project_group_OkapisJohnV3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "project_group_OkapisJohnV2.1RAB.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/cindyhfls/NMA_DL_2021_project/blob/main/project_group_OkapisJohnV3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pg0Ta9TfMBDc"
      },
      "source": [
        "\n",
        "To-do: \n",
        "* hyperparameter tuning\n",
        "* make train its own function?\n",
        "* simple correlations\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "idt_5qF9ogSJ"
      },
      "source": [
        "_____________\n",
        "# Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SHRR0PV20BqZ"
      },
      "source": [
        "#@title Import matplotlib and set defaults\n",
        "from matplotlib import rcParams \n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "\n",
        "import torch\n",
        "import copy\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from torchvision import transforms, utils\n",
        "import numpy as np\n",
        "import math\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from matplotlib import pyplot as plt\n",
        "from torch.utils.data.dataloader import default_collate\n",
        "\n",
        "\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "rcParams['figure.figsize'] = [20, 4]\n",
        "rcParams['font.size'] =15\n",
        "rcParams['axes.spines.top'] = False\n",
        "rcParams['axes.spines.right'] = False\n",
        "rcParams['figure.autolayout'] = True"
      ],
      "execution_count": 247,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MWiCcZ2Xz44h"
      },
      "source": [
        "# Data Loading\n",
        "\n",
        "#@title Data retrieval\n",
        "import os, requests\n",
        "\n",
        "fname = []\n",
        "for j in range(3):\n",
        "  fname.append('steinmetz_part%d.npz'%j)\n",
        "url = [\"https://osf.io/agvxh/download\"]\n",
        "url.append(\"https://osf.io/uv3mw/download\")\n",
        "url.append(\"https://osf.io/ehmw2/download\")\n",
        "\n",
        "for j in range(len(url)):\n",
        "  if not os.path.isfile(fname[j]):\n",
        "    try:\n",
        "      r = requests.get(url[j])\n",
        "    except requests.ConnectionError:\n",
        "      print(\"!!! Failed to download data !!!\")\n",
        "    else:\n",
        "      if r.status_code != requests.codes.ok:\n",
        "        print(\"!!! Failed to download data !!!\")\n",
        "      else:\n",
        "        with open(fname[j], \"wb\") as fid:\n",
        "          fid.write(r.content)\n",
        "\n",
        "alldat = np.array([])\n",
        "for j in range(len(fname)):\n",
        "  alldat = np.hstack((alldat, np.load('steinmetz_part%d.npz'%j, allow_pickle=True)['dat']))"
      ],
      "execution_count": 248,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zP4xE0-30qRG",
        "outputId": "d59a82b7-8dc9-4e1c-977b-93930227e30b"
      },
      "source": [
        "#@title Print Keys\n",
        "print(alldat[0].keys())"
      ],
      "execution_count": 249,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['spks', 'wheel', 'pupil', 'response', 'response_time', 'bin_size', 'stim_onset', 'contrast_right', 'contrast_left', 'brain_area', 'feedback_time', 'feedback_type', 'gocue', 'mouse_name', 'date_exp', 'trough_to_peak', 'active_trials', 'contrast_left_passive', 'contrast_right_passive', 'spks_passive', 'pupil_passive', 'wheel_passive', 'prev_reward', 'ccf', 'ccf_axes', 'cellid_orig', 'reaction_time', 'face', 'face_passive', 'licks', 'licks_passive'])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "me2qABA1PzIZ"
      },
      "source": [
        "# @title Set random seed\n",
        "\n",
        "# @markdown Executing `set_seed(seed=seed)` you are setting the seed\n",
        "\n",
        "# for DL its critical to set the random seed so that students can have a\n",
        "# baseline to compare their results to expected results.\n",
        "# Read more here: https://pytorch.org/docs/stable/notes/randomness.html\n",
        "\n",
        "# Call `set_seed` function in the exercises to ensure reproducibility.\n",
        "import random\n",
        "import torch\n",
        "\n",
        "def set_seed(seed=None, seed_torch=True):\n",
        "  if seed is None:\n",
        "    seed = np.random.choice(2 ** 32)\n",
        "  random.seed(seed)\n",
        "  np.random.seed(seed)\n",
        "  if seed_torch:\n",
        "    torch.manual_seed(seed)\n",
        "    torch.cuda.manual_seed_all(seed)\n",
        "    torch.cuda.manual_seed(seed)\n",
        "    torch.backends.cudnn.benchmark = False\n",
        "    torch.backends.cudnn.deterministic = True\n",
        "\n",
        "  print(f'Random seed {seed} has been set.')\n",
        "\n",
        "\n",
        "# In case that `DataLoader` is used\n",
        "def seed_worker(worker_id):\n",
        "  worker_seed = torch.initial_seed() % 2**32\n",
        "  np.random.seed(worker_seed)\n",
        "  random.seed(worker_seed)"
      ],
      "execution_count": 250,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jyNZF8eu2GJa"
      },
      "source": [
        "#@title Define Steinmetz Class\n",
        "class SteinmetzSession:\n",
        "  data = []\n",
        "  binSize = 10\n",
        "  nTrials = []\n",
        "  nNeurons = []\n",
        "  trialLen = 0\n",
        "  trimStart = \"trialStart\"\n",
        "  trimEnd =  \"trialEnd\"\n",
        "  def __init__(self, dataIn):\n",
        "    self.data = copy.deepcopy(dataIn)\n",
        "    dims1 = np.shape(dataIn['spks'])\n",
        "    self.nTrials = dims1[1]\n",
        "    self.nNeurons = dims1[0]\n",
        "    self.trialLen = dims1[2]\n",
        "\n",
        "  def binData(self, binSizeIn): # Inputs: data, scalar for binning. Combines binSizeIn bins together to bin data smaller Ex. binSizeIn of 5 on the original dataset combines every 5 10 ms bins into one 50 ms bin across all trials.\n",
        "    varsToRebinSum = ['spks']\n",
        "    varsToRebinMean = ['wheel', 'pupil']\n",
        "    spikes = self.data['spks']\n",
        "    histVec = range(0,self.trialLen+1, binSizeIn)\n",
        "    spikesBin = np.zeros((self.nNeurons, self.nTrials, len(histVec)))\n",
        "    print(histVec)\n",
        "    for trial in range(self.nTrials):\n",
        "      spikes1 = np.squeeze(spikes[:,trial,:])\n",
        "      for time1 in range(len(histVec)-1):\n",
        "        spikesBin[:,trial, time1] = np.sum(spikes1[:, histVec[time1]:histVec[time1+1]-1], axis=1)\n",
        "\n",
        "    spikesBin = spikesBin[:,:,:-1]\n",
        "    self.data['spks'] = spikesBin\n",
        "    self.trialLen = len(histVec) -1\n",
        "    self.binSize = self.binSize*binSizeIn\n",
        "\n",
        "    \n",
        "    s = \"Binned spikes, turning a \" + repr(np.shape(spikes)) + \" matrix into a \" + repr(np.shape(spikesBin)) + \" matrix\"\n",
        "    print(s)\n",
        "\n",
        "  def plotTrial(self, trialNum): # Basic function to plot the firing rate during a single trial. Used for debugging trimming and binning\n",
        "    plt.imshow(np.squeeze(self.data['spks'][:,trialNum,:]), cmap='gray_r', aspect = 'auto')\n",
        "    plt.colorbar()\n",
        "    plt.xlabel(\"Time (bins)\")\n",
        "    plt.ylabel(\"Neuron #\")\n",
        "    \n",
        "  def realign_data_to_movement(self,length_time_in_ms): # input has to be n * nTrials * nbins\n",
        "    align_time_in_bins = np.round(self.data['response_time']/self.binSize*1000)+ int(500/self.binSize) # has to add 0.5 s because the first 0.5 s is pre-stimulus\n",
        "    length_time_in_bins = int(length_time_in_ms/self.binSize)\n",
        "    validtrials = self.data['response']!=0\n",
        "    maxtime = self.trialLen\n",
        "    newshape = (self.nNeurons,self.nTrials)\n",
        "    newshape+=(length_time_in_bins,)\n",
