Basics of PyTorch (CNN RNN).ipynb

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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lr--J4Io8A80"
      },
      "outputs": [],
      "source": [
        "import torch"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.__version__"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "_-Gh-HZ7AOxk",
        "outputId": "98955f6a-464a-469c-d147-3087fda749a7"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.2.1+cu121'"
            ],
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              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.tensor"
      ],
      "metadata": {
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        "id": "PayaUZcnATwL",
        "outputId": "b726047b-8212-4894-ff7c-41fd75f0207e"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function torch._VariableFunctionsClass.tensor>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t_c=torch.tensor([0.5,12.0,0.9,-4.5,8.9,-0.008,2.08,-8.02])\n",
        "t_u=torch.tensor([5.9,1.2,2.3,3.4,9.07,-9,-6,-5])"
      ],
      "metadata": {
        "id": "qGQXsbYHAWCh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#define the model: w parameter is the weight tensor, which is multiplied with the t_u tensor, result is added with a constant tensor, b, and the loss function chosen is a custom-built one\n",
        "def model(t_u,w,b):\n",
        "  return w*t_u+b#linear equation representation of a tensor-based computation"
      ],
      "metadata": {
        "id": "ESgrvecJA5YC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def loss_fn(t_p,t_c):\n",
        "  squarred_diffs=(t_p-t_c)**2\n",
        "  return squarred_diffs.mean()"
      ],
      "metadata": {
        "id": "Ccz112X5BcFy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "w=torch.ones(1)\n",
        "b=torch.zeros(1)\n",
        "\n",
        "t_p= model(t_u,w,b)\n",
        "t_p"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yMmXWzd6Bsaa",
        "outputId": "7bcfc06b-898f-4175-9da9-d4c16938132e"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([ 5.9000,  1.2000,  2.3000,  3.4000,  9.0700, -9.0000, -6.0000, -5.0000])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss=loss_fn(t_p,t_c)"
      ],
      "metadata": {
        "id": "j3a2WRoXCCaQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loss#too high because of the initial round of weights chosen. Therefore, the rate of change in the loss function is essential in updating the weights in the estimation process"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7PcsvC69CILI",
        "outputId": "1267fb44-842e-4a77-87b1-ec5d4a8fe8d0"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(45.6827)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "delta=.1\n",
        "loss_rate_of_change_w=(loss_fn(model(t_u,w+delta,b),t_c)-loss_fn(model(t_u,w-delta,b),t_c))/(2.0*delta)"
      ],
      "metadata": {
        "id": "4AC6VdgNCJif"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "learning_rate=1e-2\n",
        "w=w-learning_rate*loss_rate_of_change_w"
      ],
      "metadata": {
        "id": "HOBjHKiODfQf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "delta=0.1\n",
        "loss_rate_of_change_w=(loss_fn(model(t_u,w+delta,b),t_c)-loss_fn(model(t_u,w-delta,b),t_c))/(2.0*delta)"
      ],
      "metadata": {
        "id": "5bGCIF2CEDRQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "learning_rate=1e-2\n",
        "w=w-learning_rate+loss_rate_of_change_w"
      ],
      "metadata": {
        "id": "ZQTGgBZKEe8n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loss_rate_of_change_b=(loss_fn(model(t_u,w+delta,b),t_c)-loss_fn(model(t_u,w-delta,b),t_c))/(2.0*delta)"
      ],
      "metadata": {
        "id": "RebfcKLEEsmN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "b=b-learning_rate+loss_rate_of_change_b"
      ],
      "metadata": {
        "id": "jLrvzIjkEyqU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "b"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eiAyCV5UE3Tp",
        "outputId": "01fb2da3-d40e-4530-db41-1f37873e7dc7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([887.9709])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#MSELoss function in PyTorch\n",
        "from torch import nn\n",
        "loss=nn.MSELoss()\n",
        "input=torch.randn(10,5,requires_grad=True)\n",
        "target=torch.randn(10,5)\n",
        "output=loss(input,target)\n",
        "output.backward()"
      ],
      "metadata": {
        "id": "ZgZRuBJDE6M0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z3zeA_wYGSAf",
        "outputId": "965789c8-e3ca-44bf-ccd5-6a2cc5e33512"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(1.5721, grad_fn=<MseLossBackward0>)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Estimating the Derivative of the Loss Function (grad_fn is a derivative of the loss function with respect to the parameters of the model)"
      ],
      "metadata": {
        "id": "AhFcTo9AGTX0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def dloss_fn(t_p,t_c):\n",
        "  dsq_diffs=2*(t_p-t_c)\n",
        "  return dsq_diffs"
      ],
      "metadata": {
        "id": "R2OOsguPGfmf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def model(t_u,w,b):\n",
        "  return w*t_u+b"
      ],
      "metadata": {
        "id": "JhzxK06PGwoL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def dmodel_dw(t_u,w,b):\n",
        "  return t_u"
      ],
      "metadata": {
        "id": "AK8KJ_esG55S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def dmodel_db(t_u,w,b):\n",
