BioLCNet.ipynb

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        "9042967f47494e5a92663c30648fcb74": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
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            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
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            "margin": null,
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          }
        }
      }
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Singular-Brain/bindsnet/blob/master/BioLCNet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7fTSvrK3T_GA"
      },
      "source": [
        "#Notebook setups"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lXtgP_iEPE0G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3a637115-43b1-40fc-8d4b-ea649dd4d2ea"
      },
      "source": [
        "!pip install -q git+https://github.com/Singular-Brain/bindsnet"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l\r\u001b[K     |██▊                             | 10 kB 25.0 MB/s eta 0:00:01\r\u001b[K     |█████▍                          | 20 kB 27.8 MB/s eta 0:00:01\r\u001b[K     |████████▏                       | 30 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |██████████▉                     | 40 kB 10.1 MB/s eta 0:00:01\r\u001b[K     |█████████████▋                  | 51 kB 5.8 MB/s eta 0:00:01\r\u001b[K     |████████████████▎               | 61 kB 5.7 MB/s eta 0:00:01\r\u001b[K     |███████████████████             | 71 kB 5.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▊          | 81 kB 6.0 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▌       | 92 kB 5.8 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▏    | 102 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████  | 112 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 120 kB 5.4 MB/s \n",
            "\u001b[K     |████████████████████████████████| 73 kB 1.8 MB/s \n",
            "\u001b[K     |████████████████████████████████| 280 kB 25.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 28.5 MB 45 kB/s \n",
            "\u001b[?25h  Building wheel for BindsNET (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OW7m3ugEHZP_",
        "outputId": "0cdbdbd7-39b0-43f1-b53c-ed923a833b05"
      },
      "source": [
        "!wget https://data.deepai.org/mnist.zip\n",
        "!mkdir -p ../data/MNIST/TorchvisionDatasetWrapper/raw\n",
        "!unzip mnist.zip -d ../data/MNIST/TorchvisionDatasetWrapper/raw/"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-09-07 22:47:43--  https://data.deepai.org/mnist.zip\n",
            "Resolving data.deepai.org (data.deepai.org)... 138.201.36.183\n",
            "Connecting to data.deepai.org (data.deepai.org)|138.201.36.183|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 11597176 (11M) [application/x-zip-compressed]\n",
            "Saving to: ‘mnist.zip’\n",
            "\n",
            "mnist.zip           100%[===================>]  11.06M  11.0MB/s    in 1.0s    \n",
            "\n",
            "2021-09-07 22:47:45 (11.0 MB/s) - ‘mnist.zip’ saved [11597176/11597176]\n",
            "\n",
            "Archive:  mnist.zip\n",
            "  inflating: ../data/MNIST/TorchvisionDatasetWrapper/raw/train-labels-idx1-ubyte.gz  \n",
            "  inflating: ../data/MNIST/TorchvisionDatasetWrapper/raw/train-images-idx3-ubyte.gz  \n",
            "  inflating: ../data/MNIST/TorchvisionDatasetWrapper/raw/t10k-images-idx3-ubyte.gz  \n",
            "  inflating: ../data/MNIST/TorchvisionDatasetWrapper/raw/t10k-labels-idx1-ubyte.gz  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KXcXvvsXcOlv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e5cfb2e-5102-434b-c1f0-b593a8dfc0b1"
      },
      "source": [
        "!git clone https://github.com/Singular-Brain/bindsnet/"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'bindsnet'...\n",
            "remote: Enumerating objects: 11113, done.\u001b[K\n",
            "remote: Counting objects: 100% (2073/2073), done.\u001b[K\n",
            "remote: Compressing objects: 100% (924/924), done.\u001b[K\n",
            "remote: Total 11113 (delta 1460), reused 1641 (delta 1148), pack-reused 9040\u001b[K\n",
            "Receiving objects: 100% (11113/11113), 90.53 MiB | 22.78 MiB/s, done.\n",
            "Resolving deltas: 100% (7186/7186), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BFGNAecpT-Lj"
      },
      "source": [
        "from bindsnet.network.nodes import Nodes\n",
        "import os\n",
        "import torch\n",
        "import random\n",
        "import numpy as np\n",
        "import copy\n",
        "import math\n",
        "import matplotlib.pyplot as plt\n",
        "import collections\n",
        "from torchvision import transforms\n",
        "from tqdm.notebook import tqdm\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sn\n",
        "import torch.nn.functional as fn\n",
        "\n",
        "from abc import ABC, abstractmethod\n",
