用python实现手写数字识别
时间: 2023-05-23 14:07:03 浏览: 57
A:实现手写数字识别,可以采用以下步骤:
1. 导入相关库
```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. 标准化数据
```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)
])
```
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
model.fit(train_images, train_labels, epochs=10)
```
7. 评估模型
```python
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
print('\nTest accuracy:', test_acc)
```
8. 预测结果
```python
probability_model = tf.keras.Sequential([model, tf.keras.layers.Softmax()])
predictions = probability_model.predict(test_images)
np.argmax(predictions[0])
```
以上这些是实现手写数字识别的关键步骤,根据实际需要可做相应调整。
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