前面的步骤跟乌班图安装Pytorch、Tensorflow Cuda环境 是一样。
安装GPU版本的paddle
代码语言：javascript复制python -m pip install paddlepaddle-gpu==2.3.1.post116 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html张量
代码语言：javascript复制import paddle
if __name__ == '__main__':
a = paddle.to_tensor([[1, 2], [3, 4]])
print(a)
print(a.shape)
print(a.type)
b = paddle.ones([2, 2])
print(b)
print(b.type)
c = paddle.zeros([2, 2])
print(c)
print(c.type)
d = paddle.eye(2, 2)
print(d)
print(d.type)
e = paddle.zeros_like(a)
print(e)
print(e.type)
f = paddle.ones_like(a)
print(f)
print(f.type)
g = paddle.arange(0, 11, 1)
print(g)
print(g.type)
h = paddle.linspace(2, 10, 4)
print(h)
i = paddle.rand([2, 2])
print(i)
j = paddle.normal(mean=0.0, std=paddle.rand([5]))
print(j)
k = paddle.uniform(shape=[2, 2])
print(k)
l = paddle.randperm(10)
print(l)运行结果
代码语言：javascript复制Tensor(shape=[2, 2], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[[1, 2],
[3, 4]])
[2, 2]
VarType.LOD_TENSOR
Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[1., 1.],
[1., 1.]])
VarType.LOD_TENSOR
Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0., 0.],
[0., 0.]])
VarType.LOD_TENSOR
Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[1., 0.],
[0., 1.]])
VarType.LOD_TENSOR
Tensor(shape=[2, 2], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[[0, 0],
[0, 0]])
VarType.LOD_TENSOR
Tensor(shape=[2, 2], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[[1, 1],
[1, 1]])
VarType.LOD_TENSOR
Tensor(shape=[11], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[0 , 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10])
VarType.LOD_TENSOR
Tensor(shape=[4], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[2. , 4.66666651, 7.33333349, 10. ])
Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0.17855753, 0.15026711],
[0.54343289, 0.04870688]])
Tensor(shape=[5], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[-0.07493367, -0.10425358, -1.67506480, 0.02299307, 0.38065284])
Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[ 0.01213348, -0.30467188],
[-0.81535292, 0.09958601]])
Tensor(shape=[10], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[2, 9, 4, 5, 8, 7, 0, 1, 6, 3])- 算数运算、矩阵乘法
import paddle
if __name__ == '__main__':
a = paddle.to_tensor([[1., 2.], [3., 4.]])
print(a)
b = paddle.ones([2, 2])
print(b)
c = a b
print(c)
c = paddle.add(a, b)
print(c)
d = paddle.subtract(a, b)
print(d)
e = paddle.to_tensor([2., 3.])
f = a * e
print(f)
f = paddle.multiply(a, e)
print(f)
g = a / e
print(g)
g = paddle.divide(a, e)
print(g)
h = paddle.to_tensor([[1, 2, 3], [4, 5, 6]], dtype='float32')
i = paddle.to_tensor([[2, 4], [11, 13], [7, 9]], dtype='float32')
j = paddle.mm(h, i)
print(j)
k = paddle.matmul(h, i)
print(k)运行结果
代码语言：javascript复制Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[1., 2.],
[3., 4.]])
Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[1., 1.],
[1., 1.]])
Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[2., 3.],
[4., 5.]])
Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[2., 3.],
[4., 5.]])
Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0., 1.],
[2., 3.]])
Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[2. , 6. ],
[6. , 12.]])
Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[2. , 6. ],
[6. , 12.]])
Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0.50000000, 0.66666669],
[1.50000000, 1.33333337]])
Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0.50000000, 0.66666669],
[1.50000000, 1.33333337]])
Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[45. , 57. ],
[105., 135.]])
Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[45. , 57. ],
[105., 135.]])- 平方、开方、对数运算
import paddle
if __name__ == '__main__':
a = paddle.to_tensor([1, 2, 3])
c = paddle.pow(a, 2)
print(c)
c = a**2
print(c)
a = paddle.to_tensor([2.])
c = paddle.exp(a)
print(c)
a = paddle.to_tensor([1, 2, 3], dtype='float32')
c = paddle.sqrt(a)
print(c)
c = paddle.log2(a)
print(c)
c = paddle.log10(a)
print(c)
c = paddle.log(a)
print(c)运行结果
代码语言：javascript复制Tensor(shape=[3], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[1, 4, 9])
Tensor(shape=[3], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[1, 4, 9])
Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[7.38905621])
Tensor(shape=[3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[1. , 1.41421354, 1.73205078])
Tensor(shape=[3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[0. , 1. , 1.58496249])
Tensor(shape=[3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[0. , 0.30103001, 0.47712126])
Tensor(shape=[3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[0. , 0.69314718, 1.09861231])- 取整/取余
import paddle
if __name__ == '__main__':
a = paddle.rand([2, 2])
b = paddle.multiply(a, paddle.to_tensor([10.]))
print(b)
print(paddle.floor(b))
print(paddle.ceil(b))
print(paddle.round(b))
print(paddle.trunc(b))
print(b % 2)运行结果
代码语言：javascript复制Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[2.33501291, 3.41357899],
[6.85909081, 5.18760014]])
Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[2., 3.],
[6., 5.]])
Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[3., 4.],
[7., 6.]])
Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[2., 3.],
[7., 5.]])
Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[2., 3.],
[6., 5.]])
Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0.33501291, 1.41357899],
[0.85909081, 1.18760014]])- 比较运算
import paddle
if __name__ == '__main__':
a = paddle.to_tensor([[1, 2, 3], [4, 5, 6]])
b = paddle.to_tensor([[1, 4, 9], [6, 5, 7]])
c = paddle.rand([2, 4])
d = a
print(a)
print(b)
print(paddle.equal(a, b))
print(paddle.equal(a, d))
print(paddle.greater_equal(a, b))
print(paddle.greater_than(a, b))
print(paddle.less_equal(a, b))
print(paddle.less_than(a, b))
print(paddle.not_equal(a, b))运行结果
代码语言：javascript复制Tensor(shape=[2, 3], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[[1, 2, 3],
[4, 5, 6]])
Tensor(shape=[2, 3], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[[1, 4, 9],
[6, 5, 7]])
Tensor(shape=[2, 3], dtype=bool, place=Place(gpu:0), stop_gradient=True,
[[True , False, False],
[False, True , False]])
Tensor(shape=[2, 3], dtype=bool, place=Place(gpu:0), stop_gradient=True,
[[True, True, True],
[True, True, True]])
Tensor(shape=[2, 3], dtype=bool, place=Place(gpu:0), stop_gradient=True,
[[True , False, False],
[False, True , False]])
Tensor(shape=[2, 3], dtype=bool, place=Place(gpu:0), stop_gradient=True,
[[False, False, False],
[False, False, False]])
Tensor(shape=[2, 3], dtype=bool, place=Place(gpu:0), stop_gradient=True,
[[True, True, True],
[True, True, True]])
Tensor(shape=[2, 3], dtype=bool, place=Place(gpu:0), stop_gradient=True,
[[False, True , True ],
[True , False, True ]])
Tensor(shape=[2, 3], dtype=bool, place=Place(gpu:0), stop_gradient=True,
[[False, True , True ],
[True , False, True ]])- 排序
import paddle
if __name__ == '__main__':
a = paddle.to_tensor([1, 4, 4, 3, 5])
print(paddle.sort(a))
print(paddle.sort(a, descending=True))
b = paddle.to_tensor([[1, 4, 4, 3, 5], [2, 3, 1, 3, 5]])
print(b.shape)
print(paddle.sort(b))
print(paddle.sort(b, axis=0))
print(paddle.sort(b, descending=True))
print(paddle.sort(b, axis=0, descending=True))运行结果
