如何防止模型过拟合? [英] How can I prevent my model from being overfitted?

问题描述

我是一名深度学习的新手，我尝试创建一个模型，但我不太了解模型.添加(图层).我确定输入形状(用于识别).我认为问题出在辍学上，但我不明白它的价值.

I'm a newbie with deep learning and I try to create a model and I don't really understand the model. add(layers). I m sure that the input shape (it's for recognition). I think the problem is in the Dropout, but I don't understand the value.

有人可以向我解释吗

model = models.Sequential()

model.add(layers.Conv2D(32, (3,3), activation = 'relu', input_shape = (128,128,3))) 
model.add(layers.MaxPooling2D((2,2))) 
model.add(layers.Conv2D(64, (3,3), activation = 'relu')) 
model.add(layers.MaxPooling2D((2,2))) 

model.add(layers.Flatten())
model.add(layers.Dropout(0.5))
model.add(layers.Dense(512, activation='relu')) 
model.add(layers.Dense(6, activation='softmax'))
model.compile(loss='categorical_crossentropy', 
              optimizer=optimizers.Adam(lr=1e-4), metrics=['acc'])
-------------------------------------------------------
history = model.fit(
      train_data, 
      train_labels,
      epochs=30,
      validation_data=(test_data, test_labels),
      )

这是结果:

Epoch 15/30
5/5 [==============================] - 0s 34ms/step - loss: 0.3987 - acc: 0.8536 - val_loss: 0.7021 - val_acc: 0.7143
Epoch 16/30
5/5 [==============================] - 0s 31ms/step - loss: 0.3223 - acc: 0.8891 - val_loss: 0.6393 - val_acc: 0.7778
Epoch 17/30
5/5 [==============================] - 0s 32ms/step - loss: 0.3321 - acc: 0.9082 - val_loss: 0.6229 - val_acc: 0.7460
Epoch 18/30
5/5 [==============================] - 0s 31ms/step - loss: 0.2615 - acc: 0.9409 - val_loss: 0.6591 - val_acc: 0.8095
Epoch 19/30
5/5 [==============================] - 0s 32ms/step - loss: 0.2161 - acc: 0.9857 - val_loss: 0.6368 - val_acc: 0.7143
Epoch 20/30
5/5 [==============================] - 0s 33ms/step - loss: 0.1773 - acc: 0.9857 - val_loss: 0.5644 - val_acc: 0.7778
Epoch 21/30
5/5 [==============================] - 0s 32ms/step - loss: 0.1650 - acc: 0.9782 - val_loss: 0.5459 - val_acc: 0.8413
Epoch 22/30
5/5 [==============================] - 0s 31ms/step - loss: 0.1534 - acc: 0.9789 - val_loss: 0.5738 - val_acc: 0.7460
Epoch 23/30
5/5 [==============================] - 0s 32ms/step - loss: 0.1205 - acc: 0.9921 - val_loss: 0.5351 - val_acc: 0.8095
Epoch 24/30
5/5 [==============================] - 0s 32ms/step - loss: 0.0967 - acc: 1.0000 - val_loss: 0.5256 - val_acc: 0.8413
Epoch 25/30
5/5 [==============================] - 0s 32ms/step - loss: 0.0736 - acc: 1.0000 - val_loss: 0.5493 - val_acc: 0.7937
Epoch 26/30
5/5 [==============================] - 0s 32ms/step - loss: 0.0826 - acc: 1.0000 - val_loss: 0.5342 - val_acc: 0.8254
Epoch 27/30
5/5 [==============================] - 0s 32ms/step - loss: 0.0687 - acc: 1.0000 - val_loss: 0.5452 - val_acc: 0.8254
Epoch 28/30
5/5 [==============================] - 0s 32ms/step - loss: 0.0571 - acc: 1.0000 - val_loss: 0.5176 - val_acc: 0.7937
Epoch 29/30
5/5 [==============================] - 0s 32ms/step - loss: 0.0549 - acc: 1.0000 - val_loss: 0.5142 - val_acc: 0.8095
Epoch 30/30
5/5 [==============================] - 0s 32ms/step - loss: 0.0479 - acc: 1.0000 - val_loss: 0.5243 - val_acc: 0.8095

我从来没有超过70％的平均水平，但是我有80％的平均水平，但是我认为我正在过度拟合.

I never depassed the 70% average but on this i have 80% but i think i'm on overfitting.. I evidemently searched on differents docs but i'm lost

推荐答案

您是否尝试了以下培训:

Have you try following into your training:

• 数据增强
• 预训练模型

看看每个时期的执行时间，看来您的数据集很小.另外，还不清楚数据集中是否存在类不平衡.您可能应该尝试对褶皱结果进行分层的简历培训和分析.它不会防止过度拟合，但最终将使您对模型有更多的了解，通常可以帮助减少过度拟合.但是，防止过度拟合是一个普遍的主题，请在线搜索以获取资源.您也可以尝试

Looking at the execution time per epoch, it looks like your data set is pretty small. Also, it's not clear whether there is any class imbalance in your dataset. You probably should try stratified CV training and analysis on the folds results. It won't prevent overfit but it will eventually give you more insight into your model, which generally can help to reduce overfitting. However, preventing overfitting is a general topic, search online to get resources. You can also try this

model.compile(loss='categorical_crossentropy', 
              optimizer='adam, metrics=['acc'])
-------------------------------------------------------

# src: https://keras.io/api/callbacks/reduce_lr_on_plateau/
# reduce learning rate by a factor of 0.2 if val_loss -
# won't improve within 5 epoch. 
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2,
                              patience=5, min_lr=0.00001)

# src: https://keras.io/api/callbacks/early_stopping/
# stop training if val_loss don't improve within 15 epoch.
early_stop = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=15)


history = model.fit(
      train_data, 
      train_labels,
      epochs=30,
      validation_data=(test_data, test_labels),
      callbacks=[reduce_lr, early_stop]
      )

您还可能会发现使用 ModelCheckpoint 很有用或 LearningRateScheduler .这不能保证不会过拟合，但可以采取一些措施.

You may also find it useful of using ModelCheckpoint or LearningRateScheduler. This doesn't guarantee of no overfit but some approach for that to adopt.

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