简单cnn 借助调参指南进一步提高精度
基础CNN模型代码
python
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import tensorflow as tf
from tensorflow.keras import layers, models
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.utils import to_categorical
# 加载数据
(train_images, train_labels), (test_images, test_labels) = cifar10.load_data()
# 数据预处理
train_images = train_images.astype('float32') / 255
test_images = test_images.astype('float32') / 255
train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)
# 基础CNN模型
model = models.Sequential([
layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu'),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu'),
layers.Flatten(),
layers.Dense(64, activation='relu'),
layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
history = model.fit(train_images, train_labels,
epochs=10,
batch_size=64,
validation_data=(test_images, test_labels))
改进方法
增加模型复杂度
python
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model = models.Sequential([
layers.Conv2D(64, (3, 3), activation='relu', input_shape=(32, 32, 3), padding='same'),
layers.BatchNormalization(),
layers.Conv2D(64, (3, 3), activation='relu', padding='same'),
layers.BatchNormalization(),
layers.MaxPooling2D((2, 2)),
layers.Dropout(0.25),
layers.Conv2D(128, (3, 3), activation='relu', padding='same'),
layers.BatchNormalization(),
layers.Conv2D(128, (3, 3), activation='relu', padding='same'),
layers.BatchNormalization(),
layers.MaxPooling2D((2, 2)),
layers.Dropout(0.25),
layers.Conv2D(256, (3, 3), activation='relu', padding='same'),
layers.BatchNormalization(),
layers.Conv2D(256, (3, 3), activation='relu', padding='same'),
layers.BatchNormalization(),
layers.MaxPooling2D((2, 2)),
layers.Dropout(0.25),
layers.Flatten(),
layers.Dense(512, activation='relu'),
layers.BatchNormalization(),
layers.Dropout(0.5),
layers.Dense(10, activation='softmax')
])
优化器调参
python
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from tensorflow.keras.optimizers import Adam
optimizer = Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07)
model.compile(optimizer=optimizer,
loss='categorical_crossentropy',
metrics=['accuracy'])
数据增强
python
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from tensorflow.keras.preprocessing.image import ImageDataGenerator
datagen = ImageDataGenerator(
rotation_range=15,
width_shift_range=0.1,
height_shift_range=0.1,
horizontal_flip=True,
zoom_range=0.1
)
datagen.fit(train_images)
history = model.fit(datagen.flow(train_images, train_labels, batch_size=64),
epochs=50,
validation_data=(test_images, test_labels))
早停和模型检查点
python
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from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
callbacks = [
EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True),
ModelCheckpoint('best_model.h5', monitor='val_accuracy', save_best_only=True)
]
history = model.fit(..., callbacks=callbacks, epochs=100)