Tensor 介绍¶

import paddle # 后面的示例代码默认已导入 paddle 模块
ndim_1_Tensor = paddle.to_tensor([2.0, 3.0, 4.0])
print(ndim_1_Tensor)

paddle.to_tensor(2)
paddle.to_tensor([2])

ndim_2_Tensor = paddle.to_tensor([[1.0, 2.0, 3.0],
                                  [4.0, 5.0, 6.0]])
print(ndim_2_Tensor)

ndim_3_Tensor = paddle.to_tensor([[[1, 2, 3, 4, 5],
                                   [6, 7, 8, 9, 10]],
                                  [[11, 12, 13, 14, 15],
                                   [16, 17, 18, 19, 20]]])
print(ndim_3_Tensor)

ndim_2_Tensor = paddle.to_tensor([[1.0, 2.0],
                                  [4.0, 5.0, 6.0]])

paddle.zeros([m, n])             # 创建数据全为 0，形状为 [m, n] 的 Tensor
paddle.ones([m, n])              # 创建数据全为 1，形状为 [m, n] 的 Tensor
paddle.full([m, n], 10)          # 创建数据全为 10，形状为 [m, n] 的 Tensor

paddle.arange(start, end, step)  # 创建以步长 step 均匀分隔区间[start, end)的 Tensor
paddle.linspace(start, stop, num) # 创建以元素个数 num 均匀分隔区间[start, stop)的 Tensor

paddle.arange(start=1, end=5, step=1)

import numpy as np
from PIL import Image
import paddle.vision.transforms as T
import paddle.vision.transforms.functional as F

fake_img = Image.fromarray((np.random.rand(224, 224, 3) * 255.).astype(np.uint8)) # 创建随机图片
transform = T.ToTensor()
tensor = transform(fake_img) # 使用 ToTensor()将图片转换为 Tensor
print(tensor)

import paddle

from paddle.vision.transforms import Compose, Normalize

transform = Compose([Normalize(mean=[127.5],
                               std=[127.5],
                               data_format='CHW')])

test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=transform)
print(test_dataset[0][1]) # 打印原始数据集的第一个数据的 label
loader = paddle.io.DataLoader(test_dataset)
for data in enumerate(loader):
    x, label = data[1]
    print(label) # 打印由 DataLoader 返回的迭代器中的第一个数据的 label
    break

ndim_4_Tensor = paddle.ones([2, 3, 4, 5])

print("Data Type of every element:", ndim_4_Tensor.dtype)
print("Number of dimensions:", ndim_4_Tensor.ndim)
print("Shape of Tensor:", ndim_4_Tensor.shape)
print("Elements number along axis 0 of Tensor:", ndim_4_Tensor.shape[0])
print("Elements number along the last axis of Tensor:", ndim_4_Tensor.shape[-1])

ndim_1_Tensor = paddle.to_tensor([1, 2, 3])
print("the shape of ndim_1_Tensor:", ndim_1_Tensor.shape)

reshape_Tensor = paddle.reshape(ndim_1_Tensor, [1, 3])
print("After reshape:", reshape_Tensor.shape)

print("Tensor flattened to Vector:", paddle.reshape(ndim_3_Tensor, [-1]).numpy())

origin_tensor = paddle.to_tensor([1, 2, 3])
new_tensor = paddle.reshape(origin_tensor, [1, 3]) # 非原位操作
same_tensor = paddle.reshape_(origin_tensor, [1, 3]) # 原位操作
print("origin_tensor name: ", origin_tensor.name)
print("new_tensor name: ", new_tensor.name)
print("same_tensor name: ", same_tensor.name)

