Pytorch_Tutorials.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNUA+IKgiJnjF6wvT3GePdY",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/theAkhileshRai/DeepLearning/blob/master/Pytorch_Tutorials.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "WsTaFmctm0iz"
      },
      "outputs": [],
      "source": [
        "# Pytorch object use Cuda"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v8wP8yn0s9RV",
        "outputId": "6981f147-d52e-4abd-f3b3-1f6d733cbf84"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/bin/bash: line 1: nvidia-smi: command not found\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check for GPU with Pytorch\n",
        "\n",
        "import torch\n",
        "torch.cuda.is_available()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "enkEEwBxtATM",
        "outputId": "20639811-5dca-488c-b516-7acfa161783c"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Device Agnostic code\n",
        "device  = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "eqU7LfNOtYpy",
        "outputId": "63de5637-a8d5-4b12-c4d9-b99f676c5c17"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cpu'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tensor1 = torch.tensor([1,23,34])\n",
        "tensor1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZdixQB5u4uL9",
        "outputId": "ac8a80e3-59e5-4f1c-ab62-533084e50b7d"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([ 1, 23, 34])"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tensor_on_gpu = tensor1.to(device)\n",
        "tensor_on_gpu"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EA3_PCE4BrA-",
        "outputId": "2e351328-c362-4438-a215-73987a8a8873"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([ 1, 23, 34])"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Numpy\n",
        "tensor_on_gpu.cpu().numpy()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DMFLWLe5B2MN",
        "outputId": "1eae45ef-4485-4676-e2e4-1bcf804aa95d"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1, 23, 34])"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exercises and Curriculum"
      ],
      "metadata": {
        "id": "ZMDIlPklDHdI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "torch.rand([7,7])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uQ7X91Y3CNlI",
        "outputId": "55fd901b-cc01-42f4-f1d5-f0ed03d4323a"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.5117, 0.1585, 0.0758, 0.2247, 0.0624, 0.1816, 0.9998],\n",
              "        [0.5944, 0.6541, 0.0337, 0.1716, 0.3336, 0.5782, 0.0600],\n",
              "        [0.2846, 0.2007, 0.5014, 0.3139, 0.4654, 0.1612, 0.1568],\n",
              "        [0.2083, 0.3289, 0.1054, 0.9192, 0.4008, 0.9302, 0.6558],\n",
              "        [0.0766, 0.8460, 0.3624, 0.3083, 0.0850, 0.0029, 0.6431],\n",
              "        [0.3908, 0.6947, 0.0897, 0.8712, 0.1330, 0.4137, 0.6044],\n",
              "        [0.7581, 0.9037, 0.9555, 0.1035, 0.6258, 0.2849, 0.4452]])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "random_seed = 42\n",
        "SEED = torch.manual_seed(random_seed)\n",
        "a = torch.rand([7,7])\n",
        "b = torch.rand([7,7])\n",
        "torch.matmul(a,b)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jasHd6BuEPlc",
        "outputId": "c857a880-c39b-4f15-b4b4-367986750ad2"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[2.6637, 2.0426, 3.2242, 0.8659, 1.6420, 1.8317, 2.0220],\n",
              "        [3.0324, 2.3639, 3.7553, 0.7506, 1.6955, 2.3543, 2.2479],\n",
              "        [2.0545, 1.8111, 2.4874, 1.1800, 1.3461, 1.6582, 1.6263],\n",
              "        [1.2061, 1.2405, 1.5288, 0.9969, 0.9076, 1.1420, 1.0550],\n",
              "        [1.7111, 1.7710, 2.1770, 1.2673, 1.0596, 1.9356, 1.3243],\n",
              "        [2.2473, 2.0134, 2.7834, 1.2568, 1.5264, 1.8804, 1.8302],\n",
              "        [2.5960, 2.5335, 3.4685, 1.3261, 1.7587, 2.5078, 2.1685]])"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Pytorch Workflow\n",
        "\n",
        "### 1. Data (prepare and load)\n",
        "### 2. Build Model\n",
        "### 3. Fit the model to the data (training)\n",
        "### 4. Evaluate the model (test)\n",
        "### 5. Deploy on Azure"
      ],
      "metadata": {
        "id": "JZA8liqKL932"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "what_were_covering = {1: \"data (prepare and load)\",\n",
        "    2: \"build model\",\n",
        "    3: \"fitting the model to data (training)\",\n",
        "    4: \"making predictions and evaluating a model (inference)\",\n",
        "    5: \"saving and loading a model\",\n",
        "    6: \"putting it all together\"\n",
        "}"
      ],