代码语言：javascript复制Tensor(shape=[5], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[1, 3, 4, 4, 5])
Tensor(shape=[5], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[5, 4, 4, 3, 1])
[2, 5]
Tensor(shape=[2, 5], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[[1, 3, 4, 4, 5],
[1, 2, 3, 3, 5]])
Tensor(shape=[2, 5], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[[1, 3, 1, 3, 5],
[2, 4, 4, 3, 5]])
Tensor(shape=[2, 5], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[[5, 4, 4, 3, 1],
[5, 3, 3, 2, 1]])
Tensor(shape=[2, 5], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[[2, 4, 4, 3, 5],
[1, 3, 1, 3, 5]])- Top K
import paddle
if __name__ == '__main__':
a = paddle.to_tensor([[1, 4, 4, 3, 5], [2, 3, 1, 3, 6]])
print(paddle.topk(a, k=1, axis=0))
print(paddle.topk(a, k=2, axis=0))
print(paddle.topk(a, k=2, axis=1))运行结果
代码语言：javascript复制(Tensor(shape=[1, 5], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[[2, 4, 4, 3, 6]]), Tensor(shape=[1, 5], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[[1, 0, 0, 0, 1]]))
(Tensor(shape=[2, 5], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[[2, 4, 4, 3, 6],
[1, 3, 1, 3, 5]]), Tensor(shape=[2, 5], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[[1, 0, 0, 0, 1],
[0, 1, 1, 1, 0]]))
(Tensor(shape=[2, 2], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[[5, 4],
[6, 3]]), Tensor(shape=[2, 2], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[[4, 1],
[4, 1]]))- 第 k 个最小值
import paddle
if __name__ == '__main__':
a = paddle.to_tensor([[1, 4, 4, 3, 5], [2, 3, 1, 3, 6], [4, 5, 6, 7, 8]])
print(paddle.kthvalue(a, k=2, axis=0))
print(paddle.kthvalue(a, k=2, axis=1))运行结果
代码语言：javascript复制(Tensor(shape=[5], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[2, 4, 4, 3, 6]), Tensor(shape=[5], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[1, 0, 0, 1, 1]))
(Tensor(shape=[3], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[3, 2, 5]), Tensor(shape=[3], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[3, 0, 1]))- 数据合法性校验
import paddle
import numpy as np
if __name__ == '__main__':
a = paddle.rand([2, 3])
b = paddle.to_tensor([1, 2, np.nan])
print(a)
print(paddle.isfinite(a))
print(paddle.isinf(a))
print(paddle.isnan(a))
print(paddle.isnan(b))运行结果
代码语言：javascript复制Tensor(shape=[2, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0.08867172, 0.27258149, 0.78055871],
[0.34912518, 0.62152320, 0.54573017]])
Tensor(shape=[2, 3], dtype=bool, place=Place(gpu:0), stop_gradient=True,
[[True, True, True],
[True, True, True]])
Tensor(shape=[2, 3], dtype=bool, place=Place(gpu:0), stop_gradient=True,
[[False, False, False],
[False, False, False]])
Tensor(shape=[2, 3], dtype=bool, place=Place(gpu:0), stop_gradient=True,
[[False, False, False],
[False, False, False]])
Tensor(shape=[3], dtype=bool, place=Place(gpu:0), stop_gradient=True,
[False, False, True ])- 三角函数
import paddle
if __name__ == '__main__':
a = paddle.to_tensor([0, 0, 0], dtype='float32')
print(paddle.cos(a))运行结果
代码语言：javascript复制Tensor(shape=[3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[1., 1., 1.])- 统计学函数
import paddle
if __name__ == '__main__':
a = paddle.rand([2, 2])
print(a)
print(paddle.mean(a))
print(paddle.mean(a, axis=0))
print(paddle.sum(a))
print(paddle.sum(a, axis=0))
print(paddle.prod(a))
print(paddle.prod(a, axis=0))
print(paddle.argmax(a, axis=0))
print(paddle.argmin(a, axis=0))
print(paddle.std(a))
print(paddle.var(a))
print(paddle.median(a))
print(paddle.mode(a))
a = paddle.rand([2, 2]) * 10
print(a)
print(paddle.histogram(a, 6, 0, 0))
a = paddle.randint(0, 10, [10])
print(a)
print(paddle.bincount(a))运行结果