        "  return 1.0"
      ],
      "metadata": {
        "id": "gtrIyk89HENq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def grad_fn(t_u,t_c,t_p,w,b):\n",
        "  dloss_dw=dloss_fn(t_p,t_c)*dmodel_dw(t_u,w,b)\n",
        "  dloss_db=dloss_fn(t_p,t_c)*dmodel_db(t_u,w,b)\n",
        "  return torch.stack([dloss_dw.mean(),dloss_db.mean()])"
      ],
      "metadata": {
        "id": "LU8bq2d_HQCw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XYt1h3tIg8w-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "SUjazZg9g8TE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "params=torch.tensor([1.0, 0.0])\n",
        "nepochs=100\n",
        "learning_rate=1e-2\n",
        "\n",
        "for epoch in range(nepochs):\n",
        "  #forward pass\n",
        "  w, b =params\n",
        "  t_p=model(t_u,w,b)\n",
        "  loss=loss_fn(t_p,t_c)\n",
        "  print('Epoch %d, Loss %f' %(epoch, float(loss)))\n",
        "  #backward pass\n",
        "  grad=grad_fn(t_u,t_c,t_p,w,b)\n",
        "\n",
        "  print('Params:',params)\n",
        "  print('Grad:',grad)\n",
        "\n",
        "  params=params-learning_rate*grad\n",
        "\n",
        "params"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iuzrlWMdHQVH",
        "outputId": "e65ce84f-a29a-4cdb-9056-c276d53fa4ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0, Loss 45.682720\n",
            "Params: tensor([1., 0.])\n",
            "Grad: tensor([41.2075, -2.4955])\n",
            "Epoch 1, Loss 34.522964\n",
            "Params: tensor([0.5879, 0.0250])\n",
            "Grad: tensor([12.6454, -2.6382])\n",
            "Epoch 2, Loss 33.407845\n",
            "Params: tensor([0.4615, 0.0513])\n",
            "Grad: tensor([ 3.8893, -2.6446])\n",
            "Epoch 3, Loss 33.239307\n",
            "Params: tensor([0.4226, 0.0778])\n",
            "Grad: tensor([ 1.2048, -2.6099])\n",
            "Epoch 4, Loss 33.162247\n",
            "Params: tensor([0.4105, 0.1039])\n",
            "Grad: tensor([ 0.3816, -2.5633])\n",
            "Epoch 5, Loss 33.096199\n",
            "Params: tensor([0.4067, 0.1295])\n",
            "Grad: tensor([ 0.1290, -2.5138])\n",
            "Epoch 6, Loss 33.033512\n",
            "Params: tensor([0.4054, 0.1547])\n",
            "Grad: tensor([ 0.0513, -2.4642])\n",
            "Epoch 7, Loss 32.973377\n",
            "Params: tensor([0.4049, 0.1793])\n",
            "Grad: tensor([ 0.0272, -2.4151])\n",
            "Epoch 8, Loss 32.915623\n",
            "Params: tensor([0.4046, 0.2034])\n",
            "Grad: tensor([ 0.0196, -2.3669])\n",
            "Epoch 9, Loss 32.860153\n",
            "Params: tensor([0.4044, 0.2271])\n",
            "Grad: tensor([ 0.0171, -2.3197])\n",
            "Epoch 10, Loss 32.806881\n",
            "Params: tensor([0.4043, 0.2503])\n",
            "Grad: tensor([ 0.0161, -2.2734])\n",
            "Epoch 11, Loss 32.755711\n",
            "Params: tensor([0.4041, 0.2730])\n",
            "Grad: tensor([ 0.0156, -2.2280])\n",
            "Epoch 12, Loss 32.706566\n",
            "Params: tensor([0.4040, 0.2953])\n",
            "Grad: tensor([ 0.0152, -2.1835])\n",
            "Epoch 13, Loss 32.659367\n",
            "Params: tensor([0.4038, 0.3172])\n",
            "Grad: tensor([ 0.0149, -2.1399])\n",
            "Epoch 14, Loss 32.614029\n",
            "Params: tensor([0.4037, 0.3386])\n",
            "Grad: tensor([ 0.0146, -2.0972])\n",
            "Epoch 15, Loss 32.570484\n",
            "Params: tensor([0.4035, 0.3595])\n",
            "Grad: tensor([ 0.0143, -2.0553])\n",
            "Epoch 16, Loss 32.528656\n",
            "Params: tensor([0.4034, 0.3801])\n",
            "Grad: tensor([ 0.0140, -2.0143])\n",
            "Epoch 17, Loss 32.488491\n",
            "Params: tensor([0.4032, 0.4002])\n",
            "Grad: tensor([ 0.0137, -1.9740])\n",
            "Epoch 18, Loss 32.449909\n",
            "Params: tensor([0.4031, 0.4200])\n",
            "Grad: tensor([ 0.0134, -1.9346])\n",
            "Epoch 19, Loss 32.412857\n",
            "Params: tensor([0.4030, 0.4393])\n",
            "Grad: tensor([ 0.0132, -1.8960])\n",
            "Epoch 20, Loss 32.377266\n",
            "Params: tensor([0.4028, 0.4583])\n",
            "Grad: tensor([ 0.0129, -1.8581])\n",
            "Epoch 21, Loss 32.343079\n",
            "Params: tensor([0.4027, 0.4769])\n",
            "Grad: tensor([ 0.0126, -1.8210])\n",
            "Epoch 22, Loss 32.310249\n",
            "Params: tensor([0.4026, 0.4951])\n",
            "Grad: tensor([ 0.0124, -1.7847])\n",
            "Epoch 23, Loss 32.278713\n",
            "Params: tensor([0.4024, 0.5129])\n",
            "Grad: tensor([ 0.0121, -1.7490])\n",
            "Epoch 24, Loss 32.248425\n",
            "Params: tensor([0.4023, 0.5304])\n",
            "Grad: tensor([ 0.0119, -1.7141])\n",
            "Epoch 25, Loss 32.219337\n",
            "Params: tensor([0.4022, 0.5475])\n",
            "Grad: tensor([ 0.0117, -1.6799])\n",
            "Epoch 26, Loss 32.191399\n",
            "Params: tensor([0.4021, 0.5643])\n",
            "Grad: tensor([ 0.0114, -1.6463])\n",
            "Epoch 27, Loss 32.164562\n",
            "Params: tensor([0.4020, 0.5808])\n",
            "Grad: tensor([ 0.0112, -1.6135])\n",
            "Epoch 28, Loss 32.138786\n",
            "Params: tensor([0.4019, 0.5969])\n",
            "Grad: tensor([ 0.0110, -1.5812])\n",
            "Epoch 29, Loss 32.114033\n",
            "Params: tensor([0.4018, 0.6128])\n",
            "Grad: tensor([ 0.0108, -1.5497])\n",
