EDL_5_1_Hyperparameter_Tuning.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "EDL_5_1_Hyperparameter_Tuning.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyN9fUFWEmEAyN9w5ZZKgtuw",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/cxbxmxcx/EvolutionaryDeepLearning/blob/main/EDL_5_1_Hyperparameter_Tuning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7eo9h5KA5UPe"
      },
      "source": [
        "# Setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FslekLiVp_Il"
      },
      "source": [
        "#@title Defining Imports\n",
        "#numpy\n",
        "import numpy as np\n",
        "import math\n",
        "#PyTorch\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.autograd import Variable\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import TensorDataset, DataLoader\n",
        "#plotting\n",
        "from matplotlib import pyplot as plt\n",
        "from IPython.display import clear_output\n",
        "#for performance timing\n",
        "import time"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gaFe1mDywxdu"
      },
      "source": [
        "results = []"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NpNRGhi8qShC"
      },
      "source": [
        "#@title Setting Hyperparameters\n",
        "hp_test = \"lr = 3.5e-01\" #@param {type:\"string\"}\n",
        "learning_rate = 3.5e-01\n",
        "epochs = 500\n",
        "middle_layer = 16\n",
        "batch_size = 25\n",
        "data_step = .5\n",
        "data_min = -5\n",
        "data_max = 5"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zCbrGhsr6IhK"
      },
      "source": [
        "# Creating Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s8Gzcwhhr670",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "e9756842-5cd2-42a5-8f71-3a35b8d53575"
      },
      "source": [
        "#@title Creating data from function and reshaping\n",
        "def function(x):\n",
        "  return (2*x + 3*x**2 + 4*x**3 + 5*x**4 + 6*x**5 + 10) \n",
        "\n",
        "Xi = np.reshape(np.arange(data_min,data_max, data_step), (-1, 1))\n",
        "yi = function(Xi)\n",
        "inputs = Xi.shape[1]\n",
        "yi = yi.reshape(-1, 1)\n",
        "plt.plot(Xi, yi, 'o', color='black')\n",
        "plt.plot(Xi,yi, color=\"red\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f3c30f532d0>]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qo7YjMgMaD0P"
      },
      "source": [
        "tensor_x = torch.Tensor(Xi) # transform to torch tensor\n",
        "tensor_y = torch.Tensor(yi)\n",
        "\n",
        "dataset = TensorDataset(tensor_x,tensor_y) # create your datset\n",
        "dataloader = DataLoader(dataset, batch_size= batch_size, shuffle=True) # create your dataloader"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7hDSRLfe5wAe"
      },
      "source": [
        "# Building the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eY30bijNqNBL"
      },
      "source": [
        "#@title Define the Model\n",
        "class Net(nn.Module):\n",
        "  def __init__(self, inputs, middle):\n",
        "    super().__init__()\n",
        "    self.fc1 = nn.Linear(inputs,middle)    \n",
        "    self.fc2 = nn.Linear(middle,middle)    \n",
        "    self.out = nn.Linear(middle,1)\n",
        "  def forward(self, x):\n",
        "    x = F.relu(self.fc1(x))     \n",
        "    x = F.relu(self.fc2(x))    \n",
        "    x = self.out(x)\n",
        "    return x"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4j8hXCNS5Lo0",
        "outputId": "89a02304-28d6-4a0f-c46e-3d9a307d9786"
      },
      "source": [
        "#@title Create the model\n",
        "model = Net(inputs, middle_layer)\n",
        "print(model)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Net(\n",
            "  (fc1): Linear(in_features=1, out_features=16, bias=True)\n",
            "  (fc2): Linear(in_features=16, out_features=16, bias=True)\n",
            "  (out): Linear(in_features=16, out_features=1, bias=True)\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SHKSYklV6Ag2"
      },
      "source": [
        "#@title Define loss function (criterion) and optimizer\n",
        "loss_fn = nn.MSELoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=learning_rate)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZpNxqDTNsyN3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "outputId": "86b1e73a-e5c4-4d30-eacd-a28b994224e8"
      },
      "source": [
        "#@title Training the Model\n",
        "epoch_report = 25\n",
        "history = []\n",
        "start = time.time()\n",
        "for i in range(epochs):        \n",
        "  for X, y in iter(dataloader):\n",
        "    # wrap the data in variables\n",
        "    x_batch = Variable(torch.Tensor(X))\n",
        "    y_batch = Variable(torch.Tensor(y))                   \n",
        "    # forward pass\n",
        "    y_pred = model(x_batch)        \n",
        "    # compute and print loss\n",
        "    loss = loss_fn(y_pred, y_batch)  \n",
        "    history.append(loss.data/batch_size)         \n",
        "    # reset gradients\n",
        "    optimizer.zero_grad()        \n",
        "    # backwards pass\n",
        "    loss.backward()        \n",
        "    # step the optimizer - update the weights\n",
        "    optimizer.step()  \n",
        "  if (i+1) % epoch_report == 0:\n",
        "    clear_output()\n",
        "    y_ = model(tensor_x)\n",
        "    plt.plot(Xi, yi, 'o', color='black')\n",
        "    plt.plot(Xi,y_.detach().numpy(), 'r')\n",
        "    plt.show()\n",
        "    print(f\"[{i}] Loss = {loss.data}\")\n",
        "    time.sleep(1)\n",
        "plt.plot(history)\n",
        "end = time.time() - start"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[499] Loss = 1651014.25\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nXqg7ktFuUqv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "90b58293-6970-4d34-8c2b-a83d82892831"
      },
      "source": [
        "X_a = torch.rand(100,1).clone() * 10 - 5\n",
        "y_a = model(X_a)\n",
        "y_a = y_a.detach().numpy()\n",
        "results.append([hp_test,end, X_a, y_a])\n",
        "\n",
        "fig = plt.figure()\n",
        "ax1 = fig.add_subplot(111)\n",
        "for test,t,x,y in results:\n",
        "  ax1.scatter(x, y, s=10, marker=\"s\", label=f\"{test} in {t:0.1f}s\")\n",
        "\n",
        "plt.legend(loc='upper left')\n",
        "plt.plot(Xi, yi, 'o', color='black')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}