代码语言：javascript复制Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0.28592348, 0.81242460],
[0.54838538, 0.11063743]])
Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[0.43934274])
Tensor(shape=[2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[0.41715443, 0.46153101])
Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[1.75737095])
Tensor(shape=[2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[0.83430886, 0.92306203])
Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[0.01409356])
Tensor(shape=[2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[0.15679626, 0.08988457])
Tensor(shape=[2], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[1, 0])
Tensor(shape=[2], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[0, 1])
Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[0.30695549])
Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[0.09422167])
Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[0.41715443])
(Tensor(shape=[2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[0.28592348, 0.11063743]), Tensor(shape=[2], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[0, 1]))
Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[7.70743370, 5.53660393],
[7.40494251, 3.98108697]])
Tensor(shape=[6], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[1, 0, 1, 0, 0, 2])
Tensor(shape=[10], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[0, 4, 7, 0, 3, 2, 6, 2, 1, 2])
Tensor(shape=[8], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[2, 1, 3, 1, 1, 0, 1, 1])- 随机抽样
import paddle
if __name__ == '__main__':
paddle.seed(1)
mean = paddle.rand([1, 2])
std = paddle.rand([1, 2])
print(paddle.normal(mean, std))运行结果
代码语言：javascript复制Tensor(shape=[1, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[ 1.11346483, -0.69872946]])- 范数运算
import paddle
if __name__ == '__main__':
a = paddle.rand([2, 1])
b = paddle.rand([2, 1])
print(a)
print(b)
print(paddle.dist(a, b, p=1))
print(paddle.dist(a, b, p=2))
print(paddle.dist(a, b, p=3))
print(paddle.norm(a))
print(paddle.norm(a, p=3))
print(paddle.norm(a, p='fro'))运行结果
代码语言：javascript复制Tensor(shape=[2, 1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0.25732645],
[0.40564528]])
Tensor(shape=[2, 1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0.35750133],
[0.94703859]])
Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[0.64156818])
Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[0.55058300])
Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[0.54253405])
Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[0.48038006])
Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[0.43758231])
Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[0.48038006])- 张量裁剪
import paddle
if __name__ == '__main__':
a = paddle.rand([2, 2]) * 10
print(a)
a = paddle.clip(a, 2, 5)
print(a)运行结果
代码语言：javascript复制Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[4.89272022, 6.48443699],
[0.27107078, 4.85858250]])
Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[4.89272022, 5. ],
[2. , 4.85858250]])- 张量的索引与数据筛选
import paddle
if __name__ == '__main__':
a = paddle.rand([4, 4])
b = paddle.rand([4, 4])
print(a)
print(b)
out = paddle.where(a > 0.5, a, b)
print(out)
out = paddle.where(a > b)
print(out)
out = paddle.index_select(a, axis=0, index=paddle.to_tensor([0, 3, 2]))
print(out)
out = paddle.index_select(a, axis=1, index=paddle.to_tensor([0, 3, 2]))
print(out)
a = paddle.linspace(1, 16, 16)
a = paddle.reshape(a, (4, 4))
print(a)
out = paddle.gather(a, index=paddle.to_tensor([0, 1, 3]), axis=0)
print(out)
out = paddle.gather(a, index=paddle.to_tensor([0, 1, 3]), axis=1)
print(out)
mask = paddle.greater_than(a, paddle.to_tensor([8.]))