        "    newdata = np.empty(newshape)\n",
        "    for count,align_time_curr_trial in enumerate(align_time_in_bins):\n",
        "      if (validtrials[count]==0)|(align_time_curr_trial+length_time_in_bins>maxtime) :\n",
        "        validtrials[count] = 0\n",
        "      else:\n",
        "        newdata[:,count,:]= self.data['spks'][:,count,int(align_time_curr_trial):int(align_time_curr_trial)+length_time_in_bins]\n",
        "    # newdata = newdata[:,validtrials,:]\n",
        "    self.data['spks'] = newdata\n",
        "    # self.validtrials = validtrials\n",
        "    print('spikes aligned to movement, returning validtrials')\n",
        "    return validtrials\n",
        "  \n",
        "  def get_areas(self):\n",
        "    print(set(list(self.data['brain_area'])))\n",
        "\n",
        "  def extractROI(self, region): #### extract neurons from single region\n",
        "    rmrt=list(np.where(self.data['brain_area']!=region))[0]\n",
        "    print(f' removing data from {len(rmrt)} neurons not contained in {region} ')\n",
        "    self.data['spks']=np.delete(self.data['spks'],rmrt,axis=0)\n",
        "    neur=len(self.data['spks'])\n",
        "    print(f'neurons remaining in trial {neur}')\n",
        "    self.data['brain_area']=np.delete(self.data['brain_area'],rmrt,axis=0)\n",
        "    self.data['ccf']=np.delete(self.data['ccf'],rmrt,axis=0)\n",
        "    \n",
        "  def FlattenTs(self):\n",
        "    self.data['spks']=np.hstack(self.data['spks'][:])\n",
        "\n",
        "  def removeTrialAvgFR(self):\n",
        "    mFR = self.data['spks'].mean(1)\n",
        "    mFR = np.expand_dims(mFR, 1).repeat(self.data['spks'].shape[1],axis = 1)\n",
        "    print(np.shape(self.data['spks']))\n",
        "    print(np.shape(mFR))\n",
        "    self.data['spks'] = self.data['spks'].astype(float)\n",
        "    self.data['spks'] -= mFR\n",
        "  def permdims(self):\n",
        "    return torch.permute(torch.tensor(self.data['spks']),(2,1,0))\n",
        "\n",
        "  def smoothFR(self, smoothingWidth):# TODO: Smooth the data and save it back to the data structure\n",
        "    return 0\n"
      ],
      "execution_count": 251,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tIeFNLI66MDc"
      },
      "source": [
        "#@title Set up Dataset\n",
        "\n",
        "### dataset definition\n",
        "class NeuronDataset(Dataset):\n",
        "  def __init__(self, X, Y):\n",
        "    self.X = X\n",
        "    self.Y = Y\n",
        "    \n",
        "  def __len__(self):\n",
        "    return len(self.Y)\n",
        "    \n",
        "  def __getitem__(self, idx):\n",
        "    dataP = self.Y[idx]\n",
        "    data = self.X[idx]\n",
        "    sample = {\"X\": data, \"Y\": dataP}\n",
        "    return sample"
      ],
      "execution_count": 252,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 956
        },
        "id": "S4PaVL2Y7-fW",
        "outputId": "66716752-e2aa-4056-e965-6ca88d6bf29a"
      },
      "source": [
        "#@title get input for network from session 31\n",
        "s31=SteinmetzSession(alldat[30])\n",
        "validtrials = s31.realign_data_to_movement(500) # get 500 ms from movement time, \n",
        "\n",
        "# cannot get realign and binning to work the same time =[\n",
        "\n",
        "nTr = np.argwhere(validtrials) # since the other trials were defaulted to a zero value, only plot the valid trials\n",
        "\n",
        "## plot a trial\n",
        "plt.figure()\n",
        "s31.plotTrial(nTr[1])\n",
        "plt.title('All')\n",
        "\n",
        "### print areas\n",
        "s31.get_areas()\n",
        "\n",
        "# s31.FlattenTs()\n",
        "\n",
        "MO = copy.deepcopy(s31)\n",
        "###remove all neurons not in motor cortex\n",
        "MO.extractROI('MOs')\n",
        "### plot a trial from motor neuron\n",
        "plt.figure()\n",
        "MO.plotTrial(nTr[1])\n",
        "plt.title('MOs')\n",
        "### permute the trials\n",
        "MOdata = MO.permdims().float().to(device)\n",
        "MOdata = MOdata[:,validtrials,:]\n",
        "print(MOdata.shape)\n",
        "\n",
        "TH = copy.deepcopy(s31)\n",
        "###remove all neurons not in motor cortex\n",
        "TH.extractROI('TH')\n",
        "### plot a trial from motor neuron\n",
        "plt.figure()\n",
        "TH.plotTrial(nTr[1])\n",
        "plt.title('TH')\n",
        "\n",
        "THdata = TH.permdims().float().to(device)\n",
        "THdata = THdata[:,validtrials,:]\n",
        "print(THdata.shape)"
      ],
      "execution_count": 254,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "spikes aligned to movement, returning validtrials\n",
            "{'SNr', 'CA3', 'SCm', 'OLF', 'TH', 'MOs', 'ORB', 'POST'}\n",
            " removing data from 696 neurons not contained in MOs \n",
            "neurons remaining in trial 281\n",
            "torch.Size([50, 158, 281])\n",
            " removing data from 868 neurons not contained in TH \n",
            "neurons remaining in trial 109\n",
            "torch.Size([50, 158, 109])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ushslg7nBH6_",
        "outputId": "0ddeb2bc-db27-4d67-b9d2-6634425e896a"
      },
      "source": [
        "#@title get indices for trials (split into ~60%, 30%,10%)\n",
        "N = MOdata.shape[1]\n",
        "np.random.seed(42)\n",
        "ii = torch.randperm(N).tolist()\n",
        "idx_train = ii[:math.floor(0.6*N)]\n",
        "idx_val = ii[math.floor(0.6*N):math.floor(0.9*N)]\n",
        "idx_test = ii[math.floor(0.9*N):]\n",
        "\n",
        "print(N)"
      ],
      "execution_count": 255,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "158\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zkiaD2O9preO"
      },
      "source": [
        "#@title split into train, test and validation set\n",
        "x0_train = THdata[:,idx_train,:]\n",
        "x0_val = THdata[:,idx_val,:]\n",
        "x0_test = THdata[:,idx_test,:]\n",
        "\n",
        "x1_train = MOdata[:,idx_train,:]\n",
        "x1_val = MOdata[:,idx_val,:]\n",
        "x1_test = MOdata[:,idx_test,:]"
      ],
      "execution_count": 257,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1TdDGTtCoM1-"
      },
      "source": [
        "------\n",
        "# DataLoader (skip for now), compatibility issue"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Ju90wuxV1ix"
      },
      "source": [
        "#@title Split Dataset and Create Data Loaders  \n",
        "# need to make trial first so do batches in trials\n",
        "batchsz= 20\n",
        "ND=NeuronDataset(MOdata[:,idx_train,:],MOdata[:,idx_train,:]) \n",
        "DL_Train = DataLoader(ND, batch_size=batchsz)\n",
        "\n",
        "ND=NeuronDataset(MOdata[:,idx_val,:],MOdata[:,idx_val,:]) \n",
        "DL_Val = DataLoader(ND, batch_size=batchsz)\n",
        "\n",
        "ND=NeuronDataset(MOdata[:,idx_test,:],MOdata[:,idx_test,:]) \n",
        "DL_Test = DataLoader(ND, batch_size=batchsz)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O93Dp7s5wT3m",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c558e6ca-93c4-4cd4-8661-68b9ac238a3b"
      },
      "source": [
        "#### check that the data loader is iterating correctly \n",
        "print('\\nFirst iteration of data set: ', next(iter(DL_Train)), '\\n')\n",
        "print('Length of data set: ', len(DL_Train), '\\n')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "First iteration of data set:  {'X': tensor([[[0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         ...,\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.]],\n",
            "\n",
            "        [[0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 1., 0.],\n",
            "         ...,\n",
            "         [0., 1., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [1., 0., 0.,  ..., 0., 0., 0.]],\n",
