HW1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.4"
    },
    "colab": {
      "name": "HW1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZtXoKeWrMjZG",
        "colab_type": "text"
      },
      "source": [
        "# Deep Unsupervised Learning HW1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hiK6m8snMjZM",
        "colab_type": "text"
      },
      "source": [
        "## Warmup Exercise"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zRbV60sjMjZO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn\n",
        "import torch.utils.data\n",
        "import matplotlib.pyplot as plt\n",
        "import math\n",
        "\n",
        "def sample_data():\n",
        "    count = 10000\n",
        "    rand = np.random.RandomState(0)\n",
        "    a = 0.3 + 0.1 * rand.randn(count)\n",
        "    b = 0.8 + 0.05 * rand.randn(count)\n",
        "    mask = rand.rand(count) < 0.5\n",
        "    samples = np.clip(a * mask + b * (1 - mask), 0.0, 1.0)\n",
        "    return np.digitize(samples, np.linspace(0.0, 1.0, 100))\n",
        "\n",
        "def compute_loss(theta,x):\n",
        "    #softmax\n",
        "    _theta=torch.exp(theta) / torch.sum(torch.exp(theta))\n",
        "\n",
        "    #instead of having a one hot encoded vector x, just use gather instead:\n",
        "    prob = torch.gather(_theta, dim=0, index=x)\n",
        "\n",
        "    loss = torch.sum(-torch.log2(prob))/prob.shape[0]\n",
        "    return loss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LnHTxr7HMjZZ",
        "colab_type": "text"
      },
      "source": [
        "## Create data and set parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "083UiS0bMjZc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sampled_data=sample_data()\n",
        "n=len(sampled_data)\n",
        "train_data = sampled_data[:int(0.6*n)]\n",
        "val_data = sampled_data[int(0.6*n):int(0.8*n)]\n",
        "test_data = sampled_data[int(0.8*n):]\n",
        "batch_size=512\n",
        "theta=torch.zeros(100,requires_grad=True)\n",
        "optimizer = torch.optim.Adam([theta],lr=3e-4)\n",
        "epochs=1000"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-5d7vFFLMjZj",
        "colab_type": "code",
        "outputId": "444e04a1-8744-4669-a3c7-7a601b65d780",
        "colab": {}
      },
      "source": [
        "train_iter = torch.utils.data.DataLoader(dataset=train_data,batch_size=batch_size,shuffle=True)\n",
        "val_iter = torch.utils.data.DataLoader(dataset=val_data,batch_size=batch_size,shuffle=True)\n",
        "losses = []\n",
        "val_losses = []\n",
        "\n",
        "\n",
        "for epoch in range(epochs):\n",
        "    train_loss = 0\n",
        "\n",
        "    for train_batch in train_iter:\n",
        "\n",
        "        loss= compute_loss(theta,train_batch)\n",
        "\n",
        "        loss.backward()    \n",
        "        optimizer.step()\n",
        "        optimizer.zero_grad()\n",
        "        losses.append(loss)\n",
        "\n",
        "    with torch.set_grad_enabled(False):\n",
        "\n",
        "        val_loss = compute_loss(theta,torch.from_numpy(val_data))\n",
        "        val_losses.append(val_loss)\n",
        "        \n",
        "    if epoch % 100 ==0:\n",
        "        print('Epoch {}: loss{} val_loss{} '.format(epoch,loss,val_loss))\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.plot(losses, label = \"train_loss\")\n",
        "plt.plot(np.arange(0, len(losses),int(len(losses)/len(val_losses))),val_losses, label = \"val_loss\")\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 0: loss6.640065670013428 val_loss6.639639854431152 \n",
            "Epoch 100: loss6.317140579223633 val_loss6.310300350189209 \n",
            "Epoch 200: loss6.106627941131592 val_loss6.111555576324463 \n",
            "Epoch 300: loss5.998766899108887 val_loss5.994115352630615 \n",
            "Epoch 400: loss5.871201038360596 val_loss5.927525520324707 \n",
            "Epoch 500: loss5.8062744140625 val_loss5.891590595245361 \n",
            "Epoch 600: loss5.854290008544922 val_loss5.872189521789551 \n",
            "Epoch 700: loss5.7515950202941895 val_loss5.861020088195801 \n",
            "Epoch 800: loss5.860213756561279 val_loss5.854262351989746 \n",
            "Epoch 900: loss5.874886512756348 val_loss5.850228786468506 \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8zPOMIuQMjZr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_iter = torch.utils.data.DataLoader(dataset=test_data,batch_size=batch_size,shuffle=True,num_workers=1)\n",
        "\n",
        "# Compute Test Loss\n",
        "for idx, test_batch in enumerate(test_iter):\n",
        "    test_loss = compute_loss(theta, test_batch)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "85wALQHyMjZz",
        "colab_type": "code",
        "outputId": "3ee51232-e4b1-4073-94b3-42cacb178e03",
        "colab": {}
      },
      "source": [
        "# Showing all losses\n",
        "print(\"Final Training Loss:\", losses[-1].item())\n",
        "print(\"Final Validation Loss:\", val_losses[-1].item())\n",
        "print(\"Final Test Loss:\", test_loss.item())\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Final Training Loss: 5.927191257476807\n",
            "Final Validation Loss: 5.84775447845459\n",
            "Final Test Loss: 5.824062347412109\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ub5JHSYYMjZ6",
        "colab_type": "text"
      },
      "source": [
        "# Data distributions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "44ABQ1keMjZ8",
        "colab_type": "text"
      },
      "source": [
        "Compute probability distribution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "09Cv_Zc2MjZ_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "p = torch.exp(theta) / torch.sum(torch.exp(theta))\n",
        "p = p.detach().numpy()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KEMID0MRMjaG",
        "colab_type": "code",
        "outputId": "3beccccb-fc3a-4cef-c8a1-73ed67b8854e",
        "colab": {}
      },
      "source": [
        "\n",
        "plt.figure(figsize=(18, 12))\n",
        "plt.subplot(221)\n",
        "plt.bar(np.arange(len(p)), p)\n",
        "plt.title('Model probabilities')\n",
        "plt.xlabel('x')\n",
        "plt.ylabel('$p_{\\\\theta}(x)$')\n",
        "plt.grid(True)\n",
        "\n",
        "\n",
        "plt.subplot(222)\n",
        "plt.hist(sampled_data, bins=100, density=True)\n",
        "plt.title('Data distribution')\n",
        "plt.xlabel('x')\n",
        "plt.ylabel('Frequency')\n",
        "plt.grid(True)\n",
        "\n",
        "\n",
        "plt.subplot(223)\n",
        "samples = np.random.choice(np.arange(100), p=p, size=1000)\n",
        "plt.hist(samples, bins=100, density=True)\n",
        "plt.title('1000 samples from model')\n",
        "plt.xlabel('x')\n",
        "plt.ylabel('Frequency')\n",
        "plt.grid(True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9LtzJKzdvc/ub3v1hlu/a/N+Uv3MZ63z7LfaaNVkhSZKmydXAAT3L+7frZiuzMckyYE/gOuAI4JgkfwXsBfwsyS1VdXrvzlV1BnAGwKpVq2r16tWLCnjdunUs9hi6M+t1eKzb4bBeF+6EefzYcvLKzbzu0q1/Ld5w3OoBRjRdFnvNmqyQJEnT5GLgkCQH0SQljgWe3ldmLXA88BngGODCqirgETMFkpwKbOpPVEiSpMEwWSFJkqZGVW1OchLwMWBH4KyquizJq4BLqmotcCZwdpL1wPU0CQ1JUsc5bHVpMVkhSZKmSlVdAFzQt+6Unse3AE+e4xinDiU4SZIEeDcQSZIkSZLUMSYrJEmSJElSp5iskCRJkiRJnWKyQpIkSZIkdYrJCkmSJEmS1CneDUSSJEmSpFn03w51w2lHjSmS6WPPCkmSJEmS1CkmKyRJkiRJUqeYrJAkSZIkSZ1iskKSJEmSJHWKyQpJkiRJktQpJiskSZIkSVKnmKyQJEmSJEmdYrJCkiRJkiR1iskKSZIkSZLUKSYrJEmSJElSp5iskCRJkiRJnWKyQpIkSZIkdYrJCkmSJEmS1CkmKyRJkiRJUqeYrJAkSZIkSZ1iskKSJEmSJHXKsnEHIEmaTivWnH/74w2nHTXGSCRJktQ19qyQJEmSJEmdYrJCkiRJkiR1iskKSZIkSZLUKSYrJEmSJElSp5iskCRJkiRJnWKyQpIkSZIkdYrJCkmSJEmS1CkmKyRJkiRJUqcsG3cAkiRJkiRNghVrzt9iecNpR40pkqXPnhWSJEmSJKlTTFZIkiRJkqROMVkhSZIkSZI6xWSFJEmSJEnqFJMVkiRJkiSpU0xWSJIkSZKkTjFZIUmSJEmSOmXZuAOQJAm8b7kkSZLuYM8KSZIkSZLUKSYrJEmSJElSp5iskCRJkiRJnWKyQpIkSZIkdYrJCkmSJEmS1CkmKyRJkiRJUqeYrJAkSZIkSZ0y0mRFkiOTfD3J+iRrZtm+c5Jz2+0XJVnRrj88yZfaf/+d5LdHGbckSZIkSRqdkSUrkuwIvBl4PHAo8LQkh/YVezZwQ1UdDLwBeG27/ivAqqr6ZeBI4C1Jlo0mckmSJEmSNEqj/MJ/OLC+qq4ASHIOcDRweU+Zo4FT28fnAacnSVX9qKfMLkANP1xJml4r1px/++MNpx01xkgkSZK6q7fNBLabBmmUyYr9gKt6ljcCR2ytTFVtTnITsDdwbZIjgLOAA4Hfq6rN/SdIciJwIsDy5ctZt27doJ8DAJs2bRrasaeNdTk41uXgWJdw8so73mLXrVu3xfJs+svMLC/ftTnW1rZv6xxveveHt1heud+e2/VclhKvTUmSNC0mZihFVV0E3D/J/YB3JPloVd3SV+YM4AyAVatW1erVq4cSy7p16xjWsaeNdTk41uXgWJdwQm/PiuNWb7E8m/4yM8snr9zM6y5dttXtCz3HtPPalCRJ02KUE2xeDRzQs7x/u27WMu2cFHsC1/UWqKqvApuAw4YWqSRJkiRJGptRJisuBg5JclCSnYBjgbV9ZdYCx7ePjwEurKpq91kGkORA4L7AhtGELUmSJEmSRmlkw0DaOShOAj4G7AicVVWXJXkVcElVrQXOBM5Osh64niahAfBwYE2SnwI/A55fVdeOKnZJkiRJkjQ6I52zoqouAC7oW3dKz+NbgCfPst/ZwNlDD1CSJC15SY4E/pbmx5O3VdVpfdt3Bt4JPIRmOOpTq2pDksNp58YCApxaVf88usglSZoeoxwGIkmSNFZJdgTeDDweOBR4WpJD+4o9G7ihqg4G3gC8tl3/FWBVVf0ycCTwlplhqpIkabBMVkiSpGlyOLC+qq6oqluBc4Cj+8ocDbyjfXwe8Ogkqaof9dw6fRegRhKxJElTyF8DJEnSNNkPuKpneSNwxNbKtHNu3QTsDVyb5AjgLOBA4Pd6khe3S3IicCLA8uXLWbdu3aIC3rRp06KPoTuzXofHuh0O63VuJ6+801vynJbvun37bY1/ozss9po1WSFJkjRPVXURcP8k9wPekeSj7ZxbvWXOoJ3bYtWqVbV69epFnXPdunUs9hi6M+t1eKzb4bBe53bCmvMXvM/JKzfzuksH97V4w3GrB3asSbfYa9ZhIJIkaZpcDRzQs7x/u27WMu2cFHvSTLR5u6r6KrAJOGxokUqSNMVMVkiSpGlyMXBIkoOS7ERzm/S1fWXWAse3j48BLqyqavdZBpDkQOC+wIbRhC1J0nRxGIgkSZoa7RwUJwEfo7l16VlVdVmSVwGXVNVa4Ezg7CTrgetpEhoADwfWJPkp8DPg+VV17eifhSRJS5/JCkmSNFWq6gLggr51p/Q8vgV48iz7nQ2cPfQAJUmSw0AkSZIkSVK32LNCkiRJkjRxVmzH3T80OexZIUmSJEmSOsVkhSRJkiRJ6hSTFZIkSZIkqVNMVkiSJEmSpE4xWSFJkiRJkjrFZIUkSZIkSeoUkxWSJEmSJKlTTFZIkiRJkqROMVkhSZIkSZI6xWSFJEmSJEnqFJMVkiRpIiVZOe4YJEnScJiskCRJk+rvk3wuyfOT7DnuYCRJ0uCYrJAkSROpqh4BHAccAHw+yXuSPGbMYUmSpAEwWSFJkiZWVX0DeBnwYuDXgb9L8rUkvzPeyCRJ0mIsG3cAUpetWHP+7Y83nHbUGCORJPVL8gDgWcBRwL8CT6yqLyTZF/gM8MFxxidJkrafyQpJkjSp3gS8DXhpVf14ZmVVXZPkZeMLS5IkLZbJCkmSNKmOAn5cVbcBJNkB2KWqflRVZ483NGkwent5zrC3p6bVbK8HLV3OWSFJkibVvwG79izftV0nSZImnMkKSZI0qXapqk0zC+3ju44xHkmSNCAmKyRJ0qS6OcmDZxaSPAT48TbKS5KkCeGcFZIkaVK9EHh/kmuAAD8PPHW8IUmSpEEwWSFJ8ja9mkhVdXGS+wL3aVd9vap+Os6YJEnSYJiskCRJk+yhwAqaNs2Dk1BV7xxvSJIkabEWnKxIshtwy8xtwiRJksYhydnALwJfAmbaJQWYrJAkacLNmaxo71l+LHAcza8XPwF2TnItcD7wlqpaP9QoJUmS7mwVcGhV1bgDkSRJgzWfu4F8guZXi5cAP19VB1TVzwEPBz4LvDbJM4YYoyRJ0my+QjOppiRJWmLmMwzkN2abrKqqrgc+AHwgyV0GHpkkSdK27QNcnuRzND0/Aaiq3xpfSJIkaRDmTFbMJCqS/C3wwtm6WjrztiRJGoNTxx2AJEkajvkMA5nxQ2BtO8EmSR6X5FPDCUuSJGnbquqTwAbgLu3ji4EvjDUoSZI0EPO+G0hVvSzJ04F1SW4FNgFrhhaZJEnSNiR5LnAicA+a+bX2A/4RePQ445IkSYs3754VSR4NPBe4mWaM6Auq6j+HFZgkSdIc/hD4NeAHAFX1DeDnxhqRJEkaiIUMA/k/wMurajVwDHBukkcNJSpJkqS5/aSqbp1ZSLIM8DamkiQtAQsZBvKonseXJnk8zd1AfnUYgUmSJM3hk0leCuya5DHA84GPjDkmSZI0AHP2rEiS2dZX1bdpx4RurYwkSdIQrQG+D1wK/AFwAfCysUYkSZIGYj49Kz6R5APAh6vqf2ZWJtkJ+JUkxwOfAN4+nBAlSZLurKp+Bry1/SdJkpaQ+SQrjgR+H3hvkoOAG4FdaXplfBx4Y1V9cXghSpIk3VmSK5lljoqquvcYwpHGZsWa87dY3nDaUWOKRJIGZ85kRVXdAvw98PdJ7kJzJ5AfV9WNww5OkiRpG1b1PN4FeDLNbUwlSdKEm/cEmwBV9dMkLwL2TbICuBD4y6raNITYJEmStqqqrutb9cYknwdOGUc8kiRpcBaUrGjdWlVPS/IPwD8CrwROHmxYkiRJ25bkwT2LO9D0tNieto0kSeqY7flAvz7J84Cdq+qqJDsPOihJkqR5eF3P483ABuAp4wlF2j7ONyFJs1twsqKq/jrJauD+Sc4G/mXgUUmSJM2hqh457hgkSdJwzDtZkeTxwKnAXsB/A2+oqs8MKS5pIvT+GuIvIZI0Wkn+dFvbq+r1o4pFkiSwt9QgLaRnxd8DzwAuBx4C/HWSN1fVe4cSmSRJ0ratAh4KrG2Xnwh8DvjG2CKSJEkDsZBkxfeq6lPt439L8hngIsBkhSRJGof9gQdX1Q8BkpwKnF9VzxhrVJKkRevvoaDps8MCyl6Z5NVJdmqXf0ozmZUkSdI4LAdu7Vm+tV0nSZIm3EJ6VvwM+G3guUm+AdwL+GCSQ6rK7paSpKFznhj1eSfwuST/3C4/CXjHGOORJEkDMu9kRVU9HaC9VelhwAPbf29Ncu+qutdwQpQkSbqzqnpNko8Cj2hXPauqvjjOmCRJ0mAsZBgIAFX1k6r6fFWdVVV/XFWr55uoSHJkkq8nWZ9kzSzbd05ybrv9oiQr2vWPSfL5JJe2/z9qoXFLkqQl6a7AD6rqb4GNSQ4ad0CSJGnxFjIMZFGS7Ai8GXgMsBG4OMnaqrq8p9izgRuq6uAkxwKvBZ4KXAs8saquSXIY8DFgv1HFLklLjcMptBQkeQXNHUHuA/wTcBfgXcCvjTMuaTGcVFCSGgvuWbEIhwPrq+qKqroVOAc4uq/M0dwx1vQ84NFJUlVfrKpr2vWXAbu2w1EkSdL0+m3gt4CbAdq2wh5jjUiSJA3EyHpW0PSEuKpneSNwxNbKVNXmJDcBe9P0rJjxu8AXquon/SdIciJwIsDy5ctZt27dwILvtWnTpqEde9p0vS5PXnnHDW9mi3Ou7aPU9bqcJNNQl/3X7kKWZ7O1fZbv2pxrWOeYNtNwbS7QrVVVSQogyW7jDkiSJA3GKJMVi5bk/jRDQx472/aqOgM4A2DVqlW1evXqocSxbt06hnXsaTPuupyrK/wJvduPW73g7aM07rpcSqahLvuv3YUsz2Zr+5y8cjOvu3TZ0M4xbabh2lyg9yV5C7BXkucCvw+8dcwxSdvkMA9Jmp9RDgO5GjigZ3n/dt2sZZIsA/YErmuX9wf+GXhmVX1z6NFKkqROq6q/oRk2+gGaeStOqao3zbWfE35LktR9o+xZcTFwSDtL99XAscDT+8qsBY4HPgMcA1zYdu/cCzgfWFNVnxphzJIkqYPaibv/raoeCfzrAvdzwm9JkjpuZMmKdg6Kk2g+2HcEzqqqy5K8CrikqtYCZwJnJ1kPXE+T0AA4CTgYOCXJKe26x1bV90YVv6aDd0iQpMlQVbcl+VmSPavqpgXsevuE3wBJZib87k1WHA2c2j4+Dzh9ZsLvnjK3T/g92zxakiRpcUY6Z0VVXQBc0LfulJ7HtwBPnmW/VwOvHnqAWtL6x4iajJCkibcJuDTJv9LeEQSgql6wjX0mbsJvJ1YdjmHV66VXb5k7W7nfnlsszzWZ8Hy86d0f7jvmltvHfb14zQ7HtNXrIF4r8zEzGfiwTNPfrN9ir9mJmmBTkiSpxwfbfyM16gm/nVh1OIZVr/2TBfdPBjzXZMKDMO4JiL1mh2Pa6nUUrxXg9snAh2Xcr8dxWuw1a7JCkiRNlCT3qqr/qap3bMfuC5nwe6MTfkuSNB4mK6QFcE4LSeqEDwEPBkjygar63QXs64TfkiRNAJMVkiRp0qTn8b0XsqMTfmsaOE+XpKXAZIU0QDYOJGkkaiuP57ezE35LktR5JiskSdKkeWCSH9D0sNi1fUy7XFV1t/GFJkmSBsFkhSRJmihVteO4Y5AkScO1w7gDkCRJkiRJ6mWyQpIkSZIkdYrJCkmSJEmS1CnOWaElq//OHF3RG5d3C5EkScPm3cokTSKTFVoyTAJIkiRJ0tJgskIaIn/JkCRpunW1p6ckdZ3JCkmSJGmK+eOKpC4yWSFJkiRNEXt7SJoE3g1EkiRJkiR1iskKSZIkSZLUKSYrJEmSJElSp5iskCRJkiRJneIEm9KY9U5y5ezbkiRJkmTPCkmSJEmS1DH2rNDEsAeCJEmSJE0He1ZIkiRJkqROsWeFJGli2eNKkiRpabJnhSRJkiRJ6hR7VkiSJEm6XW+vtRn2XpM0aiYrJGkKOFxCku6s/0u574/S+MyWJFsKfJ/Zfg4DkSRJkiRJnWLPCqlj/AVckqRu8BdRSRofe1ZIkiRJkqROMVkhTbm/JbIAACAASURBVLgVa85nxZrzufTqm5bsWD9JkiRJ08VhIOqE2bpZOhxCkiSpGxwSI2nUTFZIHWfSRpKk0bCHoiR1h8kKSZIkaR5mS2b4Q4IkDYdzVkiSJEmSpE6xZ4UmlsMjJEmSJGlpsmeFJEmSJEnqFJMVkiRJkiSpUxwGIkmSJGlBvJWptH187cyfPSskSZIkSVKn2LNCmjDeA16SJEnSUmfPCkmSJEmS1Cn2rJCkJchb+0qSpC5xrgYtlMkKSdKSYZJGkiRpaTBZIUmSJGlRepPFJ6/czOrxhSJpiTBZIUmSpKnQ/4XaprAkdZcTbEqSJEmSpE4xWSFJkiRJkjrFZIUkSZIkSeoUkxWSJEmSJKlTTFZIkiRJkqROcQpkjcXMbNwnr9zMCT0zc0uSJEmSZM8KSZIkSZLUKSYrJEmSJElSp5iskCRJkiRJnWKyQpIkSZIkdYrJCkmSJEmS1CkjTVYkOTLJ15OsT7Jmlu07Jzm33X5RkhXt+r2TfCLJpiSnjzJmSZIkSZI0WiNLViTZEXgz8HjgUOBpSQ7tK/Zs4IaqOhh4A/Dadv0twMuBF40oXEmSJEmSNCaj7FlxOLC+qq6oqluBc4Cj+8ocDbyjfXwe8Ogkqaqbq+q/aJIWmkAr1px/+z9JksbJnp6SJHVfqmo0J0qOAY6sque0y78HHFFVJ/WU+UpbZmO7/M22zLXt8gnAqt59+s5xInAiwPLlyx9yzjnnDOW5bNq0id13330ox16qLr36ptsfr9xvz9uXl+8K3/3xncv3lhnE8mwGfY5xnLPXTF2u3G/PbcaguS2F13hXru3e63Icr5+lZlDX5iMf+cjPV9WqAYQ0cdqenv8PeAywEbgYeFpVXd5T5vnAA6rqeUmOBX67qp6aZDfgQcBhwGFba4/0WrVqVV1yySWLinndunWsXr16UceYVtv6keTklZt53aXLFn2ODacdNe9zTouTV27mj47r/01SizXp7wX9r42uvHYG9V6wvfrrYSmZ65pNss32yPj+KkNQVWcAZ0DTOBjWi3nS3yjG4YSeN58Nx62+fXlrbw69ZQaxPJtBn2Mc5+w1U5cbjlu9xZv9Un4DHJal8BrvyrXde12O4/Wz1F4LS+Ha7IDbe3oCJJnp6Xl5T5mjgVPbx+cBp8/09AT+K8nBI4xXmkhzfTGVpLmMchjI1cABPcv7t+tmLZNkGbAncN1IopMkSdNgP+CqnuWN7bpZy1TVZuAmYO+RRCdJkoDR9qy4GDgkyUE0SYljgaf3lVkLHA98BjgGuLBGNU5FkiRpAPqGpbJu3bpFHW/Tpk2LPsa0Onnl5q1uW77rtrfPV//fZhDHnHSz1a3X8OJN+nvBXNfEuF47g3ov2F5veveH77RuqQxlXew1O7JkRVVtTnIS8DFgR+CsqrosyauAS6pqLXAmcHaS9cD1NAkNAJJsAO4G7JTkScBje8eXSpIkzcNCenpu3J6enoMelurwn+23reFlA5uz4rjV8z7ntJitbvvrSQs36e8F/a+Nrrx2xj1nxWyWyutlsdfsSP8qVXUBcEHfulN6Ht8CPHkr+64YanCSJGka2NNTkqQJ0K0UkiRJ0hDZ01OSpMlgskKSJE0Ve3pK4zfbbSq9Y4ikXiYrJEmSNHG8NaYkLW0mKyRJkjTxZvulXpI0uXYYdwCSJEmSJEm9TFZIkiRJkqROcRiIpC26zjrmV5IkSdK4mazQdun/cuuXXUmSNEzOSSFJ08VkhSRNOGfElyR13XySTX6eSeplskJ34geFJEmSJGmcTFZoKBwW0m3+fSRJkjRMDt3SYnk3EEmSJEmS1CkmKyRJkiRJUqc4DESSJEmdYxdySZpu9qyQJEmSJEmdYrJCkiRJkiR1isNApCng3T8kSZIkTRJ7VkiSJEmSpE4xWSFJkiRJkjrFYSCaU/9s3A4jkCRJkiQNk8kKSXdigkqSNGreqlSS1MtkhSRpapiIkyRJmgwmKyRJkiR1jglmabqZrJAkSZI0cUxmdItDuTRoJiskSZIkSeoIE3ENkxWStkvvm+i0voFKkiRJGo4dxh2AJEmSJElSL5MVkiRJkiSpUxwGIkkTxgmsJEmStNSZrJAkSZLUeSbrpeniMBBJkiRJktQpJiskSZIkSVKnmKyQJEmSJEmd4pwVkubUP0Z0w2lHjSmS6dRb/9a9JEmSpoHJCvlFSOoYX5OSpoGTJUqTy9evRsFhIJIkSZIkqVPsWTGF/NVWw+B1JUmSJGlQTFZIkiRp6Ow2LklaCJMVkiRJkiaeE4IPj8nG8ZrWa9s5KyRJkiRJUqfYs0LSSDinhSRJkqT5MlkhSVIPE2uSJEnjZ7JC0lD4hU+TYFrHgErSNJpt3gXf9zWJpqX9YrJCkiRJi+Lke5pU0/KlT5pETrApSZIkSZI6xZ4VU8Du+JIkaZDsSSFJGjaTFUtAfzLC5IQkSZK0cA4LkbrDZIUkjZkJRkmS1CX2nlIXmKyQNBbb6hE0s06SJGmcbJ9I42OyouN8g5Sk8XKonSRJ0uiZrJDUSSbqJElS19g+kUbHZIWkiTWpv3BPatySppfj1yWpu5ZqEs1kxYRZqheiJEmSJEkzTFYM0CASCf7iKm0/Xz+SND9ztVnsSSFNF1/zS8tS+YHbZMUizOdFPdfEbH65kuZvEl8vS+XDQls3idelJGk4Zvt+4GeDtH1MVkhasua6PWo/GxMaBG/Lq2EbxnuZv6pK22ehP16CnwPSfJmsWICZN5qTV25m9XhDkTQkK9acz8krN3PCmvPn1RtqoQkRabZGq73uJEnSOHUxqTbSZEWSI4G/BXYE3lZVp/Vt3xl4J/AQ4DrgqVW1od32EuDZwG3AC6rqYyMMXZIkLRFLvT1i0lTqtoXOGTOML42+T0yXSf17jyxZkWRH4M3AY4CNwMVJ1lbV5T3Fng3cUFUHJzkWeC3w1CSHAscC9wf2Bf4tyS9V1W2jil+SJE0+2yOSptGkflnVdBtlz4rDgfVVdQVAknOAo4HexsHRwKnt4/OA05OkXX9OVf0EuDLJ+vZ4nxlR7JIkaWmwPSKpU+ZKJPQORT/BpINGpAvDQlJVozlRcgxwZFU9p13+PeCIqjqpp8xX2jIb2+VvAkfQNBg+W1XvatefCXy0qs7rO8eJwInt4n2Arw/p6ewDXDukY08b63JwrMvBsS4Hx7ocrEHV54FVdc8BHGfiTGh7xNfRcFivw2PdDof1OhzW6/DMVbfbbI8sqQk2q+oM4IxhnyfJJVW1atjnmQbW5eBYl4NjXQ6OdTlY1udkGHR7xL/7cFivw2PdDof1OhzW6/Astm53GGQwc7gaOKBnef923axlkiwD9qSZ2Go++0qSJM3F9ogkSRNglMmKi4FDkhyUZCeaCarW9pVZCxzfPj4GuLCacSprgWOT7JzkIOAQ4HMjiluSJC0dtkckSZoAIxsGUlWbk5wEfIzmVmFnVdVlSV4FXFJVa4EzgbPbCauup2lA0JZ7H83kV5uBPxzzzNtDH2oyRazLwbEuB8e6HBzrcrCsz0Wa0PaIf/fhsF6Hx7odDut1OKzX4VlU3Y5sgk1JkiRJkqT5GOUwEEmSJEmSpDmZrJAkSZIkSZ1ismIBkhyZ5OtJ1idZM+54JkmSA5J8IsnlSS5L8sft+nsk+dck32j/v/u4Y50USXZM8sUk/9IuH5Tkovb6PLedOE7zkGSvJOcl+VqSryb5Fa/N7ZPkT9rX+FeSvDfJLl6b85PkrCTfS/KVnnWzXodp/F1bp19O8uDxRa5hsd0xOLZDhss2yeDZNhke2yqDM+y2i8mKeUqyI/Bm4PHAocDTkhw63qgmymbg5Ko6FHgY8Idt/a0B/r2qDgH+vV3W/Pwx8NWe5dcCb6iqg4EbgGePJarJ9LfA/62q+wIPpKlXr80FSrIf8AJgVVUdRjN54bF4bc7X24Ej+9Zt7Tp8PM2dKA4BTgT+YUQxakRsdwyc7ZDhsk0yeLZNhsC2ysC9nSG2XUxWzN/hwPqquqKqbgXOAY4ec0wTo6q+XVVfaB//kOYNdz+aOnxHW+wdwJPGE+FkSbI/cBTwtnY5wKOA89oi1uU8JdkT+F80s/9TVbdW1Y14bW6vZcCuSZYBdwW+jdfmvFTVf9DceaLX1q7Do4F3VuOzwF5JfmE0kWpEbHcMkO2Q4bFNMni2TYbOtsqADLvtYrJi/vYDrupZ3tiu0wIlWQE8CLgIWF5V3243fQdYPqawJs0bgT8DftYu7w3cWFWb22Wvz/k7CPg+8E9tF9a3JdkNr80Fq6qrgb8B/ofmg/8m4PN4bS7G1q5DP5OWPv/GQ2I7ZOBskwyebZMhsa0yEgNru5is0Egl2R34APDCqvpB77Zq7qPrvXTnkOQJwPeq6vPjjmWJWAY8GPiHqnoQcDN93Sq9NuenHZN4NE0ja19gN+7cNVDbyetQWjzbIYNlm2RobJsMiW2V0VrsdWqyYv6uBg7oWd6/Xad5SnIXmgbCu6vqg+3q7850/2n//9644psgvwb8VpINNN2CH0UzrnGvtjsbeH0uxEZgY1Vd1C6fR9NA8NpcuN8Arqyq71fVT4EP0lyvXpvbb2vXoZ9JS59/4wGzHTIUtkmGw7bJ8NhWGb6BtV1MVszfxcAh7UyxO9FMxLJ2zDFNjHb84pnAV6vq9T2b1gLHt4+PBz486tgmTVW9pKr2r6oVNNfhhVV1HPAJ4Ji2mHU5T1X1HeCqJPdpVz0auByvze3xP8DDkty1fc3P1KXX5vbb2nW4FnhmO7P2w4Cberpcammw3TFAtkOGwzbJcNg2GSrbKsM3sLZLmp4Zmo8kv0kzLm9H4Kyqes2YQ5oYSR4O/CdwKXeMaXwpzXjR9wH3Ar4FPKWq+idp0VYkWQ28qKqekOTeNL9q3AP4IvCMqvrJOOObFEl+mWZisJ2AK4Bn0SRzvTYXKMkrgafSzLz/ReA5NOMRvTbnkOS9wGpgH+C7wCuADzHLddg2sE6n6br6I+BZVXXJOOLW8NjuGBzbIcNnm2SwbJsMj22VwRl228VkhSRJkiRJ6hSHgUiSJEmSpE4xWSFJkiRJkjrFZIUkSZIkSeoUkxWSJEmSJKlTTFZIkiRJkqROMVkhSZIkSZI6xWSFJEmSJEnqFJMVkoYmyUOTfDnJLkl2S3JZksPGHZckSZoetkekyZSqGncMkpawJK8GdgF2BTZW1V+OOSRJkjRlbI9Ik8dkhaShSrITcDFwC/CrVXXbmEOSJElTxvaINHkcBiJp2PYGdgf2oPlFQ5IkadRsj0gTxp4VkoYqyVrgHOAg4Beq6qQxhyRJkqaM7RFp8iwbdwCSlq4kzwR+WlXvSbIj8Okkj6qqC8cdmyRJmg62R6TJZM8KSZIkSZLUKc5ZIUmSJEmSOsVkhSRJkiRJ6hSTFZIkSZIkqVNMVkiSJEmSpE4xWSFJkiRJkjrFZIUkSZIkSeoUkxWSJEmSJKlTTFZIkiRJkqROMVkhSZIkSZI6xWSFJEmSJEnqFJMVkiRJkiSpU0xWSJIkSZKkTjFZIUmSJEmSOsVkhaTtluTtSV494GPumuQjSW5K8v5BHnvckqxOsnGeZU9N8q5hxyRJkiR1kckKaYGSnJTkkiQ/SfL2WbY/OsnXkvwoySeSHNizbeckZyX5QZLvJPnT+e47RY4BlgN7V9WTxx2MJEmSpNEzWSEt3DXAq4Gz+jck2Qf4IPBy4B7AJcC5PUVOBQ4BDgQeCfxZkiPnue+0OBD4f1W1ebaNSZaNOB5JkiRJI2ayQlqgqvpgVX0IuG6Wzb8DXFZV76+qW2iSEw9Mct92+/HAn1fVDVX1VeCtwAnz3HcLSV6c5OokP0zy9SSPbtcfnuQzSW5M8u0kpyfZqWe/SvL8JN9o9/3zJL+Y5NNtj4/3zZSfGbaQ5KVJrk2yIclxW6ubJE9I8qX23J9O8oC54u3b/5XAKcBTk2xK8uwkJyT5VJI3JLkOODXJDkleluRbSb6X5J1J9myPsaJ9js9KclWSG5I8L8lDk3y5je30bTyHU5O8P8m72lgvTfJLSV7SnuuqJI/tKb9vkrVJrk+yPslze7bt2g6VuSHJ5cBD+861b5IPJPl+kiuTvGBrcUmSJEnTxGSFNFj3B/57ZqGqbga+Cdw/yd2BX+jd3j6+/1z79p8kyX2Ak4CHVtUewOOADe3m24A/AfYBfgV4NPD8vkM8DngI8DDgz4AzgGcABwCHAU/rKfvz7bH2o0m2nNGevz+mB9H0NvkDYG/gLcDadujLtuK9XVW9AvgL4Nyq2r2qzmw3HQFcQTM85DU0CZ4TaHqn3BvYHehPQBxB04vlqcAbgf8D/AZNfT4lya/3n7/HE4GzgbsDXwQ+RvN+uR/wqva5zTgH2AjsSzOE5S+SPKrd9grgF9t/j6Opv5n62gH4CM3ffD+av9MLkzxuG3FJkiRJU8FkhTRYuwM39a27Cdij3Ubf9pltc+3b7zZgZ+DQJHepqg1V9U2Aqvp8VX22qjZX1QaaL9b9X8z/qqp+UFWXAV8BPl5VV1TVTcBHgQf1lX95Vf2kqj4JnA88ZZaYTgTeUlUXVdVtVfUO4Cc0CZGtxjtP11TVm9rn9GPgOOD1bcybgJcAx/YNEfnzqrqlqj4O3Ay8t6q+V1VXA/85y3Ps9Z9V9bF2KMr7gXsCp1XVT2mSEyuS7JXkAODXgBe35/oS8Dbgme1xngK8pqqur6qrgL/rOcdDgXtW1auq6taquoKmp82xC6gXSZIkaUkyWSEN1ibgbn3r7gb8sN1G3/aZbXPtu4WqWg+8kGaoyPeSnJNkX4B2yMK/tBN4/oCmp8I+fYf4bs/jH8+yvHvP8g1tL48Z36LpRdDvQODkdpjFjUlupOmpse+24p2nq/qW923j6I1pGU3PixkLeY79+steW1W39SzT7r8vcH1V9f6NvkXTU2Imzqv6ts04ENi3r75e2vccJEmSpKlkskIarMuAB84sJNmNZgjAZVV1A/Dt3u3t48vm2ne2E1XVe6rq4TRfegt4bbvpH4CvAYdU1d1ovgBnEc/p7m0sM+5FM8lov6toehHs1fPvrlX13jninY/qW76mPU5vTJvZMskwCtcA90jS2/vlXsDV7eNv0yRserfNuAq4sq++9qiq3xxuyJIkSVL3mayQFijJsiS7ADsCOybZpWf4wT8DhyX53bbMKcCXq+pr7fZ3Ai9Lcvd24sznAm+f5769MdwnyaOS7AzcQvNr/8/azXsAPwA2tef43wN42q9MslOSRwBPoBka0e+twPOSHJHGbkmOSrLHHPFuj/cCf5LkoCS7c8c8F7PeQWRY2qEdnwb+sr0OHgA8G3hXW+R9wEvav/f+wB/17P454IftxKO7JtkxyWFJtpiEU5IkSZpGJiukhXsZzZftNTSTUv64XUdVfR/4XZpJIG+gmeSxdw6CV9BMmvkt4JPAX1fV/53nvr12Bk4DrgW+A/wczbwNAC8Cnk4zfOStLP72p99p47kGeDfwvNkSKFV1CU3y5fS2/HruuNPJtuLdHmfRTID5H8CVNAmQP9rmHsPzNGAFTf38M/CKqvq3dtsraf7WVwIfp4kZgHZYyROAX263X0sz38WeowpckiRJ6qpU9feulqRGktXAu6pq/3HHIkmSJGl62LNCkiRJkiR1iskK/f/t3X+wZ2ddH/D3x10hgWiooFubBDY0KRpIFVgT66/ZgNjQqEtnggQREwumjmbUGkdXx2LK6DR0xKgFrZHEhti60IB02wQpGNfaVkMSccwPmmEJqyQgmoDBjYSw8Okf96y9XG+ye7/33O89997Xa2Znv+ec53u+n/vs892dvPOc5wEAAIBJ8RgIAAAAMClmVgAAAACTsv3YTTampz3tab1z585R7/nwww/nyU9+8qj33Or06bj05/j06fj06fhW2qe33377A939pWtYEgDAqmzasGLnzp257bbbRr3ngQMHsnv37lHvudXp03Hpz/Hp0/Hp0/GttE+r6k/XrhoAgNXzGAgAAAAwKcIKAAAAYFKEFQAAAMCkCCsAAACASRFWAAAAAJMirAAAAAAmRVgBAAAATIqwAgAAAJgUYQUAAAAwKXMNK6rq/Kq6p6oOVtXeZa4/sareMly/pap2Dud3VtWnquqPh1//YZ51AwAAAPOzfV4fVFXbkrwxyYuS3Jfk1qra3913L2r2qiSf6O4zquqiJK9L8rLh2ge7+6vnVS8AAACwPuY5s+KcJAe7+97ufjTJviR7lrTZk+S64fUNSV5YVTXHGgEAAIB1NreZFUlOSfLhRcf3JTn3sdp095GqeijJU4drp1fV+5J8MslPdffvL/2Aqro0yaVJsmPHjhw4cGDUH+Dw4cOj33Or06fj0p/j06ezueP+hx7z2o4To09HZpwCAJvNPMOK1fhokqd394NV9fwk76iqZ3f3Jxc36u6rk1ydJLt27erdu3ePWsSBAwcy9j23On06Lv05Pn06m0v23viY1y4/+0i+Q5+OyjgFADabeT4Gcn+S0xYdnzqcW7ZNVW1PcnKSB7v70939YJJ09+1JPpjkH615xQAAAMDczTOsuDXJmVV1elU9IclFSfYvabM/ycXD6wuT3NzdXVVfOizQmap6ZpIzk9w7p7oBAACAOZrbYyDDGhSXJXlXkm1Jru3uu6rqtUlu6+79Sa5Jcn1VHUzy8SwEGknyTUleW1WfSfK5JN/X3R+fV+0AAADA/Mx1zYruvinJTUvOvWbR60eSvHSZ970tydvWvEAAAABg3c3zMRAAAACAYxJWAAAAAJMirAAAAAAmRVgBAAAATIqwAgAAAJgUYQUAAAAwKcIKAAAAYFKEFQAAAMCkCCsAAACASRFWAAAAAJMirAAAAAAmRVgBAAAATIqwAgAAAJgUYQUAAAAwKcIKAAAAYFKEFQAAAMCkCCsAAACASRFWAAAAAJMirAAAAAAmRVgBAAAATIqwAgAAAJgUYQUAAAAwKcIKAAAAYFKEFQAAAMCkCCsAAACASRFWAAAAAJMirAAAAAAmRVgBAAAATIqwAgAAAJgUYQUAAAAwKcIKAAAAYFKEFQAAAMCkCCsAAACASRFWAAAAAJMirAAAAAAmRVgBAAAATIqwAgAAAJgUYQUAAAAwKcIKAAAAYFKEFQAAAMCkCCsAAACASRFWAAAAAJMirAAAAAAmRVgBAAAATIqwAgAAAJgUYQUAAAAwKdvXuwAANr6de29c7xIAANhEzKwAAAAAJmWuYUVVnV9V91TVwarau8z1J1bVW4brt1TVziXXn15Vh6vqR+dVMwAAADBfcwsrqmpbkjcmeXGSs5K8vKrOWtLsVUk+0d1nJLkqyeuWXP/5JO9c61oBAACA9TPPmRXnJDnY3fd296NJ9iXZs6TNniTXDa9vSPLCqqokqaqXJPlQkrvmVC8AAACwDua5wOYpST686Pi+JOc+VpvuPlJVDyV5alU9kuTHk7woyWM+AlJVlya5NEl27NiRAwcOjFZ8khw+fHj0e251+nRc+nN8+vT4XH72keNuu+PE6NORGacAwGazUXYDuSLJVd19eJhosazuvjrJ1Umya9eu3r1796hFHDhwIGPfc6vTp+PSn+PTp8fnkhXsBnL52UfyY7/98N85f+jKC8YsaUsxTgGAzWaeYcX9SU5bdHzqcG65NvdV1fYkJyd5MAszMC6sqn+X5ClJPldVj3T3G9a+bAAAAGCe5hlW3JrkzKo6PQuhxEVJvnNJm/1JLk7yB0kuTHJzd3eSbzzaoKquSHJYUAEAAACb09zCimENisuSvCvJtiTXdvddVfXaJLd19/4k1yS5vqoOJvl4FgINAAAAYAuZ65oV3X1TkpuWnHvNotePJHnpMe5xxZoUBwAAAEzCPLcuBQAAADgmYQUAAAAwKcIKAAAAYFKEFQAAAMCkCCsAAACASRFWAAAAAJMirAAAAAAmZft6FwDAxrNz743rXQIAAJuYmRUAAADApAgrAAAAgEkRVgAAAACTIqwAAAAAJkVYAQAAAEyKsAIAAACYFGEFAAAAMCnCCgAAAGBShBUAAADApAgrAAAAgEkRVgAAAACTIqwAAAAAJkVYAQAAAEyKsAIAAACYFGEFAAAAMCnCCgAAAGBShBUAAADApAgrAAAAgEkRVgAAAACTIqwAAAAAJkVYAQAAAEyKsAIAAACYFGEFAAAAMCnCCgAAAGBShBUAAADApAgrAAAAgEkRVgAAAACTIqwAAAAAJkVYAQAAAEyKsAIAAACYFGEFAAAAMCnCCgAAAGBShBUAAADApAgrAAAAgEkRVgAAAACTIqwAAAAAJkVYAQAAAEyKsAIAAACYFGEFAAAAMCnb5/lhVXV+kl9Msi3Jm7r7yiXXn5jkzUmen+TBJC/r7kNVdU6Sq482S3JFd//W/CoHYK3t3Hvj5x0fuvKCdaoEAID1NtPMiqo6e4b3bEvyxiQvTnJWkpdX1VlLmr0qySe6+4wkVyV53XD+ziS7uvurk5yf5Feraq5BCwAAADAfsz4G8stV9d6q+v6qOvk433NOkoPdfW93P5pkX5I9S9rsSXLd8PqGJC+squruv+nuI8P5E5L0jHUDAAAAE1fds/13f1WdmeRfJHlpkvcm+fXufvfjtL8wyfnd/erh+JVJzu3uyxa1uXNoc99w/MGhzQNVdW6Sa5M8I8krl3sMpKouTXJpkuzYseP5+/btm+lneyyHDx/OSSedNOo9tzp9Oi79OT59urw77n9o5vfuODH52KeO3e7sU443C2el4/S88867vbt3rWFJAACrMvOjFN39gar6qSS3JfmlJM+tqkryk9399rEKXPR5tyR5dlV9ZZLrquqd3f3IkjZXZ1jbYteuXb179+5Razhw4EDGvudWp0/HpT/Hp0+Xd8mS9SVW4vKzj+T1dxz7n59Dr9g982dsNcYpALDZzLpmxT+uqquSvD/JC5J8W3d/5fD6qsd42/1JTlt0fOpwbtk2w5oUJ2dhBaPd9AAAE99JREFUoc2/1d3vT3I4yXNmqR0AAACYtllnVvz7JG/KwiyKv53M290fGWZbLOfWJGdW1elZCCUuSvKdS9rsT3Jxkj9IcmGSm7u7h/d8uLuPVNUzknxFkkMz1g7ABrB0d5Cl7BYCALB5zRpWXJDkU9392SSpqi9IcsKwEOb1y71hCBouS/KuLGxdem1331VVr01yW3fvT3JNkuur6mCSj2ch0EiSb0iyt6o+k+RzSb6/ux+YsXYAAABgwmYNK96T5Juz8DhGkjwpyf9I8nWP96buvinJTUvOvWbR60eysGDn0vddn2TZEAQAAADYXGbduvSE7j4aVGR4/aRxSgIAAAC2slnDioer6nlHD6rq+UmOYyM6AAAAgMc362MgP5zkv1TVR5JUkr+f5GWjVQUjWLo4n8X4YHbHWuwSAADGNFNY0d23VtVXJHnWcOqe7v7MeGUBAAAAW9WsMyuS5GuS7Bzu8byqSne/eZSqAAAAgC1rprCiqq5P8g+T/HGSzw6nO4mwAgAAAFiVWWdW7EpyVnf3mMUAAAAAzLobyJ1ZWFQTAAAAYFSzzqx4WpK7q+q9ST599GR3f/soVQEAAABb1qxhxRVjFgEAAABw1Kxbl/5eVT0jyZnd/Z6qelKSbeOWBgAAAGxFM61ZUVXfm+SGJL86nDolyTvGKgoAAADYumZdYPMHknx9kk8mSXd/IMmXjVUUAAAAsHXNGlZ8ursfPXpQVduT2MYUAAAAWLVZF9j8var6ySQnVtWLknx/kv82XlkArKede29c7xIAANjCZp1ZsTfJXya5I8m/THJTkp8aqygAAABg65p1N5DPJfm14RcAAADAaGYKK6rqQ1lmjYrufuaqKwIAAAC2tFnXrNi16PUJSV6a5EtWXw4AAACw1c20ZkV3P7jo1/3d/QtJLhi5NgAAAGALmvUxkOctOvyCLMy0mHWWBgAAAMDfmjVgeP2i10eSHEryHauuBgAAANjyZt0N5LyxCwEAAABIZn8M5Ece73p3//xs5QAAAABb3Wp2A/maJPuH429L8t4kHxijKAAAAGDrmjWsODXJ87r7r5Okqq5IcmN3f9dYhcEU7Nx74+cdH7rSpjcAAABrbaatS5PsSPLoouNHh3MAAAAAqzLrzIo3J3lvVf3WcPySJNeNUxIAAACwlc26G8jPVtU7k3zjcOp7uvt945UFAAAAbFWzPgaSJE9K8snu/sUk91XV6SPVBAAAAGxhM4UVVfXTSX48yU8Mp74wyW+MVRQAAACwdc26ZsU/T/LcJH+UJN39kar6otGqgg3CbiEwHb6PAACbx6yPgTza3Z2kk6SqnjxeSQAAAMBWNmtY8daq+tUkT6mq703yniS/Nl5ZAAAAwFY1624gP1dVL0ryySTPSvKa7n73qJUBAAAAW9KKw4qq2pbkPd19XhIBBQAAADCqFT8G0t2fTfK5qjp5DeoBAAAAtrhZdwM5nOSOqnp3koePnuzuHxylKgAAAGDLmjWsePvwCwAAAGBUKworqurp3f1n3X3dWhUEAAAAbG0rXbPiHUdfVNXbRq4FAAAAYMVhRS16/cwxCwEAAABIVh5W9GO8BgAAABjFShfY/Kqq+mQWZlicOLzOcNzd/cWjVseWsXPvjZ93fOjKC1Z9j3l8JgAAAONbUVjR3dvWqhAAAACAZOWPgQAAAACsKWEFAAAAMCnCCgAAAGBS5hpWVNX5VXVPVR2sqr3LXH9iVb1luH5LVe0czr+oqm6vqjuG318wz7oBAACA+VnpbiAzq6ptSd6Y5EVJ7ktya1Xt7+67FzV7VZJPdPcZVXVRktcleVmSB5J8W3d/pKqek+RdSU6ZV+1sDivdLQQAAID1Mc+ZFeckOdjd93b3o0n2JdmzpM2eJNcNr29I8sKqqu5+X3d/ZDh/Vxa2TX3iXKoGAAAA5qq6ez4fVHVhkvO7+9XD8SuTnNvdly1qc+fQ5r7h+INDmweW3Of7uvubl/mMS5NcmiQ7dux4/r59+0b9GQ4fPpyTTjpp1HtudUf79I77H/q882efcvKK77X0Hiu13Gceq64x6h6TMTq+rdqnq/0+PZ4dJyYf+9Tq7zP17+M8rXScnnfeebd39641LAkAYFXm9hjIGKrq2Vl4NORblrve3VcnuTpJdu3a1bt37x718w8cOJCx77nVHe3TS5Y8onHoFbtXfK+l91ip5T7zWHWNUfeYjNHxbdU+Xe336fFcfvaRvP6O1f/zM/Xv4zxt1XEKAGxe83wM5P4kpy06PnU4t2ybqtqe5OQkDw7Hpyb5rSTf3d0fXPNqAQAAgHUxz5kVtyY5s6pOz0IocVGS71zSZn+Si5P8QZILk9zc3V1VT0lyY5K93f2/51gzrMrSRT0PXXnBOlUCAACwccxtZkV3H0lyWRZ28nh/krd2911V9dqq+vah2TVJnlpVB5P8SJKj25teluSMJK+pqj8efn3ZvGoHAAAA5meua1Z0901Jblpy7jWLXj+S5KXLvO9nkvzMmhcIAAAArLt5rlkBAAAAcEzCCgAAAGBShBUAAADApMx1zQqYsqU7dwAAALA+zKwAAAAAJkVYAQAAAEyKsAIAAACYFGEFAAAAMCnCCgAAAGBS7AYCK3CsHUNWev3QlResuqYxTLUueDx28AEA2LzMrAAAAAAmRVgBAAAATIqwAgAAAJgUYQUAAAAwKcIKAAAAYFKEFQAAAMCkCCsAAACASRFWAAAAAJMirAAAAAAmRVgBAAAATMr29S4AGNfOvTd+3vGhKy9Yp0rYKJaOGQAAWG9mVgAAAACTIqwAAAAAJkVYAQAAAEyKsAIAAACYFGEFAAAAMCl2A4GJsZsHAACw1ZlZAQAAAEyKsAIAAACYFGEFAAAAMCnCCgAAAGBShBUAAADApNgNhFGsxw4WSz9zIzqen+FYfbv4+uVnH4mvNUvZYQYAgI3GzAoAAABgUoQVAAAAwKQIKwAAAIBJEVYAAAAAkyKsAAAAACbFtgGwwWyGXVBgHuyCAgCwcZlZAQAAAEyKsAIAAACYFGEFAAAAMCnCCgAAAGBSLLDJXKx0oTuLSI5HXwIAABuNmRUAAADApAgrAAAAgEkRVgAAAACTMtewoqrOr6p7qupgVe1d5voTq+otw/VbqmrncP6pVfW7VXW4qt4wz5oBAACA+ZpbWFFV25K8McmLk5yV5OVVddaSZq9K8onuPiPJVUleN5x/JMm/TvKjcyoXAAAAWCfz3A3knCQHu/veJKmqfUn2JLl7UZs9Sa4YXt+Q5A1VVd39cJL/VVVnzLFe2LJWunsLG4sdYgAAmLrq7vl8UNWFSc7v7lcPx69Mcm53X7aozZ1Dm/uG4w8ObR4Yji9Jsmvxe5Z8xqVJLk2SHTt2PH/fvn2j/gyHDx/OSSedNOo9N4s77n/o847PPuXk47p+tE+XXmc2O05MPvap8e+79M9zK9kM3/upfb/Wapwey2Yexysdp+edd97t3b1rDUsCAFiVec6sWHPdfXWSq5Nk165dvXv37lHvf+DAgYx9z83ikqX/J/4Vu4/r+tE+XXqd2Vx+9pG8/o7xv9ZL/zy3ks3wvZ/a92utxumxbOZxvBnGKQDAYvNcYPP+JKctOj51OLdsm6ranuTkJA/OpToAAABgEuYZVtya5MyqOr2qnpDkoiT7l7TZn+Ti4fWFSW7ueT2nAgAAAEzC3ObhdveRqrosybuSbEtybXffVVWvTXJbd+9Pck2S66vqYJKPZyHQSJJU1aEkX5zkCVX1kiTf0t13L/0cAAAAYGOb60PD3X1TkpuWnHvNotePJHnpY7x355oWBwAAAEzCPB8DAQAAADgmYQUAAAAwKcIKAAAAYFKEFQAAAMCkCCsAAACASZnrbiBw1M69NyZJLj/7SC4ZXgMAAEBiZgUAAAAwMcIKAAAAYFKEFQAAAMCkCCsAAACASbHAJmtip0UzAQAAmJGZFQAAAMCkCCsAAACASRFWAAAAAJMirAAAAAAmRVgBAAAATIrdQDimpTt7HLrygnWqBEiOvduO7+jylus3fQUAME1mVgAAAACTIqwAAAAAJkVYAQAAAEyKsAIAAACYFGEFAAAAMCl2A9mCVru7x7F2ImDzs0MMAACwlsysAAAAACZFWAEAAABMirACAAAAmBRhBQAAADApwgoAAABgUuwGssEdz84cdmpgtY41zuwOMi127AEAYKMzswIAAACYFGEFAAAAMCnCCgAAAGBShBUAAADApAgrAAAAgEmxG8jEzWOXBTsHwHT4PgIAgJkVAAAAwMQIKwAAAIBJEVYAAAAAkyKsAAAAACbFAptbgAX72CrWekHa5b5Lx/qMeSySy3hW++d1PH/fGgMAAMdmZgUAAAAwKcIKAAAAYFKEFQAAAMCkCCsAAACASRFWAAAAAJNiN5AVuOP+h3LJopXej2dF95XuxGGVeDarY30Xlo79lbafpYZjfeYs3/GV7g7C+lrtn8cs71+LcQgAsNmYWQEAAABMirACAAAAmJS5hhVVdX5V3VNVB6tq7zLXn1hVbxmu31JVOxdd+4nh/D1V9U/nWTcAAAAwP3MLK6pqW5I3JnlxkrOSvLyqzlrS7FVJPtHdZyS5KsnrhveeleSiJM9Ocn6SXx7uBwAAAGwy85xZcU6Sg919b3c/mmRfkj1L2uxJct3w+oYkL6yqGs7v6+5Pd/eHkhwc7gcAAABsMtXd8/mgqguTnN/drx6OX5nk3O6+bFGbO4c29w3HH0xybpIrkvxhd//GcP6aJO/s7huWfMalSS4dDp+V5J6Rf4ynJXlg5Htudfp0XPpzfPp0fPp0fCvt02d095euVTEAAKu1qbYu7e6rk1y9Vvevqtu6e9da3X8r0qfj0p/j06fj06fj06cAwGYzz8dA7k9y2qLjU4dzy7apqu1JTk7y4HG+FwAAANgE5hlW3JrkzKo6vaqekIUFM/cvabM/ycXD6wuT3NwLz6nsT3LRsFvI6UnOTPLeOdUNAAAAzNHcHgPp7iNVdVmSdyXZluTa7r6rql6b5Lbu3p/kmiTXV9XBJB/PQqCRod1bk9yd5EiSH+juz86r9kXW7BGTLUyfjkt/jk+fjk+fjk+fAgCbytwW2AQAAAA4HvN8DAQAAADgmIQVAAAAwKQIK45TVZ1fVfdU1cGq2rve9Ww0VXVaVf1uVd1dVXdV1Q8N57+kqt5dVR8Yfv97613rRlNV26rqfVX134fj06vqlmGsvmVY0JbjVFVPqaobqur/VtX7q+qfGKezq6p/NXzn76yq36yqE4zRlamqa6vqL6rqzkXnlh2TteCXhr79k6p63vpVDgAwO2HFcaiqbUnemOTFSc5K8vKqOmt9q9pwjiS5vLvPSvK1SX5g6MO9SX6nu89M8jvDMSvzQ0nev+j4dUmu6u4zknwiyavWpaqN6xeT/HZ3f0WSr8pC3xqnM6iqU5L8YJJd3f2cLCyufFGM0ZX6j0nOX3Luscbki7OwY9aZSS5N8itzqhEAYFTCiuNzTpKD3X1vdz+aZF+SPetc04bS3R/t7j8aXv91Fv4D8JQs9ON1Q7PrkrxkfSrcmKrq1CQXJHnTcFxJXpDkhqGJPl2Bqjo5yTdlYWeidPej3f1XMU5XY3uSE6tqe5InJflojNEV6e7/mYUdshZ7rDG5J8mbe8EfJnlKVX35fCoFABiPsOL4nJLkw4uO7xvOMYOq2pnkuUluSbKjuz86XPrzJDvWqayN6heS/FiSzw3HT03yV919ZDg2Vlfm9CR/meTXh0dr3lRVT45xOpPuvj/JzyX5syyEFA8luT3G6Bgea0z69woA2BSEFcxVVZ2U5G1Jfri7P7n4Wi/so2sv3eNUVd+a5C+6+/b1rmUT2Z7keUl+pbufm+ThLHnkwzg9fsM6CnuyEAL9gyRPzt99nIFVMiYBgM1IWHF87k9y2qLjU4dzrEBVfWEWgor/1N1vH05/7OgU5eH3v1iv+jagr0/y7VV1KAuPJr0gC+stPGWYcp8Yqyt1X5L7uvuW4fiGLIQXxulsvjnJh7r7L7v7M0nenoVxa4yu3mONSf9eAQCbgrDi+Nya5MxhBfsnZGGBuP3rXNOGMqylcE2S93f3zy+6tD/JxcPri5P813nXtlF1909096ndvTMLY/Lm7n5Fkt9NcuHQTJ+uQHf/eZIPV9WzhlMvTHJ3jNNZ/VmSr62qJw1/BxztT2N09R5rTO5P8t3DriBfm+ShRY+LAABsGLUwe5Rjqap/loX1AbYluba7f3adS9pQquobkvx+kjvy/9dX+MksrFvx1iRPT/KnSb6ju5cuJMcxVNXuJD/a3d9aVc/MwkyLL0nyviTf1d2fXs/6NpKq+uosLFj6hCT3JvmeLAS7xukMqurfJHlZFnYEel+SV2dhDQVj9DhV1W8m2Z3kaUk+luSnk7wjy4zJIRR6QxYet/mbJN/T3betR90AAKshrAAAAAAmxWMgAAAAwKQIKwAAAIBJEVYAAAAAkyKsAAAAACZFWAEAAABMirACAAAAmBRhBQAAADApwgpgzVTV11TVn1TVCVX15Kq6q6qes951AQAA01bdvd41AJtYVf1MkhOSnJjkvu7+t+tcEgAAMHHCCmBNVdUTktya5JEkX9fdn13nkgAAgInzGAiw1p6a5KQkX5SFGRYAAACPy8wKYE1V1f4k+5KcnuTLu/uydS4JAACYuO3rXQCweVXVdyf5THf/56raluT/VNULuvvm9a4NAACYLjMrAAAAgEmxZgUAAAAwKcIKAAAAYFKEFQAAAMCkCCsAAACASRFWAAAAAJMirAAAAAAmRVgBAAAATMr/A2WiwY41viiVAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1296x864 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "naVXEX4EMjaM",
        "colab_type": "text"
      },
      "source": [
        "## Two dimensional data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I-ExU_5JMjaN",
        "colab_type": "code",
        "outputId": "0fe83148-2b17-4e13-aac5-00ad57532b06",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive/')\n",
        "\n",
        "probs = np.load('/content/gdrive/My Drive/Colab Notebooks/distribution.npy')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dZzdRQ_S59K9",
        "colab_type": "text"
      },
      "source": [
        "# New Section"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oSbMtmOvMjaT",
        "colab_type": "code",
        "outputId": "59cf1253-cd3e-47af-82bc-137e938acf61",
        "colab": {}
      },
      "source": [
        "plt.imshow(probs)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f884d656d10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0yPyVrETMjac",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "samples = np.random.choice(np.arange(len(probs.ravel())), p=probs.ravel(), size=100000)\n",
        "samples_x = samples % 200\n",
        "samples_y = samples // 200\n",
        "samples = np.stack([samples_x, samples_y], axis=1)\n",
        "train = samples[0:int(0.8*len(samples))]\n",
        "test = samples[int(0.8*len(samples)):]\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ckX-U9-Mjaj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import seaborn"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FIx2hTQMMjao",
        "colab_type": "code",
        "outputId": "7cfa1832-0521-493c-ebbb-f28def31f609",
        "colab": {}
      },
      "source": [
        "with seaborn.axes_style(\"white\"):\n",
        "    seaborn.jointplot(samples_x,samples_y,kind=\"hex\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qiL6o6OtMjau",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size=1000\n",
        "n=200\n",
        "from torch.autograd import Variable\n",
        "theta=torch.zeros(n,requires_grad=True)\n",