# 创建 Tensor 时指定 dtype
ndim_1_tensor = paddle.to_tensor([2.0, 3.0, 4.0], dtype='float64')
print("Tensor dtype of ndim_1_tensor:", ndim_1_tensor.dtype)
# 创建 Tensor 时不指定 dtype，自动选择对应的默认类型
print("Tensor dtype from Python integers:", paddle.to_tensor(1).dtype)
print("Tensor dtype from Python floating point:", paddle.to_tensor(1.0).dtype)

ndim_2_Tensor = paddle.to_tensor([[(1+1j), (2+2j)],
                                  [(3+3j), (4+4j)]])
print(ndim_2_Tensor)

float32_Tensor = paddle.to_tensor(1.0)

float64_Tensor = paddle.cast(float32_Tensor, dtype='float64')
print("Tensor after cast to float64:", float64_Tensor.dtype)

int64_Tensor = paddle.cast(float32_Tensor, dtype='int64')
print("Tensor after cast to int64:", int64_Tensor.dtype)

cpu_Tensor = paddle.to_tensor(1, place=paddle.CPUPlace())
print(cpu_Tensor.place)

gpu_Tensor = paddle.to_tensor(1, place=paddle.CUDAPlace(0))
print(gpu_Tensor.place) # 显示 Tensor 位于 GPU 设备的第 0 张显卡上

pin_memory_Tensor = paddle.to_tensor(1, place=paddle.CUDAPinnedPlace())
print(pin_memory_Tensor.place)

print("Tensor name:", paddle.to_tensor(1).name)

eg = paddle.to_tensor(1)
print("Tensor stop_gradient:", eg.stop_gradient)
eg.stop_gradient = False
print("Tensor stop_gradient:", eg.stop_gradient)

x.abs()                       #逐元素取绝对值
x.ceil()                      #逐元素向上取整
x.floor()                     #逐元素向下取整
x.round()                     #逐元素四舍五入
x.exp()                       #逐元素计算自然常数为底的指数
x.log()                       #逐元素计算 x 的自然对数
x.reciprocal()                #逐元素求倒数
x.square()                    #逐元素计算平方
x.sqrt()                      #逐元素计算平方根
x.sin()                       #逐元素计算正弦
x.cos()                       #逐元素计算余弦
x.add(y)                      #逐元素相加
x.subtract(y)                 #逐元素相减
x.multiply(y)                 #逐元素相乘
x.divide(y)                   #逐元素相除
x.mod(y)                      #逐元素相除并取余
x.pow(y)                      #逐元素幂运算
x.max()                       #指定维度上元素最大值，默认为全部维度
x.min()                       #指定维度上元素最小值，默认为全部维度
x.prod()                      #指定维度上元素累乘，默认为全部维度
x.sum()                       #指定维度上元素的和，默认为全部维度

x.isfinite()                  #判断 Tensor 中元素是否是有限的数字，即不包括 inf 与 nan
x.equal_all(y)                #判断两个 Tensor 的全部元素是否相等，并返回形状为[]的布尔类 0-D Tensor
x.equal(y)                    #判断两个 Tensor 的每个元素是否相等，并返回形状相同的布尔类 Tensor
x.not_equal(y)                #判断两个 Tensor 的每个元素是否不相等
x.less_than(y)                #判断 Tensor x 的元素是否小于 Tensor y 的对应元素
x.less_equal(y)               #判断 Tensor x 的元素是否小于或等于 Tensor y 的对应元素
x.greater_than(y)             #判断 Tensor x 的元素是否大于 Tensor y 的对应元素
x.greater_equal(y)            #判断 Tensor x 的元素是否大于或等于 Tensor y 的对应元素
x.allclose(y)                 #判断 Tensor x 的全部元素是否与 Tensor y 的全部元素接近，并返回形状为[]的布尔类 0-D Tensor

x.logical_and(y)              #对两个布尔类型 Tensor 逐元素进行逻辑与操作
x.logical_or(y)               #对两个布尔类型 Tensor 逐元素进行逻辑或操作
x.logical_xor(y)              #对两个布尔类型 Tensor 逐元素进行逻辑亦或操作
x.logical_not(y)              #对两个布尔类型 Tensor 逐元素进行逻辑非操作

x.t()                         #矩阵转置
x.transpose([1, 0])           #交换第 0 维与第 1 维的顺序
x.norm('fro')                 #矩阵的弗罗贝尼乌斯范数
x.dist(y, p=2)                #矩阵（x-y）的 2 范数
x.matmul(y)                   #矩阵乘法