python - 如何防止过拟合

Question

我正在为我的最后一个项目做准备，我是 ConVnets 的新手。我想区分哪一个是真实图像和恶搞图像。我有 +-8000 数据（合并）。我想给你看一些我的训练日志。

Epoch 7/100
311/311 [==============================] - 20s 63ms/step - loss: 0.3274 - accuracy: 0.8675 - val_loss: 0.2481 - val_accuracy: 0.9002
Epoch 8/100
311/311 [==============================] - 20s 63ms/step - loss: 0.3189 - accuracy: 0.8691 - val_loss: 0.3015 - val_accuracy: 0.8684
Epoch 9/100
311/311 [==============================] - 19s 62ms/step - loss: 0.3201 - accuracy: 0.8667 - val_loss: 0.2460 - val_accuracy: 0.9036
Epoch 10/100
311/311 [==============================] - 19s 62ms/step - loss: 0.3063 - accuracy: 0.8723 - val_loss: 0.2752 - val_accuracy: 0.8901
Epoch 11/100
311/311 [==============================] - 19s 62ms/step - loss: 0.3086 - accuracy: 0.8749 - val_loss: 0.2717 - val_accuracy: 0.8988
[INFO] evaluating network...

model = Sequential()
inputShape = (height, width, depth)
chanDim = -1

if K.image_data_format() == "channels_first":
    inputShape = (depth, height, width)
    chanDim = 1

model.add(Conv2D(16, (3, 3), padding="same", input_shape=inputShape))   model.add(Activation("relu"))
model.add(BatchNormalization(axis=chanDim))
model.add(Conv2D(16, (3, 3), padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(axis=chanDim))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.3))

model.add(Conv2D(32, (5, 5), padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(axis=chanDim))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.3))

model.add(Flatten())
model.add(Dense(128))
model.add(Activation("relu"))
model.add(BatchNormalization())
model.add(Dropout(0.6))

model.add(Dense(classes))
model.add(Activation("softmax"))

输入是 32x32，它有两个类。我EarlyStopping在 keras 中使用以防止过度拟合。而且我总是改变学习率的值并尝试改变神经元节点的数量，但训练总是停止在 20 个 epoch 以下。有什么建议可以防止过拟合吗？因为我是卷积神经网络的初学者。提前致谢 ！

PS LR：0.001 BS：20 纪元：100

标签： pythonkerasdeep-learning