        "epochs=30"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "omTpy04XMjaz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "class MLP(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(MLP, self).__init__()\n",
        "        self.fc = nn.Linear(n,n)\n",
        "        self.fc2= nn.Linear(n,n)\n",
        "        self.theta = nn.Parameter(torch.zeros(n,requires_grad=True))\n",
        "        \n",
        "    def forward(self, x):\n",
        "        onehot = F.one_hot(x[:,0], num_classes=200)\n",
        "        onehot = onehot.float()\n",
        "        \n",
        "        \n",
        "        _theta=torch.exp(self.theta) / torch.sum(torch.exp(self.theta))\n",
        "        prob = torch.gather(_theta,dim=0, index=x[:,0])\n",
        "\n",
        "        prob2 = nn.functional.relu(self.fc(onehot))\n",
        "        prob2 = nn.functional.relu(self.fc2(prob2))\n",
        "        prob2 = nn.functional.softmax(prob2)\n",
        "        \n",
        "        prob2=torch.gather(prob2.T, dim=0, index=x[:,1].unsqueeze(0))\n",
        "\n",
        "        return prob2, prob\n",
        "    \n",
        "    def loss(self, x):\n",
        "        px2x1 ,px1 = self.forward(x)\n",
        "        loss = -torch.sum(torch.log2(px1+1e-10) + torch.log2(px2x1+1e-10))/(2*px2x1.shape[1])\n",
        "        \n",
        "        return loss\n",
        "\n",
        "net = MLP()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kEIrMPpHMja4",
        "colab_type": "code",
        "outputId": "3d32a819-6673-444d-e2b3-1f9b7f9afdab",
        "colab": {}
      },
      "source": [
        "print(net)\n",
        "optimizer = torch.optim.Adam(net.parameters(),lr=1e-3, weight_decay=0.0001)\n",
        "for param in net.parameters():\n",
        "    print(type(param.data), param.size())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MLP(\n",
            "  (fc): Linear(in_features=200, out_features=200, bias=True)\n",
            "  (fc2): Linear(in_features=200, out_features=200, bias=True)\n",
            ")\n",
            "<class 'torch.Tensor'> torch.Size([200])\n",
            "<class 'torch.Tensor'> torch.Size([200, 200])\n",
            "<class 'torch.Tensor'> torch.Size([200])\n",
            "<class 'torch.Tensor'> torch.Size([200, 200])\n",
            "<class 'torch.Tensor'> torch.Size([200])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ulgZbP6xMja-",
        "colab_type": "code",
        "outputId": "4c63c8ee-8f16-40b7-adb2-430289017179",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        }
      },
      "source": [
        "losses, val_losses =[],[]\n",
        "train_iter = torch.utils.data.DataLoader(dataset=train,batch_size=batch_size,shuffle=True)\n",
        "val_iter = torch.utils.data.DataLoader(dataset=test,batch_size=batch_size,shuffle=True)\n",
        "losses = []\n",
        "val_losses = []\n",
        "\n",
        "\n",
        "for epoch in range(epochs):\n",
        "    loss = 0\n",
        "\n",
        "    for train_batch in train_iter:\n",
        "        loss = net.loss(train_batch)\n",
        "        loss.backward()    \n",
        "        optimizer.step()\n",
        "        optimizer.zero_grad()\n",
        "        losses.append(loss.data.numpy())\n",
        "        del loss\n",
        "        loss = 0\n",
        "        \n",
        "    with torch.set_grad_enabled(False):\n",
        "        val_loss = net.loss(torch.from_numpy(test))\n",
        "        val_losses.append(val_loss.data.numpy())\n",
        "        \n",
        "    if epoch % 10 ==0:\n",
        "        print('Epoch {}: loss {} val_loss {} '.format(epoch,losses[-1],val_losses[-1]))\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.plot(losses, label = \"train_loss\")\n",
        "plt.plot(np.arange(0, len(losses),int(len(losses)/len(val_losses))),val_losses, label = \"val_loss\")\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-100761f31210>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mlosses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_losses\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtrain_iter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataLoader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mval_iter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataLoader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mlosses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mval_losses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'torch' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NtmJ5gPGMjbD",
        "colab_type": "code",
        "outputId": "9ef115da-fee7-4e24-d7f5-87e522e1c07e",
        "colab": {}
      },
      "source": [
        "p1,p2 = net.forward(torch.from_numpy(test))\n",
        "p1 = p1.detach().numpy()\n",
        "p2 = p2.detach().numpy()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/home/peter/anaconda3/envs/RL/lib/python3.7/site-packages/ipykernel_launcher.py:21: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F0aVNSdpMjbK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "net.theta\n",
        "p1_theta = torch.exp(net.theta) / torch.sum(torch.exp(net.theta))\n",
        "p1_theta = p1_theta.detach().numpy()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cztyJyLfMjbN",
        "colab_type": "code",
        "outputId": "5850638b-7016-448e-ca9c-84c74acaaeff",
        "colab": {}
      },
      "source": [
        "plt.bar(np.arange(len(p1_theta)), p1_theta)\n",
        "plt.title('Model probabilities')\n",
        "plt.xlabel('x')\n",
        "plt.ylabel('$p_{}(x1)$')\n",
        "plt.grid(True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9h4C8BCoMjbS",
        "colab_type": "text"
      },
      "source": [
        "Data distribution probability"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "khZSZ7A6MjbV",
        "colab_type": "code",
        "outputId": "512058c7-2865-4596-9e4f-cded3dd7dc60",
        "colab": {}
      },
      "source": [
        "plt.imshow(probs)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f99f092d110>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "luiCTuKEMjbZ",
        "colab_type": "text"
      },
      "source": [
        "Model probability"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_9XjgtHRMjba",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "inp = [(x, y) for x in range(200) for y in range(200)]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I0OCARoTMjbh",
        "colab_type": "code",
        "outputId": "b0f8280f-01b8-4353-ece5-db3ed8ce1bca",
        "colab": {}
      },
      "source": [
        "p_1,p_2=net.forward(torch.from_numpy(np.array(inp)))\n",
        "sample_data = p_2*p_1\n",
        "sample_data = sample_data.reshape(200,200).T.detach().numpy()\n",
        "plt.imshow(sample_data)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/home/peter/anaconda3/envs/RL/lib/python3.7/site-packages/ipykernel_launcher.py:21: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f99f417b7d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4cD0OC98Mjbr",
        "colab_type": "text"
      },
      "source": [
        "## MADE"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PBaxoYqfMjbs",
        "colab_type": "text"
      },
      "source": [
        "Check the paper again!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ZZioFQmMjbu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch.nn.functional as F\n",
        "import torch.nn as nn\n",
        "class MaskedLinear(nn.Linear):\n",
        "    #Linear with mask\n",
        "    \n",
        "    def __init__(self, in_features, out_features, bias=True):\n",
        "        super().__init__(in_features, out_features, bias)\n",
        "        self.register_buffer('mask', torch.ones(out_features, in_features))\n",
        "        \n",
        "    def set_mask(self, mask):\n",
        "        self.mask.data.copy_(torch.from_numpy(mask.astype(np.uint8).T))\n",
        "        \n",
        "    def forward(self, input):\n",
        "        return F.linear(input, self.mask * self.weight, self.bias)\n",
        "    \n",
        "class MADE(nn.Module):\n",
        "    def __init__(self, hidden_sizes, natural_ordering=False):\n",
        "        \n",
        "        super().__init__()\n",
        "        self.hidden_sizes = hidden_sizes\n",
        "        \n",
        "        #Compute h0\n",
        "        layers = [MaskedLinear(n,hidden_sizes[0])]\n",
        "        \n",
        "        layers = []\n",
        "        #compute h^l for l=1..L\n",
        "        for m in range(0,len(hidden_sizes)-1):\n",
        "            layers.append(nn.ReLU())\n",
        "            layers.append(MaskedLinear(hidden_sizes[m], hidden_sizes[m+1]))\n",
        "        layers.append(nn.ReLU())\n",
        "        layers.append(MaskedLinear(hidden_sizes[-1], n))\n",
        "        #compute x_hat\n",
        "\n",
        "        self.net = nn.Sequential(*layers)\n",
        "        self.softmax = nn.Softmax()\n",
        "        \n",
        "        self.seed = 0 \n",
        "        self.m = []\n",
        "        self.make_masks()\n",
        "        \n",
        "    def make_masks(self):\n",
        "        L = len(self.hidden_sizes)\n",
        "        \n",
        "        #rng = np.random.RandomState(self.seed)\n",
        "        #self.seed = (self.seed + 1)\n",
        "        \n",
        "        np.random.seed(self.seed)\n",
        "        self.seed = (self.seed + 1)\n",
        "        \n",
        "        #sampling m^l vectors\n",
        "        self.m.append(np.random.randint(0,high=n,size=n))\n",
        "        for l in range(L):\n",
        "            self.m.append(np.random.randint(self.m[l].min(),high=n,size=self.hidden_sizes[l]))\n",
        "        \n",
        "        # construct masks\n",
        "        masks = [self.m[l][:,None] <= self.m[l+1][None,:] for l in range(L)]\n",
        "        masks.append(self.m[L][:,None] < self.m[0][None,:])\n",
        "                \n",
        "        \n",
        "        # multiply the masks on each linear layer\n",
        "        layers = [l for l in self.net.modules() if isinstance(l, MaskedLinear)]\n",
        "        for l,m in zip(layers, masks):\n",
        "            l.set_mask(m)\n",
        "    \n",
        "    def forward(self, x):\n",
        "        onehot1 = F.one_hot(x[:,0], num_classes=n).float()\n",
        "        #print(F.one_hot(x, num_classes=n).float().reshape(-1,n*2)[0])\n",
        "        #onehot = torch.cat((onehot1, onehot2), 1)\n",
        "        out = self.net(onehot1)#.view(-1,2,n)\n",
        "        sout=F.softmax(out)\n",
        "        prob1 =torch.gather(sout.T, dim=0, index=x[:,0].unsqueeze(0))\n",
        "        prob2 =torch.gather(sout.T, dim=0, index=x[:,1].unsqueeze(0))\n",
        "        return prob2#prob1*prob2\n",
        "\n",
        "    def loss(self, x):\n",
