检查目标时出错：预期 dense_8 的形状为 (2,) 但得到的数组的形状为 (1,)

Question

我是迁移学习的新手，我正在研究 2 个类别的图像分类。我正在使用 InceptionV3 对这些图像进行分类。我的数据 为 .jpg 格式。并且文件夹结构采用以下格式。因为我有 2 个类别，所以我也给出了“binary_crossentropy”。但面临问题。

父文件夹/train/categorie1
父文件夹/train/categorie2

父文件夹/test/categorie1
父文件夹/test/categorie2

from keras.applications.inception_v3 import InceptionV3
from keras.preprocessing import image
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras import backend as K

# create the base pre-trained model
base_model = InceptionV3(weights='imagenet', include_top=False)

# add a global spatial average pooling layer
x = base_model.output
x = GlobalAveragePooling2D()(x)
# let's add a fully-connected layer
x = Dense(1024, activation='relu')(x)

x = Dense(512, activation='relu')(x)
x = Dense(32, activation='relu')(x)
# and a logistic layer -- we have 2 classes
predictions = Dense(2, activation='softmax')(x)

# this is the model we will train
model = Model(inputs=base_model.input, outputs=predictions)


for layer in base_model.layers:
    layer.trainable = False

# we chose to train the top 2 inception blocks, i.e. we will freeze
# the first 249 layers and unfreeze the rest:
for layer in model.layers[:249]:
   layer.trainable = False
for layer in model.layers[249:]:
   layer.trainable = True

model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"])
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(
        rescale=1./255,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True)

test_datagen = ImageDataGenerator(rescale=1./255)

training_set = train_datagen.flow_from_directory(
        'C:/Users/Desktop/Transfer/train/',
        target_size=(64, 64),
        batch_size=5,
        class_mode='binary')

test_set = test_datagen.flow_from_directory(
        'C:/Users/Desktop/Transfer/test/',
        target_size=(64, 64),
        batch_size=5,
        class_mode='binary')

model.fit_generator(
        training_set,
        steps_per_epoch=1000,
        epochs=10,
        validation_data=test_set,
        validation_steps=100)

Answer 1

替换这一行

predictions = Dense(2, activation='softmax')(x)

与：

predictions = Dense(1, activation='sigmoid')(x)

或将您的目标编码为列，例如[1,0,1] 等同于[[0,1],[1,0],[0,1]]