如何解决tensorflow错误：ValueError：图层序列的输入0与图层预期轴-1不兼容
我正在尝试修改提供给我的代码以导入图像文件，并使用keras构建训练和测试集。 我收到以下错误：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-154-b4983c6bd066> in <module>()
1 # Fit the model
----> 2 history = model.fit(X_train,y_train,batch_size = 256,epochs = 15,verbose=2,validation_data=(X_test,y_test))
~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py in _method_wrapper(self,*args,**kwargs)
106 def _method_wrapper(self,**kwargs):
107 if not self._in_multi_worker_mode(): # pylint: disable=protected-access
--> 108 return method(self,**kwargs)
109
110 # Running inside `run_distribute_coordinator` already.
~\AppData\Roaming\Python\Python37\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)
1096 batch_size=batch_size):
1097 callbacks.on_train_batch_begin(step)
-> 1098 tmp_logs = train_function(iterator)
1099 if data_handler.should_sync:
1100 context.async_wait()
~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\def_function.py in __call__(self,**kwds)
778 else:
779 compiler = "nonXla"
--> 780 result = self._call(*args,**kwds)
781
782 new_tracing_count = self._get_tracing_count()
~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\def_function.py in _call(self,**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
~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\def_function.py in _initialize(self,args,add_initializers_to)
695 self._concrete_stateful_fn = (
696 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 697 *args,**kwds))
698
699 def invalid_creator_scope(*unused_args,**unused_kwds):
~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\function.py in _get_concrete_function_internal_garbage_collected(self,**kwargs)
2853 args,kwargs = None,None
2854 with self._lock:
-> 2855 graph_function,_,_ = self._maybe_define_function(args,kwargs)
2856 return graph_function
2857
~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\function.py in _maybe_define_function(self,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,kwargs
~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\function.py in _create_graph_function(self,kwargs,override_flat_arg_shapes)
3073 arg_names=arg_names,3074 override_flat_arg_shapes=override_flat_arg_shapes,-> 3075 capture_by_value=self._capture_by_value),3076 self._function_attributes,3077 function_spec=self.function_spec,~\AppData\Roaming\Python\Python37\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)
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,~\AppData\Roaming\Python\Python37\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
~\AppData\Roaming\Python\Python37\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:
C:\Users\synar\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py:806 train_function *
return step_function(self,iterator)
C:\Users\synar\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py:796 step_function **
outputs = model.distribute_strategy.run(run_step,args=(data,))
C:\Users\synar\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\distribute\distribute_lib.py:1211 run
return self._extended.call_for_each_replica(fn,args=args,kwargs=kwargs)
C:\Users\synar\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\distribute\distribute_lib.py:2585 call_for_each_replica
return self._call_for_each_replica(fn,kwargs)
C:\Users\synar\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\distribute\distribute_lib.py:2945 _call_for_each_replica
return fn(*args,**kwargs)
C:\Users\synar\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py:789 run_step **
outputs = model.train_step(data)
C:\Users\synar\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py:747 train_step
y_pred = self(x,training=True)
C:\Users\synar\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\base_layer.py:976 __call__
self.name)
C:\Users\synar\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\input_spec.py:216 assert_input_compatibility
' but received input with shape ' + str(shape))
ValueError: Input 0 of layer sequential_41 is incompatible with the layer: expected axis -1 of input shape to have value 784 but received input with shape [None,1]
我已经实现的代码是：
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from sklearn import model_selection
from scipy.io import loadmat
data = loadmat('notMNIST_small.mat')
X_temp = data['images']/255
#for i in range(X_temp.shape[2]):
X = np.empty(shape=[X_temp.shape[2]] + [784],dtype='float32')
for i in range(X_temp.shape[2]):
X[i,:] = X_temp[:,:,i].flatten()
y = pd.get_dummies(data['labels']).to_numpy()
print(X_temp.shape)
print(X.shape)
print(y.shape)
X[1,:]
X = np.array(data['labels']).reshape(-1,1)
y = np.array(data['labels'])
X_train,X_test,y_test =train_test_split(
X,test_size=0.2,random_state=9)
stdscaler = preprocessing.StandardScaler().fit(X_train)
X_train_scaled = stdscaler.transform(X_train)
X_test_scaled = stdscaler.transform(X_test)
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.regularizers import l2,l1
from tensorflow.keras.optimizers import SGD
# Stochastic Logistic Regression
model = Sequential()
# Model
model.add(Dense(units=10,input_shape = [784,],activation = 'relu',kernel_regularizer=l2(0)))
model.add(Dense(units = 40,activation = 'relu'))
model.add(Dense(units = 10,activation = 'sigmoid'))
# Compile model
sgd = SGD(lr=0.1)
model.compile(loss='categorical_crossentropy',optimizer=sgd)
实现以下单元格时出现错误：
# Fit the model
history = model.fit(X_train,y_test))
解决这个问题的任何帮助都是很棒的，我是机器学习的新手，请原谅我的无知。
解决方法
为您的 X_train 添加额外的维度
x_train = x_train.reshape(-1,28*28)
model = Sequential()
# Model
model.add(Dense(units=10,input_dim = 784,activation = 'relu',kernel_regularizer=l2(0)))
model.add(Dense(units = 40,activation = 'relu'))
model.add(Dense(units = 10,activation = 'sigmoid'))
# Compile model
sgd = SGD(lr=0.1)
model.compile(loss='categorical_crossentropy',optimizer=sgd)
版权声明：本文内容由互联网用户自发贡献，该文观点与技术仅代表作者本人。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。如发现本站有涉嫌侵权/违法违规的内容， 请发送邮件至 dio@foxmail.com 举报，一经查实，本站将立刻删除。