Python 如何解决InvalidArgumentError：不兼容的形状：[10,34,34]与CNN模型的[10,1]相比

因此，我试图使用TensorFlow 2为猫/狗宠物分类器编写代码。我已经完成了所有的层，当我尝试拟合模型时，我得到了错误“InvalidArgumentError:不兼容的形状：[10,34,34]vs[10,1]”

我真的不确定我哪里做错了。我有大约20张狗的图片，20张猫的图片用于训练，10张用于验证

import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator 
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import InputLayer, Dense, Conv2D, Flatten, Dropout, MaxPooling2D, Activation
from tensorflow.keras.preprocessing import image
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

img_width, img_height = 150,150 
train_data_dir = r"E:\Dhivya\Python\petclassification\data\train"
validation_data_dir=r"E:\Dhivya\Python\petclassification\data\test"
nb_train_sample = 40
nb_validation_samples = 20
epochs = 50
batch_size= 10
CATEGORIES = ['cats', 'dogs']


if k.image_data_format()=='channels_first': 
    input_shape=(3, img_width, img_height)
else:
    input_shape=(img_width,img_height,3)


model = Sequential()

model.add(InputLayer(input_shape=input_shape))

model.add(Conv2D(32,(5,5))) 

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(64,(5,5)))

model.add(MaxPooling2D(pool_size=(2, 2), strides=2))

model.add(Dense(32))

model.add(Dropout(0.4))

model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])

model.summary()

model.fit_generator(train_generator,
                   steps_per_epoch=nb_train_sample,epochs=epochs,
                   validation_data=validation_generator,
                       validation_steps=nb_validation_samples)

我将感谢任何帮助解决这个问题。我是一个学习深度学习的初学者