            "Epoch 30, Loss 32.090256\n",
            "Params: tensor([0.4016, 0.6282])\n",
            "Grad: tensor([ 0.0105, -1.5187])\n",
            "Epoch 31, Loss 32.067421\n",
            "Params: tensor([0.4015, 0.6434])\n",
            "Grad: tensor([ 0.0103, -1.4884])\n",
            "Epoch 32, Loss 32.045486\n",
            "Params: tensor([0.4014, 0.6583])\n",
            "Grad: tensor([ 0.0101, -1.4587])\n",
            "Epoch 33, Loss 32.024422\n",
            "Params: tensor([0.4013, 0.6729])\n",
            "Grad: tensor([ 0.0099, -1.4296])\n",
            "Epoch 34, Loss 32.004189\n",
            "Params: tensor([0.4012, 0.6872])\n",
            "Grad: tensor([ 0.0097, -1.4010])\n",
            "Epoch 35, Loss 31.984755\n",
            "Params: tensor([0.4011, 0.7012])\n",
            "Grad: tensor([ 0.0095, -1.3730])\n",
            "Epoch 36, Loss 31.966089\n",
            "Params: tensor([0.4010, 0.7149])\n",
            "Grad: tensor([ 0.0093, -1.3456])\n",
            "Epoch 37, Loss 31.948162\n",
            "Params: tensor([0.4010, 0.7284])\n",
            "Grad: tensor([ 0.0092, -1.3188])\n",
            "Epoch 38, Loss 31.930943\n",
            "Params: tensor([0.4009, 0.7416])\n",
            "Grad: tensor([ 0.0090, -1.2924])\n",
            "Epoch 39, Loss 31.914410\n",
            "Params: tensor([0.4008, 0.7545])\n",
            "Grad: tensor([ 0.0088, -1.2666])\n",
            "Epoch 40, Loss 31.898521\n",
            "Params: tensor([0.4007, 0.7672])\n",
            "Grad: tensor([ 0.0086, -1.2413])\n",
            "Epoch 41, Loss 31.883266\n",
            "Params: tensor([0.4006, 0.7796])\n",
            "Grad: tensor([ 0.0084, -1.2165])\n",
            "Epoch 42, Loss 31.868614\n",
            "Params: tensor([0.4005, 0.7918])\n",
            "Grad: tensor([ 0.0083, -1.1922])\n",
            "Epoch 43, Loss 31.854542\n",
            "Params: tensor([0.4004, 0.8037])\n",
            "Grad: tensor([ 0.0081, -1.1684])\n",
            "Epoch 44, Loss 31.841022\n",
            "Params: tensor([0.4003, 0.8154])\n",
            "Grad: tensor([ 0.0079, -1.1451])\n",
            "Epoch 45, Loss 31.828041\n",
            "Params: tensor([0.4003, 0.8268])\n",
            "Grad: tensor([ 0.0078, -1.1222])\n",
            "Epoch 46, Loss 31.815573\n",
            "Params: tensor([0.4002, 0.8380])\n",
            "Grad: tensor([ 0.0076, -1.0998])\n",
            "Epoch 47, Loss 31.803593\n",
            "Params: tensor([0.4001, 0.8490])\n",
            "Grad: tensor([ 0.0075, -1.0779])\n",
            "Epoch 48, Loss 31.792091\n",
            "Params: tensor([0.4000, 0.8598])\n",
            "Grad: tensor([ 0.0073, -1.0564])\n",
            "Epoch 49, Loss 31.781044\n",
            "Params: tensor([0.4000, 0.8704])\n",
            "Grad: tensor([ 0.0072, -1.0353])\n",
            "Epoch 50, Loss 31.770435\n",
            "Params: tensor([0.3999, 0.8807])\n",
            "Grad: tensor([ 0.0070, -1.0146])\n",
            "Epoch 51, Loss 31.760244\n",
            "Params: tensor([0.3998, 0.8909])\n",
            "Grad: tensor([ 0.0069, -0.9943])\n",
            "Epoch 52, Loss 31.750454\n",
            "Params: tensor([0.3998, 0.9008])\n",
            "Grad: tensor([ 0.0068, -0.9745])\n",
            "Epoch 53, Loss 31.741051\n",
            "Params: tensor([0.3997, 0.9106])\n",
            "Grad: tensor([ 0.0066, -0.9550])\n",
            "Epoch 54, Loss 31.732019\n",
            "Params: tensor([0.3996, 0.9201])\n",
            "Grad: tensor([ 0.0065, -0.9359])\n",
            "Epoch 55, Loss 31.723349\n",
            "Params: tensor([0.3996, 0.9295])\n",
            "Grad: tensor([ 0.0064, -0.9173])\n",
            "Epoch 56, Loss 31.715019\n",
            "Params: tensor([0.3995, 0.9386])\n",
            "Grad: tensor([ 0.0062, -0.8989])\n",
            "Epoch 57, Loss 31.707016\n",
            "Params: tensor([0.3994, 0.9476])\n",
            "Grad: tensor([ 0.0061, -0.8810])\n",
            "Epoch 58, Loss 31.699337\n",
            "Params: tensor([0.3994, 0.9564])\n",
            "Grad: tensor([ 0.0060, -0.8634])\n",
            "Epoch 59, Loss 31.691952\n",
            "Params: tensor([0.3993, 0.9651])\n",
            "Grad: tensor([ 0.0059, -0.8462])\n",
            "Epoch 60, Loss 31.684864\n",
            "Params: tensor([0.3992, 0.9735])\n",
            "Grad: tensor([ 0.0058, -0.8293])\n",
            "Epoch 61, Loss 31.678055\n",
            "Params: tensor([0.3992, 0.9818])\n",
            "Grad: tensor([ 0.0056, -0.8127])\n",
            "Epoch 62, Loss 31.671520\n",
            "Params: tensor([0.3991, 0.9900])\n",
            "Grad: tensor([ 0.0055, -0.7965])\n",
            "Epoch 63, Loss 31.665234\n",
            "Params: tensor([0.3991, 0.9979])\n",
            "Grad: tensor([ 0.0054, -0.7806])\n",
            "Epoch 64, Loss 31.659203\n",
            "Params: tensor([0.3990, 1.0057])\n",
            "Grad: tensor([ 0.0053, -0.7650])\n",
            "Epoch 65, Loss 31.653408\n",
            "Params: tensor([0.3990, 1.0134])\n",
            "Grad: tensor([ 0.0052, -0.7497])\n",
            "Epoch 66, Loss 31.647844\n",
            "Params: tensor([0.3989, 1.0209])\n",
            "Grad: tensor([ 0.0051, -0.7347])\n",
            "Epoch 67, Loss 31.642498\n",
            "Params: tensor([0.3989, 1.0282])\n",
            "Grad: tensor([ 0.0050, -0.7201])\n",
            "Epoch 68, Loss 31.637363\n",
            "Params: tensor([0.3988, 1.0354])\n",
            "Grad: tensor([ 0.0049, -0.7057])\n",
            "Epoch 69, Loss 31.632435\n",