        "        prob = self.forward(x)\n",
        "        loss = -torch.sum(torch.log2(prob+1e-10))/(2*prob.shape[1])\n",
        "        \n",
        "        return loss\n",
        "net = MADE([200,200])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "szKGfSRJMjbx",
        "colab_type": "code",
        "outputId": "be875470-b6be-4ec4-bfd1-c616ea87280e",
        "colab": {}
      },
      "source": [
        "print(net)\n",
        "optimizer = torch.optim.Adam(net.parameters(),lr=3e-4, weight_decay=0.0001)\n",
        "for param in net.parameters():\n",
        "    print(type(param.data), param.size())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MADE(\n",
            "  (net): Sequential(\n",
            "    (0): ReLU()\n",
            "    (1): MaskedLinear(in_features=200, out_features=200, bias=True)\n",
            "    (2): ReLU()\n",
            "    (3): MaskedLinear(in_features=200, out_features=200, bias=True)\n",
            "  )\n",
            "  (softmax): Softmax(dim=None)\n",
            ")\n",
            "<class 'torch.Tensor'> torch.Size([200, 200])\n",
            "<class 'torch.Tensor'> torch.Size([200])\n",
            "<class 'torch.Tensor'> torch.Size([200, 200])\n",
            "<class 'torch.Tensor'> torch.Size([200])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QoJ-KEVjMjb1",
        "colab_type": "code",
        "outputId": "93d1e2d2-46d6-4784-b85f-b993ff730148",
        "colab": {}
      },
      "source": [
        "losses, val_losses =[],[]\n",
        "train_iter = torch.utils.data.DataLoader(dataset=train,batch_size=batch_size,shuffle=True)\n",
        "val_iter = torch.utils.data.DataLoader(dataset=test,batch_size=len(test),shuffle=True)\n",
        "losses = []\n",
        "val_losses = []\n",
        "\n",
        "for epoch in range(epochs):\n",
        "    train_loss = 0\n",
        "\n",
        "    for train_batch in train_iter:\n",
        "        loss = net.loss(train_batch)\n",
        "        loss.backward()    \n",
        "        optimizer.step()\n",
        "        optimizer.zero_grad()\n",
        "        losses.append(loss.data.numpy())\n",
        "        \n",
        "    with torch.set_grad_enabled(False):\n",
        "        val_batch = next(iter(val_iter))\n",
        "        val_loss = net.loss(val_batch)\n",
        "        val_losses.append(val_loss.data.numpy())\n",
        "        \n",
        "    if epoch % 10 ==0:\n",
        "        print('Epoch {}: loss {} val_loss {} '.format(epoch,losses[-1],val_losses[-1]))\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.plot(losses, label = \"train_loss\")\n",
        "plt.plot(np.arange(0, len(losses),int(len(losses)/len(val_losses))),val_losses, label = \"val_loss\")\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/home/peter/anaconda3/envs/RL/lib/python3.7/site-packages/ipykernel_launcher.py:70: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 0: loss 3.8199617862701416 val_loss 3.820059061050415 \n",
            "Epoch 10: loss 3.8094072341918945 val_loss 3.810323476791382 \n",
            "Epoch 20: loss 3.8022663593292236 val_loss 3.808671474456787 \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U4RlGniFMjb5",
        "colab_type": "code",
        "outputId": "131d5889-dba9-4505-e1d3-088a79533799",
        "colab": {}
      },
      "source": [
        "inp = [(x, y) for x in range(200) for y in range(200)]\n",
        "prob=net.forward(torch.from_numpy(np.array(inp)))\n",
        "prob=prob.reshape(200,200).T.detach().numpy()\n",
        "plt.imshow(prob)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/home/peter/anaconda3/envs/RL/lib/python3.7/site-packages/ipykernel_launcher.py:70: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f14c2016ad0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "Ya47pTtJMjb_",
        "colab_type": "code",
        "outputId": "4c22971d-a3d4-4b39-c8db-7e23c1762212",
        "colab": {}
      },
      "source": [
        "inp = [(x, y) for x in range(200) for y in range(200)]\n",
        "prob=net.forward(torch.from_numpy(np.array(inp)))\n",
        "prob=prob.reshape(200,200).T.detach().numpy()\n",
        "plt.imshow(prob)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/home/peter/anaconda3/envs/RL/lib/python3.7/site-packages/ipykernel_launcher.py:72: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f14c0181390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "59sa5yKTMjcB",
        "colab_type": "text"
      },
      "source": [
        "## High Dimensional data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mr8gUbJzMjcB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pickle\n",
        "import torch.nn.functional as F\n",
        "import torch.nn as nn"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DpTBq_z6MjcH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with open('/content/gdrive/My Drive/mnist-hw1.pkl', 'rb') as f:\n",
        "    data = pickle.load(f)\n",
        "\n",
        "x_train = data['train'][:20000].astype('float32')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B88Y1qzmMjcL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_val = data['train'][20000:21000].astype('float32')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "45BveaNTMjcO",
        "colab_type": "code",
        "outputId": "ca3c51d6-6bed-4914-a8e6-88340de62c08",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "x_train[0].shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(28, 28, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y3O0JvcjMjcR",
        "colab_type": "code",
        "outputId": "7b844b6a-599c-4933-cb60-439fa7ec41a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        }
      },
      "source": [
        "#plot a few MNIST examples\n",
        "idx, dim, classes, rgb  = 0, 28, 4,3\n",
        "# create empty canvas\n",
        "canvas = np.zeros((dim*classes, classes*dim ,rgb))\n",
        "\n",
        "# fill with tensors\n",
        "for i in range(classes):\n",
        "    for j in range(classes):\n",
        "        canvas[i*dim:(i+1)*dim, j*dim:(j+1)*dim, ] = x_train[idx]/rgb\n",
        "        idx += 1\n",
        "\n",
        "# visualize matrix of tensors as gray scale image\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.axis('off')\n",
        "plt.imshow(canvas, cmap='gray')\n",
        "plt.title('MNIST handwritten digits')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZcBmfEy1bAEim2AJAMsUWAJJ1kdme07J5X917Hc9vOpVurzOnXedW77T1o126V/ip95q6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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8OUn_9tJMjcT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train = torch.Tensor(x_train).permute(0,3,1,2).cuda()\n",
        "x_val = torch.Tensor(x_val).permute(0,3,1,2).cuda()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2px8JLG66GiW",
        "colab_type": "code",
        "outputId": "ff50f2b2-8964-4a88-b618-b2fdc76a5aa7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "x_train.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([20000, 3, 28, 28])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S2GWH8r1MjcX",
        "colab_type": "text"
      },
      "source": [
        "PIXELCNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cIhlWelbMjcX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch.nn as nn\n",
        "\n",
        "class MaskedConv2d(nn.Conv2d):\n",
        "    def __init__(self, mask_type, channel_in, channel_out, kernel_size, stride, padding):\n",
        "        super(MaskedConv2d, self).__init__(channel_in, channel_out, kernel_size, stride, padding, bias=False)\n",
        "        self.mask_type = mask_type\n",
        "        channel_out, channel_in, height, width = self.weight.size()\n",
        "\n",
        "        mask = torch.ones(channel_out, channel_in, height, width)\n",
        "        \n",
        "        if mask_type == 'A':\n",
        "            mask[:, :, height // 2, width // 2:] = 0\n",
        "            mask[:, :, height // 2 + 1:] = 0\n",
        "        else:\n",
        "            mask[:, :, height // 2, width // 2 + 1:] = 0\n",
        "            mask[:, :, height // 2 + 1] = 0\n",
        "        \n",
        "        #dont let self.mask appear in model.parameters() as it would then  be updated\n",
        "        self.register_buffer('mask', mask)\n",
        "\n",
        "\n",
        "    def forward(self, x):\n",
        "        self.weight.data *= self.mask\n",
        "        return super(MaskedConv2d, self).forward(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GD73wSFz9oqu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class MaskedLinear(nn.Linear):\n",
        "    def __init__(self, mask_type, *args, **kwargs):\n",
        "        super(MaskedLinear, self).__init__(*args, **kwargs)\n",
        "        \n",
        "        # Two types of masking:\n",
        "        # A) wiht colour1\n",
        "        # B) with colour1 and colour2\n",
        "        assert mask_type in {'1', '2'}\n",
        "        \n",
        "        self.register_buffer('mask', self.weight.data.clone())\n",
        "        units_out, units_in = self.weight.size()\n",
        "\n",
        "        # Making masking on half of the network\n",
        "        self.mask.fill_(1)\n",
        "        if (mask_type == '1'):\n",
        "            self.mask[:units_out//2, units_in//2:] = 0\n",
        "\n",
        "    def forward(self, x):\n",
        "        # Apply masking\n",
        "        self.weight.data *= self.mask\n",
        "        return super(MaskedLinear, self).forward(x)\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nPdxOdNYMjca",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class ResNetBlock(nn.Module):\n",
        "    def __init__(self,channels=128):\n",
        "\n",
        "        super(ResNetBlock,self).__init__()\n",
        "        self.channels=channels\n",
        "\n",
        "        self.network = nn.Sequential(nn.ReLU(),\n",
        "        nn.Conv2d(self.channels*2,self.channels, 1, stride=1, padding=0),nn.BatchNorm2d(self.channels), nn.ReLU(),\n",
        "        MaskedConv2d('B',self.channels,self.channels, 3, stride=1, padding=1),nn.BatchNorm2d(self.channels), nn.ReLU(),\n",
        "        nn.Conv2d(self.channels,self.channels*2, 1, stride=1, padding=0),nn.BatchNorm2d(2*self.channels)\n",
        "        )\n",
        "\n",
        "    def forward(self,x):\n",
        "        out= self.network(x)\n",
        "        return x+out\n",
        "        "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KLJHnTX5MjdA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch.nn.functional as F\n",
        "class PixelCNN(nn.Module):\n",
        "    \n",
        "    def __init__(self, num_layers=6,kernel=7,channels=64, final_channels=4):\n",
        "        \n",
        "        super(PixelCNN,self).__init__()\n",
        "        self.num_layers = num_layers\n",
