Val_acc不会增加

Question

我试图用迁移学习mobilenetv2训练一个模型，但我的val精度在0,60左右停止增长。我试着训练我已经构建的顶层，在那之后我还试着训练一些mobilenets层。同样的结果。我怎么才能修复它？我不得不提一下，我是深度学习的新手，我不确定我构建的顶层是正确的。请随时纠正我。

IMAGE_SIZE = 224
BATCH_SIZE = 64

train_data_dir = "/content/FER2013/Training"
validation_data_dir = "/content/FER2013/PublicTest"


datagen = tf.keras.preprocessing.image.ImageDataGenerator(
    rescale=1./255, 
    validation_split=0)

train_generator = datagen.flow_from_directory(
    train_data_dir,
    target_size=(IMAGE_SIZE, IMAGE_SIZE),
    class_mode = 'categorical',
    batch_size=BATCH_SIZE)

val_generator = datagen.flow_from_directory(
    validation_data_dir,
    target_size=(IMAGE_SIZE, IMAGE_SIZE),
    class_mode = 'categorical',
    batch_size=BATCH_SIZE)

IMG_SHAPE = (IMAGE_SIZE, IMAGE_SIZE, 3)

base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,
                                               include_top=False,
                                               weights='imagenet')

model = tf.keras.Sequential([
   base_model,
  
  tf.keras.layers.GlobalAveragePooling2D(),
  tf.keras.layers.Dense(512, activation='relu'),
  tf.keras.layers.Dense(256, activation='relu'),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(64, activation='relu'),
  tf.keras.layers.BatchNormalization(),
  tf.keras.layers.Dense(32, activation='relu'),
  tf.keras.layers.Dense(16, activation='relu'),
  tf.keras.layers.Dense(7, activation='softmax')
])

model.compile(loss='categorical_crossentropy',
              optimizer = tf.keras.optimizers.Adam(1e-5), #I've tried with .Adam as well
              metrics=['accuracy'])

from keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint
lr_reducer = ReduceLROnPlateau(monitor='val_loss', factor=0.9, patience=3)

early_stopper = EarlyStopping(monitor='val_accuracy', min_delta=0, patience=6, mode='auto')

checkpointer = ModelCheckpoint('/content/weights.hd5', monitor='val_loss', verbose=1, save_best_only=True)

epochs = 50
learning_rate = 0.004 #I've tried other values as well

history_fine = model.fit(train_generator, 
                         steps_per_epoch=len(train_generator), 
                         epochs=epochs, 
                         callbacks=[lr_reducer, checkpointer, early_stopper],
                         validation_data=val_generator, 
                         validation_steps=len(val_generator))

