Scikit learn 检查输入时出错：预期conv2d_1_输入没有形状

首先，我搜索了所有的解决方案，并尝试了这个答案，但都不起作用。所以，我在深入学习，有三个班，第一班是猫，第二班是狗，第三班是人。我想参加自己的深度学习项目。但我总是看到这样的错误：ValueError：检查输入时出错：预期conv2d_6_输入具有形状（None，200200，1），但得到了具有形状（205，1200200）的数组。当我被要求更改此代码时：

model.add(Conv2D(nb_filters, (nb_conv , nb_conv) , 
                        activation = 'relu' , 
                        input_shape=(X_train.shape)))

我犯了这个错误

ValueError: Input 0 is incompatible with layer conv2d_8: expected ndim=4, found ndim=2

ValueError: Error when checking input: expected conv2d_6_input to have shape (None, 200, 200, 1) but got array with shape (205, 1, 200, 200). 

如果我不想改变这个代码，写下这个

model.add(Conv2D(nb_filters, (nb_conv , nb_conv) , 
                        activation = 'relu' , 
                        input_shape=(200,200,1)))

没有问题，但现在model.fit出现此错误

ValueError: Input 0 is incompatible with layer conv2d_8: expected ndim=4, found ndim=2

ValueError: Error when checking input: expected conv2d_6_input to have shape (None, 200, 200, 1) but got array with shape (205, 1, 200, 200). 

所有项目代码如下。所以thnx是所有答案

from keras.models import Sequential 
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.optimizers import SGD,RMSprop,adam
from keras.utils import np_utils

#   import numpy as np
import matplotlib.pyplot as plt 
import matplotlib
from numpy import * 
#SKLEARN
#   from sklearn.utils import shuffle 
from sklearn.cross_validation import train_test_split 
import theano


#____________________________________________________________________________________________________________________
import os

non_converted_image_file_path = 'C:\\Users\\Oğuzhan\\Desktop\\DeepLearningProject\\mnist\\data\\input_data'
converted_image_file_path = 'C:\\Humansdataresized'

#_____________________________________________________________________________________________________________________
from numpy import size

list_non_converted_images = os.listdir(non_converted_image_file_path) 
number_of_non_converted_images = size(list_non_converted_images)

#_____________________________________________________________________________________________________________________
from PIL import Image

img_rows , img_cols = 200 , 200

for file in list_non_converted_images:
    imop = Image.open(non_converted_image_file_path + '\\' + file)
    img = imop.resize((img_rows,img_cols))
    gray= img.convert('L')
        #When translating a color image to black and white (mode “L”), the library uses the ITU-R 601-2 luma transform:
        #L = R * 299/1000 + G * 587/1000 + B * 114/1000
    gray.save(converted_image_file_path +'\\'+ file, "JPEG")
    #-- file , img , img_cols, img_rows , imop , gray
#_____________________________________________________________________________________________________________________
list_of_converted_images = os.listdir(converted_image_file_path)
number_of_converted_images = size(list_of_converted_images)

if (number_of_converted_images != number_of_non_converted_images):
    print('NON EUQALS ' , number_of_converted_images , " = " , number_of_non_converted_images)
    raise SystemExit
else:
    print('EQUALS ' , number_of_converted_images , " = " , number_of_non_converted_images)
#_____________________________________________________________________________________________________________________
import numpy as np

imatrix = np.array([np.array(Image.open(converted_image_file_path+ '\\'+ im2)).flatten() for im2 in list_of_converted_images], 'f')

label = np.ones((number_of_non_converted_images,),dtype=int)
label[0:84] = 0 #cat
label[85:170] = 1 #kopek
label[170:] = 2 #human 

#_____________________________________________________________________________________________________________________
from sklearn.utils import shuffle 

shuffle_imatrix , shuffle_label = shuffle(imatrix,label,random_state=2) #shuffle_imatrix=data , shuffle_label = Label
train_data = [shuffle_imatrix,shuffle_label]

"""
import matplotlib.pyplot as plt 
img = imatrix[167].reshape(img_rows,img_cols)
plt.imshow(img)
plt.imshow(img,cmap='gray')
"""
print ("train data array[0] , train values : \n" ,train_data[0] , "\n")
print ("train data array[0] , shape train values : \n " ,train_data[0].shape ,"\n")

print ("train data array[1] , label values : \n" ,train_data[1] , "\n")
print ("train data array[1] , shape label values :\n ", train_data[1].shape, "\n")
#_____________________________________________________________________________________________________________________!TAMAM!
batch_size = 32 #batch size to train 
nb_classes = 3 # we have 3 class in non processing data folder. first of cat , second dog , thirt people.
nb_epoch = 20 # number of epoch to train 

img_rows , img_cols = 200, 200

img_channels = 1
nb_filters = 32 #convolutional filter 
nb_pool = 2 #pooling
nb_conv = 3 #conv.
#_____________________________________________________________________________________________________________________
from sklearn.cross_validation import train_test_split 

