Keras 拟合自定义模型后出现值错误
我正在fashion 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度分别为128、64和32。但是在拟合模型后，它抛出一个值错误-Keras 拟合自定义模型后出现值错误,keras,deep-learning,tensorflow2.0,autoencoder,mnist,Keras,Deep Learning,Tensorflow2.0,Autoencoder,Mnist,我正在fashion 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度分别为128、64和32。但是在拟合模型后，它抛出一个值错误-ValueError:输入0与层模型_7不兼容：预期的形状=（无，784），发现的形状=（32,28,28） 守则：- #Encoder input1 = Input(shape = (784,)) hidden1 = Dense(128, activation = 'relu')(input1) hidden
ValueError:输入0与层模型_7不兼容：预期的形状=（无，784），发现的形状=（32,28,28）#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)
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, *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
C:\Anaconda\lib\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
C:\Anaconda\lib\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
C:\Anaconda\lib\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
C:\Anaconda\lib\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,
C:\Anaconda\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)
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, args, 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, 784), found shape=(32, 28, 28)
1/3时代
---------------------------------------------------------------------------
ValueError回溯（最近一次调用上次）
在里面
---->1个模型。拟合（x_系列，y_系列，历代=3）
C:\Anaconda\lib\site packages\tensorflow\python\keras\engine\training.py in fit（self、x、y、批大小、历元、冗余、回调、验证拆分、验证数据、洗牌、类权重、样本权重、初始历元、每个历元的步骤、验证步骤、验证批大小、验证频率、最大队列大小、工作者、使用多处理）
1098（r=1）：
1099回拨。列车上批次开始（步骤）
->1100 tmp_日志=self.train_函数（迭代器）
1101如果数据处理器应同步：
1102 context.async_wait（）
C:\Anaconda\lib\site packages\tensorflow\python\eager\def\u function.py in\uuuuu调用（self，*args，**kwds）
826 tracing\u count=self.experimental\u get\u tracing\u count（）
827，trace.trace（self.\u name）为tm:
-->828结果=self.\u调用（*args，**kwds）
829 compiler=“xla”如果是self.\u experimental\u编译else“nonXla”
830 new_tracing_count=self.experimental_get_tracing_count（）
C:\Anaconda\lib\site packages\tensorflow\python\eager\def_function.py in_调用（self，*args，**kwds）
869#这是uuu call uuuu的第一个调用，所以我们必须初始化。
870个初始值设定项=[]
-->871自初始化（参数、KWD、添加初始化器到=初始化器）
872最后：
873#此时我们知道初始化已完成（或更少）
C:\Anaconda\lib\site packages\tensorflow\python\eager\def_function.py in_initialize（self、args、kwds、add_initializers_to）
723 self.\u graph\u deleter=函数deleter（self.\u lifted\u initializer\u graph）
724自身的具体状态=(
-->725 self._stateful_fn._get_concrete_function_internal_garbage_collected（#pylint:disable=protected access
726*args，**科威特第纳尔）
727
C:\Anaconda\lib\site packages\tensorflow\python\eager\function.py in\u get\u concrete\u function\u internal\u garbage\u collected（self，*args，**kwargs）
2967 args，kwargs=无，无
2968带自锁：
->2969图形函数，自变量。可能定义函数（args，kwargs）
2970返回图函数
2971
C:\Anaconda\lib\site packages\tensorflow\python\eager\function.py in\u maybe\u define\u函数（self、args、kwargs）
3359
3360 self.\u function\u cache.missed.add（调用上下文\u键）
->3361图形函数=自身。创建图形函数（args、kwargs）
3362 self.\u function\u cache.primary[cache\u key]=图形\u函数
3363
C:\Anaconda\lib\site packages\tensorflow\python\eager\function.py in\u create\u graph\u函数（self、args、kwargs、override\u flat\u arg\u shapes）
3194参数名称=基本参数名称+缺少的参数名称
3195图形函数=具体函数(
->3196 func_graph_module.func_graph_from_py_func(
3197自我名称，
3198 self.\u python\u函数，
C:\Anaconda\lib\site packages\tensorflow\python\framework\func\u中的func\u graph.py from\u py\u func（名称、python\u func、args、kwargs、签名、func\u图、自动签名、自动签名、自动签名、自动签名、自动签名选项、添加控制依赖项、参数名、操作返回值、集合、按值捕获、覆盖平面参数形状）
988，original_func=tf_decorator.unwrap（python_func）
989
-->990 func_outputs=python_func（*func_args，**func_kwargs）
991
992不变量：`func#u outputs`只包含张量、复合传感器、，
C:\Anaconda\lib\site packages\tensorflow\python\eager\def_function.py in wrapped_fn（*args，**kwds）
632 xla_context.Exit（）
633其他：
-->634 out=弱包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹包裹
635返回
636
包装中的C:\Anaconda\lib\site packages\tensorflow\python\framework\func\u graph.py（*args，**kwargs）
975例外情况为e:#pylint:disable=broad except
976如果hasattr（e，“AGU错误元数据”）：
-->977将e.ag\u错误\u元数据引发到\u异常（e）
978其他：
979提高
ValueError:在用户代码中：
C:\Anaconda\lib\site packages\tensorflow\python\keras\engine\training.py:805 train\u函数*
返回步骤_函数（self、迭代器）
C:\Anaconda\lib\site packages\tensorflow\python\keras\engine\training.py:795步骤\u函数**
输出=模型。分配策略。运行（运行步骤，参数=（数据，）
C:\Anaconda\lib\site packages\tensorflow\python\distribute\distribute\u lib.py:1259 run
返回self.\u扩展。为每个\u副本调用\u（fn，args=args，kwargs=kwargs）
C:\Anaconda\lib\site packages\tensorflow\python\distribute\distribute\u lib.py:2730为每个\u副本调用\u
返回自我。为每个副本（fn、ARG、kwargs）调用
C:\Anaconda\lib\site packages\tensorflow\python\distribute\distribute\u lib.py:3417\u为每个\u副本调用\u
返回fn（*args，**kwargs）
C:\Anaconda\lib\site packages\tensorflow\python\keras\engine\training.py:788 run\u步骤**
输出=型号列车步进（数据）
C:\Anaconda\lib\site packages\tensorflow\python\keras\engine\training.py:754 train\u步骤
y_pred=self（x，training=True）
C:\Anaconda\lib\site packages\tensorflow\python\keras\engine\base\u layer.py:998\u调用__
输入\规格断言\输入\兼容性
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)