            "\n",
            "        [[0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         ...,\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.]],\n",
            "\n",
            "        ...,\n",
            "\n",
            "        [[0., 1., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         ...,\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.]],\n",
            "\n",
            "        [[0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         ...,\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.]],\n",
            "\n",
            "        [[0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         ...,\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.]]]), 'Y': tensor([[[0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         ...,\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.]],\n",
            "\n",
            "        [[0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 1., 0.],\n",
            "         ...,\n",
            "         [0., 1., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [1., 0., 0.,  ..., 0., 0., 0.]],\n",
            "\n",
            "        [[0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         ...,\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.]],\n",
            "\n",
            "        ...,\n",
            "\n",
            "        [[0., 1., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         ...,\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.]],\n",
            "\n",
            "        [[0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         ...,\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.]],\n",
            "\n",
            "        [[0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         ...,\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.]]])} \n",
            "\n",
            "Length of data set:  3 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MqPRIteEztuq",
        "outputId": "06441acd-8aaf-4e63-9ea1-3ef15481170e"
      },
      "source": [
        "# time * trial * neuron\n",
        "next(iter(DL_Train))['X'].shape\n",
        "next(iter(DL_Train))['Y'].shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([20, 94, 281])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CblqpOHboWXe"
      },
      "source": [
        "---------\n",
        "# Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "44BcPW_GuSMC"
      },
      "source": [
        "class Net(nn.Module): # our model\n",
        "  def __init__(self, ncomp, NN1, NN2, bidi=True):\n",
        "    super(Net, self).__init__()\n",
        "\n",
        "    # play with some of the options in the RNN!\n",
        "    self.rnn1 = nn.RNN(NN1, ncomp, num_layers = 1, dropout = 0, # MO\n",
        "                      bidirectional = bidi, nonlinearity = 'tanh')\n",
        "    self.rnn2 = nn.RNN(NN2,ncomp,num_layers = 1, dropout = 0, bidirectional = bidi, nonlinearity = 'tanh') #TH\n",
        "\n",
        "    if bidi == True:\n",
        "      self.fclatent = nn.Linear(ncomp*2,ncomp*2)\n",
        "    else: \n",
        "      self.fclatent = nn.Linear(ncomp,ncomp)\n",
        "\n",
        "    self.fc = nn.Linear(ncomp,NN1)\n",
        "\n",
        "  def forward(self, x0,x1):\n",
        "    y2 = self.rnn2(x0)[0] # ncomp TH\n",
        "    y =  self.rnn1(x1)[0] # ncomp MOs\n",
        "    y += self.fclatent(y2) # ncomp MOs with projection of latent TH components\n",
        "\n",
        "    if self.rnn1.bidirectional:\n",
        "      # if the rnn is bidirectional, it concatenates the activations from the forward and backward pass\n",
        "      # we want to add them instead, so as to enforce the latents to match between the forward and backward pass\n",
        "      q = (y[:, :, :ncomp] + y[:, :, ncomp:])/2\n",
        "    else:\n",
        "      q = y\n",
        "\n",
        "    # the softplus function is just like a relu but it's smoothed out so we can't predict 0\n",
        "    # if we predict 0 and there was a spike, that's an instant Inf in the Poisson log-likelihood which leads to failure\n",
        "    z = F.softplus(self.fc(q), 10)\n",
        "\n",
        "    return z, q"
      ],
      "execution_count": 258,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SeFQk7Kex5WQ",
        "outputId": "c061bbc9-0825-4d14-f6e4-c6721665af3c"
      },
      "source": [
        "#@title Set input/hyperparameters here:\n",
        "ncomp = 10\n",
        "NN1 = MOdata.shape[2]\n",
        "print(NN1)\n",
        "NN2 = THdata.shape[2]\n",
        "print(NN2)\n",
        "\n",
        "learning_rate_start = 0.002\n"
      ],
      "execution_count": 259,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "281\n",
            "109\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qrz-ONFcwXh7",
        "outputId": "459c4ff1-001d-4505-b34c-1b3c6e34d1f6"
      },
      "source": [
        "#@title first the basic network with MO => MO (no external input)\n",
        "\n",
        "net_baseline = Net(ncomp, NN1, NN2, bidi = True).to(device)\n",
        "net_baseline.fclatent.weight.data[:] = 0 # fixed weights =0 so the TH input is not considered\n",
        "net_baseline.fclatent.bias.data[:] = 0\n",
        "net_baseline.fclatent.weight.requires_grad = False\n",
        "net_baseline.fclatent.bias.requires_grad = False\n",
        "\n",
        "# special thing:  we initialize the biases of the last layer in the neural network\n",
        "# we set them as the mean firing rates of the neurons.\n",
        "# this should make the initial predictions close to the mean, because the latents don't contribute much\n",
        "net_baseline.fc.bias.data[:] = MOdata.mean((0,1))\n",
        "\n",
        "# we set up the optimizer later in the training loop\n",
        "\n",
        "print(net_baseline)"
      ],
      "execution_count": 260,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Net(\n",
            "  (rnn1): RNN(281, 10, bidirectional=True)\n",
            "  (rnn2): RNN(109, 10, bidirectional=True)\n",
            "  (fclatent): Linear(in_features=20, out_features=20, bias=True)\n",
            "  (fc): Linear(in_features=10, out_features=281, bias=True)\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2P1qGp0xrIf5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "286977d8-9e94-476b-f5c9-3e91ce130065"
      },
      "source": [
        "#@title train net_baseline\n",
        "'''\n",
        "you can keep re-running this cell if you think the cost might decrease further\n",
        "x1_train = input we are predicting (MOs)\n",
        "x0_train = input from other areas (e.g. TH)\n",
        "'''\n",
        "# we define the Poisson log-likelihood loss\n",
        "def Poisson_loss(lam, spk):\n",
        "  return lam - spk * torch.log(lam)\n",
        "\n",
        "optimizer = torch.optim.Adam(net_baseline.parameters(), lr=learning_rate_start)\n",
        "\n",
        "set_seed(seed=2021)\n",
        "\n",
        "niter = 1000\n",
        "training_cost = []\n",
        "val_cost = []\n",
        "for k in range(niter):\n",
        "  ### training\n",
        "  optimizer.zero_grad()\n",
        "  # the network outputs the single-neuron prediction and the latents\n",
        "  z, y = net_baseline(x0_train,x1_train)\n",
        "  # our log-likelihood cost\n",
        "  cost = Poisson_loss(z, x1_train).mean()\n",
        "  # train the network as usual\n",
        "  cost.backward()\n",
        "  optimizer.step()\n",
        "  training_cost.append(cost.item())\n",
        "\n",
        "  ### test on validation data\n",
        "  z_val,_ = net_baseline(x0_val,x1_val)\n",
        "  cost = Poisson_loss(z_val, x1_val).mean()\n",
        "  val_cost.append(cost.item())\n",
        "\n",
        "  if k % 100 == 0:\n",
        "    print(f'iteration {k}, cost {cost.item():.4f}')"
      ],
