00_mindspore_basics.ipynb

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   "source": [
    "# MindSpore Basics  - Tensors, Differentiaton\n",
    "\n",
    "Make Your First GAN With [MindSpore(1.6.1)](https://mindspore.cn), 2022"
   ]
  },
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   "source": [
    "import mindspore"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "## Normal python variables"
   ]
  },
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   "outputs": [
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     "text": [
      "3.5 14.25\n"
     ]
    }
   ],
   "source": [
    "# normal python variables\n",
    "\n",
    "x = 3.5\n",
    "\n",
    "y = x*x + 2\n",
    "\n",
    "print(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
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   "source": [
    "## PyTorch Tensors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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    {
     "data": {
      "text/plain": [
       "Tensor(shape=[], dtype=Float32, value= 3.5)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# simple mindspore tensor\n",
    "\n",
    "x = mindspore.Tensor(3.5)\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9awP8pdcpYkS"
   },
   "source": [
    "## Simple Arithmetic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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    "colab_type": "code",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tensor(shape=[], dtype=Float32, value= 6.5)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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    }
   ],
   "source": [
    "# simple arithmetic with tensors\n",
    "\n",
    "y = x + 3\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ljyzfEjGpf_X"
   },
   "source": [
    "## Functional Relationship Between Tensors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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    "colab_type": "code",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tensor(shape=[], dtype=Float32, value= 1.875)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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    }
   ],
   "source": [
    "# y is defined as a function of x\n",
    "\n",
    "class Func(mindspore.nn.Cell):\n",
    "    def construct(self, x):\n",
    "        y = (x-1) * (x-2) * (x-3)\n",
    "        return y\n",
    "\n",
    "func = Func()\n",
    "y = func(x)\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "## Automatic Differentiation and Gradient Calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   "source": [
    "# make net used for calculating gradients\n",
    "\n",
    "class GradNet(mindspore.nn.Cell):\n",
    "    def __init__(self, func):\n",
    "        super(GradNet, self).__init__()\n",
    "        self.func = func\n",
    "        self.grad_func = mindspore.ops.GradOperation()(self.func)\n",
    "\n",
    "    def construct(self, x):\n",
    "        return self.grad_func(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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   "outputs": [
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     "data": {
      "text/plain": [
       "Tensor(shape=[], dtype=Float32, value= 5.75)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what is gradient at x = 3.5\n",
    "\n",
    "grad_net = GradNet(func)\n",
    "grad_net(x)"
   ]
  }
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