print(mask)
out = paddle.masked_select(a, mask)
print(out)
a = paddle.flatten(a)
out = paddle.take_along_axis(a, indices=paddle.to_tensor([0, 15, 13, 10]), axis=0)
print(out)
a = paddle.to_tensor([[0, 1, 2, 0], [2, 3, 0, 1]])
out = paddle.nonzero(a)
print(out)运行结果
代码语言：javascript复制Tensor(shape=[4, 4], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0.35779023, 0.89277714, 0.24702056, 0.92913544],
[0.29648149, 0.45815185, 0.44784531, 0.94065309],
[0.26437962, 0.86828750, 0.10525739, 0.87954575],
[0.55159646, 0.11356149, 0.72669047, 0.07444657]])
Tensor(shape=[4, 4], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0.21640024, 0.85572416, 0.66002953, 0.28534794],
[0.03093199, 0.11802873, 0.36485839, 0.07965848],
[0.19432747, 0.38168678, 0.40194315, 0.19759925],
[0.31319368, 0.17183183, 0.49453658, 0.77549160]])
Tensor(shape=[4, 4], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0.21640024, 0.89277714, 0.66002953, 0.92913544],
[0.03093199, 0.11802873, 0.36485839, 0.94065309],
[0.19432747, 0.86828750, 0.40194315, 0.87954575],
[0.55159646, 0.17183183, 0.72669047, 0.77549160]])
(Tensor(shape=[12, 1], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[[0],
[0],
[0],
[1],
[1],
[1],
[1],
[2],
[2],
[2],
[3],
[3]]), Tensor(shape=[12, 1], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[[0],
[1],
[3],
[0],
[1],
[2],
[3],
[0],
[1],
[3],
[0],
[2]]))
Tensor(shape=[3, 4], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0.35779023, 0.89277714, 0.24702056, 0.92913544],
[0.55159646, 0.11356149, 0.72669047, 0.07444657],
[0.26437962, 0.86828750, 0.10525739, 0.87954575]])
Tensor(shape=[4, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0.35779023, 0.92913544, 0.24702056],
[0.29648149, 0.94065309, 0.44784531],
[0.26437962, 0.87954575, 0.10525739],
[0.55159646, 0.07444657, 0.72669047]])
Tensor(shape=[4, 4], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[1. , 2. , 3. , 4. ],
[5. , 6. , 7. , 8. ],
[9. , 10., 11., 12.],
[13., 14., 15., 16.]])
Tensor(shape=[3, 4], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[1. , 2. , 3. , 4. ],
[5. , 6. , 7. , 8. ],
[13., 14., 15., 16.]])
Tensor(shape=[4, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[1. , 2. , 4. ],
[5. , 6. , 8. ],
[9. , 10., 12.],
[13., 14., 16.]])
Tensor(shape=[4, 4], dtype=bool, place=Place(gpu:0), stop_gradient=True,
[[False, False, False, False],
[False, False, False, False],
[True , True , True , True ],
[True , True , True , True ]])
Tensor(shape=[8], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[9. , 10., 11., 12., 13., 14., 15., 16.])
Tensor(shape=[4], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[1. , 16., 14., 11.])
Tensor(shape=[5, 2], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[[0, 1],
[0, 2],
[1, 0],
[1, 1],
[1, 3]])
- 张量的组合与拼接
import paddle
if __name__ == '__main__':
a = paddle.zeros([2, 4])
b = paddle.ones([2, 4])
out = paddle.concat((a, b), axis=0)
print(out)
a = paddle.linspace(1, 6, 6)
a = paddle.reshape(a, (2, 3))
b = paddle.linspace(7, 12, 6)
b = paddle.reshape(b, (2, 3))
print(a)
print(b)
out = paddle.stack((a, b), axis=1)
print(out)
print(out.shape)
print(out[:, 0, :])
print(out[:, 1, :])运行结果
代码语言：javascript复制Tensor(shape=[4, 4], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0., 0., 0., 0.],
[0., 0., 0., 0.],
[1., 1., 1., 1.],
[1., 1., 1., 1.]])
Tensor(shape=[2, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[1., 2., 3.],
[4., 5., 6.]])
Tensor(shape=[2, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[7. , 8. , 9. ],
[10., 11., 12.]])
Tensor(shape=[2, 2, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[[1. , 2. , 3. ],
[7. , 8. , 9. ]],
[[4. , 5. , 6. ],
[10., 11., 12.]]])