        "        self.kernel = kernel\n",
        "        self.channels = channels\n",
        "        self.batch_size = 1\n",
        "        self.final_channels = final_channels\n",
        "        \n",
        "        #PixelCNN layers\n",
        "        self.layers=[MaskedConv2d('A',3,self.channels*2, self.kernel, stride=1, padding=3)]\n",
        "        for m in range(0, self.num_layers):\n",
        "            self.layers.append(ResNetBlock(self.channels))\n",
        "        self.layers.append(nn.ReLU())\n",
        "        self.layers.append(nn.Conv2d(2*self.channels, 1024, 1, 1, 0))\n",
        "        self.layers.append(nn.ReLU())\n",
        "        self.layers.append(nn.Conv2d(1024,rgb*final_channels, 1, 1, 0))\n",
        "        \n",
        "        self.network = nn.Sequential(*self.layers)\n",
        "\n",
        "        \n",
        "        # MADE layers:\n",
        "        self.MADE = nn.Sequential(\n",
        "            MaskedLinear('1', in_features=dim*dim*2*final_channels, out_features=256),\n",
        "            nn.ReLU(),\n",
        "            MaskedLinear('1', in_features=256, out_features=256),\n",
        "            nn.ReLU(),\n",
        "            MaskedLinear('1', in_features=256, out_features=dim*dim*2*final_channels)\n",
        "        )\n",
        "        \n",
        "        \n",
        "\n",
        "        \n",
        "    def forward(self,x):\n",
        "        self.batch_size = x.shape[0]\n",
        "        x = self.network(x)\n",
        "        x = x.view(self.batch_size, rgb, self.final_channels, dim, dim)\n",
        "        \n",
        "        # Find colour 1 and 1 & 2\n",
        "        col_1 = x[:,0,:,:,:]\n",
        "        col_2 = x[:,0:2,:,:,:]\n",
        "\n",
        "        # Send colour 2 and 3 trough MADE FFNN\n",
        "        col_2 = col_2.reshape(self.batch_size, -1)\n",
        "        col_2 = self.MADE(col_2)\n",
        "        col_2 = col_2.reshape(self.batch_size, self.final_channels,2, dim, dim)\n",
        "        col_2 = col_2.permute(0,2,1,3,4)\n",
        "\n",
        "\n",
        "        # [batch_size, channels, classes, height, width] \n",
        "        col_1 = col_1.unsqueeze(1)\n",
        "        x = torch.cat((col_2, col_1), 1)\n",
        "        x = x.reshape(self.batch_size, rgb,final_channels, dim, dim)\n",
        "      \n",
        "        # [batch_size, 28, 28, channels=3,classes=4]\n",
        "        x = x.permute(0, 3, 4, 1, 2)\n",
        "        F.softmax(x, dim=-1)\n",
        "        return x\n",
        "    \n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gqRIxHK7MjdC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pikzelSeNN = PixelCNN().cuda()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fs0mXEnAMjdF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "optimizer = torch.optim.Adam(pikzelSeNN.parameters(),lr=3e-4, weight_decay=0.0001)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YWcpZB1m8tfl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def recurrent_sampling(net):\n",
        "  \n",
        "    samples = torch.Tensor(np.random.choice(4, size=(1,3, 28, 28))).type(torch.FloatTensor).cuda()\n",
        "    for k in range(3):\n",
        "      for i in range(28):\n",
        "          for j in range(28):\n",
        "              out = net(samples)\n",
        "              intensity = torch.argmax(out, dim=-1).permute(0,3,1,2)\n",
        "              samples[:,k, i, j] = intensity[:,k, i, j]\n",
        "\n",
        "\n",
        "    \n",
        "    samples = samples.cpu()\n",
        "    samples = samples.permute(0,2,3,1)\n",
        "    samples = samples.type(torch.FloatTensor)\n",
        "\n",
        "      #plot a few MNIST examples\n",
        "    idx, dim, classes, rgb  = 0, 28, 1,3\n",
        "    # create empty canvas\n",
        "    canvas = np.zeros((dim*classes, classes*dim ,rgb))\n",
        "\n",
        "    # fill with tensors\n",
        "    for i in range(classes):\n",
        "        for j in range(classes):\n",
        "            canvas[i*dim:(i+1)*dim, j*dim:(j+1)*dim, ] = samples[idx]/rgb\n",
        "            idx += 1\n",
        "\n",
        "    # visualize matrix of tensors as gray scale image\n",
        "    plt.figure(figsize=(8, 8))\n",
        "    plt.axis('off')\n",
        "    plt.imshow(canvas, cmap='gray')\n",
        "    plt.title('Recurrent sampling')\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_XUTXq2K8vxL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def naive_sampling(net):\n",
        "  #sample = torch.randint(low = 0, high = 3, size = (1, 3,28, 28) ).type(torch.FloatTensor).cuda()\n",
        "  sample = torch.Tensor(np.random.choice(4, size=(1, 3,28, 28))).type(torch.FloatTensor).cuda()\n",
        "  out = net(sample)\n",
        "  sample = torch.argmax(out, dim=4)\n",
        "\n",
        "\n",
        "  sample = sample.cpu()\n",
        "  sample = sample.reshape(1, 28, 28, 3)\n",
        "  sample = sample.type(torch.FloatTensor)\n",
        "  \n",
        "      #plot a few MNIST examples\n",
        "  idx, dim, classes, rgb  = 0, 28, 1,3\n",
        "  # create empty canvas\n",
        "  canvas = np.zeros((dim*classes, classes*dim ,rgb))\n",
        "\n",
        "  # fill with tensors\n",
        "  for i in range(classes):\n",
        "      for j in range(classes):\n",
        "          canvas[i*dim:(i+1)*dim, j*dim:(j+1)*dim, ] = sample[idx]/rgb\n",
        "          idx += 1\n",
        "\n",
        "  # visualize matrix of tensors as gray scale image\n",
        "  plt.figure(figsize=(8, 8))\n",
        "  plt.axis('off')\n",
        "  plt.imshow(canvas, cmap='gray')\n",
        "  plt.title('Naive sampling')\n",
        "  plt.show()\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IctuxTGhMjdH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "criterion = nn.CrossEntropyLoss()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JN2fXAidMjdJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size = 200\n",
        "epochs=50"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZfCEmi_jMjdL",
        "colab_type": "code",
        "outputId": "32caeace-df18-4f6b-9257-1b7ead97622c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "losses, val_losses =[],[]\n",
        "train_iter = torch.utils.data.DataLoader(dataset=x_train,batch_size=batch_size,shuffle=True)\n",
        "val_iter = torch.utils.data.DataLoader(dataset=x_val,batch_size=len(x_val),shuffle=True)\n",
        "losses = []\n",
        "val_losses = []\n",
        "\n",
        "for epoch in range(epochs):\n",
        "    train_loss = 0\n",
        "    \n",
        "    pikzelSeNN.train()\n",
        "    for train_batch in train_iter:\n",
        "        x_batch = train_batch.view(-1).type(torch.cuda.LongTensor)\n",
        "        output = pikzelSeNN.forward(train_batch).cuda().contiguous().view(-1,4)\n",
        "        \n",
        "        loss = criterion(output, x_batch)\n",
        "        loss.backward()    \n",
        "        optimizer.step()\n",
        "        optimizer.zero_grad()\n",
        "        losses.append(loss.data.cpu().numpy())\n",
        "    \n",
        "    pikzelSeNN.eval()\n",
        "    with torch.set_grad_enabled(False):\n",
        "        val_batch = next(iter(val_iter))\n",
        "        \n",
        "        x_batch = val_batch.view(-1).type(torch.cuda.LongTensor)\n",
        "        output = pikzelSeNN.forward(val_batch).cuda()\n",
        "        output = output.contiguous().view(-1,4)\n",
        "        \n",
        "        val_loss = criterion(output, x_batch)\n",
        "        val_losses.append(val_loss.data.cpu().numpy())\n",
        "       \n",
        "    if epoch % 1 ==0:\n",
        "        print('Epoch {}: loss {} val_loss {} '.format(epoch,losses[-1],val_losses[-1]))\n",
        "        \n",
        "        # Samples\n",
        "    if (epoch) % 10 == 0:\n",
        "      naive_sampling(pikzelSeNN)\n",
        "      \n",
        "      recurrent_sampling(pikzelSeNN)\n",
        "      \n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure()\n",
        "plt.plot(losses, label = \"train_loss\")\n",
        "plt.plot(np.arange(0, len(losses),int(len(losses)/len(val_losses))),val_losses, label = \"val_loss\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 0: loss 1.25369393825531 val_loss 1.3101563453674316 \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dl2lnYdSvGR530s+ZdHNWVe2C7Xcb9uemktOWdpImmyfXqyrvFe1gra6w+3cfrJf4vKVl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\nAsAQoQoAQ4QqAAz5P1U4z09pWvtkAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1: loss 1.1178562641143799 val_loss 1.2099567651748657 \n",
            "Epoch 2: loss 0.9560273289680481 val_loss 1.1037710905075073 \n",
            "Epoch 3: loss 0.8463925719261169 val_loss 1.0071089267730713 \n",
            "Epoch 4: loss 0.7613190412521362 val_loss 0.9557914733886719 \n",
            "Epoch 5: loss 0.7561118602752686 val_loss 0.907609224319458 \n",
            "Epoch 6: loss 0.6975374817848206 val_loss 0.8626845479011536 \n",
            "Epoch 7: loss 0.6908344030380249 val_loss 0.8104488849639893 \n",
            "Epoch 8: loss 0.6509169340133667 val_loss 0.75146484375 \n",
            "Epoch 9: loss 0.646844744682312 val_loss 0.688583254814148 \n",
            "Epoch 10: loss 0.6409180164337158 val_loss 0.6529959440231323 \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EiCoAhIgqAISIKgCEGP22oZ0diWUa1pmy7/7ePZMPLjN3+ZKPa3undK86qQJAiKgCQIio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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 11: loss 0.6251391172409058 val_loss 0.6270456910133362 \n",
            "Epoch 12: loss 0.6097828149795532 val_loss 0.6217369437217712 \n",
            "Epoch 13: loss 0.6070655584335327 val_loss 0.6108220219612122 \n",
            "Epoch 14: loss 0.5856457948684692 val_loss 0.5971375703811646 \n",
            "Epoch 15: loss 0.582455575466156 val_loss 0.6006162762641907 \n",
            "Epoch 16: loss 0.5862744450569153 val_loss 0.58431476354599 \n",
            "Epoch 17: loss 0.594789445400238 val_loss 0.5835209488868713 \n",
            "Epoch 18: loss 0.5813959240913391 val_loss 0.5729270577430725 \n",
            "Epoch 19: loss 0.5588673949241638 val_loss 0.5697452425956726 \n",
            "Epoch 20: loss 0.5575021505355835 val_loss 0.5613974928855896 \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 21: loss 0.5515694618225098 val_loss 0.5606887936592102 \n",
            "Epoch 22: loss 0.5383657217025757 val_loss 0.5535042881965637 \n",
            "Epoch 23: loss 0.5340765118598938 val_loss 0.5485324263572693 \n",
            "Epoch 24: loss 0.5462764501571655 val_loss 0.5422031283378601 \n",
            "Epoch 25: loss 0.5355138778686523 val_loss 0.5409820675849915 \n",
            "Epoch 26: loss 0.527492880821228 val_loss 0.5360251069068909 \n",
