pytorch_basics.ipynb

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    "colab": {
      "name": "pytorch_basics.ipynb",
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      "collapsed_sections": []
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "N_NYvE-Gdadi"
      },
      "source": [
        "# importing the libraries\r\n",
        "import pandas as pd\r\n",
        "import numpy as np\r\n",
        "\r\n",
        "# for reading and displaying images\r\n",
        "from skimage.io import imread\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "%matplotlib inline\r\n",
        "\r\n",
        "# for creating validation set\r\n",
        "from sklearn.model_selection import train_test_split\r\n",
        "\r\n",
        "# for evaluating the model\r\n",
        "from sklearn.metrics import accuracy_score\r\n",
        "from tqdm import tqdm\r\n",
        "\r\n",
        "# PyTorch libraries and modules\r\n",
        "import torch\r\n",
        "from torch.autograd import Variable\r\n",
        "from torch.nn import Linear, ReLU, CrossEntropyLoss, Sequential, Conv2d, MaxPool2d, Module, Softmax, BatchNorm2d, Dropout\r\n",
        "from torch.optim import Adam, SGD"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9M5o1_5Xdkq2",
        "outputId": "39d422b7-c327-4595-f855-684d9fc0b846"
      },
      "source": [
        "import torch\r\n",
        "a=torch.tensor([0,1,2.1],dtype=torch.int32)\r\n",
        "b=a.view(3,1)\r\n",
        "print(a)\r\n",
        "c=torch.tensor([[1,2],[2,3],[3,4]])\r\n",
        "print(c.type)\r\n",
        "#new_float_tensor = old_int_tensor.type(torch.FloatTensor)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([0, 1, 2], dtype=torch.int32)\n<built-in method type of Tensor object at 0x0000018CA5043740>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_MZhOCH2GmTv",
        "outputId": "be8266d7-d3f2-4b79-e67b-ea2d0e6c56b1"
      },
      "source": [
        "a=torch.tensor(2.0,requires_grad=True)\r\n",
        "b=a**3\r\n",
        "b.backward()\r\n",
        "print(a.grad)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(12.)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-jxAJw7vCLSF",
        "outputId": "a72febbf-4c83-4560-ae90-ec4f008ce6c2"
      },
      "source": [
        "a=np.array([1,2,3])\r\n",
        "b=torch.from_numpy(a)\r\n",
        "c=b.numpy()\r\n",
        "print(b)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([1, 2, 3], dtype=torch.int32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KXexeXEsTZ2v",
        "outputId": "1a81bb64-bd47-45cf-b8a9-7185cd726eb1"
      },
      "source": [
        "# Basic Linear Output\r\n",
        "import torch\r\n",
        "\r\n",
        "b=torch.tensor(-1.0,requires_grad=True)\r\n",
        "w=torch.tensor(2.0,requires_grad=True)\r\n",
        "x=torch.tensor([1.0])\r\n",
        "def forward(x):\r\n",
        "  y=w*x+b\r\n",
        "  return y\r\n",
        "y_hat=forward(x)\r\n",
        "print(y_hat)\r\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([1.], grad_fn=<AddBackward0>)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bITvJpNGWucx",
        "outputId": "8c504d2c-bd97-4ca3-b88b-522603772303"
      },
      "source": [
        "#Basic Linear output using Linear - Pytorch Tensor\r\n",
        "\r\n",
        "from torch.nn import Linear\r\n",
        "torch.manual_seed(1)\r\n",
        "lr = Linear(in_features=1, out_features=1, bias=True)\r\n",
        "print(\"Parameters w and b: \", list(lr.parameters()))\r\n",
        "print(\"Python dictionary: \",lr.state_dict())\r\n",
        "print(\"keys: \",lr.state_dict().keys())\r\n",
        "print(\"values: \",lr.state_dict().values())\r\n",
        "x = torch.tensor([[1.0], [2.0]])\r\n",
        "yhat = lr(x)\r\n",
        "print(\"The prediction: \", yhat)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Parameters w and b:  [Parameter containing:\n",
            "tensor([[0.5153]], requires_grad=True), Parameter containing:\n",
            "tensor([-0.4414], requires_grad=True)]\n",
            "Python dictionary:  OrderedDict([('weight', tensor([[0.5153]])), ('bias', tensor([-0.4414]))])\n",
            "keys:  odict_keys(['weight', 'bias'])\n",
            "values:  odict_values([tensor([[0.5153]]), tensor([-0.4414])])\n",
            "The prediction:  tensor([[0.0739],\n",
            "        [0.5891]], grad_fn=<AddmmBackward>)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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        },
        "id": "_U0EXTC_OLTd",
        "outputId": "a026df08-c781-4419-e601-707ac72397e7"
      },
      "source": [
        "\r\n",
        "m = torch.nn.Linear(20, 30)\r\n",
        "input = torch.randn(128, 20)\r\n",
        "output = m(input)\r\n",
        "print(output.size())"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([128, 30])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {},
      "outputs": [
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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#gradient descent\n",
        "import torch\n",
        "w=torch.tensor(-10.0,requires_grad=True)\n",
        "x=torch.arange(-3,3,0.1).view(-1,1)\n",
        "f=-3*x\n",
        "#plt.plot(x.numpy(),f.numpy())\n",
        "#plt.show()\n",
        "y=f+0.1*torch.randn(x.size())\n",
        "#plt.plot(x.numpy(),y.numpy(),'r')\n",
        "#plt.show()\n",
        "cost=[]\n",
        "def forward(x):\n",
        "    return w*x\n",
        "def criteron(yhat,y):\n",
        "    return torch.mean((yhat-y)**2)\n",
        "\n",
        "lr=0.1\n",
        "for epoch in range(10):\n",
        "    yhat=forward(x)\n",
        "    loss=criteron(yhat,y)\n",
        "    loss.backward()\n",
