CSV MNIST 数据集：ValueError: Shapes (None, 10) 和 (None, 28, 10) 不兼容

如何解决CSV MNIST 数据集：ValueError: Shapes (None, 10) 和 (None, 28, 10) 不兼容

我想用 keras 对 MINST 数据集 (csv) 进行分类。这是我的代码，但运行后出现此错误。你知道我该如何解决吗ValueError：Shapes (None,10) and (None,28,10) is incompatible

from keras import models 
import numpy as np
from keras import layers
import tensorflow as  tf
from tensorflow.keras.models import Sequential
from keras.utils import np_utils
from tensorflow.keras.layers import Dense,Dropout,LSTM,BatchNormalization
from keras.utils import to_categorical,plot_model
mnist = tf.keras.datasets.mnist
#Load dataset
(x_train,y_train),(x_test,y_test) = mnist.load_data()

y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
model = Sequential()
model.add(Dense(units=32,activation='sigmoid',input_shape=(x_train.shape[1:])))
model.add(Dense(units=64,activation='sigmoid'))
model.add(Dense(units=10,activation='softmax')) 
model.compile(optimizer="sgd",loss='categorical_crossentropy',metrics=['accuracy'])
history = model.fit(x_train,y_train,batch_size=32,epochs=100,validation_split=.3)

plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['training','validation'],loc='best')
plt.show()

这里我从代码中得到了错误。我知道这是因为输入形状的原因，但我不知道应该如何定义它。 x_train.shape 是 (60000,28) y_train.shape 是 (60000,10)

ValueError                                Traceback (most recent call last)
<ipython-input-112-7c9220a71c0e> in <module>
      1 model.compile(optimizer="sgd",metrics=['accuracy'])
----> 2 history = model.fit(x_train,validation_split=.3)
      3 
      4 plt.plot(history.history['accuracy'])
      5 plt.plot(history.history['val_accuracy'])

~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in _method_wrapper(self,*args,**kwargs)
     64   def _method_wrapper(self,**kwargs):
     65     if not self._in_multi_worker_mode():  # pylint: disable=protected-access
---> 66       return method(self,**kwargs)
     67 
     68     # Running inside `run_distribute_coordinator` already.

~\anaconda3\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)
    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.

~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in __call__(self,**kwds)
    578         xla_context.Exit()
    579     else:
--> 580       result = self._call(*args,**kwds)
    581 
    582     if tracing_count == self._get_tracing_count():

~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in _call(self,**kwds)
    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

~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in _initialize(self,args,add_initializers_to)
    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))
    507 

~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _get_concrete_function_internal_garbage_collected(self,**kwargs)
   2444       args,kwargs = None,None
   2445     with self._lock:
-> 2446       graph_function,_,_ = self._maybe_define_function(args,kwargs)
   2447     return graph_function
   2448 

~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _maybe_define_function(self,kwargs)
   2775 
   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,kwargs

~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _create_graph_function(self,kwargs,override_flat_arg_shapes)
   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,~\anaconda3\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)
    979         _,original_func = tf_decorator.unwrap(python_func)
    980 
--> 981       func_outputs = python_func(*func_args,**func_kwargs)
    982 
    983       # invariant: `func_outputs` contains only Tensors,CompositeTensors,~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in wrapped_fn(*args,**kwds)
    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)
    442     weak_wrapped_fn = weakref.ref(wrapped_fn)
    443 

~\anaconda3\lib\site-packages\tensorflow\python\framework\func_graph.py in 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

th
        raise ValueError("Shapes %s and %s are incompatible" % (self,other))

    ValueError: Shapes (None,10) are incompatible

解决方法

由于密集层无法处理图像等二维数据，您应该首先将输入展平为向量，然后将其传递给您的模型，否则，您将在输出中获得其他维度，然后是您的标签和logits（模型输出）不兼容，你会得到错误。

像这样向模型添加一个展平层：

model.add(Flatten(input_shape=(x_train.shape[1:]))) #add this
model.add(Dense(units=32,activation='sigmoid'))
model.add(Dense(units=64,activation='sigmoid'))
model.add(Dense(units=10,activation='softmax'))