      "execution_count": 261,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Random seed 2021 has been set.\n",
            "iteration 0, cost 0.1554\n",
            "iteration 100, cost 0.0710\n",
            "iteration 200, cost 0.0655\n",
            "iteration 300, cost 0.0637\n",
            "iteration 400, cost 0.0628\n",
            "iteration 500, cost 0.0623\n",
            "iteration 600, cost 0.0619\n",
            "iteration 700, cost 0.0616\n",
            "iteration 800, cost 0.0615\n",
            "iteration 900, cost 0.0614\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5QLSmrzerkYl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "outputId": "43e2c482-26e9-456e-f557-7235f450cd8c"
      },
      "source": [
        "#@title Plot training progress\n",
        "plt.plot(training_cost,'b')\n",
        "plt.plot(val_cost,'r')\n",
        "plt.hlines(np.min(training_cost),0,niter,'b',linestyles = '--')\n",
        "plt.hlines(np.min(val_cost),0,niter,'r',linestyles = '--')\n",
        "\n",
        "plt.legend(['training cost','validation cost','min training cost','min validation cost'])\n",
        "plt.title('Training cost over epochs')\n",
        "plt.ylabel('cost')\n",
        "plt.xlabel('epochs')"
      ],
      "execution_count": 262,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'epochs')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 262
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 988
        },
        "id": "zasQt3A-Nilm",
        "outputId": "5e790c3c-7e4e-4e8f-c34d-fb8fd04ae3cb"
      },
      "source": [
        "#@title Plot performance on training data\n",
        "rpred = z.detach().cpu().numpy()\n",
        "rates = x1_train\n",
        "\n",
        "nTr = 5\n",
        "nNeuron = 0\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(rates[:,nTr, nNeuron])\n",
        "plt.plot(rpred[:,nTr, nNeuron])\n",
        "\n",
        "plt.legend(['spikes', 'rates (predicted)'])\n",
        "plt.title(f'training set Trial {nTr}, Neuron {nNeuron}')\n",
        "plt.show()\n",
        "\n",
        "plt.figure(figsize = (12, 8))\n",
        "plt.subplot(121)\n",
        "plt.imshow(rates[:, nTr, :].T, cmap='gray_r')\n",
        "plt.xlabel('Time (ms)')\n",
        "plt.ylabel('Cell #')\n",
        "plt.title(f'True rates (training set trial {nTr})')\n",
        "\n",
        "plt.subplot(122)\n",
        "plt.imshow(rpred[:, nTr, :].T, cmap='gray_r')\n",
        "plt.xlabel('Time (ms)')\n",
        "plt.ylabel('Cell #')\n",
        "plt.title(f'Inferred rates (training set trial {nTr})')\n",
        "plt.show()"
      ],
      "execution_count": 266,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lIEBSZ__tcX0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 988
        },
        "outputId": "82ceaf3b-30e0-48e5-b5e4-7527ce4ccbbc"
      },
      "source": [
        "#@title Performance on validation data\n",
        "rpred = z_val.detach().cpu().numpy()\n",
        "rates = x1_val\n",
        "\n",
        "nTr = 5\n",
        "nNeuron = 160\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(rates[:,nTr, nNeuron])\n",
        "plt.plot(rpred[:,nTr, nNeuron])\n",
        "\n",
        "plt.legend(['spikes', 'rates (predicted)'])\n",
        "plt.title(f'validation set Trial {nTr}, Neuron {nNeuron}')\n",
        "plt.show()\n",
        "\n",
        "plt.figure(figsize = (12, 8))\n",
        "plt.subplot(121)\n",
        "plt.imshow(rates[:, nTr, :].T, cmap='gray_r')\n",
        "plt.xlabel('Time (ms)')\n",
        "plt.ylabel('Cell #')\n",
        "plt.title(f'True rates (validation set trial {nTr})')\n",
        "\n",
        "plt.subplot(122)\n",
        "plt.imshow(rpred[:, nTr, :].T, cmap='gray_r')\n",
        "plt.xlabel('Time (ms)')\n",
        "plt.ylabel('Cell #')\n",
        "plt.title(f'Inferred rates (validation set trial {nTr})')\n",
        "plt.show()"
      ],
      "execution_count": 270,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sB7CqS2NiX4Y"
      },
      "source": [
        "# save model (first on colab environment but you can download it, see <https://neptune.ai/blog/google-colab-dealing-with-files>)\n",
        "PATH = 'steinmetz_model_baseline.pt'\n",
        "torch.save(net_baseline.state_dict(), PATH) \n",
        "del net_baseline"
      ],
      "execution_count": 271,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mB1zaljtiwVA"
      },
      "source": [
        "# to prevent further training net_baseline, we are going to start from loading the model directly from now on."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mRE5Zbvsio2S",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fad002a7-a826-421b-d17f-28f7ae6cecca"
      },
      "source": [
        " # load saved model\n",
        "net_baseline = Net(ncomp, NN1, NN2, bidi = True).to(device)\n",
        "net_baseline.load_state_dict(torch.load('steinmetz_model_baseline.pt'))"
      ],
      "execution_count": 272,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 272
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F1pAhkMGwded",
        "outputId": "6fc6116a-2391-45ca-ba47-70789058b2ce"
      },
      "source": [
        "# after training the baseline network, get the weights of rnn1 and freeze it\n",
        "net_withinput  = copy.deepcopy(net_baseline)\n",
        "\n",
        "net_withinput.fclatent.weight.requires_grad = True\n",
        "net_withinput.fclatent.bias.requires_grad = True\n",
        "\n",
        "# # set weight initalization to random\n",
        "net_withinput.fclatent.reset_parameters()\n",
        "\n",
        "\n",
        "net_withinput.rnn1.weight_ih_l0.requires_grad = False\n",
        "net_withinput.rnn1.weight_hh_l0.requires_grad = False\n",
        "net_withinput.rnn1.bias_ih_l0.requires_grad = False\n",
        "net_withinput.rnn1.bias_hh_l0.requires_grad = False\n",
        "net_withinput.rnn1.weight_ih_l0_reverse.requires_grad = False\n",
        "net_withinput.rnn1.weight_hh_l0_reverse.requires_grad = False\n",
        "net_withinput.rnn1.bias_ih_l0_reverse.requires_grad = False\n",
        "net_withinput.rnn1.bias_hh_l0_reverse.requires_grad = False\n",
        "    \n",
        "print(net_withinput)"
      ],
      "execution_count": 273,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Net(\n",
            "  (rnn1): RNN(281, 10, bidirectional=True)\n",
            "  (rnn2): RNN(109, 10, bidirectional=True)\n",
            "  (fclatent): Linear(in_features=20, out_features=20, bias=True)\n",
            "  (fc): Linear(in_features=10, out_features=281, bias=True)\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SLTgv4h0t4Nv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f669394b-3c32-4ab5-9cfc-45a2abcb65a9"
      },
      "source": [
        "#@title train net_withinput\n",
        "'''\n",
        "you can keep re-running this cell if you think the cost might decrease further\n",
        "x1_train = input we are predicting (MOs)\n",
        "x0_train = input from other areas (e.g. TH)\n",
        "'''\n",
        "# we define the Poisson log-likelihood loss\n",
        "# def Poisson_loss(lam, spk):\n",
        "#   return lam - spk * torch.log(lam)\n",
        "loss = nn.MSELoss()\n",
        "optimizer = torch.optim.Adam(net_withinput.parameters(), lr=learning_rate_start) # this is very important\n",
        "\n",
        "set_seed(seed=2021)\n",
        "\n",
        "niter = 1000\n",
        "training_cost = []\n",
        "val_cost = []\n",
        "for k in range(niter):\n",
        "  ### training\n",
        "  optimizer.zero_grad()\n",
        "  # the network outputs the single-neuron prediction and the latents\n",
        "  z, y = net_withinput(x0_train,x1_train)\n",
        "  # our log-likelihood cost\n",
        "  cost = loss(z, x1_train).mean()\n",
        "  # train the network as usual\n",
        "  cost.backward()\n",
        "  optimizer.step()\n",
        "  training_cost.append(cost.item())\n",