[2, 2, 3]
Tensor(shape=[2, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[1., 2., 3.],
[4., 5., 6.]])
Tensor(shape=[2, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[7. , 8. , 9. ],
[10., 11., 12.]])
- 张量切片
import paddle
if __name__ == '__main__':
a = paddle.rand([3, 4])
print(a)
out = paddle.chunk(a, (2, 1), axis=0)
print(out)
out = paddle.chunk(a, 2, axis=1)
print(out)
out = paddle.split(a, (2, 1), axis=0)
print(out)
out = paddle.split(a, 2, axis=1)
print(out)
out = paddle.split(a, (1, 1, 1), axis=0)
print(out)运行结果
代码语言：javascript复制Tensor(shape=[3, 4], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0.72375304, 0.28191790, 0.45890489, 0.79828680],
[0.10114241, 0.24494733, 0.85273385, 0.31621015],
[0.78064203, 0.37038296, 0.75661004, 0.32411623]])
[Tensor(shape=[2, 4], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0.72375304, 0.28191790, 0.45890489, 0.79828680],
[0.10114241, 0.24494733, 0.85273385, 0.31621015]]), Tensor(shape=[1, 4], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0.78064203, 0.37038296, 0.75661004, 0.32411623]])]
[Tensor(shape=[3, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0.72375304, 0.28191790],
[0.10114241, 0.24494733],
[0.78064203, 0.37038296]]), Tensor(shape=[3, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0.45890489, 0.79828680],
[0.85273385, 0.31621015],
[0.75661004, 0.32411623]])]
[Tensor(shape=[2, 4], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0.72375304, 0.28191790, 0.45890489, 0.79828680],
[0.10114241, 0.24494733, 0.85273385, 0.31621015]]), Tensor(shape=[1, 4], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0.78064203, 0.37038296, 0.75661004, 0.32411623]])]
[Tensor(shape=[3, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0.72375304, 0.28191790],
[0.10114241, 0.24494733],
[0.78064203, 0.37038296]]), Tensor(shape=[3, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0.45890489, 0.79828680],
[0.85273385, 0.31621015],
[0.75661004, 0.32411623]])]
[Tensor(shape=[1, 4], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0.72375304, 0.28191790, 0.45890489, 0.79828680]]), Tensor(shape=[1, 4], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0.10114241, 0.24494733, 0.85273385, 0.31621015]]), Tensor(shape=[1, 4], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0.78064203, 0.37038296, 0.75661004, 0.32411623]])]- 张量变形
import paddle
if __name__ == '__main__':
a = paddle.rand([2, 3])
print(a)
out = paddle.reshape(a, (3, 2))
print(out)
print(paddle.t(out))
a = paddle.rand([1, 2, 3])
print(a)
out = paddle.transpose(a, (1, 0, 2))
print(out)
out = paddle.squeeze(a)
print(out)
out = paddle.unsqueeze(a, -1)
print(out)
out = paddle.unbind(a, axis=1)
print(out)
out = paddle.flip(a, axis=1)
print(out)
out = paddle.flip(a, axis=2)
print(out)
out = paddle.flip(a, axis=[1, 2])
print(out)
out = paddle.rot90(a)
print(out)
out = paddle.rot90(a, -1)
print(out)
运行结果
代码语言：javascript复制Tensor(shape=[2, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0.57933182, 0.92746025, 0.43314070],
[0.13385081, 0.11243574, 0.38549340]])
Tensor(shape=[3, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0.57933182, 0.92746025],
[0.43314070, 0.13385081],
[0.11243574, 0.38549340]])
Tensor(shape=[2, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0.57933182, 0.43314070, 0.11243574],
[0.92746025, 0.13385081, 0.38549340]])
Tensor(shape=[1, 2, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[[0.31357428, 0.54367834, 0.89613014],
