pytorch_tutorial1b.ipynb

{
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  "metadata": {
    "colab": {
      "name": "pytorch_tutorial1b.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNIWtLfUMBemMbuiN5YY6Bz",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rstager/ARoboCar/blob/master/pytorch_tutorial1b.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ri5PDh4yRTab",
        "colab_type": "text"
      },
      "source": [
        "# Setup the environment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J1mkFeIUIIXl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from __future__ import print_function\n",
        "import torch\n",
        "import numpy as np\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import animation, rc\n",
        "from IPython.display import HTML\n",
        "import copy\n",
        "from IPython.core.debugger import set_trace\n",
        "from IPython.display import clear_output\n",
        "import math\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "from functools import partial\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t5i_26lOZ3nU",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "East Bay Machine Learning pytorch tutorial series.\n",
        "\n",
        "---\n",
        "\n",
        "This is the second part of the pytorch tutorial series first meeting.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WxpEAveycl2y",
        "colab_type": "text"
      },
      "source": [
        "We willl used a google doc as a shared online clipboard. \n",
        "https://docs.google.com/document/d/1N-5Ue0rk7g8CImayet-cgnaCkHJzXky7aeHrwUocXEM/edit?usp=sharing\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RzWZwUrJBVxC",
        "colab_type": "text"
      },
      "source": [
        "#Agenda\n",
        "\n",
        "* in the section we will explore torch.nn\n",
        "\n",
        "Lets get *started*\n",
        "\n",
        "* first bring a few of our utility functions from the previous notebook"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bNh4Ez0Pn4Cg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# just run on cpu for now\n",
        "device=\"cpu\"\n",
        "\n",
        "# Data Generation\n",
        "def dataset_from_function_with_noise(f,npoints=100,noise=0.1,device=\"cpu\"):\n",
        "  torch.manual_seed(42)\n",
        "  x_train_tensor = torch.rand((npoints,1),device=device)\n",
        "  y_train_tensor = f(x_train_tensor)\n",
        "  y_train_tensor += torch.randn(y_train_tensor.shape)*noise\n",
        "  return x_train_tensor,y_train_tensor\n",
        "\n",
        "class Movie:\n",
        "  def __init__(self,X,Y,rng=None,axis=None):\n",
        "    self.X=X.cpu()\n",
        "    self.Y=Y.cpu()\n",
        "    self.axis=plt.gca() if axis is None else axis\n",
        "    self.scat=self.axis.scatter(self.X,self.Y,c='b')\n",
        "    self.artists=[]\n",
        "    self.rng=torch.linspace(min(self.X).item(),max(self.X).item(),100).unsqueeze(-1) if rng is None else rng\n",
        "\n",
        "\n",
        "  def __call__(self,model):\n",
        "    l1,=self.axis.plot(list(self.rng),list(model(self.rng)),c='r')\n",
        "    self.artists.append([self.scat,l1])    \n",
        "\n",
        "\n",
        "def plot_hook(model,X,Y):\n",
        "  if X is not None:\n",
        "    plt.scatter(X.cpu(),Y.cpu(),c='b')\n",
