本文示例源码地址：
https://github.com/tianlanlandelan/DL4JTest/blob/master/src/test/java/com/dl4j/demo/Nd4jTest.java
## maven安装DL4J
pom文件引入：
org.nd4j
nd4j-native
1.0.0-beta3
org.deeplearning4j
deeplearning4j-core
1.0.0-beta3
## 创建矩阵
//生成一个全0二维矩阵
INDArray tensorA = Nd4j.zeros(4,5);
println("全0二维矩阵",tensorA);
//生成一个全1二维矩阵
INDArray tensorB = Nd4j.ones(4,5);
println("全1二维矩阵",tensorB);
//生成一个全1二维矩阵
INDArray tensorC = Nd4j.rand(4,5);
println("随机二维矩阵",tensorC)
运行结果：
====全0二维矩阵===
[[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0]]
====全1二维矩阵===
[[ 1.0000, 1.0000, 1.0000, 1.0000, 1.0000],
[ 1.0000, 1.0000, 1.0000, 1.0000, 1.0000],
[ 1.0000, 1.0000, 1.0000, 1.0000, 1.0000],
[ 1.0000, 1.0000, 1.0000, 1.0000, 1.0000]]
====随机二维矩阵===
[[ 0.5017, 0.9461, 0.3255, 0.2155, 0.9273],
[ 0.0239, 0.5130, 0.8028, 0.5011, 0.3680],
[ 0.3644, 0.0864, 0.0342, 0.4126, 0.5553],
[ 0.2027, 0.7989, 0.6696, 0.0402, 0.7059]]
## 矩阵运算--拼接
println("水平拼接若干矩阵，矩阵必须有相同的行数", Nd4j.hstack(tensorA,tensorB));
println("垂直拼接若干矩阵，矩阵必须有相同的列数", Nd4j.vstack(tensorA,tensorB));
运行结果：
====水平拼接若干矩阵，矩阵必须有相同的行数===
[[ 0, 0, 0, 0, 0, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000],
[ 0, 0, 0, 0, 0, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000],
[ 0, 0, 0, 0, 0, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000],
[ 0, 0, 0, 0, 0, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000]]
====垂直拼接若干矩阵，矩阵必须有相同的列数===
[[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 1.0000, 1.0000, 1.0000, 1.0000, 1.0000],
[ 1.0000, 1.0000, 1.0000, 1.0000, 1.0000],
[ 1.0000, 1.0000, 1.0000, 1.0000, 1.0000],
[ 1.0000, 1.0000, 1.0000, 1.0000, 1.0000]]
## 矩阵运算-加减
注意，每个运算函数都有一个加i的函数，如 add和addi，加i的函数运算后会覆盖掉原矩阵
println("矩阵元素加上一个标量",tensorA.add(10));
println("矩阵相加",tensorA.add(tensorB));
println("矩阵元素加上标量后覆盖原矩阵tensorA",tensorA.addi(10));
println("矩阵相减",tensorA.sub(tensorB));
运行结果：
====矩阵元素加上一个标量===
[[ 10.0000, 10.0000, 10.0000],
[ 10.0000, 10.0000, 10.0000]]
====矩阵相加===
[[ 0.2202, 0.1473, 0.1217],
[ 0.8428, 0.6761, 0.8127]]
====矩阵元素加上标量后覆盖原矩阵tensorA===
[[ 10.0000, 10.0000, 10.0000],
[ 10.0000, 10.0000, 10.0000]]
====矩阵相减===
[[ 9.7798, 9.8527, 9.8783],
[ 9.1572, 9.3239, 9.1873]]
## 矩阵运算-乘除
println("矩阵对应元素相乘",tensorA.mul(tensorB));
println("矩阵元素除以一个标量",tensorA.div(2));
println("矩阵对应元素相除",tensorA.div(tensorB));
/*
矩阵A*B=C
需要注意：
1、当矩阵A的列数等于矩阵B的行数时，A与B可以相乘。
2、矩阵C的行数等于矩阵A的行数，C的列数等于B的列数。（ A:2,3; B:3,4; C:2,4 ）
3、乘积C的第m行第n列的元素等于矩阵A的第m行的元素与矩阵B的第n列对应元素乘积之和。
*/
println("矩阵相乘",tensorA.mmul(tensorB));
运算结果：
```bash
====矩阵对应元素相乘===
[[ 2.2015, 1.4728, 1.2173],
[ 8.4281, 6.7608, 8.1272]]
====矩阵元素除以一个标量===
[[ 5.0000, 5.0000, 5.0000],
[ 5.0000, 5.0000, 5.0000]]
====矩阵对应元素相除===
[[ 45.4231, 67.8989, 82.1506],
[ 11.8650, 14.7911, 12.3043]]
====矩阵相乘===
[[ 4.8916, 23.3161],
[ 4.8916, 23.3161]]
## 矩阵运算-翻转
println("矩阵转置",tensorB.transpose());
println("矩阵转置后替换原矩阵tensorB",tensorB.transposei());
运算结果：
====矩阵转置===
[[ 0.2202, 0.8428],
[ 0.1473, 0.6761],
[ 0.1217, 0.8127]]
====矩阵转置后替换原矩阵tensorB===
[[ 0.2202, 0.8428],
[ 0.1473, 0.6761],
[ 0.1217, 0.8127]]
## 三维矩阵
三维矩阵和二维矩阵操作一样
//创建一个三维矩阵 2*3*4
INDArray tensor3d_1 = Nd4j.create(new int[]{2,3,4});
println("创建空的三维矩阵",tensor3d_1);
//创建一个随机的三维矩阵 2*3*4
INDArray tensor3d_2 = Nd4j.rand(new int[]{2,3,4});
println("创建随机三维矩阵",tensor3d_2);
//矩阵的每个元素减去一个标量后覆盖原矩阵
println("矩阵元素减去一个标量",tensor3d_1.subi(-5));
//矩阵相减
println("三维矩阵相减",tensor3d_1.sub(tensor3d_2));
运算结果：
====创建空的三维矩阵===
[[[ 0, 0, 0, 0],
[ 0, 0, 0, 0],
[ 0, 0, 0, 0]],
[[ 0, 0, 0, 0],
[ 0, 0, 0, 0],
[ 0, 0, 0, 0]]]
====创建随机三维矩阵===
[[[ 0.7030, 0.0575, 0.3288, 0.8928],
[ 0.7067, 0.4539, 0.6318, 0.8632],
[ 0.2914, 0.7980, 0.3350, 0.8783]],
[[ 0.8559, 0.7396, 0.6039, 0.1946],
[ 0.5336, 0.9253, 0.4747, 0.2658],
[ 0.9690, 0.3269, 0.0520, 0.1754]]]
====矩阵元素减去一个标量===
[[[ 5.0000, 5.0000, 5.0000, 5.0000],
[ 5.0000, 5.0000, 5.0000, 5.0000],
[ 5.0000, 5.0000, 5.0000, 5.0000]],
[[ 5.0000, 5.0000, 5.0000, 5.0000],
[ 5.0000, 5.0000, 5.0000, 5.0000],
[ 5.0000, 5.0000, 5.0000, 5.0000]]]
====三维矩阵相减===
[[[ 4.2970, 4.9425, 4.6712, 4.1072],
[ 4.2933, 4.5461, 4.3682, 4.1368],
[ 4.7086, 4.2020, 4.6650, 4.1217]],
[[ 4.1441, 4.2604, 4.3961, 4.8054],
[ 4.4664, 4.0747, 4.5253, 4.7342],
[ 4.0310, 4.6731, 4.9480, 4.8246]]