            "Epoch 27: loss 0.5113461017608643 val_loss 0.5362863540649414 \n",
            "Epoch 28: loss 0.5103186964988708 val_loss 0.5322985053062439 \n",
            "Epoch 29: loss 0.5118075609207153 val_loss 0.5250212550163269 \n",
            "Epoch 30: loss 0.5045768022537231 val_loss 0.5183904767036438 \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EFQBCrln6ADiCgwX3vVlw33vqYMnrPWnjfjlxa75fZpzWe81KFQBCRBUAQkQVAEJEFQBC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\nAISIKgCEiCoAhPw/aXFs5kCPkwgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 31: loss 0.5035494565963745 val_loss 0.5139111876487732 \n",
            "Epoch 32: loss 0.4980493187904358 val_loss 0.5167849063873291 \n",
            "Epoch 33: loss 0.49994349479675293 val_loss 0.5091966986656189 \n",
            "Epoch 34: loss 0.47819024324417114 val_loss 0.503372073173523 \n",
            "Epoch 35: loss 0.4737059473991394 val_loss 0.5071669816970825 \n",
            "Epoch 36: loss 0.4655284583568573 val_loss 0.5102062225341797 \n",
            "Epoch 37: loss 0.47048699855804443 val_loss 0.4969656765460968 \n",
            "Epoch 38: loss 0.4677937924861908 val_loss 0.5017699599266052 \n",
            "Epoch 39: loss 0.4716375172138214 val_loss 0.4953812062740326 \n",
            "Epoch 40: loss 0.5016443133354187 val_loss 0.49831855297088623 \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QIqoAECKqABAiqgAQIqoAECKqABAiqgAQIqoAECKqABAiqgAQIqoAECKqABAiqgAQIqoA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\nqAJAyP8DOB+WfSShuDMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 41: loss 0.46593043208122253 val_loss 0.49572086334228516 \n",
            "Epoch 42: loss 0.46835383772850037 val_loss 0.4939548075199127 \n",
            "Epoch 43: loss 0.4578666687011719 val_loss 0.4936428666114807 \n",
            "Epoch 44: loss 0.4648205041885376 val_loss 0.48338544368743896 \n",
            "Epoch 45: loss 0.456072062253952 val_loss 0.4812715947628021 \n",
            "Epoch 46: loss 0.4466181695461273 val_loss 0.48091527819633484 \n",
            "Epoch 47: loss 0.4425674378871918 val_loss 0.49030834436416626 \n",
            "Epoch 48: loss 0.43761593103408813 val_loss 0.4755080044269562 \n",
            "Epoch 49: loss 0.4348250925540924 val_loss 0.4771527945995331 \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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44j60mxf/KR/Nnrpw7EH9eeKmWSzeWsJrP+5DaxgbG8hHd453ddlCCCdRSiVp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\nSbgLIUQXJOEuhBBd0P8Hmia3JsvcxAIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MbI08JuPMnVZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W_dAVwQ40a5F",
        "colab_type": "code",
        "outputId": "e9e248aa-fd67-4550-b065-17d2856e1a8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "losses, val_losses =[],[]\n",
        "train_iter = torch.utils.data.DataLoader(dataset=x_train,batch_size=batch_size,shuffle=True)\n",
        "val_iter = torch.utils.data.DataLoader(dataset=x_val,batch_size=len(x_val),shuffle=True)\n",
        "losses = []\n",
        "val_losses = []\n",
        "\n",
        "for epoch in range(epochs):\n",
        "    train_loss = 0\n",
        "    \n",
        "    pikzelSeNN.train()\n",
        "    for train_batch in train_iter:\n",
        "        x_batch = train_batch.permute(0,2,3,1)\n",
        "        x_batch = x_batch.contiguous().view(-1).type(torch.cuda.LongTensor)\n",
        "        output = pikzelSeNN.forward(train_batch).cuda().contiguous().view(-1,4)\n",
        "        loss = criterion(output, x_batch)\n",
        "        loss.backward()    \n",
        "        optimizer.step()\n",
        "        optimizer.zero_grad()\n",
        "        losses.append(loss.data.cpu().numpy())\n",
        "    \n",
        "    pikzelSeNN.eval()\n",
        "    with torch.set_grad_enabled(False):\n",
        "        val_batch = next(iter(val_iter))\n",
        "        x_batch = val_batch.permute(0,2,3,1)\n",
        "        x_batch = x_batch.contiguous().view(-1).type(torch.cuda.LongTensor)\n",
        "        output = pikzelSeNN.forward(val_batch).cuda()\n",
        "        output = output.contiguous().view(-1,4)\n",
        "        \n",
        "        val_loss = criterion(output, x_batch)\n",
        "        val_losses.append(val_loss.data.cpu().numpy())\n",
        "       \n",
        "    if epoch % 1 ==0:\n",
        "        print('Epoch {}: loss {} val_loss {} '.format(epoch,losses[-1],val_losses[-1]))\n",
        "        \n",
        "        # Samples\n",
        "    if (epoch) % 10 == 0:\n",
        "      naive_sampling(pikzelSeNN)\n",
        "      \n",
        "      recurrent_sampling(pikzelSeNN)\n",
        "      \n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure()\n",
        "plt.plot(losses, label = \"train_loss\")\n",
        "plt.plot(np.arange(0, len(losses),int(len(losses)/len(val_losses))),val_losses, label = \"val_loss\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 0: loss 0.3675821125507355 val_loss 0.37776219844818115 \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VJ/fL9LyPv9cGDYVVVbW7wtTtVAEgRKgCQIhQBYAQoQoAIUIVAEKEKgCECFUACBGqABAi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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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logoAJaIKACWiCgAlogoAJaIKACWiCgAlogoAJZd19Btw69amuVRbx85tO5wDOORzdsjx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\nlPwf7WEn/Y++qmAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1: loss 0.29038095474243164 val_loss 0.3020133674144745 \n",
            "Epoch 2: loss 0.2498313933610916 val_loss 0.25974583625793457 \n",
            "Epoch 3: loss 0.23661544919013977 val_loss 0.2407030612230301 \n",
            "Epoch 4: loss 0.20941300690174103 val_loss 0.22307251393795013 \n",
            "Epoch 5: loss 0.20203101634979248 val_loss 0.2122582048177719 \n",
            "Epoch 6: loss 0.19140122830867767 val_loss 0.20425207912921906 \n",
            "Epoch 7: loss 0.18542718887329102 val_loss 0.19479858875274658 \n",
            "Epoch 8: loss 0.17681443691253662 val_loss 0.18985658884048462 \n",
            "Epoch 9: loss 0.1701827198266983 val_loss 0.18138520419597626 \n",
            "Epoch 10: loss 0.175497367978096 val_loss 0.17681990563869476 \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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j8ub1Jd71ptPjPn29TPtQ592/W5YHT8Y+r32oU7Pztba8L+6Ha33iOrcHT6oAECJUASBE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\nIESoAkCIUAWAEKEKACH/D5M9N4M9hTPRAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Lzf15WakCQIioAkCIqAJAiKgCQIioAkCIqAJAiKgCQIioAkCIqAJAiKgCQIioAkCIqAJA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\nf+0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 11: loss 0.16165347397327423 val_loss 0.17255833745002747 \n",
            "Epoch 12: loss 0.1577131450176239 val_loss 0.1664600819349289 \n",
            "Epoch 13: loss 0.14129896461963654 val_loss 0.16106334328651428 \n",
            "Epoch 14: loss 0.1527898758649826 val_loss 0.15623174607753754 \n",
            "Epoch 15: loss 0.13365274667739868 val_loss 0.1518363505601883 \n",
            "Epoch 16: loss 0.14497308433055878 val_loss 0.1477503925561905 \n",
            "Epoch 17: loss 0.13430190086364746 val_loss 0.14366139471530914 \n",
            "Epoch 18: loss 0.12449683994054794 val_loss 0.1389635056257248 \n",
            "Epoch 19: loss 0.13525965809822083 val_loss 0.1358865350484848 \n",
            "Epoch 20: loss 0.12377110123634338 val_loss 0.13201308250427246 \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fzj2R7PeW/ZhJ/21V3jOZnLMtj9uGwr7l5Jx32CO7kvO94fW9i9f5tXlSBYAhQhUAhghV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\nKgAMEaoAMESoAsAQoQoAQ/4PR4NBnJp6ttAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kuL7Z9U+YGqE2udkzI/d61WPnTu40x68tyZUqAISIKgCEiCoAhIgqAISIKgCEiCoAhIgq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\nhIgqAISIKgCE/C9/jH+nDfxvxQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 21: loss 0.11660213768482208 val_loss 0.13053438067436218 \n",
            "Epoch 22: loss 0.11378677934408188 val_loss 0.12853117287158966 \n",
            "Epoch 23: loss 0.1142430528998375 val_loss 0.12600316107273102 \n",
            "Epoch 24: loss 0.11240660399198532 val_loss 0.12343545258045197 \n",
            "Epoch 25: loss 0.1072002649307251 val_loss 0.1232711672782898 \n",
            "Epoch 26: loss 0.10540588945150375 val_loss 0.12008652091026306 \n",
            "Epoch 27: loss 0.10923592001199722 val_loss 0.12051773071289062 \n",
            "Epoch 28: loss 0.10418428480625153 val_loss 0.11765513569116592 \n",
            "Epoch 29: loss 0.10536883026361465 val_loss 0.11560145765542984 \n",
            "Epoch 30: loss 0.10369610041379929 val_loss 0.11459949612617493 \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qADQRqgDQRKgCQBOhCgBNhCoANLnlqKPFvYHb9efVeninaSzqRM2O+yRY87RrcYm6QbMu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\nAGgiVAGgiVAFgCZCFQCaCFUAaPL/DcCYg70KLucAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 31: loss 0.09660506993532181 val_loss 0.11297198385000229 \n",
            "Epoch 32: loss 0.10168521106243134 val_loss 0.11230553686618805 \n",
            "Epoch 33: loss 0.09645003080368042 val_loss 0.11046218872070312 \n",
            "Epoch 34: loss 0.09866681694984436 val_loss 0.11063376069068909 \n",
            "Epoch 35: loss 0.09368065744638443 val_loss 0.1083717867732048 \n",
            "Epoch 36: loss 0.09488273411989212 val_loss 0.10680994391441345 \n",
            "Epoch 37: loss 0.09403655678033829 val_loss 0.10594023764133453 \n",
            "Epoch 38: loss 0.09270624816417694 val_loss 0.10614119470119476 \n",
            "Epoch 39: loss 0.09128834307193756 val_loss 0.1062522754073143 \n",
            "Epoch 40: loss 0.09252705425024033 val_loss 0.10547596961259842 \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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aCFUAaCJUAaCJUAWAJhfbpxo7vNtzFzYmHkW9oll333G0/cJ+zLDbMznjR1EnaVV2zdKu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\nAJoIVQBoIlQBoMn/AzGNZMqJ1io4AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 41: loss 0.08789166063070297 val_loss 0.1032622754573822 \n",
            "Epoch 42: loss 0.08934328705072403 val_loss 0.10348385572433472 \n",
            "Epoch 43: loss 0.08640611916780472 val_loss 0.10218247771263123 \n",
            "Epoch 44: loss 0.09267854690551758 val_loss 0.10192390531301498 \n",
            "Epoch 45: loss 0.08587218821048737 val_loss 0.10361061245203018 \n",
            "Epoch 46: loss 0.08590041846036911 val_loss 0.09989962726831436 \n",
            "Epoch 47: loss 0.08460681885480881 val_loss 0.09873257577419281 \n",
            "Epoch 48: loss 0.08111125230789185 val_loss 0.09880249947309494 \n",
