【发布时间】：2020-03-14 09:43:14
【问题描述】：
我使用 ResNet50 训练了一个模型，并在训练集上获得了惊人的 95% 准确率。 我用同样的训练集进行验证，准确率似乎很差。(
from keras.preprocessing.image import ImageDataGenerator
train_set = ImageDataGenerator(horizontal_flip=True,rescale=1./255,shear_range=0.2,zoom_range=0.2).flow_from_directory(data,target_size=(256,256),classes=['airplane','airport','baseball_diamond',
'basketball_court','beach','bridge',
'chaparral','church','circular_farmland',
'commercial_area','dense_residential','desert',
'forest','freeway','golf_course','ground_track_field',
'harbor','industrial_area','intersection','island',
'lake','meadow','medium_residential','mobile_home_park',
'mountain','overpass','parking_lot','railway','rectangular_farmland',
'roundabout','runway'],batch_size=31)
from keras.applications import ResNet50
from keras.applications.resnet50 import preprocess_input
from keras import layers,Model
conv_base = ResNet50(
include_top=False,
weights='imagenet')
for layer in conv_base.layers:
layer.trainable = False
x = conv_base.output
x = layers.GlobalAveragePooling2D()(x)
x = layers.Dense(128, activation='relu')(x)
predictions = layers.Dense(31, activation='softmax')(x)
model = Model(conv_base.input, predictions)
# here you will write the path for train data or if you create your val data then you can test using that too.
# test_dir = ""
test_datagen = ImageDataGenerator(rescale=1. / 255)
test_generator = test_datagen.flow_from_directory(
data,
target_size=(256, 256), classes=['airplane','airport','baseball_diamond',
'basketball_court','beach','bridge',
'chaparral','church','circular_farmland',
'commercial_area','dense_residential','desert',
'forest','freeway','golf_course','ground_track_field',
'harbor','industrial_area','intersection','island',
'lake','meadow','medium_residential','mobile_home_park',
'mountain','overpass','parking_lot','railway','rectangular_farmland',
'roundabout','runway'],batch_size=1,shuffle=True)
model.compile(loss='categorical_crossentropy',optimizer='Adam',metrics=['accuracy'])
model.fit_generator(train_set,steps_per_epoch=1488//31,epochs=10,verbose=True,validation_data = test_generator,
validation_steps = test_generator.samples // 31)
Epoch 1/10
48/48 [==============================] - 27s 553ms/step - loss: 1.9631 - acc: 0.4825 - val_loss: 4.3134 - val_acc: 0.0208
Epoch 2/10
48/48 [==============================] - 22s 456ms/step - loss: 0.6395 - acc: 0.8212 - val_loss: 4.7584 - val_acc: 0.0833
Epoch 3/10
48/48 [==============================] - 23s 482ms/step - loss: 0.4325 - acc: 0.8810 - val_loss: 5.3852 - val_acc: 0.0625
Epoch 4/10
48/48 [==============================] - 23s 476ms/step - loss: 0.2925 - acc: 0.9153 - val_loss: 6.0963 - val_acc: 0.0208
Epoch 5/10
48/48 [==============================] - 23s 477ms/step - loss: 0.2275 - acc: 0.9341 - val_loss: 5.6571 - val_acc: 0.0625
Epoch 6/10
48/48 [==============================] - 23s 478ms/step - loss: 0.1855 - acc: 0.9489 - val_loss: 6.2440 - val_acc: 0.0208
Epoch 7/10
48/48 [==============================] - 23s 483ms/step - loss: 0.1704 - acc: 0.9543 - val_loss: 7.4446 - val_acc: 0.0208
Epoch 8/10
48/48 [==============================] - 23s 487ms/step - loss: 0.1828 - acc: 0.9476 - val_loss: 7.5198 - val_acc: 0.0417
可能是什么原因？！
【问题讨论】：
-
你不应该使用相同的数据进行训练和验证，这违背了验证的想法，试图找出你的模型的泛化程度。请发布一个可重现的示例
-
@Alistair 谢谢你的回复！这只是一个实验。只是想了解使用相同数据集作为验证时准确性低的原因。
-
如果数据相同，准确率应该相同。可能是代码问题
-
@Alistair 不，检查代码。当我从互联网上的各种博客文章中阅读时，这个问题可能是由于过度拟合造成的。但我希望从这里的任何人那里找到一些直观的解释。
-
@Samuel Liew♦ 我请求您重新提出问题。
标签： python tensorflow keras