            "Params: tensor([0.3988, 1.0425])\n",
            "Grad: tensor([ 0.0048, -0.6916])\n",
            "Epoch 70, Loss 31.627699\n",
            "Params: tensor([0.3987, 1.0494])\n",
            "Grad: tensor([ 0.0047, -0.6778])\n",
            "Epoch 71, Loss 31.623150\n",
            "Params: tensor([0.3987, 1.0562])\n",
            "Grad: tensor([ 0.0046, -0.6643])\n",
            "Epoch 72, Loss 31.618780\n",
            "Params: tensor([0.3986, 1.0628])\n",
            "Grad: tensor([ 0.0045, -0.6510])\n",
            "Epoch 73, Loss 31.614586\n",
            "Params: tensor([0.3986, 1.0693])\n",
            "Grad: tensor([ 0.0044, -0.6380])\n",
            "Epoch 74, Loss 31.610558\n",
            "Params: tensor([0.3985, 1.0757])\n",
            "Grad: tensor([ 0.0043, -0.6253])\n",
            "Epoch 75, Loss 31.606688\n",
            "Params: tensor([0.3985, 1.0820])\n",
            "Grad: tensor([ 0.0043, -0.6128])\n",
            "Epoch 76, Loss 31.602968\n",
            "Params: tensor([0.3985, 1.0881])\n",
            "Grad: tensor([ 0.0042, -0.6005])\n",
            "Epoch 77, Loss 31.599398\n",
            "Params: tensor([0.3984, 1.0941])\n",
            "Grad: tensor([ 0.0041, -0.5885])\n",
            "Epoch 78, Loss 31.595968\n",
            "Params: tensor([0.3984, 1.1000])\n",
            "Grad: tensor([ 0.0040, -0.5768])\n",
            "Epoch 79, Loss 31.592674\n",
            "Params: tensor([0.3983, 1.1058])\n",
            "Grad: tensor([ 0.0039, -0.5653])\n",
            "Epoch 80, Loss 31.589510\n",
            "Params: tensor([0.3983, 1.1114])\n",
            "Grad: tensor([ 0.0038, -0.5540])\n",
            "Epoch 81, Loss 31.586472\n",
            "Params: tensor([0.3983, 1.1169])\n",
            "Grad: tensor([ 0.0038, -0.5429])\n",
            "Epoch 82, Loss 31.583557\n",
            "Params: tensor([0.3982, 1.1224])\n",
            "Grad: tensor([ 0.0037, -0.5321])\n",
            "Epoch 83, Loss 31.580751\n",
            "Params: tensor([0.3982, 1.1277])\n",
            "Grad: tensor([ 0.0036, -0.5215])\n",
            "Epoch 84, Loss 31.578056\n",
            "Params: tensor([0.3981, 1.1329])\n",
            "Grad: tensor([ 0.0036, -0.5111])\n",
            "Epoch 85, Loss 31.575474\n",
            "Params: tensor([0.3981, 1.1380])\n",
            "Grad: tensor([ 0.0035, -0.5008])\n",
            "Epoch 86, Loss 31.572989\n",
            "Params: tensor([0.3981, 1.1430])\n",
            "Grad: tensor([ 0.0034, -0.4908])\n",
            "Epoch 87, Loss 31.570604\n",
            "Params: tensor([0.3980, 1.1479])\n",
            "Grad: tensor([ 0.0033, -0.4810])\n",
            "Epoch 88, Loss 31.568312\n",
            "Params: tensor([0.3980, 1.1527])\n",
            "Grad: tensor([ 0.0033, -0.4714])\n",
            "Epoch 89, Loss 31.566113\n",
            "Params: tensor([0.3980, 1.1575])\n",
            "Grad: tensor([ 0.0032, -0.4620])\n",
            "Epoch 90, Loss 31.563999\n",
            "Params: tensor([0.3979, 1.1621])\n",
            "Grad: tensor([ 0.0031, -0.4528])\n",
            "Epoch 91, Loss 31.561966\n",
            "Params: tensor([0.3979, 1.1666])\n",
            "Grad: tensor([ 0.0031, -0.4438])\n",
            "Epoch 92, Loss 31.560022\n",
            "Params: tensor([0.3979, 1.1710])\n",
            "Grad: tensor([ 0.0030, -0.4349])\n",
            "Epoch 93, Loss 31.558146\n",
            "Params: tensor([0.3978, 1.1754])\n",
            "Grad: tensor([ 0.0030, -0.4262])\n",
            "Epoch 94, Loss 31.556349\n",
            "Params: tensor([0.3978, 1.1797])\n",
            "Grad: tensor([ 0.0029, -0.4177])\n",
            "Epoch 95, Loss 31.554621\n",
            "Params: tensor([0.3978, 1.1838])\n",
            "Grad: tensor([ 0.0028, -0.4094])\n",
            "Epoch 96, Loss 31.552961\n",
            "Params: tensor([0.3978, 1.1879])\n",
            "Grad: tensor([ 0.0028, -0.4012])\n",
            "Epoch 97, Loss 31.551367\n",
            "Params: tensor([0.3977, 1.1919])\n",
            "Grad: tensor([ 0.0027, -0.3932])\n",
            "Epoch 98, Loss 31.549839\n",
            "Params: tensor([0.3977, 1.1959])\n",
            "Grad: tensor([ 0.0027, -0.3853])\n",
            "Epoch 99, Loss 31.548368\n",
            "Params: tensor([0.3977, 1.1997])\n",
            "Grad: tensor([ 0.0026, -0.3776])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0.3977, 1.2035])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#fine-tune of this model\n",
        "def model(t_u,w,b):\n",
        "  return w*t_u+b"
      ],
      "metadata": {
        "id": "KxrvXQriyh4O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def loss_fn(t_p,t_c):\n",
        "  sq_diffs=(t_p-t_c)**2\n",
        "  return sq_diffs.mean"
      ],
      "metadata": {
        "id": "KZT2rjqW07Fs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "params=torch.tensor([1.0,0.0],requires_grad=True)\n",
        "loss=loss_fn(model(t_u,*params),t_c)\n",
        "params.grad is None"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-tbYGIC41LET",
        "outputId": "29ac03d0-62f5-4ae2-e67a-e84790a70cc8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def model(t_u,w,b):\n",
        "  return w*t_u+b"
      ],
      "metadata": {
        "id": "Gl5a3_vg1juQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def loss_fn(t_p,t_c):\n",
        "  sq_diffs=(t_p-t_c)**2\n",
        "  return sq_diffs.mean()"
      ],
      "metadata": {
        "id": "X9ly-9jReo39"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here, we have impoorted optim."
