简单cnn 借助调参指南进一步提高精度

基础CNN模型代码

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

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

from tensorflow.keras.optimizers import Adamoptimizer = 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

from tensorflow.keras.preprocessing.image import ImageDataGeneratordatagen = 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

from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpointcallbacks = [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)