        "\n",
        "  ### test on validation data\n",
        "  z_val,_ = net_withinput(x0_val,x1_val)\n",
        "  cost = loss(z_val, x1_val).mean()\n",
        "  val_cost.append(cost.item())\n",
        "\n",
        "  if k % 100 == 0:\n",
        "    print(f'iteration {k}, cost {cost.item():.4f}')"
      ],
      "execution_count": 274,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Random seed 2021 has been set.\n",
            "iteration 0, cost 0.0172\n",
            "iteration 100, cost 0.0152\n",
            "iteration 200, cost 0.0152\n",
            "iteration 300, cost 0.0153\n",
            "iteration 400, cost 0.0153\n",
            "iteration 500, cost 0.0154\n",
            "iteration 600, cost 0.0154\n",
            "iteration 700, cost 0.0155\n",
            "iteration 800, cost 0.0156\n",
            "iteration 900, cost 0.0157\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rS2ZTu2KvXXb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "outputId": "e780ce98-0688-4b31-c158-116a9fbfebe3"
      },
      "source": [
        "#@title Plot training progress\n",
        "plt.plot(training_cost,'b')\n",
        "plt.plot(val_cost,'r')\n",
        "plt.hlines(np.min(training_cost),0,niter,'b',linestyles = '--')\n",
        "plt.hlines(np.min(val_cost),0,niter,'r',linestyles = '--')\n",
        "\n",
        "plt.legend(['training cost','validation cost','min training cost','min validation cost'])\n",
        "plt.title('Training cost over epochs')\n",
        "plt.ylabel('cost')\n",
        "plt.xlabel('epochs')"
      ],
      "execution_count": 275,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'epochs')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 275
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fK34xhVbu4VV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 988
        },
        "outputId": "14fb96ff-d81e-4a91-a051-8967caee1dce"
      },
      "source": [
        "#@title Plot performance on training data\n",
        "rpred = z.detach().cpu().numpy()\n",
        "rates = x1_train\n",
        "\n",
        "nTr = 2\n",
        "nNeuron = 0\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(rates[:,nTr, nNeuron])\n",
        "plt.plot(rpred[:,nTr, nNeuron])\n",
        "\n",
        "plt.legend(['spikes', 'rates (predicted)'])\n",
        "plt.title(f'Trial {nTr}, Neuron {nNeuron}')\n",
        "plt.show()\n",
        "\n",
        "plt.figure(figsize = (12, 8))\n",
        "plt.subplot(121)\n",
        "plt.imshow(rates[:, nTr, :].T, cmap='gray_r')\n",
        "plt.xlabel('Time (ms)')\n",
        "plt.ylabel('Cell #')\n",
        "plt.title(f'True rates (trial {nTr})')\n",
        "\n",
        "plt.subplot(122)\n",
        "plt.imshow(rpred[:, nTr, :].T, cmap='gray_r')\n",
        "plt.xlabel('Time (ms)')\n",
        "plt.ylabel('Cell #')\n",
        "plt.title(f'Inferred rates (trial {nTr})')\n",
        "plt.show()"
      ],
      "execution_count": 276,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DR5FKVmEvWh7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 988
        },
        "outputId": "c85ae0bb-d660-48da-ea48-52f84df04305"
      },
      "source": [
        "#@title Performance on validation data\n",
        "rpred = z_val.detach().cpu().numpy()\n",
        "rates = x1_val\n",
        "\n",
        "nTr = 2\n",
        "nNeuron = 0\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(rates[:,nTr, nNeuron])\n",
        "plt.plot(rpred[:,nTr, nNeuron])\n",
        "\n",
        "plt.legend(['spikes', 'rates (predicted)'])\n",
        "plt.title(f'Trial {nTr}, Neuron {nNeuron}')\n",
        "plt.show()\n",
        "\n",
        "plt.figure(figsize = (12, 8))\n",
        "plt.subplot(121)\n",
        "plt.imshow(rates[:, nTr, :].T, cmap='gray_r')\n",
        "plt.xlabel('Time (ms)')\n",
        "plt.ylabel('Cell #')\n",
        "plt.title(f'True rates (trial {nTr})')\n",
        "\n",
        "plt.subplot(122)\n",
        "plt.imshow(rpred[:, nTr, :].T, cmap='gray_r')\n",
        "plt.xlabel('Time (ms)')\n",
        "plt.ylabel('Cell #')\n",
        "plt.title(f'Inferred rates (trial {nTr})')\n",
        "plt.show()"
      ],
      "execution_count": 277,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eA3Q8CRLfqbm"
      },
      "source": [
        "#compare with just training the previous network for another 100 epochs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w4lRfQpofqbp",
        "outputId": "d94212d3-3794-49c1-fc6d-7c7192c0fba9"
      },
      "source": [
        "#@title train net_baseline\n",
        "'''\n",
        "you can keep re-running this cell if you think the cost might decrease further\n",
        "x1_train = input we are predicting (MOs)\n",
        "x0_train = input from other areas (e.g. TH)\n",
        "'''\n",
        "# we define the Poisson log-likelihood loss\n",
        "# def Poisson_loss(lam, spk):\n",
        "#   return lam - spk * torch.log(lam)\n",
        "loss = nn.MSELoss()\n",
        "optimizer = torch.optim.Adam(net_baseline.parameters(), lr=learning_rate_start)\n",
        "set_seed(seed=2021)\n",
        "\n",
        "niter = 100\n",
        "training_cost = []\n",
        "val_cost = []\n",
        "for k in range(niter):\n",
        "  ### training\n",
        "  optimizer.zero_grad()\n",
        "  # the network outputs the single-neuron prediction and the latents\n",
        "  z, y = net_baseline(x0_train,x1_train)\n",
        "  cost = loss(z,x1_train).mean()\n",
        "  # # our log-likelihood cost\n",
        "  # cost = Poisson_loss(z, x1_train).mean()\n",
        "  # train the network as usual\n",
        "  cost.backward()\n",
        "  optimizer.step()\n",
        "  training_cost.append(cost.item())\n",
        "\n",
        "  ### test on validation data\n",
        "  z_val,_ = net_baseline(x0_val,x1_val)\n",
        "  cost = loss(z_val,x1_val).mean()\n",
        "  # cost = Poisson_loss(z_val, x1_val).mean()\n",
        "  val_cost.append(cost.item())\n",
        "\n",
        "  if k % 100 == 0:\n",
        "    print(f'iteration {k}, cost {cost.item():.4f}')"
      ],
      "execution_count": 278,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Random seed 2021 has been set.\n",
            "iteration 0, cost 0.0152\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "YLHeN4mxfqbq",
        "outputId": "4f31d8c2-da25-4f41-b60f-a9e41e97e98c"
      },
      "source": [
        "#@title Plot training progress\n",
        "plt.plot(training_cost,'b')\n",
        "plt.plot(val_cost,'r')\n",
        "plt.hlines(np.min(training_cost),0,niter,'b',linestyles = '--')\n",
        "plt.hlines(np.min(val_cost),0,niter,'r',linestyles = '--')\n",
        "\n",
        "plt.legend(['training cost','validation cost','min training cost','min validation cost'])\n",
        "plt.title('Training cost over epochs')\n",
        "plt.ylabel('cost')\n",
        "plt.xlabel('epochs')"
      ],
      "execution_count": 279,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'epochs')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 279
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V_IYDqvRheqa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3a0e2f26-1124-4dab-e772-03dc704d5590"
      },
      "source": [
        "#@title what if we don't freeze the weights\n",
        "\n",
        "# after training the baseline network, get the weights of rnn1 and freeze it\n",
        "net_withinput2  = copy.deepcopy(net_baseline)\n",
        "\n",
        "net_withinput2.fclatent.weight.requires_grad = True\n",
        "net_withinput2.fclatent.bias.requires_grad = True\n",
        "\n",
        "# # set weight initalization to random\n",
        "net_withinput2.fclatent.reset_parameters()\n",
        "\n",
        "print(net_withinput2)"
      ],
      "execution_count": 280,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Net(\n",
            "  (rnn1): RNN(281, 10, bidirectional=True)\n",