        "  plt.plot(list(rng),list(model(rng)),c='r')\n",
        "  plt.pause(0.5)\n",
        "  clear_output()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vVOi537K91_m",
        "colab_type": "text"
      },
      "source": [
        "# Create our dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "20b0f4be-bea4-480f-c5ca-6bb89cbe5648",
        "id": "IPx0qbCoB7uw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "torch.manual_seed(42)\n",
        "X_train,Y_train = dataset_from_function_with_noise(lambda x:1 + 4 * (x-0.5)**2,noise=0.01 ,npoints=100 )\n",
        "plt.scatter(X_train,Y_train,c='b')\n",
        "plt.show()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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trpNA8VD2COg10lmwEef8eSaokK5enQSKh/JBQK+ZRiOYdIoL6svLZB1g6zpTE8+fl0ZH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\nqAkCOgDUBAEdAGqCgA4ANUFAB4Ca+H/08xBtn00NdQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QWM_ZCcn8EaA",
        "colab_type": "text"
      },
      "source": [
        "# OK. Lets play with pytorch.nn\n",
        "* torch.Tensor is like numpy\n",
        "* torch.nn,torch.optim,torch.loss,torch.data are like keras\n",
        "\n",
        "* torch.nn.Model is a container for parameters (torch.nn.Parameters) and a forward(x) function. It may also include additional sub models.\n",
        "https://pytorch.org/docs/stable/nn.html\n",
        "\n",
        "\n",
        "Thanks to \n",
        "https://medium.com/@benjamin.phillips22/simple-regression-with-neural-networks-in-pytorch-313f06910379 for the example.\n",
        "\n",
        "First we will do a quick implementation of the network that we finished with in the previous notebook, and then we will do a deeper dive into the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NXHlLXRBpzvU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def initializer(model):\n",
        "  for p in model.parameters():\n",
        "    try:\n",
        "      torch.nn.init.kaiming_uniform_(p, a=math.sqrt(5))\n",
        "    except:\n",
        "      torch.nn.init.uniform_(p)\n",
        "\n",
        "def sgd(model,data_loader,criterion,optimizer,n_epochs=100,hook=lambda m,x,y,l:None,\n",
        "        initialize=initializer):\n",
        "  initialize(model)\n",
        "  for i in range(n_epochs):\n",
        "    for X,Y in my_dataloader:\n",
        "      Yhat = model(X)\n",
        "      loss = criterion(Yhat, Y)\n",
        "      optimizer.zero_grad()   # zero the gradient buffers\n",
        "      loss.backward()\n",
        "      optimizer.step()    # Does the update\n",
        "    hook(model)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KcdR_Zsv8thR",
        "colab_type": "code",
        "outputId": "fb48841c-bca8-455d-c2ab-10ab88365809",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        }
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torch.utils import data\n",
        "\n",
        "my_dataset = data.TensorDataset(X_train,Y_train) # create your datset\n",
        "my_dataloader = data.DataLoader(my_dataset,batch_size=64) \n",
        "\n",
        "torch.manual_seed(42)\n",
        "\n",
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        self.fc1 = nn.Linear(1, 10)\n",
        "        self.fc2 = nn.Linear(10, 1)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = F.leaky_relu(self.fc1(x))\n",
        "        x = self.fc2(x)\n",