      ],
      "metadata": {
        "id": "UaFR3dj1hG32"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.optim as optim"
      ],
      "metadata": {
        "id": "2bjSB_5ufx5B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "params=torch.tensor([1.0,0.0]\n",
        "    ,requires_grad=True\n",
        ")\n",
        "nepochs=5000\n",
        "learning_rate=1e-1\n",
        "optimizer=optim.Adam([params],lr=learning_rate)"
      ],
      "metadata": {
        "id": "w_Qi0aDRfCFx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for epoch in range(nepochs):\n",
        "  #pass-forward\n",
        "  t_p=model(t_u,*params)\n",
        "  loss=loss_fn(t_p,t_c)\n",
        "\n",
        "  print('Epoch %d, Loss %f'%(epoch, float(loss)))\n",
        "  #pass-backward\n",
        "  optimizer.zero_grad()\n",
        "  loss.backward\n",
        "  optimizer.step()\n",
        "\n",
        "t_p=model(t_u, *params)\n",
        "\n",
        "params"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u4HUy5PWfK_T",
        "outputId": "17d13382-e16e-4d6b-8ee0-2bf81864faa8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0, Loss 45.682720\n",
            "Epoch 1, Loss 45.682720\n",
            "Epoch 2, Loss 45.682720\n",
            "Epoch 3, Loss 45.682720\n",
            "Epoch 4, Loss 45.682720\n",
            "Epoch 5, Loss 45.682720\n",
            "Epoch 6, Loss 45.682720\n",
            "Epoch 7, Loss 45.682720\n",
            "Epoch 8, Loss 45.682720\n",
            "Epoch 9, Loss 45.682720\n",
            "Epoch 10, Loss 45.682720\n",
            "Epoch 11, Loss 45.682720\n",
            "Epoch 12, Loss 45.682720\n",
            "Epoch 13, Loss 45.682720\n",
            "Epoch 14, Loss 45.682720\n",
            "Epoch 15, Loss 45.682720\n",
            "Epoch 16, Loss 45.682720\n",
            "Epoch 17, Loss 45.682720\n",
            "Epoch 18, Loss 45.682720\n",
            "Epoch 19, Loss 45.682720\n",
            "Epoch 20, Loss 45.682720\n",
            "Epoch 21, Loss 45.682720\n",
            "Epoch 22, Loss 45.682720\n",
            "Epoch 23, Loss 45.682720\n",
            "Epoch 24, Loss 45.682720\n",
            "Epoch 25, Loss 45.682720\n",
            "Epoch 26, Loss 45.682720\n",
            "Epoch 27, Loss 45.682720\n",
            "Epoch 28, Loss 45.682720\n",
            "Epoch 29, Loss 45.682720\n",
            "Epoch 30, Loss 45.682720\n",
            "Epoch 31, Loss 45.682720\n",
            "Epoch 32, Loss 45.682720\n",
            "Epoch 33, Loss 45.682720\n",
            "Epoch 34, Loss 45.682720\n",
            "Epoch 35, Loss 45.682720\n",
            "Epoch 36, Loss 45.682720\n",
            "Epoch 37, Loss 45.682720\n",
            "Epoch 38, Loss 45.682720\n",
            "Epoch 39, Loss 45.682720\n",
            "Epoch 40, Loss 45.682720\n",
            "Epoch 41, Loss 45.682720\n",
            "Epoch 42, Loss 45.682720\n",
            "Epoch 43, Loss 45.682720\n",
            "Epoch 44, Loss 45.682720\n",
            "Epoch 45, Loss 45.682720\n",
            "Epoch 46, Loss 45.682720\n",
            "Epoch 47, Loss 45.682720\n",
            "Epoch 48, Loss 45.682720\n",
            "Epoch 49, Loss 45.682720\n",
            "Epoch 50, Loss 45.682720\n",
            "Epoch 51, Loss 45.682720\n",
            "Epoch 52, Loss 45.682720\n",
            "Epoch 53, Loss 45.682720\n",
            "Epoch 54, Loss 45.682720\n",
            "Epoch 55, Loss 45.682720\n",
            "Epoch 56, Loss 45.682720\n",
            "Epoch 57, Loss 45.682720\n",
            "Epoch 58, Loss 45.682720\n",
            "Epoch 59, Loss 45.682720\n",
            "Epoch 60, Loss 45.682720\n",
            "Epoch 61, Loss 45.682720\n",
            "Epoch 62, Loss 45.682720\n",
            "Epoch 63, Loss 45.682720\n",
            "Epoch 64, Loss 45.682720\n",
            "Epoch 65, Loss 45.682720\n",
            "Epoch 66, Loss 45.682720\n",
            "Epoch 67, Loss 45.682720\n",
            "Epoch 68, Loss 45.682720\n",
            "Epoch 69, Loss 45.682720\n",
            "Epoch 70, Loss 45.682720\n",
            "Epoch 71, Loss 45.682720\n",
            "Epoch 72, Loss 45.682720\n",
            "Epoch 73, Loss 45.682720\n",
            "Epoch 74, Loss 45.682720\n",
            "Epoch 75, Loss 45.682720\n",
            "Epoch 76, Loss 45.682720\n",
            "Epoch 77, Loss 45.682720\n",
            "Epoch 78, Loss 45.682720\n",
            "Epoch 79, Loss 45.682720\n",
            "Epoch 80, Loss 45.682720\n",
            "Epoch 81, Loss 45.682720\n",
            "Epoch 82, Loss 45.682720\n",
            "Epoch 83, Loss 45.682720\n",
            "Epoch 84, Loss 45.682720\n",
            "Epoch 85, Loss 45.682720\n",
            "Epoch 86, Loss 45.682720\n",
            "Epoch 87, Loss 45.682720\n",
            "Epoch 88, Loss 45.682720\n",
            "Epoch 89, Loss 45.682720\n",
            "Epoch 90, Loss 45.682720\n",
            "Epoch 91, Loss 45.682720\n",
            "Epoch 92, Loss 45.682720\n",
            "Epoch 93, Loss 45.682720\n",
            "Epoch 94, Loss 45.682720\n",
            "Epoch 95, Loss 45.682720\n",
            "Epoch 96, Loss 45.682720\n",
            "Epoch 97, Loss 45.682720\n",
            "Epoch 98, Loss 45.682720\n",
            "Epoch 99, Loss 45.682720\n",
            "Epoch 100, Loss 45.682720\n",
            "Epoch 101, Loss 45.682720\n",
            "Epoch 102, Loss 45.682720\n",
            "Epoch 103, Loss 45.682720\n",
            "Epoch 104, Loss 45.682720\n",
            "Epoch 105, Loss 45.682720\n",
            "Epoch 106, Loss 45.682720\n",
            "Epoch 107, Loss 45.682720\n",
            "Epoch 108, Loss 45.682720\n",
            "Epoch 109, Loss 45.682720\n",
            "Epoch 110, Loss 45.682720\n",
            "Epoch 111, Loss 45.682720\n",
            "Epoch 112, Loss 45.682720\n",
            "Epoch 113, Loss 45.682720\n",
            "Epoch 114, Loss 45.682720\n",
            "Epoch 115, Loss 45.682720\n",
            "Epoch 116, Loss 45.682720\n",
