EDL_5_2_RS_HPO.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "EDL_5_2_RS_HPO.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyOR8UmWt9KkCKxXFc/0M2VW",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/cxbxmxcx/EvolutionaryDeepLearning/blob/main/EDL_5_2_RS_HPO.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7eo9h5KA5UPe"
      },
      "source": [
        "# Setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FslekLiVp_Il"
      },
      "source": [
        "#@title Defining Imports\n",
        "#numpy\n",
        "import numpy as np\n",
        "#PyTorch\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.autograd import Variable\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import TensorDataset, DataLoader\n",
        "#plotting\n",
        "from matplotlib import pyplot as plt\n",
        "from matplotlib import cm\n",
        "from IPython.display import clear_output\n",
        "#for performance timing\n",
        "import time\n",
        "#ranodm\n",
        "import random"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "MQCdK2oWJlid",
        "outputId": "9d78bce3-f18b-4eb1-d70a-3cfc2d2e7e9b"
      },
      "source": [
        "def function(x):\n",
        "  return (2*x + 3*x**2 + 4*x**3 + 5*x**4 + 6*x**5 + 10) \n",
        "\n",
        "data_min = -5\n",
        "data_max = 5\n",
        "data_step = .5\n",
        "Xi = np.reshape(np.arange(data_min, data_max, data_step), (-1, 1))\n",
        "yi = function(Xi)\n",
        "inputs = Xi.shape[1]\n",
        "yi = yi.reshape(-1, 1)\n",
        "plt.plot(Xi, yi, 'o', color='black')\n",
        "plt.plot(Xi,yi, color=\"red\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f6cd0155fd0>]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eY30bijNqNBL"
      },
      "source": [
        "#@title Define the Model\n",
        "class Net(nn.Module):\n",
        "  def __init__(self, inputs, middle):\n",
        "    super().__init__()\n",
        "    self.fc1 = nn.Linear(inputs,middle)    \n",
        "    self.fc2 = nn.Linear(middle,middle)    \n",
        "    self.out = nn.Linear(middle,1)\n",
        "  def forward(self, x):\n",
        "    x = F.relu(self.fc1(x))     \n",
        "    x = F.relu(self.fc2(x))    \n",
        "    x = self.out(x)\n",
        "    return x"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nsBRAusqy7Uz"
      },
      "source": [
        "#@title Define Hyperparameters Class\n",
        "class Hyperparameters(object):\n",
        "  def __init__(self, **kwargs):\n",
        "    self.__dict__.update(kwargs)\n",
        "  \n",
        "  def __str__(self):\n",
        "    out = \"\"\n",
        "    for d in self.__dict__:\n",
        "      ds = self.__dict__[d]\n",
        "      out += f\"{d} = {ds}   \"\n",
        "    return out\n",
        "\n",
        "  def next(self):\n",
        "    dict = {}\n",
        "    for d in self.__dict__:\n",
        "      dict[d] = next(self.__dict__[d])\n",
        "    return Hyperparameters(**dict)\n",
        "\n",
        "def sampler(func, min, max):\n",
        "  while True:\n",
        "    yield func(min,max)\n",
        "\n",
        "def static(val):\n",
        "  while True:\n",
        "    yield val"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TrxTh7lrTjgz"
      },
      "source": [
        "# Create the Hyperparameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ciFUE2XDzhMk",
        "outputId": "a3837412-5f77-45e6-9aae-2eee526442a9"
      },
      "source": [
        "#@title Instantiate Hyperparameters \n",
        "hp = Hyperparameters(\n",
        "  epochs = static(200),\n",
        "  middle_layer = sampler(random.randint, 8, 64),  \n",
        "  learning_rate = sampler(random.uniform,3.5e-01,3.5e-03),\n",
        "  batch_size = static(16)  \n",
        ")\n",
        "\n",
        "#sample\n",
        "print(hp.next())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epochs = 200   middle_layer = 57   learning_rate = 0.18565852269060848   batch_size = 16   \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zCbrGhsr6IhK"
      },
      "source": [
        "# Create the Training Function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "rm843_KFxy6Y",
        "outputId": "8961591f-c2a1-45bc-de21-6cd024b0237a"
      },
      "source": [
        "def sample_train_function(hp):\n",
        "  hp = hp.next() \n",
        "  \n",
        "  X = np.reshape(\n",
        "      np.arange(\n",
        "          data_min, \n",
        "          data_max, \n",
        "          data_step)\n",
        "      , (-1, 1))\n",
        "  y = function(X)\n",
        "  inputs = X.shape[1]\n",
        "  y = y.reshape(-1, 1)\n",
        "  plt.plot(X, y, 'o', color='black')\n",
        "\n",
        "sample_train_function(hp)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "je4Jf9Vp88Rq",
        "outputId": "3741b788-ac90-4817-9185-70ac7c01ad60"
      },
      "source": [
        "loss_fn = nn.MSELoss()\n",
        "#@title The Main Training Function\n",
        "def train_function(hp):\n",
        "  hp = hp.next()\n",
        " \n",
        "  X = np.reshape(\n",
        "      np.arange(\n",
        "          data_min, \n",
        "          data_max, \n",
        "          data_step)\n",
        "      , (-1, 1))\n",
        "  y = function(X)\n",
        "  inputs = X.shape[1]\n",
        "  \n",
        "  tensor_x = torch.Tensor(X) # transform to torch tensor\n",
        "  tensor_y = torch.Tensor(y)\n",
        "  \n",
        "  dataset = TensorDataset(tensor_x,tensor_y) # create your datset\n",
        "  dataloader = DataLoader(dataset, batch_size= hp.batch_size, shuffle=True) # create your dataloader\n",
        "\n",
        "  model = Net(inputs, hp.middle_layer)\n",
        "  optimizer = optim.Adam(model.parameters(), lr=hp.learning_rate)\n",
        "  \n",
        "  history=[]  \n",
        "  start = time.time()\n",
        "  for i in range(hp.epochs):        \n",
        "    for X, y in iter(dataloader):\n",
        "      # wrap the data in variables\n",
        "      x_batch = Variable(torch.Tensor(X))\n",
        "      y_batch = Variable(torch.Tensor(y))                   \n",
        "      # forward pass\n",
        "      y_pred = model(x_batch)        \n",
        "      # compute and print loss\n",
        "      loss = loss_fn(y_pred, y_batch)  \n",
        "      ll = loss.data\n",
        "      history.append(ll)                    \n",
        "      # reset gradients\n",
        "      optimizer.zero_grad()        \n",