        "from typing import Union, Tuple, Optional, Sequence\n",
        "from torch.nn.modules.utils import _pair\n",
        "\n",
        "from bindsnet.datasets import MNIST\n",
        "from bindsnet.encoding import PoissonEncoder\n",
        "from bindsnet.network import Network\n",
        "from bindsnet.network.nodes import Input, LIFNodes, AdaptiveLIFNodes, IFNodes\n",
        "from bindsnet.network.topology import LocalConnection, Connection, LocalConnectionOrig, MaxPool2dLocalConnection\n",
        "from bindsnet.network.monitors import Monitor, AbstractMonitor, TensorBoardMonitor\n",
        "from bindsnet.learning import PostPre, MSTDP, MSTDPET, WeightDependentPostPre, Hebbian\n",
        "from bindsnet.learning.reward import DynamicDopamineInjection, DopaminergicRPE\n",
        "from bindsnet.analysis.plotting import plot_locally_connected_weights,plot_locally_connected_weights_meh,plot_spikes,plot_locally_connected_weights_meh2,plot_convergence_and_histogram,plot_locally_connected_weights_meh3\n",
        "from bindsnet.analysis.visualization import plot_weights_movie, plot_spike_trains_for_example,summary, plot_voltage\n",
        "from bindsnet.utils import reshape_locally_connected_weights, reshape_locally_connected_weights_meh, reshape_conv2d_weights"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ULGGHW43UksI"
      },
      "source": [
        "## Sets up Gpu use and manual seed\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LiUmFrpcUfmR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9cb56fe6-047f-4840-d220-cf1a1858b368"
      },
      "source": [
        "if torch.cuda.is_available():\n",
        "    device =  torch.device(\"cuda\")\n",
        "    gpu = True\n",
        "else:\n",
        "    device =  torch.device(\"cpu\")\n",
        "    gpu = False\n",
        "\n",
        "def manual_seed(seed):\n",
        "    random.seed(seed)\n",
        "    torch.manual_seed(seed)\n",
        "    np.random.seed(seed)\n",
        "    if torch.cuda.is_available():\n",
        "        torch.cuda.manual_seed_all(seed)\n",
        "\n",
        "\n",
        "SEED = 2045 # The Singularity is Near!\n",
        "manual_seed(SEED)\n",
        "\n",
        "torch.set_num_threads(os.cpu_count() - 1)\n",
        "print(\"Running on Device = \", device)\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running on Device =  cuda\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MBKedMpIleMr"
      },
      "source": [
        "# Custom Monitors"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6tfqpsr2a1WV"
      },
      "source": [
        "## Reward Monitor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M44GJ65GleMs"
      },
      "source": [
        "class RewardMonitor(AbstractMonitor):\n",
        "    # language=rst\n",
        "    \"\"\"\n",
        "    Records state variables of interest.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(\n",
        "        self,\n",
        "        time: None,\n",
        "        batch_size: int = 1,\n",
        "        device: str = \"cpu\",\n",
        "    ):\n",
        "        # language=rst\n",
        "        \"\"\"\n",
        "        Constructs a ``Monitor`` object.\n",
        "\n",
        "        :param obj: An object to record state variables from during network simulation.\n",
        "        :param state_vars: Iterable of strings indicating names of state variables to record.\n",
        "        :param time: If not ``None``, pre-allocate memory for state variable recording.\n",
        "        :param device: Allow the monitor to be on different device separate from Network device\n",
        "        \"\"\"\n",
        "        super().__init__()\n",
        "\n",
        "        self.time = time\n",
        "        self.batch_size = batch_size\n",
        "        self.device = device\n",
        "\n",
        "        # if time is not specified the monitor variable accumulate the logs\n",
        "        if self.time is None:\n",
        "            self.device = \"cpu\"\n",
        "\n",
        "        self.recording = []\n",
        "        self.reset_state_variables()\n",
        "\n",
        "    def get(self,) -> torch.Tensor:\n",
        "        # language=rst\n",
        "        \"\"\"\n",
        "        Return recording to user.\n",
        "\n",
        "        :return: Tensor of shape ``[time, n_1, ..., n_k]``, where ``[n_1, ..., n_k]`` is the shape of the recorded state\n",
        "        variable.\n",
        "        Note, if time == `None`, get return the logs and empty the monitor variable\n",
        "\n",
        "        \"\"\"\n",
        "        # return_logs = torch.as_tensor(self.recording)\n",
        "        # if self.time is None:\n",
        "        #     self.recording = []\n",
        "        return self.recording\n",
        "\n",
        "    def record(self, **kwargs) -> None:\n",
        "        # language=rst\n",
        "        \"\"\"\n",