[0.09769047, 0.61672699, 0.02827156]]])
Tensor(shape=[2, 1, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[[0.31357428, 0.54367834, 0.89613014]],
[[0.09769047, 0.61672699, 0.02827156]]])
Tensor(shape=[2, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0.31357428, 0.54367834, 0.89613014],
[0.09769047, 0.61672699, 0.02827156]])
Tensor(shape=[1, 2, 3, 1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[[[0.31357428],
[0.54367834],
[0.89613014]],
[[0.09769047],
[0.61672699],
[0.02827156]]]])
[Tensor(shape=[1, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0.31357428, 0.54367834, 0.89613014]]), Tensor(shape=[1, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[0.09769047, 0.61672699, 0.02827156]])]
Tensor(shape=[1, 2, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[[0.09769047, 0.61672699, 0.02827156],
[0.31357428, 0.54367834, 0.89613014]]])
Tensor(shape=[1, 2, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[[0.89613014, 0.54367834, 0.31357428],
[0.02827156, 0.61672699, 0.09769047]]])
Tensor(shape=[1, 2, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[[0.02827156, 0.61672699, 0.09769047],
[0.89613014, 0.54367834, 0.31357428]]])
Tensor(shape=[2, 1, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[[0.09769047, 0.61672699, 0.02827156]],
[[0.31357428, 0.54367834, 0.89613014]]])
Tensor(shape=[2, 1, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[[0.31357428, 0.54367834, 0.89613014]],
[[0.09769047, 0.61672699, 0.02827156]]])- 张量填充
import paddle
if __name__ == '__main__':
a = paddle.full((2, 3), 10)
print(a)运行结果
代码语言：javascript复制Tensor(shape=[2, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[10., 10., 10.],
[10., 10., 10.]])- 求导数
import paddle
if __name__ == '__main__':
x = paddle.ones([2, 2])
x.stop_gradient = False
y = x 2
print(y)
y.backward()
print(x.grad)
x = paddle.ones([2, 2])
x.stop_gradient = False
y = x 2
z = y**2 * 3
z.backward()
print(x.grad)运行结果
代码语言：javascript复制Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=False,
[[3., 3.],
[3., 3.]])
Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=False,
[[1., 1.],
[1., 1.]])
Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=False,
[[18., 18.],
[18., 18.]])神经网络的搭建
波士顿房价预测
代码语言：javascript复制import paddle
from sklearn import datasets
if __name__ == '__main__':
boston = datasets.load_boston()
X = paddle.to_tensor(boston.data)
y = paddle.to_tensor(boston.target)
y = paddle.unsqueeze(y, -1)
data = paddle.concat((X, y), axis=-1)
# print(data)
y = paddle.squeeze(y)
train_data = data[:496]
X_test = X[496:]
y_test = y[496:]
class Net(paddle.nn.Layer):
def __init__(self, n_feature, n_output):
super(Net, self).__init__()
self.hidden = paddle.nn.Linear(n_feature, 100)
self.relu = paddle.nn.ReLU()
self.predict = paddle.nn.Linear(100, n_output)
def forward(self, x):
out = self.hidden(x)
out = self.relu(out)
out = self.predict(out)
return out
net = Net(13, 1)
train_loader = paddle.io.DataLoader(train_data, batch_size=10, shuffle=True)
loss_func = paddle.nn.MSELoss()
optimizer = paddle.optimizer.Adam(learning_rate=0.01, parameters=net.parameters())
EPOCH_NUM = 1000
for epoch in range(EPOCH_NUM):
for batch_id, data in enumerate(train_loader):
X_train = data[:, :13].astype('float32')
y_train = data[:, 13:].astype('float32')
predict = net(X_train)
loss = loss_func(predict, y_train) * 0.001