            "  (rnn2): RNN(109, 10, bidirectional=True)\n",
            "  (fclatent): Linear(in_features=20, out_features=20, bias=True)\n",
            "  (fc): Linear(in_features=10, out_features=281, bias=True)\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dXEPLyXMh1h8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d31ec457-718c-4b99-9a4d-a2e1067a1bd4"
      },
      "source": [
        "#@title train the input model without freezing weights\n",
        "'''\n",
        "you can keep re-running this cell if you think the cost might decrease further\n",
        "x1_train = input we are predicting (MOs)\n",
        "x0_train = input from other areas (e.g. TH)\n",
        "'''\n",
        "# we define the Poisson log-likelihood loss\n",
        "# def Poisson_loss(lam, spk):\n",
        "#   return lam - spk * torch.log(lam)\n",
        "loss = nn.MSELoss()\n",
        "optimizer = torch.optim.Adam(net_withinput2.parameters(), lr=learning_rate_start)\n",
        "set_seed(seed=2021)\n",
        "\n",
        "niter = 1000\n",
        "training_cost = []\n",
        "val_cost = []\n",
        "for k in range(niter):\n",
        "  ### training\n",
        "  optimizer.zero_grad()\n",
        "  # the network outputs the single-neuron prediction and the latents\n",
        "  z, y = net_withinput2(x0_train,x1_train)\n",
        "  cost = loss(z,x1_train).mean()\n",
        "  # # our log-likelihood cost\n",
        "  # cost = Poisson_loss(z, x1_train).mean()\n",
        "  # train the network as usual\n",
        "  cost.backward()\n",
        "  optimizer.step()\n",
        "  training_cost.append(cost.item())\n",
        "\n",
        "  ### test on validation data\n",
        "  z_val,_ = net_withinput2(x0_val,x1_val)\n",
        "  cost = loss(z_val,x1_val).mean()\n",
        "  # cost = Poisson_loss(z_val, x1_val).mean()\n",
        "  val_cost.append(cost.item())\n",
        "\n",
        "  if k % 100 == 0:\n",
        "    print(f'iteration {k}, cost {cost.item():.4f}')"
      ],
      "execution_count": 281,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Random seed 2021 has been set.\n",
            "iteration 0, cost 0.0227\n",
            "iteration 100, cost 0.0130\n",
            "iteration 200, cost 0.0126\n",
            "iteration 300, cost 0.0123\n",
            "iteration 400, cost 0.0122\n",
            "iteration 500, cost 0.0121\n",
            "iteration 600, cost 0.0121\n",
            "iteration 700, cost 0.0121\n",
            "iteration 800, cost 0.0121\n",
            "iteration 900, cost 0.0121\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eTYQXfXClbYq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "6d431b4f-6f27-4ced-bdc8-a903f55eb3e7"
      },
      "source": [
        "#@title Plot training progress\n",
        "plt.plot(training_cost,'b')\n",
        "plt.plot(val_cost,'r')\n",
        "plt.hlines(np.min(training_cost),0,niter,'b',linestyles = '--')\n",
        "plt.hlines(np.min(val_cost),0,niter,'r',linestyles = '--')\n",
        "\n",
        "plt.legend(['training cost','validation cost','min training cost','min validation cost'])\n",
        "plt.title('Training cost over epochs')\n",
        "plt.ylabel('cost')\n",
        "plt.xlabel('epochs')"
      ],
      "execution_count": 282,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'epochs')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 282
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8XRIIHV8rRxo"
      },
      "source": [
        "--------------------- The end -------------------\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2mlyk4z-rZtS"
      },
      "source": [
        "--------------------------\n",
        "# Below are some old stuff"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5V9llXOYsNzI"
      },
      "source": [
        "for (idx, batch) in enumerate(DL_DS):\n",
        "  pass\n",
        "      # print(idx, 'X:', batch['X'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cHZuIqCsDb7o"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "id": "iONxRXblKYHy",
        "outputId": "c9a74295-17ed-4e04-b0eb-8142a190dac6"
      },
      "source": [
        "#Parameters\n",
        "params = {'batch_size': 64,\n",
        "          'shuffle': True,\n",
        "          'num_workers': 6}\n",
        "max_epochs = 100\n",
        "\n",
        "# Datasets\n",
        "partition = # IDs\n",
        "labels = # Labels\n",
        "\n",
        "# Generators\n",
        "training_set = Dataset(partition['train'], labels)\n",
        "training_generator = torch.utils.data.DataLoader(training_set, **params)\n",
        "\n",
        "validation_set = Dataset(partition['validation'], labels)\n",
        "validation_generator = torch.utils.data.DataLoader(validation_set, **params)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-53-5cc229967d05>\"\u001b[0;36m, line \u001b[0;32m8\u001b[0m\n\u001b[0;31m    partition = # IDs\u001b[0m\n\u001b[0m                     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k9bBuzK7QfF4",
        "outputId": "015a37d6-a32c-4a8b-a97a-3853bf85b157"
      },
      "source": [
        "import torch\n",
        "\n",
        "# @title Set device (GPU or CPU). Execute `set_device()`\n",
        "\n",
        "# inform the user if the notebook uses GPU or CPU.\n",
        "\n",
        "def set_device():\n",
        "  device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "  if device != \"cuda\":\n",
        "    print(\"WARNING: For this notebook to perform best, \"\n",
        "        \"if possible, in the menu under `Runtime` -> \"\n",
        "        \"`Change runtime type.`  select `GPU` \")\n",
        "  else:\n",
        "    print(\"GPU is enabled in this notebook.\")\n",
        "\n",
        "  return device\n",
        "\n",
        "\n",
        "\n",
        "device = set_device()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "GPU is enabled in this notebook.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zu2mW21QlTVQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        },
        "outputId": "5afea644-e8f3-43a6-dfae-e1baa3405687"
      },
      "source": [
        "# set the seed\n",
        "np.random.seed(42)\n",
        "\n",
        "# 100 trials is typical of neural data\n",
        "ntrials = 100\n",
        "\n",
        "# we simulate 200 neurons\n",
        "NN = 200\n",
        "\n",
        "# we will pretend like every \"bin\" is 10ms, so the trial length is 2500ms\n",
        "NT = 250\n",
        "\n",
        "# let's use 10 latent components\n",
        "ncomp = 10\n",
        "\n",
        "# this is the recurrent dynamics matrix, which we made diagonal for simplicity\n",
        "# values have to be smaller than 1 for stability\n",
        "A0 =  np.diag(.8 + .2 * np.random.rand(ncomp,))\n",
        "\n",
        "# this is the projection matrix from components to neurons\n",
        "C0 = .025 * np.random.randn(ncomp, NN)\n",
        "\n",
        "# We generate the dynamics of the low-d system. We initialize the latent state.\n",
        "\n",
        "# start by initializing the latents\n",
        "y       = 2 * np.random.randn(ncomp)\n",
        "latents = np.zeros((NT, ntrials, ncomp))\n",
        "# we run the dynamics forward and add noise (or \"innovations\") at each timestep\n",
        "for t in range(NT):\n",
        "  y = y @ A0 +  np.random.randn(ntrials, ncomp)\n",
        "  latents[t] = y\n",
        "\n",
        "# we now project the latents to the neuron space and threshold to generate firing rates\n",
        "rates = np.maximum(0, latents @ C0)\n",
        "\n",
        "# now we draw poisson counts to simulate the number of spikes a neuron fires randomly\n",
        "x = np.random.poisson(rates)\n",
        "x0 = torch.from_numpy(x[:, :, :200]).to(device).float()\n",
        "x1 = torch.from_numpy(x[:, :, 200:]).to(device).float()\n",
        "\n",
        "x0 = torch.permute(x0, (2,1,0))\n",
