CIFAR10数据集上的EfficientNet模型代码实现
时间: 2024-03-14 18:46:13 浏览: 10
以下是在CIFAR10数据集上实现EfficientNet模型的代码:
```python
import tensorflow as tf
from tensorflow.keras.layers import Conv2D, Dropout, BatchNormalization, Dense, GlobalAveragePooling2D
from tensorflow.keras.models import Sequential
from tensorflow.keras.regularizers import l2
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau
from efficientnet.tfkeras import EfficientNetB0
# 加载CIFAR10数据集
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
# 数据预处理
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
y_train = tf.keras.utils.to_categorical(y_train, num_classes=10)
y_test = tf.keras.utils.to_categorical(y_test, num_classes=10)
# 创建EfficientNet模型
model = Sequential()
model.add(EfficientNetB0(input_shape=(32, 32, 3), weights=None, include_top=False))
model.add(GlobalAveragePooling2D())
model.add(Dense(10, activation='softmax'))
# 编译模型
model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.001), metrics=['accuracy'])
# 训练模型
checkpoint = ModelCheckpoint('efficientnet.h5', monitor='val_loss', save_best_only=True)
earlystop = EarlyStopping(monitor='val_loss', patience=10)
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=5)
callbacks = [checkpoint, earlystop, reduce_lr]
history = model.fit(x_train, y_train, batch_size=128, epochs=100, validation_data=(x_test, y_test), callbacks=callbacks)
```
这段代码使用EfficientNetB0作为特征提取器,然后添加全局平均池化层和一个全连接层。编译模型使用Adam优化器和分类交叉熵损失函数。在训练期间,使用了ModelCheckpoint、EarlyStopping和ReduceLROnPlateau回调函数来优化模型的训练过程。