if batch_id % 20 == 0:
print("epoch: {}, batch_id: {}, loss is: {}".format(epoch, batch_id, loss.numpy()))
loss.backward()
optimizer.step()
optimizer.clear_grad()
predict = net(X_test.astype('float32'))
loss_test = loss_func(predict, y_test.astype('float32')) * 0.001
print("epoch: {}, test_loss is: {}".format(epoch, loss_test.numpy()))运行结果 (部分)
代码语言：javascript复制epoch: 999, batch_id: 0, loss is: [0.01588636]
epoch: 999, batch_id: 20, loss is: [0.02083369]
epoch: 999, batch_id: 40, loss is: [0.00961253]
epoch: 999, test_loss is: [0.02289972]手写数字识别
代码语言：javascript复制import paddle
import paddle.nn as nn
import paddle.dataset.mnist as mnist
if __name__ == '__main__':
train_reader = paddle.batch(mnist.train(), batch_size=4)
test_reader = paddle.batch(mnist.test(), batch_size=4)
class CNN(nn.Layer):
def __init__(self):
super(CNN, self).__init__()
self.conv = nn.Sequential(
nn.Conv2D(1, 32, 5, stride=1, padding=2),
nn.BatchNorm2D(32),
nn.ReLU(),
nn.MaxPool2D(2)
)
self.fc = nn.Linear(14 * 14 * 32, 10)
def forward(self, x):
out = self.conv(x)
out = paddle.reshape(out, (out.shape[0], -1))
out = self.fc(out)
return out
cnn = CNN()
loss_func = nn.CrossEntropyLoss()
optimizer = paddle.optimizer.Adam(learning_rate=0.01, parameters=cnn.parameters())
EPOCH_NUM = 5
best_acc = 0
for epoch in range(EPOCH_NUM):
for batch_id, data in enumerate(train_reader()):
images = paddle.to_tensor(data[0][0], dtype='float32')
images = paddle.unsqueeze(images, 0)
for i in range(1, len(data)):
tmp = paddle.to_tensor(data[i][0], dtype='float32')
tmp = paddle.unsqueeze(tmp, 0)
images = paddle.concat((images, tmp), axis=0)
images = paddle.reshape(images, (4, 1, 28, 28))
predict = cnn(images)
labels = paddle.to_tensor(data[0][1])
labels = paddle.unsqueeze(labels, 0)
for i in range(1, len(data)):
tmp = paddle.to_tensor(data[i][1])
tmp = paddle.unsqueeze(tmp, 0)
labels = paddle.concat((labels, tmp), axis=0)
loss = loss_func(predict, labels)
loss.backward()
optimizer.step()
optimizer.clear_grad()
print("epoch is {}, batch_id is {}, loss is {}".format(epoch 1, batch_id, loss.item()))
loss_test = 0
accuracy = 0
total = 0
for batch_id, data in enumerate(test_reader()):
images = paddle.to_tensor(data[0][0], dtype='float32')
images = paddle.unsqueeze(images, 0)
for i in range(1, len(data)):
tmp = paddle.to_tensor(data[i][0], dtype='float32')
tmp = paddle.unsqueeze(tmp, 0)
images = paddle.concat((images, tmp), axis=0)
images = paddle.reshape(images, (4, 1, 28, 28))
predict = cnn(images)
labels = paddle.to_tensor(data[0][1])
labels = paddle.unsqueeze(labels, 0)
for i in range(1, len(data)):
tmp = paddle.to_tensor(data[i][1])
tmp = paddle.unsqueeze(tmp, 0)
labels = paddle.concat((labels, tmp), axis=0)
loss_test = loss_func(predict, labels)
pred = paddle.argmax(predict, axis=1)
accuracy = (pred == paddle.squeeze(labels)).sum().item()
total = batch_id
total *= 4
accuracy = accuracy / total
if accuracy > best_acc:
best_acc = accuracy
loss_test = loss_test / (total // 4)
print("epoch is {}, accuracy is {}, loss test is {}, best_acc is {}".format(epoch 1, accuracy, loss_test.item(), best_acc))
运行结果 (部分)
代码语言：javascript复制epoch is 5, batch_id is 14997, loss is 0.0013393799308687449
epoch is 5, batch_id is 14998, loss is 0.0260640699416399
epoch is 5, batch_id is 14999, loss is 0.00019078730838373303
epoch is 5, accuracy is 0.9109643857543017, loss test is 0.30904391407966614, best_acc is 0.970188075230092
