Chapter6_DeepNeuralNetworks.ipynb

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  "metadata": {
    "colab": {
      "name": "Chapter6_DeepNeuralNetworks.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMxqUTwZY9Q5sEkhoIZv3Wp",
      "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/SokichiFujita/PyTorch-for-Deep-Learning-and-Computer-Vision/blob/master/Chapter6_DeepNeuralNetworks.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rIPRLRLP6MDd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import matplotlib.pyplot as plt \n",
        "import numpy as np\n",
        "from sklearn import datasets"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_1MKfwwNGZkw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c83fd1c6-4649-4d18-e156-781500275d75"
      },
      "source": [
        "number_points = 500\n",
        "X, y = datasets.make_circles(n_samples=number_points, random_state=123, noise=0.1, factor=0.2)\n",
        "print(X.shape, X)\n",
        "print(y.shape, y)\n",
        "\n",
        "def scatter_plot():\n",
        "  plt.scatter(X[y==0, 0], X[y==0,1])\n",
        "  plt.scatter(X[y==1, 0], X[y==1,1])\n",
        "\n",
        "scatter_plot()\n",
        "\n",
        "x_data = torch.Tensor(X)\n",
        "y_data = torch.Tensor(y.reshape(500,1))"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(500, 2) [[ 6.98553633e-01 -3.78289607e-01]\n",
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            " [ 1.00816914e+00  2.07309295e-01]\n",
            " [ 4.39780516e-02 -2.80236072e-01]\n",
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          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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XsHSF09LwZ9/qXLB66gsljY3fm0dRKFiZhCRJsyaWTI6oDHF5CdssncAHwsuM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\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CkevxFYRJgoA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Model(nn.Module):\n",
        "  def __init__(self,input_size, H1, output_size):\n",
        "    super().__init__()\n",
        "    self.linear = nn.Linear(input_size, H1)\n",
        "    self.linear2 = nn.Linear(H1, output_size)\n",
        "  def forward(self, x):\n",
        "    x = torch.sigmoid(self.linear(x))\n",
        "    x = torch.sigmoid(self.linear2(x))\n",
        "    return x\n",
        "  def predict(self, x):\n",
        "    pred = self.forward(x)\n",
        "    if pred >= 0.5:\n",
        "      return 1\n",
        "    else:\n",
        "      return 0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OOiwTxocKGLS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "5db9e24a-7b0b-41ed-fcdb-a63312a5d088"
      },
      "source": [
        "torch.manual_seed(2)\n",
        "model = Model(2,4,1)\n",
        "print(list(model.parameters()))"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parameter containing:\n",
            "tensor([[ 0.1622, -0.1683],\n",
            "        [ 0.1939, -0.0361],\n",
            "        [ 0.3021,  0.1683],\n",
            "        [-0.0813, -0.5717]], requires_grad=True), Parameter containing:\n",
            "tensor([ 0.1614, -0.6260,  0.0929,  0.0470], requires_grad=True), Parameter containing:\n",
            "tensor([[-0.1099,  0.4088,  0.0334,  0.2073]], requires_grad=True), Parameter containing:\n",
            "tensor([0.2116], requires_grad=True)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fq8hSAzBKHj3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "criterion = nn.BCELoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=0.1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4OgHSJP_KPWS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "epochs = 1000\n",
        "losses = []\n",
        "\n",
        "for i in range(epochs):\n",
        "  y_pred = model.forward(x_data)\n",
        "  loss = criterion(y_pred, y_data)\n",
        "\n",
        "  losses.append(loss.item())\n",
        "  optimizer.zero_grad()\n",
        "  loss.backward()\n",
        "  optimizer.step()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hj93_8HQKxYg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "037f1a25-7770-4091-ec02-db64b4620bc8"
      },
      "source": [
        "plt.plot(range(epochs), losses)\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('epoch')"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'epoch')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0XRT/RKiJ64Ql/SywNyLur3Qt8ygHvBL4eERcDhzl5KECoLb2MUByWGMTxRA8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\n+Laf66EAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9oJof9CILFmQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_decision_boundary(X,y):\n",
        "  x_span = np.linspace(min(X[:,0]), max(X[:,0]))\n",
        "  y_span = np.linspace(min(X[:,1]), max(X[:,1]))\n",
        "  xx, yy = np.meshgrid(x_span, y_span)\n",
        "  grid = torch.Tensor(np.c_[xx.ravel(), yy.ravel()])\n",
        "  pred_func = model.forward(grid)\n",
        "  z = pred_func.view(xx.shape).detach().numpy()\n",
        "  plt.contourf(xx,yy,z)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7K_bz2XINz_t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "2de30234-0356-4c79-b89a-8eb017ea15f4"
      },
      "source": [
        "plot_decision_boundary(X,y)\n",
        "scatter_plot()"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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uG7+v3sSqjyP2gNpQi2PAJXHMNIlt4RNRGcBXAPwygBkAzxLRo0KIH/te9kkA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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S9yoJeqeOMST",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "3e6d2161-82f9-4c0a-8db5-a20eb688ca09"
      },
      "source": [
        "x = 0.025\n",
        "y = 0.025\n",
        "point = torch.Tensor([x,y])\n",
        "prediction = model.predict(point)\n",
        "plt.plot([x],[y],marker='o',markersize=10,color=\"red\")\n",
        "print(\"prediction is\", prediction)\n",
        "plot_decision_boundary(X,y)"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "prediction is 1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NJ8MZ2d3O3Zv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}