迁移学习模型学得不多，验证稳定在 45%，而训练则高达 90%

Question

我一直在研究这个图像分类模型。我有 70000 张图像和 375 个类。我尝试过使用 Vgg16、Xception、Resnet 和 Mobilenet 来训练它......并且我在验证中总是得到 45% 的相同限制。

我尝试添加丢失层和正则化，它得到了相同的验证结果。 数据增强也没有多大帮助 有什么想法为什么这不起作用吗？

这是我使用的最后一个模型的代码片段：

from keras.models import Sequential
from keras.layers import Dense
from tensorflow import keras
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from keras import regularizers
from PIL import Image
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True

validation_datagen = ImageDataGenerator(rescale=1./255)
target_size = (height, width)

datagen = ImageDataGenerator(rescale=1./255,
    validation_split=0.2)

train_generator = datagen.flow_from_directory(
    path,
    target_size=(height, width),
    batch_size=batchSize,
    shuffle=True,
    class_mode='categorical',
    subset='training')

validation_generator = datagen.flow_from_directory(
    path,
    target_size=(height, width),
    batch_size=batchSize,
    class_mode='categorical',
    subset='validation')

num_classes = len(train_generator.class_indices)



xception_model = Xception(weights='imagenet',input_shape=(width, height, 3), include_top=False,classes=num_classes)
x = xception_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(512, activation='relu')(x)
out = Dense(num_classes, activation='softmax')(x)

opt = Adam()
model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])


n_epochs = 15

history = model.fit(
    train_generator,
    steps_per_epoch = train_generator.samples // batchSize,
    validation_data = validation_generator, 
    validation_steps = validation_generator.samples // batchSize,
    verbose=1, 
    epochs = n_epochs)

Answer 1

是的，您可能需要数据集中每个类别之间的平衡数据集，以获得更好的模型训练性能。请更改

class_mode='sparse'

和

loss='sparse_categorical_crossentropy'

重试，因为您正在使用图像数据集。同时冻结预训练的模型层

'xception_model.trainable = False'

。

检查下面的代码：（我使用了 5 个类别的 flower 数据集）

xception_model = tf.keras.applications.Xception(weights='imagenet',input_shape=(width, height, 3), include_top=False,classes=num_classes)
xception_model.trainable = False
x = xception_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(32, activation='relu')(x)
out = Dense(num_classes, activation='softmax')(x)

opt = tf.keras.optimizers.Adam()
model = keras.Model(inputs=xception_model.input, outputs=out)
model.compile(optimizer=opt, loss='sparse_categorical_crossentropy', metrics=['accuracy'])
history = model.fit(train_generator,  epochs=10, validation_data=validation_generator)

输出：

Epoch 1/10
217/217 [==============================] - 23s 95ms/step - loss: 0.5945 - accuracy: 0.7793 - val_loss: 0.4610 - val_accuracy: 0.8337
Epoch 2/10
217/217 [==============================] - 20s 91ms/step - loss: 0.3439 - accuracy: 0.8797 - val_loss: 0.4550 - val_accuracy: 0.8419
Epoch 3/10
217/217 [==============================] - 20s 93ms/step - loss: 0.2570 - accuracy: 0.9150 - val_loss: 0.4437 - val_accuracy: 0.8384
Epoch 4/10
217/217 [==============================] - 20s 91ms/step - loss: 0.2040 - accuracy: 0.9340 - val_loss: 0.4592 - val_accuracy: 0.8477
Epoch 5/10
217/217 [==============================] - 20s 91ms/step - loss: 0.1649 - accuracy: 0.9494 - val_loss: 0.4686 - val_accuracy: 0.8512
Epoch 6/10
217/217 [==============================] - 20s 92ms/step - loss: 0.1301 - accuracy: 0.9589 - val_loss: 0.4805 - val_accuracy: 0.8488
Epoch 7/10
217/217 [==============================] - 20s 93ms/step - loss: 0.0966 - accuracy: 0.9754 - val_loss: 0.4993 - val_accuracy: 0.8442
Epoch 8/10
217/217 [==============================] - 20s 91ms/step - loss: 0.0806 - accuracy: 0.9806 - val_loss: 0.5488 - val_accuracy: 0.8372
Epoch 9/10
217/217 [==============================] - 20s 91ms/step - loss: 0.0623 - accuracy: 0.9864 - val_loss: 0.5802 - val_accuracy: 0.8360
Epoch 10/10
217/217 [==============================] - 22s 100ms/step - loss: 0.0456 - accuracy: 0.9896 - val_loss: 0.6005 - val_accuracy: 0.8360