CIFAR10数据集上的EfficientNet模型代码实现

以下是CIFAR10数据集上的EfficientNet模型代码实现的示例：

import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, BatchNormalization, Activation, Dropout, GlobalAveragePooling2D, Dense
from tensorflow.keras.models import Model
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping

from efficientnet.tfkeras import EfficientNetB0


# Load the CIFAR10 dataset
(x_train, y_train), (x_test, y_test) = cifar10.load_data()

# Normalize the pixel values
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.

# Convert the labels to one-hot encoded vectors
num_classes = 10
y_train = to_categorical(y_train, num_classes)
y_test = to_categorical(y_test, num_classes)

# Define the EfficientNetB0 model
input_shape = (32, 32, 3)
inputs = Input(shape=input_shape)
x = inputs

x = EfficientNetB0(include_top=False, input_tensor=x, pooling='avg')(x)
x = Dropout(0.2)(x)
outputs = Dense(num_classes, activation='softmax')(x)

model = Model(inputs, outputs)

# Compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Define the callbacks
checkpoint = ModelCheckpoint(filepath='cifar10_efficientnetB0.h5', monitor='val_accuracy', save_best_only=True, verbose=1)
early_stop = EarlyStopping(monitor='val_accuracy', patience=5, verbose=1)

# Train the model
batch_size = 64
epochs = 50
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_test, y_test), callbacks=[checkpoint, early_stop])

说明：

- 代码中使用了EfficientNetB0预训练模型的权重，因此需要先安装EfficientNet库：`pip install efficientnet`
- 代码中使用了EarlyStopping回调函数，当验证集上的准确率连续5个epoch没有提升时，停止训练
- 训练完成后，checkpoint回调函数会保存验证集上准确率最高的模型权重到`cifar10_efficientnetB0.h5`文件中