06_gan_simple_pattern.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "06_gan_simple_pattern.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DpII-qgno6EX",
        "colab_type": "text"
      },
      "source": [
        "# Simple 1010 Pattern\n",
        "\n",
        "Make Your First GAN With PyTorch, 2020"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7bkrfSHHAPUB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# import libraries\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "import pandas\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "import numpy"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o91c-ALVmw0n",
        "colab_type": "text"
      },
      "source": [
        "## Data Functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hS9SJ5JI5rIW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# function to generate real data\n",
        "\n",
        "def generate_real():\n",
        "    real_data = torch.FloatTensor(\n",
        "        [random.uniform(0.8, 1.0),\n",
        "         random.uniform(0.0, 0.2),\n",
        "         random.uniform(0.8, 1.0),\n",
        "         random.uniform(0.0, 0.2)])\n",
        "    return real_data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "px_g8nYPBD3A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# function to generate uniform random data\n",
        "\n",
        "def generate_random(size):\n",
        "    random_data = torch.rand(size)\n",
        "    return random_data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AJ_E9wgYQybx",
        "colab_type": "text"
      },
      "source": [
        "## Discriminator Network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MrgKlTsRAfSk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# discriminator class\n",
        "\n",
        "class Discriminator(nn.Module):\n",
        "    \n",
        "    def __init__(self):\n",
        "        # initialise parent pytorch class\n",
        "        super().__init__()\n",
        "        \n",
        "        # define neural network layers\n",
        "        self.model = nn.Sequential(\n",
        "            nn.Linear(4, 3),\n",
        "            nn.Sigmoid(),\n",
        "            nn.Linear(3, 1),\n",
        "            nn.Sigmoid()\n",
        "        )\n",
        "        \n",
        "        # create loss function\n",
        "        self.loss_function = nn.MSELoss()\n",
        "\n",
        "        # create optimiser, simple stochastic gradient descent\n",
        "        self.optimiser = torch.optim.SGD(self.parameters(), lr=0.01)\n",
        "\n",
        "        # counter and accumulator for progress\n",
        "        self.counter = 0;\n",
        "        self.progress = []\n",
        "\n",
        "        pass\n",
        "    \n",
        "    \n",
        "    def forward(self, inputs):\n",
        "        # simply run model\n",
        "        return self.model(inputs)\n",
        "    \n",
        "    \n",
        "    def train(self, inputs, targets):\n",
        "        # calculate the output of the network\n",
        "        outputs = self.forward(inputs)\n",
        "        \n",
        "        # calculate loss\n",
        "        loss = self.loss_function(outputs, targets)\n",
        "\n",
        "        # increase counter and accumulate error every 10\n",
        "        self.counter += 1;\n",
        "        if (self.counter % 10 == 0):\n",
        "            self.progress.append(loss.item())\n",
        "            pass\n",
        "        if (self.counter % 10000 == 0):\n",
        "            print(\"counter = \", self.counter)\n",
        "            pass\n",
        "\n",
        "        # zero gradients, perform a backward pass, update weights\n",
        "        self.optimiser.zero_grad()\n",
        "        loss.backward()\n",
        "        self.optimiser.step()\n",
        "\n",
        "        pass\n",
        "    \n",
        "    \n",
        "    def plot_progress(self):\n",
        "        df = pandas.DataFrame(self.progress, columns=['loss'])\n",
        "        df.plot(ylim=(0, 1.0), figsize=(16,8), alpha=0.1, marker='.', grid=True, yticks=(0, 0.25, 0.5))\n",
        "        pass\n",
        "    \n",
        "    pass"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IFajrD3xCgsb",
        "colab_type": "text"
      },
      "source": [
        "## Test Discriminator"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AXgCZ9DiCl3A",
        "colab_type": "code",
        "outputId": "d055f70a-380c-46b8-97a8-1c12bdc817ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# test discriminator can separate real data from random noise\n",