        "    w.data=w.data-lr*w.grad.data\n",
        "    w.grad.data.zero_()\n",
        "    cost.append(loss.item())\n",
        "    #print(w)\n",
        "    #print(yhat)\n",
        "plt.plot(cost,'g')\n",
        "plt.show()\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#batch gradient descent\n",
        "import torch\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "w=torch.tensor(-10.0,requires_grad=True)\n",
        "b=torch.tensor(-10.0,requires_grad=True)\n",
        "x=torch.arange(-3,3,0.1).view(-1,1)\n",
        "f=-3*x+3\n",
        "plt.plot(x.numpy(),f.numpy())\n",
        "plt.show()\n",
        "y=f+0.3*torch.randn(x.size())\n",
        "plt.plot(x.numpy(),y.numpy(),'r')\n",
        "plt.show()\n",
        "cost=[]\n",
        "def forward(x,b):\n",
        "    return w*x+b\n",
        "def criteron(yhat,y):\n",
        "    return torch.mean((yhat-y)**2)\n",
        "\n",
        "lr=0.1\n",
        "for epoch in range(20):\n",
        "    yhat=forward(x,b)\n",
        "    loss=criteron(yhat,y)\n",
        "    loss.backward()\n",
        "    w.data=w.data-lr*w.grad.data\n",
        "    w.grad.data.zero_()\n",
        "    b.data=b.data-lr*b.grad.data\n",
        "    b.grad.data.zero_()\n",
        "    cost.append(loss.item())\n",
        "    #print(w)\n",
        "    #print(yhat)\n",
        "plt.plot(cost,'g')\n",
        "plt.grid()\n",
        "plt.show()\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(2.8485, requires_grad=True)\ntensor(-2.9731, requires_grad=True)\n"
          ]
        }
      ],
      "source": [
        "print(b)\n",
        "print(w)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# stochastic gradient descent basic implementation\n",
        "#check out implementation of dataloader set\n",
        "import torch\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "w=torch.tensor(-10.0,requires_grad=True)\n",
        "b=torch.tensor(-10.0,requires_grad=True)\n",
        "#view is used to reshape the data. Here row vector is converted to column vector.\n",
        "X=torch.arange(-3,3,0.1).view(-1,1)\n",
        "Y=-3*x+3+0.2*torch.randn(x.size())\n",
        "#print(y)\n",
        "cost=[]\n",
        "def forward(x,b):\n",
        "    return w*x+b\n",
        "def criteron(yhat,y):\n",
        "    return torch.mean((yhat-y)**2)\n",
        "\n",
        "lr=0.01\n",
        "for epoch in range(10):\n",
        "    total=0\n",
        "    for x,y in zip(X,Y):\n",
        "        yhat=forward(x,b)\n",
        "        loss=criteron(yhat,y)\n",
        "        loss.backward()\n",
        "        w.data=w.data-lr*w.grad.data\n",
        "        b.data=b.data-lr*b.grad.data\n",
        "        w.grad.data.zero_()\n",
        "        b.grad.data.zero_()\n",
        "        total+=loss.item()\n",
        "    cost.append(total)\n",
        "plt.plot(cost,'g')\n",
        "plt.grid()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[69.75442504882812, 69.75444030761719, 69.75442504882812, 69.75444030761719, 69.75442504882812, 69.75444030761719, 69.75442504882812, 69.75444030761719, 69.75442504882812, 69.75444030761719]\n"
          ]
        }
      ],
      "source": [
        "print(cost)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[1, 2],\n        [3, 4],\n        [5, 6]])\n"
          ]
        }
      ],
      "source": [
        "\n",
        "embedding = torch.nn.Embedding(10, 3)\n",
        "#print(embedding)\n",
        "#embedding(torch.tensor([1,2,3]))\n",
        "a=torch.tensor([[1,2,3],[4,5,6]])\n",
        "b=a.reshape(-1,2)\n",
        "print(b)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {},
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[1, 0, 0, 0, 0],\n",
              "        [0, 1, 0, 0, 0],\n",
              "        [0, 0, 1, 0, 0],\n",
              "        [0, 0, 0, 1, 0],\n",
              "        [0, 0, 0, 0, 1]])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "torch.nn.functional.one_hot(torch.arange(0,5),num_classes=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {},
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n",
              "        [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n",
              "        [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "target = torch.randint(0, 10, (3,))\n",
        "torch.nn.functional.one_hot(target,num_classes=11)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {},
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(0)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "x = torch.tensor([0.25,0.5,0.25])\n",
        "c = torch.distributions.categorical.Categorical(x)\n",
        "c.sample() # error"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[1, 2, 3, 4],\n        [5, 6, 7, 8]])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-1, -1, -1, -1],\n",
              "        [-1, -1, -1, -1]])"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ],
      "source": [
        "import  numpy as np\n",
        "#clamps all element in the range of .clamp(min,max)\n",
        "a=torch.tensor([[1,2,3,4],[5,6,7,8]])\n",
        "print(a)\n",
        "a.clamp(0,-1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([0, 0, 0, 0])\ntensor([[0.9534],\n        [0.3180],\n        [1.0476],\n        [0.7668]])\n"
          ]
        }
      ],
      "source": [
        "a = torch.randn(4, 1)\n",
        "print(torch.argmin(a,dim=-1))\n",
        "print(a)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[-0.2503, -1.0120],\n        [ 0.5515, -1.7247],\n        [-0.2516,  0.9143]])\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "a = torch.randn(2,3)\n",
        "print(torch.transpose(a,0,1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ]
}