AI安全
python机器学习库:PyTorch
python手写数字识别模型
# 导入人工智能相关的库
import tensorflow as tf
import keras
import numpy as np
# 加载数据集
from keras.datasets import mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# 数据预处理
x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
# 将标签转换为独热编码
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)
# 构建模型
model = keras.models.Sequential()
model.add(keras.layers.Dense(512, activation='relu', input_shape=(784,)))
model.add(keras.layers.Dropout(0.2))
model.add(keras.layers.Dense(10, activation='softmax'))
# 编译模型
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# 训练模型
model.fit(x_train, y_train,
batch_size=128,
epochs=20,
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])