机器学习02


from mxnet import nd

x = nd.ones(3)
nd.save('x', x)
x2 = nd.load('x')
y = nd.zeros(4)
nd.save('xy', [x, y])

x3, y2 = nd.load('xy')
print(y2)
print(x3)
mydict = {'x': x, 'y': y}
nd.save('mydict', mydict)

rd = nd.load('mydict')

print(rd)
x = nd.array([1, 2, 3])
print(x.context)
'''
mxnet使用GPU
去https://dist.mxnet.io/python选择合适cuda版本的mxnet下载

使用pip install **.whl
'''
# a = nd.array([1, 2, 3], ctx=mx.gpu(1))
#
# print(a)

# from mxnet.gluon import nn
#
# net = nn.Sequential()
#
# net.add(nn.Dense(1))
# net.initialize(ctx=mx.gpu())

print('hello gll')

from mxnet import autograd, nd


def corr2d(X, K):
    h, w = K.shape
    print(h, w)
    y = nd.zeros((X.shape[0] - h + 1, X.shape[1] - w + 1))

    for i in range(y.shape[0]):
        for j in range(y.shape[1]):
            y[i, j] = (X[i: i + h, j: j + w] * K).sum()
            print(X[i: i + h, j: j + w])
    return y


X = nd.array([[0, 1, 2], [3, 4, 5], [6, 7, 8]])

'''
[[0. 1. 2.]
 [3. 4. 5.]
 [6. 7. 8.]]
 ###遍历矩阵
 #卷积
print(X[0:3, 0:2])

[[0. 1.]
 [3. 4.]
 [6. 7.]]
'''
print(X)

K = nd.array([[0, 1], [2, 3]])
a = corr2d(X, K)
print(a)

# # cuda
# # https://developer.nvidia.com/zh-cn/cuda-downloads

# import json
# # 集合
# # 不重复、无序
# # {}, set 表示
# #
#
# names = {'1', '2', '3', '2'}
#
# # ctrl + n 搜索类
# # 空集合 set()
# print(names)
#
# # eval()
# # 可执行代码
#
# a = '1 + 1'
#
# print(eval(a))
# # JSON 使用
# #
#
#
# str1 = {'name': 'gll'}
# m = json.dumps(str1)
#
# print(m)
# # 类型
# print(type(m))
#
# s = json.loads(m)
# # 转类型、转回原类型
# print(s)
# # []列表 () 元组 {} 集合
#
# print(type(s))
#
# ''''
# 函数
#
# '''
#
#
# def fun():
#     print('函数')
#
#
# # 参数
# def fun1(parameters1, parameters2):
#     r_parameters1 = parameters1
#     r_parameters2 = parameters2
#
#     print('函数1')
#     print(r_parameters2)
#     print(r_parameters1)
#
#
# # 函数返回值
# def fun2():
#     back = 1 + 10
#     back2 = 1 + 99
#     back3 = 10 + 11
#     return back, back2, back3
#
#
# fun1(1, 2)
# ba_ck, ba_ck2, ba_ck3 = fun2()
#
# print(ba_ck)
# print(ba_ck2)
# print(ba_ck3)

# '''
# 函数注释
#
# '''
#
# int 参数类型限制
# def add1(a: int, b: int):
#     """
#
#     :param a:数1
#     :param b:数2
#     :return:数1 与 数2 相加结果
#     """
#     return a + b
#
#
# # 帮助
# add1(100, 11)
# help(add1)

x = 10
y = 11

print('x是{},y是{}'.format(x, y))

# 函数缺省参数


def fun():

    print("{},{},{}".format())


print('hi', 'll', sep='-----')
print('he',end=' ')
print('is')
#
# hi-----ll
# he is

# 缺省参数必须放在可变参数之后
# **kargs 表示可变关键字参数


def fun_(a, b, *args, n=1, **kwargs):
    # print('a={},b={}'.format(a, b))
    # print('args = {}'.format(args))
    print('kwargs{}'.format(kwargs))
    '''
    多余的关键字参数字典形式保存
    kargs{'m': 12, 'p': 10}
    '''
    c = a + b + n
    for arg in args:
        c += arg

    return c
#     多余参数元组形式保存在args中
'''
a=1,b=2
args = (3, 8)
'''
# 可变参数
# fun_(1, 2, 3, 8)

se = fun_(1, 2, 3, 8, m=12, p=10)

print(se)

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