        "\n",
        "D = Discriminator()\n",
        "\n",
        "for i in range(10000):\n",
        "    # real data\n",
        "    D.train(generate_real(), torch.FloatTensor([1.0]))\n",
        "    # fake data\n",
        "    D.train(generate_random(4), torch.FloatTensor([0.0]))\n",
        "    pass"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "counter =  10000\n",
            "counter =  20000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aBh8AGJFDhcU",
        "colab_type": "code",
        "outputId": "71b31b63-fa20-498f-b73d-f7cc4f032f38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        }
      },
      "source": [
        "# plot discriminator loss\n",
        "\n",
        "D.plot_progress()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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AAABwGrLCKi+t4jBQEjE3Nw8IqAAAADhXhAichqyw+u6dTcWB0VIr1q2VNs+vOUBABQAA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\n1njutc5u9/pvSXsOD2eF1d3NUbWetbB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Agv1ehL95M4U0Fi/PK9hpgYwyHA9iDAcpfnic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\nPMdmM3LbOrs3IzVtFQVs+A6BbRHnBWWp1gqK0LW1QVKu0xA3e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Uvk29wYRdwY6YjhF+5M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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MUMJn6efDkH5",
        "colab_type": "code",
        "outputId": "2219d02b-9152-4187-d8e9-17cf11c6ff7c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# manually run discriminator to check it can tell real data from fake\n",
        "\n",
        "print( D.forward( generate_real() ).item() )\n",
        "print( D.forward( generate_random(4) ).item() )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8236010670661926\n",
            "0.22374509274959564\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SsE9-KrguowG",
        "colab_type": "text"
      },
      "source": [
        "## Generator Network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j-JwJlKDh_Ej",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# generator class\n",
        "\n",
        "class Generator(nn.Module):\n",
        "    \n",
        "    def __init__(self):\n",
        "        # initialise parent pytorch class\n",
        "        super().__init__()\n",
        "        \n",
        "        # define neural network layers\n",
        "        self.model = nn.Sequential(\n",
        "            nn.Linear(1, 3),\n",
        "            nn.Sigmoid(),\n",
        "            nn.Linear(3, 4),\n",
        "            nn.Sigmoid()\n",
        "        )\n",
        "\n",
        "        # create optimiser, simple stochastic gradient descent\n",
        "        self.optimiser = torch.optim.SGD(self.parameters(), lr=0.01)\n",
        "\n",
        "        # counter and accumulator for progress\n",
        "        self.counter = 0;\n",
        "        self.progress = []\n",
        "        \n",
        "        pass\n",
        "    \n",
        "    \n",
        "    def forward(self, inputs):        \n",
        "        # simply run model\n",
        "        return self.model(inputs)\n",
        "    \n",
        "    \n",
        "    def train(self, D, inputs, targets):\n",
        "        # calculate the output of the network\n",
        "        g_output = self.forward(inputs)\n",
        "        \n",
        "        # pass onto Discriminator\n",
        "        d_output = D.forward(g_output)\n",
        "        \n",
        "        # calculate error\n",
        "        loss = D.loss_function(d_output, targets)\n",
        "\n",
        "        # increase counter and accumulate error every 10\n",
        "        self.counter += 1;\n",
        "        if (self.counter % 10 == 0):\n",
        "            self.progress.append(loss.item())\n",
        "            pass\n",
        "\n",
        "        # zero gradients, perform a backward pass, update weights\n",
        "        self.optimiser.zero_grad()\n",
        "        loss.backward()\n",
        "        self.optimiser.step()\n",
        "\n",
        "        pass\n",
        "    \n",
        "    \n",
        "    def plot_progress(self):\n",
        "        df = pandas.DataFrame(self.progress, columns=['loss'])\n",
        "        df.plot(ylim=(0, 1.0), figsize=(16,8), alpha=0.1, marker='.', grid=True, yticks=(0, 0.25, 0.5))\n",
        "        pass\n",
        "    \n",
        "    pass"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SwjycPNXxF7G",
        "colab_type": "text"
