使用遗传算法优化面部情绪识别模型超参数

Question

我正在构建一个面部情绪识别系统，可以对快乐、悲伤、愤怒、惊讶等情绪进行分类。我使用 TensorFlow/Keras 训练了一个卷积神经网络模型，目前它的准确率达到了 50 左右%。然而，我相信微调超参数可能会进一步提高准确性。

现在，我有兴趣优化模型的超参数以实现更好的准确性。我听说过使用遗传算法进行超参数优化，但我不确定如何继续。有人可以指导我如何应用遗传算法来微调模型的超参数吗？具体来说，我如何修改我的代码以纳入遗传算法以进行超参数优化？

这是我的代码摘要：

import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras import models,layers

# Data Augmentation
augmentor = ImageDataGenerator(
    rescale=1.0/255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True
)

# Loading data and resizing images to 48x48 pixels
augmented_trained_data = augmentor.flow_from_directory(
    "Facial Recognition Dataset/Train",
    target_size=(48, 48),
    batch_size=32,
    color_mode="grayscale",
    class_mode="categorical"
)

augmented_validation_data = augmentor.flow_from_directory(
    "Facial Recognition Dataset/Validation",
    target_size=(48, 48),
    batch_size=32,
    color_mode="grayscale",
    class_mode="categorical"
)

augmented_testing_data = augmentor.flow_from_directory(
    "Facial Recognition Dataset/Test",
    target_size=(48, 48),
    batch_size=32,
    color_mode="grayscale",
    class_mode="categorical"
)

# Model Definition
model = models.Sequential([
    layers.Conv2D(32, (2, 2), activation="relu", input_shape=(48, 48, 1)),
    layers.MaxPool2D((2, 2)),
    layers.Conv2D(64, (2, 2), activation="relu"),
    layers.MaxPool2D((2, 2)),
    layers.Conv2D(128, (2, 2), activation="relu"),
    layers.MaxPool2D((2, 2)),
    layers.Flatten(),
    layers.Dense(128, activation="relu"),
    layers.Dropout(0.25),
    layers.Dense(6, activation="softmax")
])

# Model Compilation
model.compile(
    optimizer='adam',
    loss=tf.keras.losses.CategoricalCrossentropy(from_logits=False),
    metrics=["accuracy"]
)

# Model Training
model.fit(
    augmented_trained_data,
    validation_data=augmented_validation_data,
    epochs=10
)

# Model Evaluation
test_loss, test_accuracy = model.evaluate(augmented_testing_data)
print(f"Test Accuracy: {test_accuracy * 100:.2f}%")'''

Answer 1

也许您需要尝试另一种像这样的情绪识别系统https://luxand.cloud/face-recognition-blog/tutorial-facial-emotion-recognition-with-python

我用过它，训练起来更容易