1 expects 1 inputs, but it received 2 input tensors. Inputs received: [<tf.Tensor 'IteratorGetNext:0' shape=(None, 28, 28, 1) dtype=float32>, <tf.Tensor 'IteratorGetNext:1' shape=(None, 10) dtype=float32>]
帮帮孩子吧！！！！！