        "      # backwards pass\n",
        "      loss.backward()        \n",
        "      # step the optimizer - update the weights\n",
        "      optimizer.step()  \n",
        "  end = time.time() - start\n",
        "  return end, history, model, hp\n",
        "\n",
        "best = float(\"inf\")\n",
        "span, history, model, hp_out = train_function(hp)\n",
        "plt.plot(history)\n",
        "print(min(history).item())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "186.2595977783203\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OMrbj-9WUA9x"
      },
      "source": [
        "# Automatic HPO"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 815
        },
        "id": "0G12K_KMFCsm",
        "outputId": "5efec7db-5c7a-4f23-f008-0cda5a909266"
      },
      "source": [
        "#@title Run the HPO Automation\n",
        "runs = 10000\n",
        "best = float(\"inf\")\n",
        "best_hp = None\n",
        "run_history = []\n",
        "for i in range(runs):\n",
        "  span, history, model, hp_out = train_function(hp)\n",
        "  y_ = model(torch.Tensor(Xi))   \n",
        "  fitness = loss_fn(y_, torch.Tensor(yi)).data.item() \n",
        "  run_history.append([fitness,*hp_out.__dict__.values()]) \n",
        "  if fitness < best:\n",
        "    best = fitness\n",
        "    best_hp = hp_out\n",
        "  clear_output()    \n",
        "\n",
        "  fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(18,6))\n",
        "  fig.suptitle(f\"Best Fitness {best}\\n{best_hp} \")\n",
        "  fig.text(0,0,f\"Current Fitness {fitness} \\n{hp_out}\")\n",
        "  ax1.plot(history)\n",
        "  ax1.set_xlabel(\"iteration\") \n",
        "  ax1.set_ylabel(\"loss\")\n",
        "\n",
        "  ax2.plot(Xi, yi, 'o', color='black') \n",
        "  ax2.plot(Xi,y_.detach().numpy(), 'r') \n",
        "  ax2.set_xlabel(\"X\") \n",
        "  ax2.set_ylabel(\"Y\")\n",
        "  \n",
        "  rh = np.array(run_history)    \n",
        "  hexbins = ax3.hexbin(rh[:, 2], rh[:, 3], C=rh[:, 0], \n",
        "                        bins=25, gridsize=25, cmap=cm.get_cmap('gray'))\n",
        "  ax3.set_xlabel(\"middle_layer\")\n",
        "  ax3.set_ylabel(\"learning_rate\")    \n",
        "      \n",
        "  plt.show()\n",
        "  time.sleep(1)\n",
        "    \n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-8-9d590a2a7eb6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mrun_history\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      6\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[0mruns\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----> 7\u001b[0;31m   \u001b[0mspan\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhistory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhp_out\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhp\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      8\u001b[0m   \u001b[0my_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXi\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      9\u001b[0m   \u001b[0mfitness\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\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<ipython-input-7-d1fd64577b62>\u001b[0m in \u001b[0;36mtrain_function\u001b[0;34m(hp)\u001b[0m\n\u001b[1;32m     40\u001b[0m       \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\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     41\u001b[0m       \u001b[0;31m# step the optimizer - update the weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m       \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\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     43\u001b[0m   \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhistory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/optim/optimizer.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     86\u001b[0m                 \u001b[0mprofile_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Optimizer.step#{}.step\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprofiler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecord_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprofile_name\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---> 88\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\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     89\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/autograd/grad_mode.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\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---> 28\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\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     29\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/optim/adam.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m    142\u001b[0m                    \u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgroup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lr'\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    143\u001b[0m                    \u001b[0mweight_decay\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgroup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'weight_decay'\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--> 144\u001b[0;31m                    eps=group['eps'])\n\u001b[0m\u001b[1;32m    145\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/optim/_functional.py\u001b[0m in \u001b[0;36madam\u001b[0;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, amsgrad, beta1, beta2, lr, weight_decay, eps)\u001b[0m\n\u001b[1;32m     85\u001b[0m         \u001b[0;31m# Decay the first and second moment running average coefficient\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m         \u001b[0mexp_avg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmul_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbeta1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mbeta1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 87\u001b[0;31m         \u001b[0mexp_avg_sq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmul_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbeta2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddcmul_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mbeta2\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     88\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mamsgrad\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m             \u001b[0;31m# Maintains the maximum of all 2nd moment running avg. till now\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    }
  ]
}