机器学习手写数字识别
时间: 2023-12-25 20:06:58 浏览: 38
以下是机器学习手写数字识别的步骤:
1. MNIST数据集的解读以及导入
```python
from tensorflow.keras.datasets import mnist
# 加载MNIST数据集
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
```
2. 数据集的划分
```python
# 对数据进行预处理
train_images = train_images.reshape((60000, 28 * 28))
train_images = train_images.astype('float32') / 255
test_images = test_images.reshape((10000, 28 * 28))
test_images = test_images.astype('float32') / 255
```
3. 模型构建、训练和评估
```python
from tensorflow.keras import models
from tensorflow.keras import layers
network = models.Sequential()
network.add(layers.Dense(512, activation='relu', input_shape=(28 * 28,)))
network.add(layers.Dense(10, activation='softmax'))
network.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
network.fit(train_images, train_labels, epochs=5, batch_size=128)
test_loss, test_acc = network.evaluate(test_images, test_labels)
print('test_acc:', test_acc)
```
4. 应用模型
```python
# 使用模型进行预测
digit = test_images[0]
print(network.predict(test_images)[0])
```
5. 可视化
```python
import matplotlib.pyplot as plt
# 可视化手写数字
plt.imshow(test_images[0].reshape(28, 28), cmap='gray')
plt.show()
```