        "        return x\n",
        "\n",
        "net = Net()\n",
        "print(net)\n",
        "\n",
        "criterion = nn.MSELoss()\n",
        "#optimizer = optim.SGD(net.parameters(),lr=1e-2,nesterov=True,momentum=1e-10)\n",
        "optimizer = optim.Adam(net.parameters(), lr=0.1)\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "torch.manual_seed(42)\n",
        "movie=Movie(X_train,Y_train)\n",
        "sgd(net,my_dataloader,criterion,optimizer,hook=movie)\n",
        "HTML(animation.ArtistAnimation(fig, movie.artists,interval=50, blit=True,repeat_delay=1000).to_html5_video()) "
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Net(\n",
            "  (fc1): Linear(in_features=1, out_features=10, bias=True)\n",
            "  (fc2): Linear(in_features=10, out_features=1, bias=True)\n",
            ")\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
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C1WEOwegDS0SgtwCOAM4ETgfuNLN9o51oZgPMbKaZzVy+PLOnz0cu0DWbQxjBnVzCc5zL\nZCAYvxwiQVFYCCfwPs9zEV9wOOfyMmX8V41zgrDoXiICfTHwpnNuvXNuBfA+cGi0E51zY5xzXZ1z\nXTt16pSAl06fyM0wAO7jNr7gMEYxiA6UBuKXQyQodiz5klf5LQvIpxevs452NY5n8lDFSIkI9FeA\n482shZnlAEcD3ybgeZu0qoW7cnP911VNLx1YyajsGwLxyyESCN9/z4ysnqymPT2YQSkdaxzOzQ1O\nn1c8wxYnAh8D+5nZYjPrb2YDzWwggHPuW2A68DXwGfCkcy7mEMcgKSiAFSuguNh3qHxjh/DX9ndy\nUcVECnJeTnfxROSnn6BHD9rllHN26xksZo/qQ2YwaJD/Gw5CmIN2LEq8zZvhqKPYOH8J/91uDl//\nmEtenn87F5RfGpGMsGoVnHgizJ8P775L6LsjKSz0/VuZ/DepHYtSqWVLpl04jharS7l18Y3aXFok\nHTZsgLPOgu++gylT4Mgjq5fCDsQEohgU6Elw3ZhDuZc7KOBZzmYKoKGMIimzeTNceCGVH3/CwO2f\nJavHac1mmWsFehIsXAh/5Hb+yW8YzUA6UFp9v4gkUWUlXHEFvP46N7QczRMrzm9W75IV6EmQl+dH\nvVzJ0+RSykhurL5fRJLEObjpJnj2We7f4U88XnZNjcPN4V2yAj0Jqsaof8VvqpteLmo1RUMZRZLp\n3nvhr3+FoUP5/c/Dop4S9HfJCvQkiNxc+j5+z+zsQxm5eSBD+pY2m7Y8kZR6/HG46y7o1w8eeIC8\nLhb1tKC/S1agJ0lBga+pt8ttxWUV4+jgSnmYIc2mLU8kZZ57Dq6/Hnr3hiefBLM6M7khOLNB66NA\nT5Kq3cRLS33TSxGF9CVEb15pFm15Iikxfbrf1PeEE3ywt2gB1HyXbEbGb1wRL00sSpL8fN+zXqUl\nZXzGUezMUg5kDj9bByor01Y8kcz38cdw2mmw777w3nvQvn26S5QSmliUBrU7XzbTiisYR0dW8AhD\nAt+WJ5JUc+bAmWfCrrvCG280mzBviAI9SaIF9lf8hj9yO5dRzLjzpqa+UCJBsGAB9OgBrVvDW2/B\nLruku0RNhgI9SaJ1ygCM7lDIqrxDOHnitVt2yRCR+Cxb5sN8wwZ4803Yc890l6hJUaAnSbROmeJi\nWFLaih2njPNLvN18c7qLKZI51qyBnj1h8WKYNg0OPjjdJWpyFOhJFGsxoNDcw3ik7e0wYQLnZL+K\nGRqfLlKfTZv8sMTZs+Gll+DYY9NdoiZJgZ5iVcMZb11dyNcczOOV19KBUo1PF4mlvBz69IG//x3G\nj4czziAU8pWgrCxVhiIp0FOssNA3/22mFf0YTy6ljKcfRqXGp4vU5hxccw288gqMHAmXXlpdKSop\noVktvBUPBXqKRQ5n/JLDuIWHOItp3MoDdY6LNGvOwa23wrhxMHw4oQ43kJ8Pffv6SlEkVYY8BXqK\n1R7O+DiDmcRFFFHICbyv8ekiVf78Z/jLX+C66wjtPby6Vh6LKkMK9JSru5aEcTVP8gO/4jn68NDQ\nH9NRLJGm5ckn4bbbfNv5yJEU3mF1auW1qTKkQE+5ggK/y3ikdbTjAl6kna3jvMe6wdKl6SmcSFMw\neTJcey2cfrrvBM3KarD23RwW3oqHAj0NHnmk7qSjeTkH8/Edr8OiRdC9u1/VS6S5eecduOQSOOoo\nPzyxVSug/tp3c1l4Kx4K9DSItRJcj3uOh6lT/ca2PXrAzz+nu6giqTNrFpx9Nuyzj5841LZt9aFY\ny+EWFwd3w+et0WCgm9lYM1tmZt80cN6RZlZuZhckrnjBFXMH8m7d/FvO2bPhjDNg7do0llIkRf71\nLz8LNDfXT+nv0KHG4ea6HG5jxVNDHwf0rO8EM8sG7gdmJKBM0quXX9v588/97LiGeoNEMtnixf4d\nqZlfbKtz56inxawESbUGA9059z7Q0CpSNwAvAcsSUSgBzjsPJkzws+POPRd++SXdJRJJvNLSLc2L\nb75J6LN9NAN0G2xzG7qZdQbOBUbFce4AM5tpZjOXL1++rS8dfJde6odvzZgBF10Emzenu0QiibNu\nnX83Om8eTJ1KaO5hmgG6jRLRKfowMMw51+D+O865Mc65rs65rp06dUrASzcDV10Fjz7qO0v79oWK\ninSXSGTb/fKLfxc6axY8/zycdFL1shiRNAO0cRIR6F2B58xsAXAB8LiZnZOA55Ww0A7XcVfOg/D8\n84xr0Z8sq6RjR9VcJENVVMDll/v28qee8v1ExJ4Fqhmg8WuxrU/gnKteYd7MxgGvOeembOvzihcK\n+Up6WdlQHBsYwV1spA2DSx/nqqsMUOeQZBDn4LrrfK38wQehXz/A/56b+cO1aQZo