        "        Appends the current value of the recorded state variables to the recording.\n",
        "        \"\"\"\n",
        "        if \"reward\" in kwargs:\n",
        "            self.recording.append(kwargs[\"reward\"])\n",
        "        # remove the oldest element (first in the list)\n",
        "        # if self.time is not None:\n",
        "        #     self.recording.pop(0)\n",
        "\n",
        "    def reset_state_variables(self) -> None:\n",
        "        # language=rst\n",
        "        \"\"\"\n",
        "        Resets recordings to empty ``List``s.\n",
        "        \"\"\"\n",
        "        self.recording = []\n"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8clxN_npa1WY"
      },
      "source": [
        "## Plot Eligibility trace"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SshGlRwpa1WZ"
      },
      "source": [
        "class PlotET(AbstractMonitor):\n",
        "    # language=rst\n",
        "    \"\"\"\n",
        "    Records state variables of interest.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(\n",
        "        self,\n",
        "        i,\n",
        "        j,\n",
        "        source,\n",
        "        target,\n",
        "        connection,\n",
        "    ):\n",
        "        # language=rst\n",
        "        \"\"\"\n",
        "        Constructs a ``Monitor`` object.\n",
        "\n",
        "        :param obj: An object to record state variables from during network simulation.\n",
        "        :param state_vars: Iterable of strings indicating names of state variables to record.\n",
        "        :param time: If not ``None``, pre-allocate memory for state variable recording.\n",
        "        :param device: Allow the monitor to be on different device separate from Network device\n",
        "        \"\"\"\n",
        "        super().__init__()\n",
        "        self.i = i\n",
        "        self.j = j\n",
        "        self.source = source\n",
        "        self.target = target\n",
        "        self.connection = connection\n",
        "\n",
        "        self.reset_state_variables()\n",
        "\n",
        "    def get(self,) -> torch.Tensor:\n",
        "        # language=rst\n",
        "        \"\"\"\n",
        "        Return recording to user.\n",
        "\n",
        "        :return: Tensor of shape ``[time, n_1, ..., n_k]``, where ``[n_1, ..., n_k]`` is the shape of the recorded state\n",
        "        variable.\n",
        "        Note, if time == `None`, get return the logs and empty the monitor variable\n",
        "\n",
        "        \"\"\"\n",
        "        # return_logs = torch.as_tensor(self.recording)\n",
        "        # if self.time is None:\n",
        "        #     self.recording = []\n",
        "        return self.recording\n",
        "\n",
        "    def record(self, **kwargs) -> None:\n",
        "        # language=rst\n",
        "        \"\"\"\n",
        "        Appends the current value of the recorded state variables to the recording.\n",
        "        \"\"\"\n",
        "        if hasattr(self.connection.update_rule, 'p_plus'):\n",
        "            self.recording['spikes_i'].append(self.source.s.ravel()[self.i].item())\n",
        "            self.recording['spikes_j'].append(self.target.s.ravel()[self.j].item())\n",
        "            self.recording['p_plus'].append(self.connection.update_rule.p_plus[self.i].item())\n",
        "            self.recording['p_minus'].append(self.connection.update_rule.p_minus[self.j].item())\n",
        "            self.recording['eligibility'].append(self.connection.update_rule.eligibility[self.i,self.j].item())\n",
        "            self.recording['eligibility_trace'].append(self.connection.update_rule.eligibility_trace[self.i,self.j].item())\n",
        "            self.recording['w'].append(self.connection.w[self.i,self.j].item())\n",
        "\n",
        "    def plot(self):\n",
        "\n",
        "        fig, axs  = plt.subplots(7)\n",
        "        fig.set_size_inches(10, 20)\n",
        "        for i, (name, p) in enumerate(self.recording.items()):\n",
        "            axs[i].plot(p[-250:])\n",
        "            axs[i].set_title(name)\n",
        "    \n",
        "        fig.show()\n",
        "\n",
        "    def reset_state_variables(self) -> None:\n",
        "        # language=rst\n",
        "        \"\"\"\n",
        "        Resets recordings to empty ``List``s.\n",
        "        \"\"\"\n",
        "        self.recording = {\n",
        "        'spikes_i': [],\n",
        "        'spikes_j': [],\n",
        "        'p_plus':[],\n",
        "        'p_minus':[],\n",
        "        'eligibility':[],\n",
        "        'eligibility_trace':[],\n",
        "        'w': [],\n",
        "        }\n"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R_YGE1XjvIkZ"
      },
      "source": [
        "## Kernel "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-4hp2V46vOUv"
