phthon的手写数字自动识别怎么写
时间: 2023-04-08 09:00:29 浏览: 73
在实现手写数字自动识别这个问题时,需要使用机器学习算法。你需要准备一个手写数字数据集,用于训练模型,并对模型进行评估。
以下是使用 Python 实现的一个简单的例子:
1. 导入必要的库:numpy,matplotlib,keras。
```
import numpy as np
import matplotlib.pyplot as plt
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import to_categorical
```
2. 加载 MNIST 数据集:
```
(X_train, y_train), (X_test, y_test) = mnist.load_data()
```
3. 对图像数据进行预处理:
```
num_pixels = X_train.shape[1] * X_train.shape[2]
X_train = X_train.reshape(X_train.shape[0], num_pixels).astype('float32')
X_test = X_test.reshape(X_test.shape[0], num_pixels).astype('float32')
X_train = X_train / 255
X_test = X_test / 255
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
num_classes = y_test.shape[1]
```
4. 创建模型:
```
model = Sequential()
model.add(Dense(num_pixels, input_dim=num_pixels, kernel_initializer='normal', activation='relu'))
model.add(Dense(num_classes, kernel_initializer='normal', activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
```
5. 训练模型:
```
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=200, verbose=2)
```
6. 对模型进行评估:
```
scores = model.evaluate(X_test, y_test, verbose=0)
print("Accuracy: %.2f%%" % (scores[1