/BoMdDObCJwM\ndDSzxcBwoCWAc250UksnFBZCWZn//F7uoA0buZ0/sYnW3FL2EIWFpkCXzHHXXfDEE35a/9ChgA/z\nfv2ih7mZZoA2RoOB7py7JN4nc85dsU2lkTpqvt00Cikihw3czMOspy13ltybrqKJNM7DD8O998LV\nV8Mf/whs2R8gVteQc3oH2hjb3OQiyZWXV7tt0biZ/0cbNnIHRfxibQD1GkkT98wzfsvF886D0aN9\n1RsYMqT+aRZduqSofAGhqf9NXFFR9XIWEYxBjGI8lzPC3QEPPZSOoonEZ9o0uPJKOPVUCIUIPZdN\nx44+0+tbskgLbjWeauhNXNXbzb59a97vyKI/T5FjG7lw6FBo3RoGD059AUXq88EHcMEF8JvfwJQp\nhF5qHe7kr/9h2dma2r81VEPPALF+qStoQYErht/+1o8cePrp1BZMpD5ff+1/N/Py4I03oF27Gp38\n9Rk/XmG+NRToGSJWW+JuXVr5IWDdu0P//jBxYmoLJhLNDz/49czbtfMzncMTCeMZU56bqzDfWgr0\nDBFr+dCiInxzy5QpcOKJcNll8PLLaSmjCABLlvj1WTZv9mEeURtpaEx5To7fL0C2jgI9QzS4fGhO\nDrz6Khx5JFx8sX+LK5Jqq1b5mvnSpfD667D//jUON9TJqXbzbWMu2mj+FOjataubOXNmWl470H7+\n2a+pPneuH11w6qnpLpE0Fxs2+Jr5Z5/5373u3aOe1rFj9NEtXbr4ZXGlfmY2yznXNdox1dCDZocd\n/Nvcvff2HVIffZTuEklzsHmzXxH0H//ws4VihDlE34JRQxQTQ4EeRLm58Le/we67+12PPv883SWS\nIKus9AsOTZsGo0bBhRfWe7p2H0oeNbkE2eLFvqP055/h3Xfh0EPTXSIJGufgpptg5Ehfxb799nSX\nKPDU5NKMhEJs2fHl+N2ZcuM7frPd007z7eoiiVRU5MP85pvh97+vcajG72K+lntOBQV6gFQtdBS5\n40tBYT7DT3iHZata8NOBp3Fy53/rD0sSY9QouPNOv7b5gw+CWXWIm/kRtNp9KLXU5BIg+fmxNwk4\ngDn8nZPYQA49Wn/AnU92UZulbL3nn4c+feDMM2HyZGjZsrpC0dBiWxrJsm3U5NJM1DcLby4H0p23\naMdapm06lZHDfkxdwSRYZszwiwsdf7wP9pYtAaJuIVebdh9KLgV6gDQ0C+9LDqMn09mJZYz/sZuf\n/CHSGJ9+6pfAPeAAv89tmzbVh+IJa+0+lFwK9ACJtjxAbZ9xNGcyjTwWsurI7rByZWoKJ5lv7lzo\n1Qt22QWmT4cddqjR8ZnVQJporHnyKdADJHJ8b30+4ER6M5U2i76jtGsPWL06NQWUzFVS4meBtmrl\nm1x22aVOJ3y0XYfC+1horHmKKNADpqDAdzoVF9dfW3+b0zifl2g3/2s+2qEXB+St0wgEiW75ch/m\n69fDm2/CXnsBsdvMs7O3TBh65hkf9gsWKMxTQRtcBFTVH09hoW/bjDaY6XXO5BImMomLeXRRb865\nYhrQRn94ssWaNX628aJF8NZbcMgh1YditZlXVvqbpJ5q6AFWVVuvrIzdDDOZ8+nHeE7mPSaVn8et\nN/6S0jJKE7ZpE5xzDnz1Fbz4Ihx3XI3DsTo41fGZPg0GupmNNbNlZvZNjOMFZva1mc02s3+YmeaX\nN0H1dZg+SwHX8L+cwXQeW9nHL7QkzVt5OVxyiV8yYtw43xlaS71r9EtaxFNDHwf0rOf4fOAk59zB\nwAhgTALKJQnWUIfpWPpzPX/lXKb4KX7RerikeXAOrr3Wb5ryyCMxG7+1yFbTE9dMUTPLB15zzh3U\nwHk7At845zo39JyaKZo+9c0o/R97kAfcrbzQ9grKRj1FwWVqlWt2hg2DP//ZT+u/5550l0ZqSeVM\n0f6Atspp4oqKqif31fGg+x/u4m4uXD+O9VddT6g4PUtDSJr8+c/+Nngw3H13uksjjZSwQDezU/CB\nPqyecwaY2Uwzm7l8+fJEvbQ0UkEBPP20Xza9SuSkkBHcyX0MY0D5KNZcM5T8Lk4r5jUHY8fCsGEs\nOOZi9nptJFnZpp95pnHONXgD8vFNKbGOHwL8AOwbz/M55zjiiCOcNB1mzvnG06pbpXuYG50DN4LC\n6vtzcpwrLk53aSXhXn7Zuaws9+PBPVz7Nr/U+F0wc27QoHQXUKoAM12MXN3mGrqZ5QGTgcucc99t\n6/NJetQdambcxMOM4RruoIjb8UMXNmzwY9slQN57z6+ceOSRnPbzS6ze2KrGYedg9GjV1DNBPMMW\nJwIfA/uZ2WIz629mA81sYPiUu4Bc4HEz+9LM1NOZgaIPazQGMYpn6EsRd3AzDwFaMS9QvvgCeveG\nX/0Kpk3j20XbRT3NOf0jzwQNzhR1zl3SwPGrgasTViJJi9ozS/PyYMUKWL8+myt5mtZs4iGGspE2\njHaDyM/3/wQ0RC2Dffcd9OwJHTrAjBmEpudiFn1WMegfeSbQ1H+pVlBQM6A7dvTLd1TQggJCtGYT\noxjMRtowvuQKBgzY8jjJMIsXQ/fu/vMZM6BzZwoLY4c5aAZoJtAgY4kpcmXdzbTiQl5gBt15iv5c\nzHNqT89UpaVw+umwahW88Qbsuy9Qfw1cM0AzgwJdYqpdI/uF1pzDFD7keIrpy9lM0dvwTLNund82\n7ocf/AYVRxxRfShWDTw7WzNAM4UCXWKK1lG6kRzO4jVm0pXnuYgLtpuund0zRVkZnH8+lZ99zoDt\nnyPr1JNr/Mxirc0yfrzCPGPEGs+Y7JvGoWeG4mLnunTZMh65amxye1a5LzjMbaC1O4W3a4xbbttW\nY9WbkuJi5/bMK3cTudg5cFdnj63x84qcW1D18zbzH/VzbHqoZxy6Al3iVvuPfZ8dl7vZHOjW0tYd\ny4e1JiY5l5urQEi34mLnctpUuscY5By4oTxQ5+cE/ucpmaG+QI9rca5k0OJcmS8rC3Zy/+F9TmRn\nltKNt5lF3TWDcnPrXbRPkig/H64sGc5w7uF+fsdt3B/1PDNtSpEpUrk4lzQjeXmwlF3oxtuspAMz\n6MEhfFXnvNJSv/ek2tdTI3Lj5t4lIxnOPTzFVdzGfTEfoyGJwaBAl61WVORrdovZg1N5h/W05S26\n82u+rXOuhjimxuDBfjn7khK4xIUYyRAmcy7X8gRgUR+jIYnBoUCXrVZQAAPDC0AsYE+68TaVZPE2\n3fgV39c5v6TET1Yy87eOHVVrT6RQyK+54hycweuM4wre4RQu5VkqIuYQtmzpm8G0KUXwKNBlmzz+\nOBQX+4D4N/tyGn+jFWW8TTfyqLuLRmlpzc+vukqhnihVMz2P5SNe5AK+5hDOYQq/0BrYEuBPP+2X\ndais9HvOKsyDQ4Eu26ygwAeEc/D74oO4qP0M2rOat+nGrvwE+DCJpqxMTTGNFdlGHjmOfOFCOIjZ\nvMZZLGIPzuAN1rI94M9VgAefAl0SqqAA3vn5cD75w3R2saW8TTcO331ZvWuEVDXFdOyoCUoNCYV8\nB3NJif8HWlICffv6791h7efxJqeznrb0YAbL2an6cZWV+p42Bxq2KMnz/vt+Nb999uHQle/y9eIO\ncT80J0dtu9HE2g92Z/7DhxzPjqziBD7gWw6oc06XLr6GLplNwxYlPU48EV55Bf7v/3in5enktlgd\n90M1Kia6aGvntOdnptOTXfgPvXg9apjHeqwEiwJdkqt7d3jxRXIXfcncvc5kjw7r435otJpokMRq\nC491P9QdL96GDUylNwcwl/OYzGccHfP1NNa8GYg1hTTZN039b2ZeeMG5rCznTj3VuQ0bqteHaegW\n1KUDiov9Giq111QZNKju/VSvbcMAAAiQSURBVJF7ekY+7gC+cbM50FVg7kIm1ft91F6wwYHWcpEm\n4ZlnfDqdcYZ79ulNdYIr1i2Ia8LE+w8tMtSrvgehcWXu5rZPuA20dv9hJ9edN6M+Jjtbi2wFUX2B\nrh2LJHX69oVNm+Caa7ik9SW4xydx+/CWDTatVC0dAHU7SUOhmtvmZcq2ePW3Zzs6soK9mFd929PN\nZ8/+8+CO+Vy6aBGXVlSw5KDT6P3zM8xcvEudrePUqdxMxUr6ZN9UQ2/GHnnEVyH79HGuvDxq80M8\nKwLGarZoqrXR4mL/bgOca8N6dwDfuDN51d3AI+4hbnJT6O2+4mC3hu3qXPxP7OI+4r+dKyhw7o47\nnJs0ybmKihrPrWVvmwe02qI0OfffD7fdBldcAU89RWhiFv36QUVF7IfUXhEw1hC+JjE8r6KClx/9\nkUl/mkfO0vkc0GYeu26cz57hGvcuLK1x+npyquvjW87yny8gnw20bRrXJWlX37BFNblIegwbBhs3\nwt13Q5s2FDz2GGAMGOCHLEZTe5RGrGaLkhI/QiSpTTCVlX567KJFMH8+zJu35eO8eVTML+Hcis2c\nGz69YmMWC8ljPnvyGmfVCO757MlyOhFr8SzQAloSnwYD3czGAmcBy5xzB0U5bsAjQC9gA3CFc+6L\nRBdUAmj4cJ/eDzzgQ/3BBwFjyJCaa75A9EDLy4s9tNGFZ1HWaXt3zr/m2rWwZo3/WHWL9+uVK2Hp\nUigvr/Gam7bLZd1Oe/HJfw7nm4rza9SyF5JHOS0b9e3JzfUvlUl9A5Je8dTQxwGPAhNiHD8D2Cd8\nOxoYFf4oUj8z3/SycSM89BD88AMFfftSsLAXoZdzqjs799yjnD8VruOiE9bAnC0BO+HsNYRGr6VV\n2VraUfe2PWtot2EtHa5cC7eFA3ndurh2cqjEKG/TjlYd2kG78G377WGnnaBrV74p3ZWxb+zK/M2d\nq0P7l7LtcYtg8+Zt/7YMHOgXPhNpjAYD3Tn3vpnl13PK2cCEcGP9J2a2g5nt6pxbkqAySpCZ+e2M\ndtzRr/36yiuQk0NB584UbFoDrdfAwo1wbd2Hnhi+VSmjZXWcr2F71tKOVezIws157NNj+y3BHBnQ\n4c/f+Gh7bitqx7JN/vHraUuOGWPuj14zPisfSmoHd9m2fRuc8+3/qo3L1oqrUzQc6K/FaHJ5DbjP\nOfdh+Ou3gWHOuTo9nmY2ABgAkJeXd0RJ0KcCSuOUl8MHH8BLL/m2hhjhG+u+/F+33upO0sZ2sGZl\n1RwmGI9Wrfxa5OvXb3mOykqFuDROk+kUdc6NAcaAH+WSyteWDNCiBZxyir9thaIi6nSqxtuZGKuD\nNdb99bXfR6PQllRIxFouPwJ7RHy9e/g+kZQqKPCTabp0afxuPLHWOYl1f1GR/2cRqaoGHiknx28A\nonXIJRUSEehTgcvNOwZYrfZzSZeCAh+ejd3MIVpA11e7j/bPY+xYvxvQ1vxDEUmEBtvQzWwicDLQ\nEVgKDAc//so5Nzo8bPFRoCd+2OKV0drPa9PEImlqMnUZAWle6mtD10xREZEMog0uRESaAQW6iEhA\nKNBFRAJCgS4iEhAKdBGRgFCgi4gEhAJdRCQg0jYO3cyWA9u6OldHYEUCipNJdM3NQ3O8Zmie193Y\na+7inOsU7UDaAj0RzGxmrAH2QaVrbh6a4zVD87zuRF6zmlxERAJCgS4iEhCZHuhj0l2ANNA1Nw/N\n8ZqheV53wq45o9vQRURki0yvoYuISJgCXUQkIDIi0M2sp5n9y8y+N7Pbohz/LzObFD7+aXhT64wW\nxzXfYmZzzexrM3vbzLqko5yJ1NA1R5x3vpk5M8v44W3xXLOZXRT+Wc8xs2dTXcZEi+N3O8/M3jWz\nf4Z/v3ulo5yJZGZjzWyZmX0T47iZ2cjw9+RrMzt8q17IOdekb0A28AOwF9AK+Ao4oNY5g4HR4c/7\nAJPSXe4UXPMpQE7480HN4ZrD57UD3gc+Abqmu9wp+DnvA/wT2DH89U7pLncKrnkMMCj8+QHAgnSX\nOwHXfSJwOPBNjOO9gDcAA44BPt2a18mEGvpRwPfOuXnOuTLgOeDsWuecDYwPf/4i0C28NV6mavCa\nnXPvOueq9rf/BL85dyaL5+cMMAK4H9iUysIlSTzXfA3wmHNuFYBzblmKy5ho8VyzA7YPf94e+CmF\n5UsK59z7wMp6TjkbmOC8T4AdzGzXxr5OJgR6Z2BRxNeLw/dFPcc5Vw6sBnJTUrrkiOeaI/XH/3fP\nZA1ec/ht6B7OuWmpLFgSxfNz3hfY18w+MrNPzKxnykqXHPFc8x+Avma2GHgduCE1RUurxv7NR9Ui\nYcWRtDCzvkBX4KR0lyWZzCwLeAi4Is1FSbUW+GaXk/Hvwt43s4Odcz+ntVTJdQkwzjn3FzP7b+AZ\nMzvIOVeZ7oI1dZlQQ/8R2CPi693D90U9x8xa4N+mlaakdMkRzzVjZqcBhUBv59wvKSpbsjR0ze2A\ng4D3zGwBvp1xaoZ3jMbzc14MTHXObXbOzQe+wwd8pornmvsDzwM45z4GWuMXsAqyuP7mG5IJgf45\nsI+Z7WlmrfCdnlNrnTMV6Bf+/ALgHRfuachQDV6zmR0GPIEP80xvV4UGrtk5t9o519E5l++cy8f3\nG/R2zs1MT3ETIp7f7Sn42jlm1hHfBDMvlYVMsHiueSHQDcDM9scH+vKUljL1pgKXh0e7HAOsds4t\nafSzpLv3N84e4l74mskPQGH4vnvwf9Dgf+AvAN8DnwF7pbvMKbjmvwFLgS/Dt6npLnOyr7nWue+R\n4aNc4vw5G76paS4wG+iT7jKn4JoPAD7Cj4D5EuiR7jIn4JonAkuAzfh3Xf2BgcDAiJ/zY+Hvyeyt\n/d3W1H8RkYDIhCYXERGJgwJdRCQgFOgiIgGhQBcRCQgFuohIQCjQRUQCQoEuIhIQ/x9Vr5ctGdgN\nagAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DIiUekLLh1uh",
