异常:检查模型目标时出错:期望dense_3具有形状(None, 1000)，但得到形状(32,2)的数组

Question

如何为我的数据创建VGG-16序列?

数据如下:

model = Sequential() 
model.add(ZeroPadding2D((1, 1), input_shape=(3, img_width, img_height))) model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_1')) model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_2'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1))) 
model.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_1')) model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_2'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1))) 
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_1')) model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_2'))
model.add(ZeroPadding2D((1, 1))) 
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_3')) model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1))) 
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_1')) model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_2'))
model.add(ZeroPadding2D((1, 1))) 
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_3')) model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1))) 
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_1')) model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_2'))
model.add(ZeroPadding2D((1, 1))) 
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_3')) model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(Flatten()) 
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5)) 
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5)) 
model.add(Dense(1000, activation='softmax'))
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='categorical_crossentropy')
train_datagen = ImageDataGenerator(
        rescale=1./255,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
        train_data_dir,
        target_size=(img_width, img_height),
        batch_size=32)
validation_generator = test_datagen.flow_from_directory(
        validation_data_dir,
        target_size=(img_width, img_height),
        batch_size=32)
model.fit_generator(
        train_generator,
        samples_per_epoch=2000,
        nb_epoch=1,
        verbose=1,
        validation_data=validation_generator,
        nb_val_samples=800)
json_string = model.to_json()  
open('my_model_architecture.json','w').write(json_string) 
model.save_weights('Second_try.h5')

我得到一个错误:

Exception: Error when checking model target: expected dense_3 to haveshape (None, 32)但得到shape (32, 2)

我如何改变Dense使其工作?

Answer 1

我有10个物种，
我已经解决了这个问题
改变:

model.add(Dense(1000, activation='softmax'))

:

model.add(Dense(10, activation='softmax'))

Answer 2

这里不是1000个，而是类的总数，因为它是输出层。

model.add(Dense(1000, activation='softmax')) 

标签的形状(或Y_train/Y_test)应该是(总类别数，总记录数)

这帮助我解决了类似的错误