使用sklearn调用CCN训练mnist数据集
时间: 2024-12-20 09:24:33 浏览: 54
在Python的scikit-learn库中,并没有直接提供叫做"CCN" (可能是你打错了,因为常见的是卷积神经网络(Convolutional Neural Network,CNN)而非CCN)的模型。如果你想用scikit-learn训练MNIST数据集,通常会使用Keras或者TensorFlow等深度学习库,因为它们更适合处理图像分类任务,比如CNN。
如果你确实想要使用scikit-learn并结合其他库(如Keras、Torch等)来训练MNIST,可以采用以下步骤:
1. **安装必要的库**:
- `pip install scikit-learn`
- 可能还需要`tensorflow`或`keras`,例如`pip install tensorflow keras`
2. **导入所需的模块**:
```python
from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
```
3. **加载MNIST数据**:
```python
mnist = fetch_openml('mnist_784', version=1)
X, y = mnist["data"], mnist["target"]
```
4. **预处理数据**:
- 归一化像素值到0-1之间
- 将图像数据转化为适合CNN的4D张量格式
```python
X = X.reshape((-1, 28, 28, 1)) / 255.
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```
5. **构建和编译模型**:
```python
model = Sequential([
Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)),
MaxPooling2D(pool_size=(2, 2)),
Conv2D(64, (3, 3), activation='relu'),
MaxPooling2D(pool_size=(2, 2)),
Flatten(),
Dense(128, activation='relu'),
Dense(10, activation='softmax') # 输出层有10个节点,对应MNIST的10个类别
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
```
6. **训练模型**:
```python
model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test))
```
7. **评估和保存模型**:
```python
_, accuracy = model.evaluate(X_test, y_test)
print(f'Test accuracy: {accuracy * 100:.2f}%')
model.save('mnist_cnn.h5')
```
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