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      "source": [
        "#%%\n",
        "import torch\n",
        "import torchsummary\n",
        "from tqdm.auto import tqdm\n",
        "from plotly import express as px\n",
        "def swissRoll(samples=1000, noise=0.0):\n",
        "    t = 1.5 * 3.14 * (1 + 2 * torch.rand(samples, 1))\n",
        "    x = t * torch.cos(t)\n",
        "    y = t * torch.sin(t)\n",
        "    X = torch.concat([x, y], axis=1)\n",
        "\n",
        "    if noise > 0:\n",
        "        X += noise * torch.randn(samples, 2)\n",
        "\n",
        "    return X\n",
        "\n",
        "# %%\n",
        "def makeBlock(in_features,out_features,bn=True):\n",
        "    if bn:\n",
        "        return torch.nn.Sequential(\n",
        "            torch.nn.Linear(in_features,out_features),\n",
        "            torch.nn.BatchNorm1d(out_features),\n",
        "            torch.nn.ReLU()\n",
        "        )\n",
        "    else:\n",
        "        return torch.nn.Sequential(\n",
        "            torch.nn.Linear(in_features,out_features),\n",
        "            torch.nn.ReLU()\n",
        "        )\n",
        "\n",
        "def hinge(x,maximize=True):\n",
        "    if maximize:\n",
        "        return torch.relu(1-x)\n",
        "    else:\n",
        "        return torch.relu(1+x)\n",
        "\n",
        "encoder=torch.nn.Sequential(\n",
        "    makeBlock(2,64,bn=False),\n",
        "    makeBlock(64,64),\n",
        "    makeBlock(64,64),\n",
        "    makeBlock(64,64),\n",
        "    makeBlock(64,64),\n",
        "    torch.nn.Linear(64,1)\n",
        ").cuda()\n",
        "\n",
        "decoder=torch.nn.Sequential(\n",
        "    makeBlock(2,64),\n",
        "    makeBlock(64,64),\n",
        "    makeBlock(64,64),\n",
        "    makeBlock(64,64),\n",
        "    makeBlock(64,64),\n",
        "    torch.nn.Linear(64,2)\n",
        ").cuda()\n",
        "\n",
        "optimizerD=torch.optim.Adam(decoder.parameters(),lr=5e-4)\n",
        "optimizerE=torch.optim.Adam(encoder.parameters(),lr=1e-3)\n",
        "\n",
        "#%%\n",
        "loss=0.0\n",
        "#Training Loop\n",
        "for i in (pbar:=tqdm(range(8000))):\n",
        "    sample=swissRoll(1024,0.5).cuda()\n",
        "    latent=torch.randn_like(sample)\n",
        "\n",
        "    # generator (decoder)\n",
        "\n",
        "    \"\"\"\n",
        "\n",
        "    The loss function used for training the generator aims to maximize the discriminator's error on fake data.\n",
        "    This is achieved by minimizing the hinge loss hinge(encoder(sampleFake), maximize=True).\n",
        "    The generator tries to fool the discriminator into classifying fake data as real.\n",
        "\n",
        "\n",
        "    \"\"\"\n",
        "\n",
        "    optimizerD.zero_grad()\n",
        "    sampleFake=decoder(latent)\n",
        "    generatorLoss=hinge(encoder(sampleFake),maximize=True).mean()\n",
        "    generatorLoss.backward()\n",
        "    optimizerD.step()\n",
        "\n",
        "\n",
        "    # discriminator (encoder)\n",
        "\n",
        "    \"\"\"\n",
        "\n",
        "    Minimizing the hinge loss on fake data: hinge(encoder(sampleFake.detach()), maximize=False)\n",
        "\n",
        "    Maximizing the hinge loss on real data: hinge(encoder(sample), maximize=True)\n",
        "    This combination helps the discriminator distinguish between real and fake data effectively.\n",
        "\n",
        "    \"\"\"\n",
        "\n",
        "    optimizerE.zero_grad()\n",
        "    discriminatorLoss= ( hinge(encoder(sampleFake.detach()),maximize=False) + hinge(encoder(sample),maximize=True) ).mean()\n",
        "    discriminatorLoss.backward()\n",
        "    optimizerE.step()\n",
        "\n",
        "    if i%100==0:\n",
        "        pbar.set_postfix({'Generator Loss':generatorLoss.item()})\n",
        "\n",
        "#%%\n",
        "sR=swissRoll(500,0.5)\n",
        "latent=torch.randn_like(latent)\n",
        "generatedData=decoder(latent).cpu().detach()\n",
        "\n",
        "px.scatter(x=sR[:,0],y=sR[:,1],width=512,height=512,template='plotly_dark',range_x=[-15,15],range_y=[-15,15]).show()\n",
        "px.scatter(x=generatedData[:,0],y=generatedData[:,1],width=512,height=512,template='plotly_dark',range_x=[-15,15],range_y=[-15,15]).show()\n",
        "# %%\n"
      ]
    }
  ]
}