Python卷积神经网络字迹识别

Python卷积神经网络可以用于手写字迹识别。下面是一个简单的示例代码：

import numpy as np
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.datasets import mnist


# 加载数据集
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# 转换为4D张量
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)

# 将像素值归一化到0到1之间
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255

# 将标签转换为分类格式
num_classes = 10
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

# 创建模型
model = Sequential()

model.add(Conv2D(32, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=(28, 28, 1)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

# 编译模型
model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adadelta(),
              metrics=['accuracy'])

# 训练模型
batch_size = 128
epochs = 12
model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          validation_data=(x_test, y_test))

# 评估模型
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

这段代码使用了Keras库，它提供了一个方便的API来构建神经网络。这个模型是一个卷积神经网络，包括两个卷积层、一个最大池化层、两个Dropout层和两个全连接层。它使用MNIST数据集进行训练和测试。最终输出测试集上的准确率。