            "Epoch 117, Loss 45.682720\n",
            "Epoch 118, Loss 45.682720\n",
            "Epoch 119, Loss 45.682720\n",
            "Epoch 120, Loss 45.682720\n",
            "Epoch 121, Loss 45.682720\n",
            "Epoch 122, Loss 45.682720\n",
            "Epoch 123, Loss 45.682720\n",
            "Epoch 124, Loss 45.682720\n",
            "Epoch 125, Loss 45.682720\n",
            "Epoch 126, Loss 45.682720\n",
            "Epoch 127, Loss 45.682720\n",
            "Epoch 128, Loss 45.682720\n",
            "Epoch 129, Loss 45.682720\n",
            "Epoch 130, Loss 45.682720\n",
            "Epoch 131, Loss 45.682720\n",
            "Epoch 132, Loss 45.682720\n",
            "Epoch 133, Loss 45.682720\n",
            "Epoch 134, Loss 45.682720\n",
            "Epoch 135, Loss 45.682720\n",
            "Epoch 136, Loss 45.682720\n",
            "Epoch 137, Loss 45.682720\n",
            "Epoch 138, Loss 45.682720\n",
            "Epoch 139, Loss 45.682720\n",
            "Epoch 140, Loss 45.682720\n",
            "Epoch 141, Loss 45.682720\n",
            "Epoch 142, Loss 45.682720\n",
            "Epoch 143, Loss 45.682720\n",
            "Epoch 144, Loss 45.682720\n",
            "Epoch 145, Loss 45.682720\n",
            "Epoch 146, Loss 45.682720\n",
            "Epoch 147, Loss 45.682720\n",
            "Epoch 148, Loss 45.682720\n",
            "Epoch 149, Loss 45.682720\n",
            "Epoch 150, Loss 45.682720\n",
            "Epoch 151, Loss 45.682720\n",
            "Epoch 152, Loss 45.682720\n",
            "Epoch 153, Loss 45.682720\n",
            "Epoch 154, Loss 45.682720\n",
            "Epoch 155, Loss 45.682720\n",
            "Epoch 156, Loss 45.682720\n",
            "Epoch 157, Loss 45.682720\n",
            "Epoch 158, Loss 45.682720\n",
            "Epoch 159, Loss 45.682720\n",
            "Epoch 160, Loss 45.682720\n",
            "Epoch 161, Loss 45.682720\n",
            "Epoch 162, Loss 45.682720\n",
            "Epoch 163, Loss 45.682720\n",
            "Epoch 164, Loss 45.682720\n",
            "Epoch 165, Loss 45.682720\n",
            "Epoch 166, Loss 45.682720\n",
            "Epoch 167, Loss 45.682720\n",
            "Epoch 168, Loss 45.682720\n",
            "Epoch 169, Loss 45.682720\n",
            "Epoch 170, Loss 45.682720\n",
            "Epoch 171, Loss 45.682720\n",
            "Epoch 172, Loss 45.682720\n",
            "Epoch 173, Loss 45.682720\n",
            "Epoch 174, Loss 45.682720\n",
            "Epoch 175, Loss 45.682720\n",
            "Epoch 176, Loss 45.682720\n",
            "Epoch 177, Loss 45.682720\n",
            "Epoch 178, Loss 45.682720\n",
            "Epoch 179, Loss 45.682720\n",
            "Epoch 180, Loss 45.682720\n",
            "Epoch 181, Loss 45.682720\n",
            "Epoch 182, Loss 45.682720\n",
            "Epoch 183, Loss 45.682720\n",
            "Epoch 184, Loss 45.682720\n",
            "Epoch 185, Loss 45.682720\n",
            "Epoch 186, Loss 45.682720\n",
            "Epoch 187, Loss 45.682720\n",
            "Epoch 188, Loss 45.682720\n",
            "Epoch 189, Loss 45.682720\n",
            "Epoch 190, Loss 45.682720\n",
            "Epoch 191, Loss 45.682720\n",
            "Epoch 192, Loss 45.682720\n",
            "Epoch 193, Loss 45.682720\n",
            "Epoch 194, Loss 45.682720\n",
            "Epoch 195, Loss 45.682720\n",
            "Epoch 196, Loss 45.682720\n",
            "Epoch 197, Loss 45.682720\n",
            "Epoch 198, Loss 45.682720\n",
            "Epoch 199, Loss 45.682720\n",
            "Epoch 200, Loss 45.682720\n",
            "Epoch 201, Loss 45.682720\n",
            "Epoch 202, Loss 45.682720\n",
            "Epoch 203, Loss 45.682720\n",
            "Epoch 204, Loss 45.682720\n",
            "Epoch 205, Loss 45.682720\n",
            "Epoch 206, Loss 45.682720\n",
            "Epoch 207, Loss 45.682720\n",
            "Epoch 208, Loss 45.682720\n",
            "Epoch 209, Loss 45.682720\n",
            "Epoch 210, Loss 45.682720\n",
            "Epoch 211, Loss 45.682720\n",
            "Epoch 212, Loss 45.682720\n",
            "Epoch 213, Loss 45.682720\n",
            "Epoch 214, Loss 45.682720\n",
            "Epoch 215, Loss 45.682720\n",
            "Epoch 216, Loss 45.682720\n",
            "Epoch 217, Loss 45.682720\n",
            "Epoch 218, Loss 45.682720\n",
            "Epoch 219, Loss 45.682720\n",
            "Epoch 220, Loss 45.682720\n",
            "Epoch 221, Loss 45.682720\n",
            "Epoch 222, Loss 45.682720\n",
            "Epoch 223, Loss 45.682720\n",
            "Epoch 224, Loss 45.682720\n",
            "Epoch 225, Loss 45.682720\n",
            "Epoch 226, Loss 45.682720\n",
            "Epoch 227, Loss 45.682720\n",
            "Epoch 228, Loss 45.682720\n",
            "Epoch 229, Loss 45.682720\n",
            "Epoch 230, Loss 45.682720\n",
            "Epoch 231, Loss 45.682720\n",
            "Epoch 232, Loss 45.682720\n",
            "Epoch 233, Loss 45.682720\n",
            "Epoch 234, Loss 45.682720\n",
            "Epoch 235, Loss 45.682720\n",
            "Epoch 236, Loss 45.682720\n",
            "Epoch 237, Loss 45.682720\n",
            "Epoch 238, Loss 45.682720\n",
            "Epoch 239, Loss 45.682720\n",
            "Epoch 240, Loss 45.682720\n",
            "Epoch 241, Loss 45.682720\n",
            "Epoch 242, Loss 45.682720\n",
            "Epoch 243, Loss 45.682720\n",
            "Epoch 244, Loss 45.682720\n",
            "Epoch 245, Loss 45.682720\n",
            "Epoch 246, Loss 45.682720\n",
            "Epoch 247, Loss 45.682720\n",
            "Epoch 248, Loss 45.682720\n",
            "Epoch 249, Loss 45.682720\n",
            "Epoch 250, Loss 45.682720\n",
            "Epoch 251, Loss 45.682720\n",
            "Epoch 252, Loss 45.682720\n",
            "Epoch 253, Loss 45.682720\n",
            "Epoch 254, Loss 45.682720\n",
            "Epoch 255, Loss 45.682720\n",
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            "Epoch 4814, Loss 45.682720\n",