        "colab_type": "text"
      },
      "source": [
        "# Let's dive into the libraries a little\n",
        "\n",
        "optimizers (to be explored in later session) :https://pytorch.org/docs/stable/optim.html [SGD source ](https://pytorch.org/docs/stable/_modules/torch/optim/sgd.html#SGD)\n",
        "\n",
        "\n",
        "loss functions: https://pytorch.org/docs/stable/nn.functional.html#loss-functions\n",
        "\n",
        "torch functions: https://pytorch.org/docs/stable/nn.functional.html\n",
        "\n",
        "data utilities: (to be explored in later session) https://pytorch.org/docs/stable/data.html\n",
        "\n",
        "\n",
        "* [torch.utils.data](https://pytorch.org/docs/stable/data.html)\n",
        "* [torch.nn.Linear](https://pytorch.org/docs/stable/nn.html#linear)  [F.linear](https://pytorch.org/docs/stable/_modules/torch/nn/functional.html#linear)\n",
        "* [torch.functional.relu](https://)\n",
        "* [torch.optim](https://pytorch.org/docs/master/optim.html)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sQUgs3Q88BbC",
        "colab_type": "text"
      },
      "source": [
        "# Now your turn.\n",
        "\n",
        "Play with optimizers, loss functions,learning rate, target signal, target noise, # points, batchsize, epochs ,model structure, etc. Try it on a GPU.\n",
        "\n",
        "Find an interesting result and post it to the shared board. Try to keep training time down so we can share during class."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mLpxA7Fv_D3M",
        "colab_type": "text"
      },
      "source": [
        "# Loss surface of high dimensionality parameter space\n",
        "\n",
        "With the dimensionality of the parameter space dimensionality larger than 2, we can no longer create an intuitive loss surface representation. A proxy is to take a number of 1 dimensional slices in random directions in the N dimensional space around a point in the parameters space. This does not give us a view of the entire surface, but gives us a sense of the curvature at that point."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rfm5GIE9_ynU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "4598e690-8334-4dcc-a466-4f3b2bdafdb9"
      },
      "source": [
        "torch.manual_seed(42)\n",
        "def slicemodel(model, nslices=10,magitude=1):  # generate n random vectors in the paramater space of model\n",
        "  lr=0.1\n",
        "  with torch.no_grad():\n",
        "    slices = [[torch.randn_like(p)*lr  for p in model.parameters(recurse=True)] for n in range(10)]\n",
        "    # todo: each vector should be magnitude length\n",
        "    return slices\n",
        "\n",
        "def slicelosses(slices,model,X,Y,criterion,npoints=10):\n",
        "  losses=[]\n",
        "  for slice in slices:\n",
        "    loss=[]\n",
        "    for tmp in range(0,npoints):\n",
        "      factor=(tmp-npoints/2)/npoints\n",
        "      # perturb the model parameters in direction of the slice\n",
        "      workingmodel=copy.deepcopy(model)\n",
        "      with torch.no_grad():     \n",
        "        workingmodel.requires_grad=False\n",
        "        for p,slicep in zip(workingmodel.parameters(recurse=True),slice):\n",