X_data_set , y_label_set = (train_data[0],train_data[1]) #train_data[0] = shuffle_imatrix , train_data[1] = shuffle_label

X_train, X_test, y_train , y_test = train_test_split(X_data_set , y_label_set , test_size=0.2 , random_state=4)

X_train = X_train.reshape(X_train.shape[0], 1 , img_rows , img_cols)#X_train.shape[0] = number of trained data , 1 = siyah beyaz olduğu için tek resim kanalı
X_test = X_test.reshape(X_test.shape[0], 1 , img_rows, img_cols)

X_train = X_train.astype('float32')
X_test = X_test.astype('float32')

X_train /= 255
X_test /= 255 

print('X_train shape :', X_train.shape)
print(X_train.shape[0] , 'train samples' )
print(X_test.shape[0] , 'test samples') 

from keras import utils
from keras.utils import np_utils

#Convert class vectors to binary class matrices 
Y_train = np_utils.to_categorical(y_train , nb_classes)
Y_test = np_utils.to_categorical(y_test , nb_classes)

i=100 
import matplotlib.pyplot as plt 
plt.imshow(X_train[i , 0] , interpolation='nearest')
print("label : " , Y_train[i,:])
#_____________________________________________________________________________________________________________________
from keras.models import Sequential 
from keras.layers.convolutional import MaxPooling2D

model = Sequential()

from keras.layers import Conv2D

model.add(Conv2D(nb_filters, (nb_conv , nb_conv) , 
                        activation = 'relu' , 
                        input_shape=(X_train.shape)))


model.add(Conv2D(nb_filters, (nb_conv , nb_conv) , 
                        activation = 'relu'))


model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(128))
model.add(Activation('relu'))

model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy' , optimizer='adadelta')
#_____________________________+________________________________________________________________________________________

model.fit(X_train , Y_train , batch_size = batch_size , nb_epoch = nb_epoch ,
           verbose = 1 , validation_data=(X_test , y_test))

model.fit(X_train, Y_train , batch_size=batch_size , nb_epoch = nb_epoch,
          verbose = 1 , validation_split = 0.2 )
#_____________________________________________________________________________________________________________________
score=model.evaluate(X_test , show_accuracy = True , verbose = 0)
print('Test score : ' , score[0])
print('Test accuracy : ' , score[1])
print(model.predict_classes(X_test[1:5]))
print(Y_test[1:5])
#_____________________________________________________________________________________________________________________
#%% 

#visualizing intermediate Layers

output_layer = model.layers[0].get_output()
output_fn = theano.function([model.layers[0].get])
#_____________________________________________________________________________________________________________________

input_image= X_train[0:1,:,:,:]
print(input_image.shape)

plt.imshow(input_image[0,0,:,:], cmap='gray')
plt.imshow(input_image[0,0,:,:])

output_image = output_fn(input_image)
print(output_image.shape)

# Rearrnge dimension so we can plot the result as RGB images
output_image = np.rollaxis(np.rollaxis(output_image , 3 , 1) , 3 , 1)
print(output_image.shape)

fig = plt.figure(figsize=(8,8))
for i in range(32):
    ax = fig.add_subplot(6, 6, i+1)
    #♠ax.imshow(output_image[0,:,:,i],interpolation='nearest') 
    ax.imshow(output_image[0,:,:,i],cmap=matplotlib.cm.gray)
    plt.xticks(np.array([]))
    plt.yticks(np.array([]))
    plt.tight_layout()

---

尝试：

input\u shape=（X\u train.shape）[1:][/code>thnx但我得到了这个错误：ValueError:conv2d\u 9/卷积（op:'conv2d'）的输入形状从1中减去3会导致负维度大小：[？，1200200]，[3,3200,32]。尝试删除您的重塑
x_列
和
x_测试
我删除了这两行重塑行，'#x#u列=x#u列。重塑（x#train.shape[0]，1，img#u列，img#u列。shape[0]=训练数据的数量，1=siyah beyaz oldu u#u#u için tek resim kanal l#x#u测试=x#u测试'并尝试运行，但现在出现以下错误：'ValueError:Input 0与层conv2d_11不兼容：预期ndim=4，发现ndim=2'当然，我正在更改
model.compile（loss='classifical\u crossentropy'，optimizer='adadelta'）
当我来到这个fit行时
model.fit（X_列，Y_列，batch_size=batch_size，nb_epoch=nb_epoch，verbose=1，validation_data=（X_test，Y_test））
再次出现相同错误omg
ValueError:检查目标时出错：预期激活10具有形状（无，3），但获得具有形状（52，1）的数组