      },
      "source": [
        "## Test Generator Output"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "84vbfjGRwodN",
        "colab_type": "code",
        "outputId": "77578817-8fca-46d4-b0e6-96fba89db611",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# check the generator output is of the right type and shape\n",
        "\n",
        "G = Generator()\n",
        "\n",
        "G.forward(torch.FloatTensor([0.5]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0.4369, 0.5104, 0.3246, 0.5549], grad_fn=<SigmoidBackward>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5k9SRwIkxTLi",
        "colab_type": "text"
      },
      "source": [
        "## Train GAN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "radA76P9xWr1",
        "colab_type": "code",
        "outputId": "8b5b1470-58a3-4551-9763-faf62ba0d8a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "%%time \n",
        "\n",
        "# create Discriminator and Generator\n",
        "\n",
        "D = Discriminator()\n",
        "G = Generator()\n",
        "\n",
        "image_list = []\n",
        "\n",
        "# train Discriminator and Generator\n",
        "\n",
        "for i in range(10000):\n",
        "    \n",
        "    # train discriminator on true\n",
        "    D.train(generate_real(), torch.FloatTensor([1.0]))\n",
        "    \n",
        "    # train discriminator on false\n",
        "    # use detach() so gradients in G are not calculated\n",
        "    D.train(G.forward(torch.FloatTensor([0.5])).detach(), torch.FloatTensor([0.0]))\n",
        "    \n",
        "    # train generator\n",
        "    G.train(D, torch.FloatTensor([0.5]), torch.FloatTensor([1.0]))\n",
        "    \n",
        "    # add image to list every 1000\n",
        "    if (i % 1000 == 0):\n",
        "      image_list.append( G.forward(torch.FloatTensor([0.5])).detach().numpy() )\n",
        "\n",
        "    pass"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "counter =  10000\n",
            "counter =  20000\n",
            "CPU times: user 15 s, sys: 1.29 s, total: 16.3 s\n",
            "Wall time: 16.3 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WfGwR21QxYtV",
        "colab_type": "code",
        "outputId": "1cbff4da-8f15-4306-e1c7-341f5e63200e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        }
      },
      "source": [
        "# plot discriminator error\n",
        "\n",
        "D.plot_progress()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CAAAwCKPbfQIAAADcWc5sTPLs+c1MZ33GbZNp12fa1YzbkrTjpRsdJ6ACAABvaJNZnxcu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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SIx0tJBixbGM",
        "colab_type": "code",
        "outputId": "1c6f7723-bc0f-4a30-a599-38c3e36c696f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        }
      },
      "source": [
        "# plot generator error\n",
        "\n",
        "G.plot_progress()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3Mm6bLLuacdtkazr4QwcAAOBtGHyv7GTU5ENnNvLY5jgpNZf3DvP8xd3MFt2DPjQAAADu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\nkiRJg2CBKkmSJEkaBAtUSZIkSdIgvA2B4/qwr1xWNAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vddYtD84xkmI",
        "colab_type": "text"
      },
      "source": [
        "## Manually Run Generator"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mePLPy3Yxj9A",
        "colab_type": "code",
        "outputId": "350aedcf-49c2-48bd-8e09-2bd2ebcaf844",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# manually run generator to see it's outputs\n",
        "\n",
        "G.forward(torch.FloatTensor([0.5]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0.9387, 0.0527, 0.9389, 0.0474], grad_fn=<SigmoidBackward>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0gZ4BG6O53SZ",
        "colab_type": "text"
      },
      "source": [
        "## Visualise Pattern During Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jxHrZmUyxm-z",
        "colab_type": "code",
        "outputId": "678d51ca-14f5-4579-a24f-296314913284",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        }
      },
      "source": [
        "# plot images collected during training\n",
        "\n",
        "plt.figure(figsize = (16,8))\n",
        "\n",
        "plt.imshow(numpy.array(image_list).T, interpolation='none', cmap='Blues')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f6f317b26d8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6oAAAGICAYAAABIhx0KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABDEaoAAAAMRagCAAAwlP8HwvaHWklDLeQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}