      "metadata": {
        "id": "naEfrA7rEVyW"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "torch.__version__\n",
        "\n"
      ],
      "metadata": {
        "id": "0Ml24FDXLsmR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "a4ca1147-f36f-4191-c911-e84d314bdc89"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.3.1+cu121'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "RANDOM_SEED = 42\n",
        "torch.manual_seed(RANDOM_SEED) #w=100\n",
        "X = torch.rand(1,100).mul(100).floor()\n",
        "y = X + 10"
      ],
      "metadata": {
        "id": "uMcOBzuNvKcT"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X,y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2cWF7aeIxpVZ",
        "outputId": "5c0b811e-5358-4d83-d037-3bba04ef5e4f"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([[88., 91., 38., 95., 39., 60., 25., 79., 94., 13., 93., 59., 86., 56.,\n",
              "          74., 42., 88., 57., 26., 62., 26., 44., 29., 83., 10., 26., 35., 19.,\n",
              "          54.,  0., 95.,  7., 88., 58., 33., 80., 57., 90., 55., 34., 63., 36.,\n",
              "          71., 94., 78., 28., 78., 58., 75., 19.,  0., 30., 11., 91., 64., 70.,\n",
              "          65., 49., 89., 14., 53., 15., 65., 32., 65., 39., 91., 20., 20., 20.,\n",
              "          94., 66., 98.,  8.,  0., 10., 16., 70., 67., 91., 24., 15., 76., 29.,\n",
              "          80., 38., 78., 11., 24., 65., 60., 37., 79., 83., 13., 23., 95., 33.,\n",
              "          32.,  1.]]),\n",
              " tensor([[ 98., 101.,  48., 105.,  49.,  70.,  35.,  89., 104.,  23., 103.,  69.,\n",
              "           96.,  66.,  84.,  52.,  98.,  67.,  36.,  72.,  36.,  54.,  39.,  93.,\n",
              "           20.,  36.,  45.,  29.,  64.,  10., 105.,  17.,  98.,  68.,  43.,  90.,\n",
              "           67., 100.,  65.,  44.,  73.,  46.,  81., 104.,  88.,  38.,  88.,  68.,\n",
              "           85.,  29.,  10.,  40.,  21., 101.,  74.,  80.,  75.,  59.,  99.,  24.,\n",
              "           63.,  25.,  75.,  42.,  75.,  49., 101.,  30.,  30.,  30., 104.,  76.,\n",
              "          108.,  18.,  10.,  20.,  26.,  80.,  77., 101.,  34.,  25.,  86.,  39.,\n",
              "           90.,  48.,  88.,  21.,  34.,  75.,  70.,  47.,  89.,  93.,  23.,  33.,\n",
              "          105.,  43.,  42.,  11.]]))"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.numpy()[0][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HoDT_oGzy5yN",
        "outputId": "84b607e7-b268-4f87-a6b7-f478c654093b"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "88.0"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(X.numpy()[0],y.numpy()[0])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "nKy3Op23x2Xd",
        "outputId": "77f2cfa2-8481-4c35-ec99-f71b5d591cc7"
      },
      "execution_count": 47,
      "outputs": [
        {
          "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": [
        "len(X[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TCB4z1ep06Yy",
        "outputId": "2056024d-38bf-4a76-cc32-c8e4268c522e"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_splot = 0.8\n",
        "train_size = int(train_splot*len(X[0]))\n",
        "train_size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EJUgFh9Rx_5Q",
        "outputId": "28c3c8d3-9b63-4fe8-c04e-4567a097c810"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "80"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train,y_train = X[0][:train_size],y[0][:train_size]\n",
        "X_test,y_test = X[0][train_size:],y[0][train_size:]"
      ],
      "metadata": {
        "id": "x-0xuqKtyvvv"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MI1GfowK7Tpo",
        "outputId": "e7162c3b-334a-4d21-f7ce-f9763f42ebc7"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "80"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "60TAsdA21RbW",
        "outputId": "bfcfee3a-5d83-4d0e-a76e-652f3e26a52c"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([24., 15., 76., 29., 80., 38., 78., 11., 24., 65., 60., 37., 79., 83.,\n",
              "        13., 23., 95., 33., 32.,  1.])"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yBNqNaku7MmO",
        "outputId": "56feaf6e-12d4-4ff1-d756-33c472936da5"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([ 34.,  25.,  86.,  39.,  90.,  48.,  88.,  21.,  34.,  75.,  70.,  47.,\n",
              "         89.,  93.,  23.,  33., 105.,  43.,  42.,  11.])"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class LineaRegressionModel(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.weights = nn.Parameter(torch.randn(1,requires_grad=True))\n",