        "x0 = np.reshape(x0, (-1,200))\n",
        "\n",
        "\n",
        "x1 = torch.permute(x1, (2,1,0))\n",
        "x1 = np.reshape(x1, (-1,200))\n",
        "print(x1.shape)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36m_wrapfunc\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m     57\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[0;32m---> 58\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mbound\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[0mkwds\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     59\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: reshape() got an unexpected keyword argument 'order'",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-23-53c7a8b1c7d3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0mx0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpermute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\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[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m \u001b[0mx0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m200\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mreshape\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36mreshape\u001b[0;34m(a, newshape, order)\u001b[0m\n\u001b[1;32m    297\u001b[0m            [5, 6]])\n\u001b[1;32m    298\u001b[0m     \"\"\"\n\u001b[0;32m--> 299\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_wrapfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'reshape'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnewshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\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    300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36m_wrapfunc\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m     65\u001b[0m         \u001b[0;31m# Call _wrapit from within the except clause to ensure a potential\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m         \u001b[0;31m# exception has a traceback chain.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_wrapit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\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[0mkwds\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     68\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36m_wrapit\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m         \u001b[0mwrap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m)\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[0mkwds\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     45\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mwrap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\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/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m     81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m     \"\"\"\n\u001b[0;32m---> 83\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\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     84\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/_tensor.py\u001b[0m in \u001b[0;36m__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m    641\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mhandle_torch_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__array__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    642\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 643\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\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    644\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[1;32m    645\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Or4Zk6X4fN19"
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torchvision \n",
        "class AE(nn.Module):\n",
        "    def __init__(self, **kwargs):\n",
        "        super().__init__()\n",
        "        self.encoder_hidden_layer = nn.Linear(\n",
        "            in_features=kwargs[\"input_shape\"], out_features=128\n",
        "        )\n",
        "        self.encoder_output_layer = nn.Linear(\n",
        "            in_features=128, out_features=128\n",
        "        )\n",
        "        self.decoder_hidden_layer = nn.Linear(\n",
        "            in_features=128, out_features=128\n",
        "        )\n",
        "        self.decoder_output_layer = nn.Linear(\n",
        "            in_features=128, out_features=kwargs[\"input_shape\"]\n",
        "        )\n",
        "\n",
        "    def forward(self, features):\n",
        "        activation = self.encoder_hidden_layer(features)\n",
        "        activation = torch.relu(activation)\n",
        "        code = self.encoder_output_layer(activation)\n",
        "        code = torch.relu(code)\n",
        "        activation = self.decoder_hidden_layer(code)\n",
        "        activation = torch.relu(activation)\n",
        "        activation = self.decoder_output_layer(activation)\n",
        "        reconstructed = torch.relu(activation)\n",
        "        return reconstructed"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dJzSEFcff0JT",
        "outputId": "a96a0355-f8db-4a02-c161-1ba281a51712"
      },
      "source": [
        "#  use gpu if available\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "print(np.shape(x0))\n",
        "\n",
        "# create a model from `AE` autoencoder class\n",
        "# load it to the specified device, either gpu or cpu\n",
        "model = AE(input_shape=200).to(device)\n",
        "\n",
        "# create an optimizer object\n",
        "# Adam optimizer with learning rate 1e-3\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n",
        "\n",
        "# mean-squared error loss\n",
        "criterion = nn.MSELoss()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([25000, 200])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TMxo8WqsgeLn",
        "outputId": "02361a98-19d1-4ed7-f50d-6945e58c0b6e"
      },
      "source": [
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b2TmwgcKiYEQ",
        "outputId": "9a185142-a4f4-40c5-802f-1ea14b453bb0"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torchvision.datasets.mnist.MNIST"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vNBGqLl1gI50",
        "outputId": "61c3c8f3-72f3-40e5-b779-710771ffcb17"
      },
      "source": [
        "epochs = 100\n",
        "for epoch in range(epochs):\n",
        "    loss = 0\n",
        "\n",
        "    # reshape mini-batch data to [N, 784] matrix\n",
        "    # load it to the active device\n",
        "    \n",
        "    # reset the gradients back to zero\n",
        "    # PyTorch accumulates gradients on subsequent backward passes\n",
        "    optimizer.zero_grad()\n",
        "    \n",
        "    # compute reconstructions\n",
        "    print(type(x0))\n",
        "    outputs = model(x0)\n",
        "    \n",
        "    # compute training reconstruction loss\n",
        "    train_loss = criterion(outputs, x0)\n",
        "    \n",
        "    # compute accumulated gradients\n",
        "    train_loss.backward()\n",
        "    \n",
        "    # perform parameter update based on current gradients\n",
        "    optimizer.step()\n",
        "    \n",
        "    # add the mini-batch training loss to epoch loss\n",
        "    loss += train_loss.item()\n",
        "    \n",
        "\n",
        "    \n",
        "    # display the epoch training loss\n",
        "    print(\"epoch : {}/{}, loss = {:.6f}\".format(epoch + 1, epochs, loss))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'torch.Tensor'>\n",
