首先，我搜索所有解决方案并尝试这个答案，但一切都行不通。所以，我正在深入学习并拥有3个班级，第一只猫，第二只狗，第三只人。我想养活自己的深度学习项目。但我总是看到这个错误：ValueError：检查输入时出错：期望conv2d_6_input有形状（无，200,200,1）但是有形状的数组（205,1,200,200）。当我改变这段代码时：
model.add(Conv2D(nb_filters, (nb_conv , nb_conv) ,
activation = 'relu' ,
input_shape=(X_train.shape)))
我收到此错误
ValueError: Input 0 is incompatible with layer conv2d_8: expected ndim=4, found ndim=2
如果我不更改此代码，请写下
model.add(Conv2D(nb_filters, (nb_conv , nb_conv) ,
activation = 'relu' ,
input_shape=(200,200,1)))
不是问题，但现在model.fit收到此错误
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。所以答案都是thnx。
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()