请写出手写数字识别代码
时间: 2023-06-19 11:10:42 浏览: 53
好的,下面是一个基于Python和TensorFlow的手写数字识别代码:
1. 导入依赖库
```python
import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt
```
2. 加载数据集
```python
mnist = keras.datasets.mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
```
3. 数据预处理
```python
train_images = train_images / 255.0
test_images = test_images / 255.0
```
4. 构建模型
```python
model = keras.Sequential([
keras.layers.Flatten(input_shape=(28, 28)),
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(10, activation='softmax')
])
```
5. 编译模型
```python
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
```
6. 训练模型
```python
model.fit(train_images, train_labels, epochs=5)
```
7. 评估模型
```python
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
print('\nTest accuracy:', test_acc)
```
8. 使用模型进行预测
```python
predictions = model.predict(test_images)
```
完整代码如下:
```python
import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt
mnist = keras.datasets.mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
train_images = train_images / 255.0
test_images = test_images / 255.0
model = keras.Sequential([
keras.layers.Flatten(input_shape=(28, 28)),
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=5)
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
print('\nTest accuracy:', test_acc)
predictions = model.predict(test_images)
```
希望这个手写数字识别代码可以帮到你。