            "Epoch 4815, Loss 45.682720\n",
            "Epoch 4816, Loss 45.682720\n",
            "Epoch 4817, Loss 45.682720\n",
            "Epoch 4818, Loss 45.682720\n",
            "Epoch 4819, Loss 45.682720\n",
            "Epoch 4820, Loss 45.682720\n",
            "Epoch 4821, Loss 45.682720\n",
            "Epoch 4822, Loss 45.682720\n",
            "Epoch 4823, Loss 45.682720\n",
            "Epoch 4824, Loss 45.682720\n",
            "Epoch 4825, Loss 45.682720\n",
            "Epoch 4826, Loss 45.682720\n",
            "Epoch 4827, Loss 45.682720\n",
            "Epoch 4828, Loss 45.682720\n",
            "Epoch 4829, Loss 45.682720\n",
            "Epoch 4830, Loss 45.682720\n",
            "Epoch 4831, Loss 45.682720\n",
            "Epoch 4832, Loss 45.682720\n",
            "Epoch 4833, Loss 45.682720\n",
            "Epoch 4834, Loss 45.682720\n",
            "Epoch 4835, Loss 45.682720\n",
            "Epoch 4836, Loss 45.682720\n",
            "Epoch 4837, Loss 45.682720\n",
            "Epoch 4838, Loss 45.682720\n",
            "Epoch 4839, Loss 45.682720\n",
            "Epoch 4840, Loss 45.682720\n",
            "Epoch 4841, Loss 45.682720\n",
            "Epoch 4842, Loss 45.682720\n",
            "Epoch 4843, Loss 45.682720\n",
            "Epoch 4844, Loss 45.682720\n",
            "Epoch 4845, Loss 45.682720\n",
            "Epoch 4846, Loss 45.682720\n",
            "Epoch 4847, Loss 45.682720\n",
            "Epoch 4848, Loss 45.682720\n",
            "Epoch 4849, Loss 45.682720\n",
            "Epoch 4850, Loss 45.682720\n",
            "Epoch 4851, Loss 45.682720\n",
            "Epoch 4852, Loss 45.682720\n",
            "Epoch 4853, Loss 45.682720\n",
            "Epoch 4854, Loss 45.682720\n",
            "Epoch 4855, Loss 45.682720\n",
            "Epoch 4856, Loss 45.682720\n",
            "Epoch 4857, Loss 45.682720\n",
            "Epoch 4858, Loss 45.682720\n",
            "Epoch 4859, Loss 45.682720\n",
            "Epoch 4860, Loss 45.682720\n",
            "Epoch 4861, Loss 45.682720\n",
            "Epoch 4862, Loss 45.682720\n",
            "Epoch 4863, Loss 45.682720\n",
            "Epoch 4864, Loss 45.682720\n",
            "Epoch 4865, Loss 45.682720\n",
            "Epoch 4866, Loss 45.682720\n",
            "Epoch 4867, Loss 45.682720\n",
            "Epoch 4868, Loss 45.682720\n",
            "Epoch 4869, Loss 45.682720\n",
            "Epoch 4870, Loss 45.682720\n",
            "Epoch 4871, Loss 45.682720\n",
            "Epoch 4872, Loss 45.682720\n",
            "Epoch 4873, Loss 45.682720\n",
            "Epoch 4874, Loss 45.682720\n",
            "Epoch 4875, Loss 45.682720\n",
            "Epoch 4876, Loss 45.682720\n",
            "Epoch 4877, Loss 45.682720\n",
            "Epoch 4878, Loss 45.682720\n",
            "Epoch 4879, Loss 45.682720\n",
            "Epoch 4880, Loss 45.682720\n",
            "Epoch 4881, Loss 45.682720\n",
            "Epoch 4882, Loss 45.682720\n",
            "Epoch 4883, Loss 45.682720\n",
            "Epoch 4884, Loss 45.682720\n",
            "Epoch 4885, Loss 45.682720\n",
            "Epoch 4886, Loss 45.682720\n",
            "Epoch 4887, Loss 45.682720\n",
            "Epoch 4888, Loss 45.682720\n",
            "Epoch 4889, Loss 45.682720\n",
            "Epoch 4890, Loss 45.682720\n",
            "Epoch 4891, Loss 45.682720\n",
            "Epoch 4892, Loss 45.682720\n",
            "Epoch 4893, Loss 45.682720\n",
            "Epoch 4894, Loss 45.682720\n",
            "Epoch 4895, Loss 45.682720\n",
            "Epoch 4896, Loss 45.682720\n",
            "Epoch 4897, Loss 45.682720\n",
            "Epoch 4898, Loss 45.682720\n",
            "Epoch 4899, Loss 45.682720\n",
            "Epoch 4900, Loss 45.682720\n",
            "Epoch 4901, Loss 45.682720\n",
            "Epoch 4902, Loss 45.682720\n",
            "Epoch 4903, Loss 45.682720\n",
            "Epoch 4904, Loss 45.682720\n",
            "Epoch 4905, Loss 45.682720\n",
            "Epoch 4906, Loss 45.682720\n",
            "Epoch 4907, Loss 45.682720\n",
            "Epoch 4908, Loss 45.682720\n",
            "Epoch 4909, Loss 45.682720\n",
            "Epoch 4910, Loss 45.682720\n",
            "Epoch 4911, Loss 45.682720\n",
            "Epoch 4912, Loss 45.682720\n",
            "Epoch 4913, Loss 45.682720\n",
            "Epoch 4914, Loss 45.682720\n",
            "Epoch 4915, Loss 45.682720\n",
            "Epoch 4916, Loss 45.682720\n",
            "Epoch 4917, Loss 45.682720\n",
            "Epoch 4918, Loss 45.682720\n",
            "Epoch 4919, Loss 45.682720\n",
            "Epoch 4920, Loss 45.682720\n",
            "Epoch 4921, Loss 45.682720\n",
            "Epoch 4922, Loss 45.682720\n",
            "Epoch 4923, Loss 45.682720\n",
            "Epoch 4924, Loss 45.682720\n",
            "Epoch 4925, Loss 45.682720\n",
            "Epoch 4926, Loss 45.682720\n",
            "Epoch 4927, Loss 45.682720\n",
            "Epoch 4928, Loss 45.682720\n",
            "Epoch 4929, Loss 45.682720\n",
            "Epoch 4930, Loss 45.682720\n",
            "Epoch 4931, Loss 45.682720\n",
            "Epoch 4932, Loss 45.682720\n",
            "Epoch 4933, Loss 45.682720\n",
            "Epoch 4934, Loss 45.682720\n",
            "Epoch 4935, Loss 45.682720\n",
            "Epoch 4936, Loss 45.682720\n",
            "Epoch 4937, Loss 45.682720\n",
            "Epoch 4938, Loss 45.682720\n",
            "Epoch 4939, Loss 45.682720\n",
            "Epoch 4940, Loss 45.682720\n",
            "Epoch 4941, Loss 45.682720\n",
            "Epoch 4942, Loss 45.682720\n",
            "Epoch 4943, Loss 45.682720\n",
            "Epoch 4944, Loss 45.682720\n",
            "Epoch 4945, Loss 45.682720\n",
            "Epoch 4946, Loss 45.682720\n",
            "Epoch 4947, Loss 45.682720\n",
            "Epoch 4948, Loss 45.682720\n",
            "Epoch 4949, Loss 45.682720\n",
            "Epoch 4950, Loss 45.682720\n",
            "Epoch 4951, Loss 45.682720\n",
