用python写一段代码:采用自主设计的网络模型对CIFA-100数据集分类,进行模型训练和模型评估
时间: 2024-05-08 11:16:53 浏览: 10
以下是一个简单的用Python实现的网络模型对CIFAR-100数据集分类的代码示例:
```
import tensorflow as tf
from tensorflow.keras.datasets import cifar100
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
# 加载数据集
(x_train, y_train), (x_test, y_test) = cifar100.load_data()
# 数据预处理
x_train = x_train / 255.0
x_test = x_test / 255.0
# 定义模型结构
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=x_train.shape[1:]))
model.add(Conv2D(32, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))
model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(100, activation='softmax'))
# 编译模型
model.compile(loss='sparse_categorical_crossentropy', optimizer=Adam(learning_rate=0.001), metrics=['accuracy'])
# 设置早停回调
early_stopping = EarlyStopping(monitor='val_loss', patience=5, verbose=1)
# 设置模型保存回调
model_checkpoint = ModelCheckpoint('cifar100_model.h5', monitor='val_loss', save_best_only=True, verbose=1)
# 训练模型
history = model.fit(x_train, y_train, batch_size=128, epochs=50, validation_data=(x_test, y_test), callbacks=[early_stopping, model_checkpoint])
# 评估模型
test_loss, test_acc = model.evaluate(x_test, y_test, verbose=0)
print('Test Loss:', test_loss)
print('Test Accuracy:', test_acc)
```
这个示例代码定义了一个包含两个卷积层和两个池化层的卷积神经网络,用于CIFAR-100数据集的分类任务。模型使用Adam优化算法进行训练,并在验证集上使用早停回调进行了训练过程的控制。最后,通过在测试集上评估模型的性能来获得模型的准确性和损失。