            "epoch : 1/100, loss = 0.114980\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 2/100, loss = 0.114161\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 3/100, loss = 0.113437\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 4/100, loss = 0.112906\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 5/100, loss = 0.112515\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 6/100, loss = 0.112154\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 7/100, loss = 0.111754\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 8/100, loss = 0.111305\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 9/100, loss = 0.110838\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 10/100, loss = 0.110377\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 11/100, loss = 0.109895\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 12/100, loss = 0.109346\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 13/100, loss = 0.108746\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 14/100, loss = 0.108141\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 15/100, loss = 0.107535\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 16/100, loss = 0.106912\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 17/100, loss = 0.106286\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 18/100, loss = 0.105669\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 19/100, loss = 0.105014\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 20/100, loss = 0.104320\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 21/100, loss = 0.103633\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 22/100, loss = 0.102956\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 23/100, loss = 0.102281\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 24/100, loss = 0.101601\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 25/100, loss = 0.100894\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 26/100, loss = 0.100167\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 27/100, loss = 0.099419\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 28/100, loss = 0.098644\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 29/100, loss = 0.097851\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 30/100, loss = 0.097028\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 31/100, loss = 0.096209\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 32/100, loss = 0.095421\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 33/100, loss = 0.094655\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 34/100, loss = 0.093890\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 35/100, loss = 0.093119\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 36/100, loss = 0.092333\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 37/100, loss = 0.091517\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 38/100, loss = 0.090674\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 39/100, loss = 0.089854\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 40/100, loss = 0.089080\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 41/100, loss = 0.088353\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 42/100, loss = 0.087705\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 43/100, loss = 0.087114\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 44/100, loss = 0.086246\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 45/100, loss = 0.085433\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 46/100, loss = 0.084962\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 47/100, loss = 0.084341\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 48/100, loss = 0.083513\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 49/100, loss = 0.082948\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 50/100, loss = 0.082413\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 51/100, loss = 0.081720\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 52/100, loss = 0.081085\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 53/100, loss = 0.080576\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 54/100, loss = 0.079948\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 55/100, loss = 0.079289\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 56/100, loss = 0.078787\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 57/100, loss = 0.078231\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 58/100, loss = 0.077581\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 59/100, loss = 0.077066\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 60/100, loss = 0.076511\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 61/100, loss = 0.075924\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 62/100, loss = 0.075410\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 63/100, loss = 0.074871\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 64/100, loss = 0.074350\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 65/100, loss = 0.073958\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 66/100, loss = 0.073591\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 67/100, loss = 0.073084\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 68/100, loss = 0.072384\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 69/100, loss = 0.071931\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 70/100, loss = 0.071575\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 71/100, loss = 0.071025\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 72/100, loss = 0.070519\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 73/100, loss = 0.070131\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 74/100, loss = 0.069650\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 75/100, loss = 0.069191\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 76/100, loss = 0.068777\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 77/100, loss = 0.068318\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 78/100, loss = 0.067902\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 79/100, loss = 0.067479\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 80/100, loss = 0.067043\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 81/100, loss = 0.066620\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 82/100, loss = 0.066212\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 83/100, loss = 0.065798\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 84/100, loss = 0.065379\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 85/100, loss = 0.064975\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 86/100, loss = 0.064572\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 87/100, loss = 0.064169\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 88/100, loss = 0.063772\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 89/100, loss = 0.063380\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 90/100, loss = 0.063012\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 91/100, loss = 0.062665\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 92/100, loss = 0.062389\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 93/100, loss = 0.062303\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 94/100, loss = 0.062295\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 95/100, loss = 0.061824\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 96/100, loss = 0.060929\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 97/100, loss = 0.060821\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 98/100, loss = 0.060834\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 99/100, loss = 0.060179\n",
            "<class 'torch.Tensor'>\n",
            "epoch : 100/100, loss = 0.059797\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "YIWKAdEiUP9S",
        "outputId": "fbf59953-8291-4a42-de89-60735593f5db"
      },
      "source": [
        "s1.plotTrial(1)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "id": "rGn5L_PjQ8H2",
        "outputId": "07180641-2003-4a73-dc85-53186d06f681"
      },
      "source": [
        "s1.binData(10)\n",
        "s1.plotTrial(1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "range(0, 251, 10)\n",
            "Binned spikes, turning a (734, 214, 250) matrix into a (734, 214, 25) matrix\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}