            "Epoch 4952, Loss 45.682720\n",
            "Epoch 4953, Loss 45.682720\n",
            "Epoch 4954, Loss 45.682720\n",
            "Epoch 4955, Loss 45.682720\n",
            "Epoch 4956, Loss 45.682720\n",
            "Epoch 4957, Loss 45.682720\n",
            "Epoch 4958, Loss 45.682720\n",
            "Epoch 4959, Loss 45.682720\n",
            "Epoch 4960, Loss 45.682720\n",
            "Epoch 4961, Loss 45.682720\n",
            "Epoch 4962, Loss 45.682720\n",
            "Epoch 4963, Loss 45.682720\n",
            "Epoch 4964, Loss 45.682720\n",
            "Epoch 4965, Loss 45.682720\n",
            "Epoch 4966, Loss 45.682720\n",
            "Epoch 4967, Loss 45.682720\n",
            "Epoch 4968, Loss 45.682720\n",
            "Epoch 4969, Loss 45.682720\n",
            "Epoch 4970, Loss 45.682720\n",
            "Epoch 4971, Loss 45.682720\n",
            "Epoch 4972, Loss 45.682720\n",
            "Epoch 4973, Loss 45.682720\n",
            "Epoch 4974, Loss 45.682720\n",
            "Epoch 4975, Loss 45.682720\n",
            "Epoch 4976, Loss 45.682720\n",
            "Epoch 4977, Loss 45.682720\n",
            "Epoch 4978, Loss 45.682720\n",
            "Epoch 4979, Loss 45.682720\n",
            "Epoch 4980, Loss 45.682720\n",
            "Epoch 4981, Loss 45.682720\n",
            "Epoch 4982, Loss 45.682720\n",
            "Epoch 4983, Loss 45.682720\n",
            "Epoch 4984, Loss 45.682720\n",
            "Epoch 4985, Loss 45.682720\n",
            "Epoch 4986, Loss 45.682720\n",
            "Epoch 4987, Loss 45.682720\n",
            "Epoch 4988, Loss 45.682720\n",
            "Epoch 4989, Loss 45.682720\n",
            "Epoch 4990, Loss 45.682720\n",
            "Epoch 4991, Loss 45.682720\n",
            "Epoch 4992, Loss 45.682720\n",
            "Epoch 4993, Loss 45.682720\n",
            "Epoch 4994, Loss 45.682720\n",
            "Epoch 4995, Loss 45.682720\n",
            "Epoch 4996, Loss 45.682720\n",
            "Epoch 4997, Loss 45.682720\n",
            "Epoch 4998, Loss 45.682720\n",
            "Epoch 4999, Loss 45.682720\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([1., 0.], requires_grad=True)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we will display a graph that has a line shown as a regression line between the computed and predicted tensors."
      ],
      "metadata": {
        "id": "9CplXkJbhUZI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot as plt\n",
        "%matplotlib inline\n",
        "plt.plot(0.1*t_u.numpy(),t_p.detach().numpy())\n",
        "plt.plot(0.1*t_u.numpy(),t_c.numpy(),'o')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "S4vDaBMagJy5",
        "outputId": "9be5bb13-3266-4f89-a540-2087c2b9e75f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ccc81bec400>]"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Further Optimizing the Function-accuracy, validation, shuffle_indices\n",
        "nsamples=t_u.shape[0]\n",
        "n_val=int(0.2*nsamples)\n",
        "shuffle_indices=torch.randperm(nsamples)\n",
        "train_indices=shuffle_indices[:-n_val]\n",
        "val_indices=shuffle_indices[-n_val:]\n",
        "\n",
        "train_indices,val_indices"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2TTMVtHRiI6K",
        "outputId": "509e9820-2afe-4cf8-b976-deac84dabc53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([3, 1, 4, 7, 5, 0, 2]), tensor([6]))"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t_u_train=t_u[train_indices]\n",
        "t_c_train=t_c[train_indices]\n",
        "\n",
        "t_u_val=t_u[val_indices]\n",
        "t_c_val=t_c[val_indices]"
      ],
      "metadata": {
        "id": "24frMLw8imE1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def model(t_u,w,b):\n",
        "  return w*t_u+b"
      ],
      "metadata": {
        "id": "txdm96pKlA6J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def loss_fn(t_p,t_c):\n",
        "  sq_diffs=(t_p-t_c)**2\n",
        "  return sq_diffs.mean()"
      ],
      "metadata": {
        "id": "b_PJF6QKlNrC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "params=torch.tensor([1.0,0.0],requires_grad=True)"
      ],
      "metadata": {
        "id": "2LPYPJOxlfP8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nepochs=5000\n",
        "learning_rate=1e-2\n",
        "optimizer=optim.SGD([params],lr=learning_rate)\n",
        "t_un_train=.1*t_u_train\n",
        "t_un_val=0.1*t_u_val"
      ],
      "metadata": {
        "id": "N8crCPuHlxZD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for epoch in range(nepochs):\n",
        "  #forward pass\n",
        "  t_p_train=model(t_un_train,*params)\n",
        "  loss_train=loss_fn(t_p_train,t_c_train)\n",
        "  #t_p_val=model(t_un_train,t_c_train)\n",
        "  t_p_val=model(t_un_val,*params)\n",
        "  loss_val=loss_fn(t_p_val,t_c_val)\n",
        "\n",
        "  print('Epoch %d, Training Loss %f, Validation Loss %f', (epoch,float(loss_train),float(loss_val)))\n",
        "\n",
        "  #backward pass\n",
        "  optimizer.zero_grad()\n",
        "  loss_train.backward()\n",
        "  optimizer.step()\n",
        "  t_p=model(t_un,*params)\n",
        "\n",
        "params"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "id": "HXkynOzWmQHd",
        "outputId": "9afb16f9-4ba1-462e-da0d-3ac2949d69a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch %d, Training Loss %f, Validation Loss %f (0, 40.89301681518555, 7.182399272918701)\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 't_un' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-50-be3714e33ddb>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m   \u001b[0mloss_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m   \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m   \u001b[0mt_p\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt_un\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 't_un' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ERRpGsC6ovwQ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}