cifar10数据集分类 python
时间: 2023-09-04 12:08:20 浏览: 46
CIFAR-10是一个包含60000张32x32像素的彩色图片的数据集。这些图片包含了10个不同的类别:飞机、汽车、鸟、猫、鹿、狗、青蛙、马、船和卡车。
下面是使用Python进行CIFAR-10数据集分类的基本步骤:
1. 导入必要的库和模块:
```python
import tensorflow as tf
from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt
```
2. 加载数据集:
```python
(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()
```
3. 数据预处理:
```python
train_images, test_images = train_images / 255.0, test_images / 255.0
```
4. 定义模型:
```python
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10))
```
5. 编译模型:
```python
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
```
6. 训练模型:
```python
history = model.fit(train_images, train_labels, epochs=10,
validation_data=(test_images, test_labels))
```
7. 评估模型:
```python
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
print(test_acc)
```
完整代码示例:
```python
import tensorflow as tf
from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt
# 加载数据集
(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()
# 数据预处理
train_images, test_images = train_images / 255.0, test_images / 255.0
# 定义模型
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10))
# 编译模型
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
# 训练模型
history = model.fit(train_images, train_labels, epochs=10,
validation_data=(test_images, test_labels))
# 评估模型
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
print(test_acc)
```
这样就可以使用Python对CIFAR-10数据集进行分类了。