Epoch 1/50
448/448 [==============================] - ETA: 0s - loss: 1.7362 - accuracy: 0.2929
Epoch 00001: val_loss improved from inf to 1.58818, saving model to /content/weights.hd5
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.
Instructions for updating:
This property should not be used in TensorFlow 2.0, as updates are applied automatically.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.
Instructions for updating:
This property should not be used in TensorFlow 2.0, as updates are applied automatically.
INFO:tensorflow:Assets written to: /content/weights.hd5/assets
448/448 [==============================] - 166s 370ms/step - loss: 1.7362 - accuracy: 0.2929 - val_loss: 1.5882 - val_accuracy: 0.4249
Epoch 2/50
448/448 [==============================] - ETA: 0s - loss: 1.3852 - accuracy: 0.4664
Epoch 00002: val_loss improved from 1.58818 to 1.31690, saving model to /content/weights.hd5
INFO:tensorflow:Assets written to: /content/weights.hd5/assets
448/448 [==============================] - 165s 368ms/step - loss: 1.3852 - accuracy: 0.4664 - val_loss: 1.3169 - val_accuracy: 0.4827
Epoch 3/50
448/448 [==============================] - ETA: 0s - loss: 1.2058 - accuracy: 0.5277
Epoch 00003: val_loss improved from 1.31690 to 1.21979, saving model to /content/weights.hd5
INFO:tensorflow:Assets written to: /content/weights.hd5/assets
448/448 [==============================] - 165s 368ms/step - loss: 1.2058 - accuracy: 0.5277 - val_loss: 1.2198 - val_accuracy: 0.5271
Epoch 4/50
448/448 [==============================] - ETA: 0s - loss: 1.0828 - accuracy: 0.5861
Epoch 00004: val_loss improved from 1.21979 to 1.18972, saving model to /content/weights.hd5
INFO:tensorflow:Assets written to: /content/weights.hd5/assets
448/448 [==============================] - 166s 370ms/step - loss: 1.0828 - accuracy: 0.5861 - val_loss: 1.1897 - val_accuracy: 0.5533
Epoch 5/50
448/448 [==============================] - ETA: 0s - loss: 0.9754 - accuracy: 0.6380
Epoch 00005: val_loss improved from 1.18972 to 1.13336, saving model to /content/weights.hd5
INFO:tensorflow:Assets written to: /content/weights.hd5/assets
448/448 [==============================] - 165s 368ms/step - loss: 0.9754 - accuracy: 0.6380 - val_loss: 1.1334 - val_accuracy: 0.5743
Epoch 6/50
448/448 [==============================] - ETA: 0s - loss: 0.8761 - accuracy: 0.6848
Epoch 00006: val_loss did not improve from 1.13336
448/448 [==============================] - 153s 342ms/step - loss: 0.8761 - accuracy: 0.6848 - val_loss: 1.1348 - val_accuracy: 0.5882
Epoch 7/50
448/448 [==============================] - ETA: 0s - loss: 0.7783 - accuracy: 0.7264
Epoch 00007: val_loss did not improve from 1.13336
448/448 [==============================] - 153s 341ms/step - loss: 0.7783 - accuracy: 0.7264 - val_loss: 1.1392 - val_accuracy: 0.5893
Epoch 8/50
448/448 [==============================] - ETA: 0s - loss: 0.6832 - accuracy: 0.7638
Epoch 00008: val_loss did not improve from 1.13336
448/448 [==============================] - 153s 342ms/step - loss: 0.6832 - accuracy: 0.7638 - val_loss: 1.1542 - val_accuracy: 0.6052

Answer 1

由于你的验证损失在增加，而训练损失在减少，我认为你可能存在训练集过拟合的问题。以下是一些可能有帮助的东西：

• 使用密度较低的层。我认为regularization.

的数量太多了，但我可能错了，因为我不知道你在解决什么问题。

• 在每个密集层之后添加数据增强层。

• 在你的训练集上使用数据增强(因为你已经在使用ImageDataGenerator了，这不会很难)。

• 减少了密集层中神经元的数量。

• 使用dropout

您可以尝试同时应用其中的任何一个或多个。调整您的模型是大量的试验和错误，做一些实验，并保持模型达到最佳性能。

Answer 2

随着训练损失的减少和验证损失的增加，你似乎处于过适应的状态。你的模型越复杂，发生这种情况的可能性就越高，所以我建议你尽量简化你的模型。试着移除所有的密层，除了最上面的一层，它产生了你的分类。运行它，看看它是如何工作的。我使用MobileNet的经验是它应该工作得很好。如果不是，则添加一个具有大约128个神经元的额外致密层，然后是一个退出层。使用丢失值来调整过拟合。如果你仍然过度拟合，你可能想要考虑添加正则化到这个密集的层。文档是here.的也可能是你需要更多的训练样本。使用图像数据生成器来扩充您的数据集。例如，设置horizontal_flip-True。我注意到您没有在图像数据生成器中包含MobileNetV2预处理功能。我认为这是必要的，所以修改代码如下所示。我还发现，如果我训练整个模型，我使用MobileNet会得到最好的结果。因此，在编译模型之前，请添加以下代码。

ImageDataGenerator(preprocessing_function=tf.keras.applications.mobilenet_v2.preprocess_input)
for layer in model.layers:
    layer.trainable=True

顺便说一句，你可以删除模型中的那一层，然后在MobileNet中设置参数pooling='max‘。我发现你一开始的学习率很低，试试.005之类的吧。由于您有ReduceLROnPlateau回调，如果它太大，这将调整它，但会让您更快地收敛。