        "          p.add_(slicep*factor)\n",
        "        Yhat = workingmodel(X)\n",
        "        loss.append(criterion(Yhat, Y))\n",
        "    losses.append(loss)\n",
        "  return losses\n",
        "\n",
        "class SliceMovie(Movie):\n",
        "  def __init__(self,model,criterion,X,Y,axes=None,slices=None):\n",
        "    self.axes=axes if axes is not None else [plt.subplot(121),plt.subplot(122)]\n",
        "    super().__init__(X,Y,axis=self.axes[0])\n",
        "    self.model=model\n",
        "    self.criterion=criterion\n",
        "    self.slices=slicemodel(self.model, nslices=10,magitude=1) if slices is None else slices\n",
        "    self.scat=self.axes[0].scatter(X.cpu(),Y.cpu(),c='b')\n",
        "\n",
        "  def __call__(self,model):\n",
        "    l1,=self.axes[0].plot(list(self.rng),list(model(self.rng)),c='r')\n",
        "    a2=[l1]\n",
        "    for loss in slicelosses(self.slices,model,self.X,self.Y,self.criterion):\n",
        "      a2.append(self.axes[1].plot(loss)[0])\n",
        "    self.artists.append(a2)\n",
        "    \n",
        "def slice_movie_hook(model,X,Y):\n",
        "  global criterion,slices,artists,slice_axes\n",
        "  scat=slice_axes[0].scatter(X.cpu(),Y.cpu(),c='b')\n",
        "  l1,=slice_axes[0].plot(list(rng),list(model(rng)),c='r')\n",
        "  artists.append([scat,l1]+[slice_axes[1].plot(loss)[0] for loss in slicelosses(slices,model,X,Y,None)])\n",
        "\n",
        "movie=SliceMovie(net,criterion,X=X_train,Y=Y_train)\n",
        "movie(net)\n",
        "plt.pause(1)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JnLAW9PZsZyE3sANRbu3pEkb4HmDfxwYA8hja5hYAU5QFy8gYWvTBqCZAwl7Q2wQhn1wS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\nkyMn4651v+w6sBiNpO7eRvLm9ejrahkUn8D4WXOIGjm6jaB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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tk88i67BZFxY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 633
        },
        "outputId": "890bf7d6-cf38-4991-8e88-313a4a2bf095"
      },
      "source": [
        "torch.manual_seed(42)\n",
        "torch.manual_seed(60)\n",
        "net = Net()\n",
        "print(net)\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "optimizer = optim.Adam(net.parameters(), lr=0.05)\n",
        "#optimizer = optim.SGD(net.parameters(),lr=2e-1)#,nesterov=True,momentum=1e-10)\n",
        "slices=slicemodel(net,30)\n",
        "\n",
        "movie=SliceMovie(net,criterion,X=X_train,Y=Y_train,slices=slices)\n",
        "movie.axes[1].set_ylim(0,0.1)\n",
        "torch.manual_seed(42)\n",
        "sgd(net,my_dataloader,criterion,optimizer,hook=movie,n_epochs=100)\n",
        "HTML(animation.ArtistAnimation(fig, movie.artists,interval=50, blit=True,repeat_delay=1000).to_html5_video()) "
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Net(\n",
            "  (fc1): Linear(in_features=1, out_features=10, bias=True)\n",
            "  (fc2): Linear(in_features=10, out_features=1, bias=True)\n",
            ")\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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          "metadata": {
            "tags": []
          },
          "execution_count": 7
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          "output_type": "display_data",