        "        self.bias = nn.Parameter(torch.randn(1,requires_grad=True))\n",
        "\n",
        "    def forward(self,x) -> torch.Tensor:\n",
        "        return self.weights*x + self.bias\n"
      ],
      "metadata": {
        "id": "oDLp4l6p1R4e"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Pytorch model building essentials\n",
        "\n",
        "model_0 = LineaRegressionModel()\n",
        "list(model_0.parameters())"
      ],
      "metadata": {
        "id": "FjVLydPaEkDu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2f06ea39-1dc2-4df6-a008-e02fac1be819"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Parameter containing:\n",
              " tensor([-0.2858], requires_grad=True),\n",
              " Parameter containing:\n",
              " tensor([-1.0935], requires_grad=True)]"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### MAke predicgions using torch.inference_mode()\n",
        "\n",
        "with torch.inference_mode():\n",
        "    y_preds = model_0(X_test)\n",
        "y_preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SlFncGi820nE",
        "outputId": "1187b07f-86ee-4a5d-fd87-340e06eb27e6"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([ -7.9516,  -5.3798, -22.8109,  -9.3804, -23.9539, -11.9522, -23.3824,\n",
              "         -4.2368,  -7.9516, -19.6676, -18.2388, -11.6664, -23.6682, -24.8112,\n",
              "         -4.8083,  -7.6659, -28.2402, -10.5234, -10.2377,  -1.3793])"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import math"
      ],
      "metadata": {
        "id": "5aE2JJe_7p-u"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "math.exp(-5.3798)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AkqJzCsn7lPu",
        "outputId": "3597c229-a06c-4a96-fdda-4d577859fcda"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.004608743586541331"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZtdWpbX567Xt",
        "outputId": "5177a1b4-3a18-4a96-f823-ec95372d225c"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([ 34.,  25.,  86.,  39.,  90.,  48.,  88.,  21.,  34.,  75.,  70.,  47.,\n",
              "         89.,  93.,  23.,  33., 105.,  43.,  42.,  11.])"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7QLQSi2ZANQa"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# how does model perform use loss function\n",
        "\n",
        "loss_fn = nn.L1Loss()\n",
        "optimizer = optim.SGD(params=model_0.parameters(),lr=0.01)\n",
        "# loss = loss_fn(y_preds,y_test,input_lenths=len())\n",
        "# loss"
      ],
      "metadata": {
        "id": "iba6HgU_9fmT"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "epochs = 5000\n",
        "\n",
        "for epoch in range(epochs):\n",
        "    ## training\n",
        "    model_0.train()\n",
        "    y_pred = model_0(X_train)\n",
        "    loss = loss_fn(y_pred,y_train)\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    ## testing\n",
        "    model_0.eval()\n",
        "    with torch.inference_mode():\n",
        "        test_pred = model_0(X_test)\n",
        "        test_loss = loss_fn(test_pred,y_test)"
      ],
      "metadata": {
        "id": "HIGpuqYrEoII"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_test\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0bj7029Jkw47",
        "outputId": "0190afcc-4d3b-4128-ab49-65d2ee4eab84"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([ 34.,  25.,  86.,  39.,  90.,  48.,  88.,  21.,  34.,  75.,  70.,  47.,\n",
              "         89.,  93.,  23.,  33., 105.,  43.,  42.,  11.])"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XVrFJwXa-C0H",
        "outputId": "e836f34a-91ca-45a4-ff66-8ce9c014947d"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(25.6004, grad_fn=<MeanBackward0>)"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_pred\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "COpLaQ5ClDPt",
        "outputId": "6f01800e-d632-41f5-e207-6727201bb3df"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([ 30.8109,  21.5640,  84.2369,  35.9480,  88.3466,  45.1948,  86.2917,\n",
              "         17.4543,  30.8109,  72.9352,  67.7981,  44.1674,  87.3192,  91.4289,\n",
              "         19.5092,  29.7834, 103.7579,  40.0577,  39.0302,   7.1801])"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(test_pred.numpy(),y_test.numpy())\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "IEUq5ebjlExQ",
        "outputId": "468bd502-960e-444c-cff4-b0577b08a492"
      },
      "execution_count": 75,
      "outputs": [
        {
          "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": [],
      "metadata": {
        "id": "UVRqLm7QlUXo"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}