飞桨paddle技术点整理

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

python -m pip install paddlepaddle-gpu==2.3.1.post116 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html

张量

import paddleif __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

Tensor(shape=[2, 2], dtype=int64, place=Place(gpu:0), stop_gradient=True,       [[1, 2],        [3, 4]])[2, 2]VarType.LOD_TENSORTensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,       [[1., 1.],        [1., 1.]])VarType.LOD_TENSORTensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,       [[0., 0.],        [0., 0.]])VarType.LOD_TENSORTensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,       [[1., 0.],        [0., 1.]])VarType.LOD_TENSORTensor(shape=[2, 2], dtype=int64, place=Place(gpu:0), stop_gradient=True,       [[0, 0],        [0, 0]])VarType.LOD_TENSORTensor(shape=[2, 2], dtype=int64, place=Place(gpu:0), stop_gradient=True,       [[1, 1],        [1, 1]])VarType.LOD_TENSORTensor(shape=[11], dtype=int64, place=Place(gpu:0), stop_gradient=True,       [0 , 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10])VarType.LOD_TENSORTensor(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 paddleif __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

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 paddleif __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

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 paddleif __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

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]])