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KjlZ3/ASlWglIQheR84HngZuNMccC8ZrhZuarU/AibdbRIz6pr1kDO3eyOGG2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\nmRcamwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ognv63mOraPm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "brqvwbmBYSG0",
        "colab_type": "text"
      },
      "source": [
        "# Question\n",
        "\n",
        "Why doesnt the linear model above use all of the hidden layer activations to make a closer fit?\n",
        "\n",
        "* I tried 3000 epoch and it never improved"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sSpnoeWNEkBR",
        "colab_type": "text"
      },
      "source": [
        "## Links and stuff\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o47PMk2HLG7g",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "https://pytorch.org/docs/master/named_tensor.html\n",
        "https://en.wikipedia.org/wiki/Automatic_differentiation\n",
        "https://pytorch.org/tutorials/beginner/examples_autograd/two_layer_net_custom_function.html\n",
        "https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/module.py\n",
        "https://www.analyticsvidhya.com/blog/2019/09/introduction-to-pytorch-from-scratch/\n",
        "https://blog.paperspace.com/pytorch-101-advanced/\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "onZg1fqBTr4s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import time\n",
        "import numpy as np\n",
        "def pltit(recorder,name,c):\n",
        "  plt.title(name)\n",
        "  for name in [name]:\n",
        "    tl=[t.detach().numpy() for t in recorder[name]]\n",
        "    print(\"len(tl) \",len(tl),\" shape \",name,\" \",tl[0].shape)\n",
        "    if len(tl[0].shape)==2:\n",
        "      if tl[0].shape[1]==1:\n",
        "        v=np.hstack(tl) \n",
        "        print(\"first v shape \",v.shape)          \n",
        "        print(\"tl[0] shape\",tl[0].shape)  \n",
        "      elif tl[0].shape[0]==1:\n",
        "        v=np.hstack([t.detach().numpy().T for t in recorder[name]])\n",
        "        print(\"other v shape \",v.shape)\n",
        "      else:\n",
        "        v=np.hstack([t.detach().numpy().T for t in recorder[name]])[:,::10]\n",
        "        print(\"v shape \",v.shape)\n",
        "        print(\"v[:2,:10] \",v[:2,:10])\n",
        "        print(\"tl[0] shape\",tl[0].shape)        \n",
        "    elif len(tl[0].shape)==1:\n",
        "      v=np.hstack([np.expand_dims(t.detach().numpy(), axis=-1) for t in recorder[name]])\n",
        "      print(\"one v shape \",v.shape)\n",
        "    else:\n",
        "      print(\"unexpected shape\",tl[0].shape) \n",
        "    for l in v:\n",
        "      plt.plot(l,c=c,label=name)\n",
        "\n",
        "def pltresults(recorder):\n",
        "  print(recorder.shape)\n",
        "  fig= plt.figure(figsize=(20,10))\n",
        "  for idx,trace in enumerate( ['w1','w2','b1','b2','w1.grad','w2.grad','b1.grad','b2.grad']):\n",
        "    c='r' if '2' in trace else 'b'\n",
        "    plt.subplot(2,4,idx+1)\n",
        "    pltit(recorder,trace,c)\n",
        "\n",
        "  plt.pause(1)\n",
        "  fig= plt.figure(figsize=(20,10))\n",
        "  plt.subplot(2,1,1)\n",
        "  pltit(recorder,'h1','b')\n",
        "  plt.subplot(2,1,2)\n",
        "  pltit(recorder,'h2','r')\n",
        "  plt.pause(1)\n"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}