如何解决拟合我的自定义模型后的值错误
我正在时尚 MNIST 数据集上创建一个编码器。编码器由三层组成，每个输入图像被展平为784维。三个编码器层的输出维数分别为128、64、32。但是在拟合模型后，它抛出了一个值错误 - ValueError: Input 0 is incompatible with layer model_7: expected shape=(None,784),found shape=(32,28,28)
代码：-
#Encoder
input1 = Input(shape = (784,))
hidden1 = Dense(128,activation = 'relu')(input1)
hidden2 = Dense(64,activation = 'relu')(hidden1)
hidden3 = Dense(32,activation = 'relu')(hidden2)
model = Model(inputs = input1,outputs = hidden3)
模型摘要：
Model: "model_7"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_17 (InputLayer) [(None,784)] 0
_________________________________________________________________
dense_43 (Dense) (None,128) 100480
_________________________________________________________________
dense_44 (Dense) (None,64) 8256
_________________________________________________________________
dense_45 (Dense) (None,32) 2080
=================================================================
Total params: 110,816
Trainable params: 110,816
Non-trainable params: 0
拟合模型后的误差：-
Epoch 1/3
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-42-3295f6ac1688> in <module>
----> 1 model.fit(x_train,y_train,epochs = 3)
C:\Anaconda\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)
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()
C:\Anaconda\lib\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()
C:\Anaconda\lib\site-packages\tensorflow\python\eager\def_function.py in _call(self,**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
C:\Anaconda\lib\site-packages\tensorflow\python\eager\def_function.py in _initialize(self,args,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
C:\Anaconda\lib\site-packages\tensorflow\python\eager\function.py in _get_concrete_function_internal_garbage_collected(self,**kwargs)
2967 args,kwargs = None,None
2968 with self._lock:
-> 2969 graph_function,_ = self._maybe_define_function(args,kwargs)
2970 return graph_function
2971
C:\Anaconda\lib\site-packages\tensorflow\python\eager\function.py in _maybe_define_function(self,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
C:\Anaconda\lib\site-packages\tensorflow\python\eager\function.py in _create_graph_function(self,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,C:\Anaconda\lib\site-packages\tensorflow\python\framework\func_graph.py in func_graph_from_py_func(name,python_func,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,C:\Anaconda\lib\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
C:\Anaconda\lib\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
ValueError: in user code:
C:\Anaconda\lib\site-packages\tensorflow\python\keras\engine\training.py:805 train_function *
return step_function(self,iterator)
C:\Anaconda\lib\site-packages\tensorflow\python\keras\engine\training.py:795 step_function **
outputs = model.distribute_strategy.run(run_step,args=(data,))
C:\Anaconda\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:1259 run
return self._extended.call_for_each_replica(fn,args=args,kwargs=kwargs)
C:\Anaconda\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2730 call_for_each_replica
return self._call_for_each_replica(fn,kwargs)
C:\Anaconda\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:3417 _call_for_each_replica
return fn(*args,**kwargs)
C:\Anaconda\lib\site-packages\tensorflow\python\keras\engine\training.py:788 run_step **
outputs = model.train_step(data)
C:\Anaconda\lib\site-packages\tensorflow\python\keras\engine\training.py:754 train_step
y_pred = self(x,training=True)
C:\Anaconda\lib\site-packages\tensorflow\python\keras\engine\base_layer.py:998 __call__
input_spec.assert_input_compatibility(self.input_spec,inputs,self.name)
C:\Anaconda\lib\site-packages\tensorflow\python\keras\engine\input_spec.py:271 assert_input_compatibility
raise ValueError('Input ' + str(input_index) +
ValueError: Input 0 is incompatible with layer model_7: expected shape=(None,28)
这个错误是什么意思，我需要改变输入维度吗？如果是，那么我的输入维度是什么？
训练和测试数据的形状-
解决方法
您的输入图像是 28 x 28 的图像，需要转换为 784 个值的向量。虽然有多种方法可以做到这一点，但最简单的方法是使用 Keras 提供的 Flatten 层。在这种情况下，您对模型的输入将是 28 x 28 的图像：
input1 = Input(shape = (28,28))
flattened_input = Flatten()(input1)
hidden1 = Dense(128,activation = 'relu')(flattened_input)
hidden2 = Dense(64,activation = 'relu')(hidden1)
hidden3 = Dense(32,activation = 'relu')(hidden2)
model = Model(inputs = input1,outputs = hidden3)
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