            "Epoch 49: loss 0.08186409622430801 val_loss 0.09893087297677994 \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qQ95OBk30s10MJD7rp8iqKbBa8qmhwGrjN2ootE/oTc1+xwNaa3JB6smggQz2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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4uX_mZP7MjdN",
        "colab_type": "code",
        "outputId": "d972c465-46c0-4daa-86cf-180e8a92d6d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        }
      },
      "source": [
        "recurrent_sampling(pikzelSeNN)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EiCoAhIgqAISIKgCEiCoAhIgqAISIKgCEiCoAhIgqAISIKgCEiCoAhIgqAISIKgCE7Hee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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ilxrKbxQ2wqa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def recurrent_samplings(net):\n",
        "  \n",
        "    samples = torch.Tensor(np.random.choice(4, size=(4,3, 28, 28))).type(torch.FloatTensor).cuda()\n",
        "    for i in range(28):\n",
        "        for j in range(28):\n",
        "            out = net(samples)\n",
        "            dist = torch.distributions.Categorical(logits=out)\n",
        "            #dist.log_prob(samples).mean()/np.log(2) loss\n",
        "            samples [:,:, i, j] = dist.sample()\n",
        "            #intensity = torch.argmax(out, dim=-1).permute(0,3,1,2)\n",
        "            #samples[:,k, i, j] = intensity[:,k, i, j]\n",
        "\n",
        "\n",
        "    \n",
        "    samples = samples.cpu()\n",
        "    samples = samples.permute(0,2,3,1)\n",
        "    samples = samples.type(torch.FloatTensor)\n",
        "\n",
        "      #plot a few MNIST examples\n",
        "    idx, dim, classes, rgb  = 0, 28, 2,3\n",
        "    # create empty canvas\n",
        "    canvas = np.zeros((dim*classes, classes*dim ,rgb))\n",
        "\n",
        "    # fill with tensors\n",
        "    for i in range(classes):\n",
        "        for j in range(classes):\n",
        "            canvas[i*dim:(i+1)*dim, j*dim:(j+1)*dim, ] = samples[idx]/rgb\n",
        "            idx += 1\n",
        "\n",
        "    # visualize matrix of tensors as gray scale image\n",
        "    plt.figure(figsize=(8, 8))\n",
        "    plt.axis('off')\n",
        "    plt.imshow(canvas, cmap='gray')\n",
        "    plt.title('Recurrent sampling')\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U6pym6WFMjdQ",
        "colab_type": "code",
        "outputId": "1ca0730e-b66b-4064-b9fc-5ac50eecee63",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        }
      },
      "source": [
        "recurrent_samplings(pikzelSeNN)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-104-4ba3289ee610>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrecurrent_samplings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpikzelSeNN\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-103-5105b210ff69>\u001b[0m in \u001b[0;36mrecurrent_samplings\u001b[0;34m(net)\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m             \u001b[0mcanvas\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msamples\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mrgb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     27\u001b[0m             \u001b[0midx\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mIndexError\u001b[0m: index 9 is out of bounds for dimension 0 with size 9"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tJXggcDXMjdZ",
        "colab_type": "code",
        "outputId": "5b8111af-31e4-4b7c-9d2f-13e7d6218813",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "torch.save(pikzelSeNN.state_dict(), '/content/gdrive/My Drive/')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "IsADirectoryError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIsADirectoryError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-105-9666fc3679f8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpikzelSeNN\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'/content/gdrive/My Drive/'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(obj, f, pickle_module, pickle_protocol)\u001b[0m\n\u001b[1;32m    222\u001b[0m         \u001b[0;34m>>\u001b[0m\u001b[0;34m>\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m     \"\"\"\n\u001b[0;32m--> 224\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_with_file_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"wb\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0m_save\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_protocol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36m_with_file_like\u001b[0;34m(f, mode, body)\u001b[0m\n\u001b[1;32m    145\u001b[0m             \u001b[0;34m(\u001b[0m\u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mversion_info\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m3\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpathlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    146\u001b[0m         \u001b[0mnew_fd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 147\u001b[0;31m         \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    148\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mIsADirectoryError\u001b[0m: [Errno 21] Is a directory: '/content/gdrive/My Drive/'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B9ciMRAlMjde",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def NLLloss(output,inp):\n",
        "  out = torch.FloatTensor(torch.argmax(output,dim=-1).cuda(),requires_grad=True)\n",
        "  prob=torch.gather(out, dim=-1, index=inp).type(torch.cuda.FloatTensor)\n",
        "  loss = -torch.sum(torch.log2(prob+1e-10))/(out.shape[0])\n",
        "  return loss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-RtwzAqEMjdg",
        "colab_type": "code",
        "outputId": "6171b4ee-897f-43c5-9826-8c75178845ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        }
      },
      "source": [
        "losses, val_losses =[],[]\n",
        "train_iter = torch.utils.data.DataLoader(dataset=x_train,batch_size=batch_size,shuffle=True)\n",
        "val_iter = torch.utils.data.DataLoader(dataset=x_val,batch_size=len(x_val),shuffle=True)\n",
        "losses = []\n",
        "val_losses = []\n",
        "\n",
        "for epoch in range(epochs):\n",
        "    train_loss = 0\n",
        "    \n",
        "    pikzelSeNN.train()\n",
        "    for train_batch in train_iter:\n",
        "        x_batch = train_batch.permute(0,2,3,1)\n",
        "        x_batch = x_batch.contiguous().view(-1).type(torch.cuda.LongTensor)\n",
        "        output = pikzelSeNN.forward(train_batch).cuda().contiguous().view(-1,4)\n",
        "        \n",
        "        loss = NLLloss(output,x_batch)\n",
        "        print(loss)\n",
        "        loss.backward()    \n",
        "        optimizer.step()\n",
        "        optimizer.zero_grad()\n",
        "        losses.append(loss.data.cpu().numpy())\n",
        "    \n",
        "    pikzelSeNN.eval()\n",
        "    with torch.set_grad_enabled(False):\n",
        "        val_batch = next(iter(val_iter))\n",
        "        x_batch = val_batch.permute(0,2,3,1)\n",
        "        x_batch = x_batch.contiguous().view(-1).type(torch.cuda.LongTensor)\n",
        "        output = pikzelSeNN.forward(val_batch).cuda()\n",
        "        output = output.contiguous().view(-1,4)\n",
        "        \n",
        "        val_loss = NLLloss(output,x_batch)\n",
        "        val_losses.append(val_loss.data.cpu().numpy())\n",
        "       \n",
        "    if epoch % 1 ==0:\n",
        "        print('Epoch {}: loss {} val_loss {} '.format(epoch,losses[-1],val_losses[-1]))\n",
        "        \n",
        "        # Samples\n",
        "    if (epoch) % 1 == 0:\n",
        "      naive_sampling(pikzelSeNN)\n",
        "      \n",
        "      recurrent_sampling(pikzelSeNN)\n",
        "      \n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure()\n",
        "plt.plot(losses, label = \"train_loss\")\n",
        "plt.plot(np.arange(0, len(losses),int(len(losses)/len(val_losses))),val_losses, label = \"val_loss\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-39-0bea3873ca03>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0mx_batch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_batch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpermute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0mx_batch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx_batch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontiguous\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLongTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpikzelSeNN\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontiguous\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNLLloss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-18-da72db5693e7>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     37\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnetwork\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrgb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfinal_channels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    491\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 493\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    494\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    495\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m     90\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_modules\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m             \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    491\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 493\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    494\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    495\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-15-1410514661ae>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m*=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMaskedConv2d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    336\u001b[0m                             _pair(0), self.dilation, self.groups)\n\u001b[1;32m    337\u001b[0m         return F.conv2d(input, self.weight, self.bias, self.stride,\n\u001b[0;32m--> 338\u001b[0;31m                         self.padding, self.dilation, self.groups)\n\u001b[0m\u001b[1;32m    339\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: CUDA out of memory. Tried to allocate 78.00 MiB (GPU 0; 11.17 GiB total capacity; 10.47 GiB already allocated; 36.81 MiB free; 347.41 MiB cached)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OIcPYZffMjdj",
        "colab_type": "code",
        "outputId": "df85fc15-44a8-496b-d9ee-9606c7fce6a5",
        "colab": {}
      },
      "source": [
        "plt.imshow(sampel.view(dim,dim,rgb), cmap='gray')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f14b980c890>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 173
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HbpxtDpyMjdl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}