      },
      "source": [
        "class AbstractKernel(ABC):\n",
        "    def __init__(self, kernel_size):\n",
        "        \"\"\"\n",
        "        Base class for generating image filter kernels such as Gabor, DoG, etc. Each subclass should override :attr:`__call__` function.\n",
        "        Instantiates a ``Filter Kernel`` object.\n",
        "        :param window_size : The size of the kernel (int)\n",
        "        \"\"\"\n",
        "        self.window_size = kernel_size\n",
        "\n",
        "    def __call__(self):\n",
        "        pass\n"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PL2L6_ABwBH4"
      },
      "source": [
        "class DoGKernel(AbstractKernel):\n",
        "    def __init__(self, kernel_size: Union[int, Tuple[int, int]], sigma1 : float, sigma2 : float):\n",
        "        \"\"\"\n",
        "        Generates DoG filter kernels.\n",
        "        :param kernel_size: Horizontal and vertical size of DOG kernels.(If pass int, we consider it as a square filter) \n",
        "        :param sigma1 : The sigma parameter for the first Gaussian function.\n",
        "        :param sigma2 : The sigma parameter for the second Gaussian function.\n",
        "        \"\"\"\n",
        "        super(DoGKernel, self).__init__(kernel_size)\n",
        "        self.sigma1 = sigma1\n",
        "        self.sigma2 = sigma2\n",
        "        \n",
        "    def __call__(self):\n",
        "        k = self.window_size//2\n",
        "        x, y = np.mgrid[-k:k+1:1, -k:k+1:1]\n",
        "        a = 1.0 / (2 * math.pi)\n",
        "        prod = x*x + y*y\n",
        "        f1 = (1/(self.sigma1*self.sigma1)) * np.exp(-0.5 * (1/(self.sigma1*self.sigma1)) * (prod))\n",
        "        f2 = (1/(self.sigma2*self.sigma2)) * np.exp(-0.5 * (1/(self.sigma2*self.sigma2)) * (prod))\n",
        "        dog = a * (f1-f2)\n",
        "        dog_mean = np.mean(dog)\n",
        "        dog = dog - dog_mean\n",
        "        dog_max = np.max(dog)\n",
        "        dog = dog / dog_max\n",
        "        dog_tensor = torch.from_numpy(dog)\n",
        "        # returns a 2d tensor corresponding to the requested DoG filter\n",
        "        return dog_tensor.float()"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zBUT0IUZDXxW"
      },
      "source": [
        "class Filter():\n",
        "    \"\"\"\n",
        "    Applies a filter transform. Each filter contains a sequence of :attr:`FilterKernel` objects.\n",
        "    The result of each filter kernel will be passed through a given threshold (if not :attr:`None`).\n",
        "    Args:\n",
        "        filter_kernels (sequence of FilterKernels): The sequence of filter kernels.\n",
        "        padding (int, optional): The size of the padding for the convolution of filter kernels. Default: 0\n",
        "        thresholds (sequence of floats, optional): The threshold for each filter kernel. Default: None\n",
        "        use_abs (boolean, optional): To compute the absolute value of the outputs or not. Default: False\n",
        "    .. note::\n",
        "        The size of the compund filter kernel tensor (stack of individual filter kernels) will be equal to the \n",
        "        greatest window size among kernels. All other smaller kernels will be zero-padded with an appropriate \n",
        "        amount.\n",
        "    \"\"\"\n",
        "    # filter_kernels must be a list of filter kernels\n",
        "    # thresholds must be a list of thresholds for each kernel\n",
        "    def __init__(self, filter_kernels, padding=0, thresholds=None, use_abs=False):\n",
        "        tensor_list = []\n",
        "        self.max_window_size = 0\n",
        "        for kernel in filter_kernels:\n",
        "            if isinstance(kernel, torch.Tensor):\n",
        "                tensor_list.append(kernel)\n",
        "                self.max_window_size = max(self.max_window_size, kernel.size(-1))\n",
        "            else:\n",
        "                tensor_list.append(kernel().unsqueeze(0))\n",
        "                self.max_window_size = max(self.max_window_size, kernel.window_size)\n",
        "        for i in range(len(tensor_list)):\n",
        "            p = (self.max_window_size - filter_kernels[i].window_size)//2\n",
        "            tensor_list[i] = fn.pad(tensor_list[i], (p,p,p,p))\n",
        "\n",
        "        self.kernels = torch.stack(tensor_list)\n",
        "        self.number_of_kernels = len(filter_kernels)\n",
        "        self.padding = padding\n",
        "        if isinstance(thresholds, list):\n",
        "            self.thresholds = thresholds.clone().detach()\n",
        "            self.thresholds.unsqueeze_(0).unsqueeze_(2).unsqueeze_(3)\n",
        "        else:\n",
        "            self.thresholds = thresholds\n",
        "        self.use_abs = use_abs\n",
        "\n",
        "    # returns a 4d tensor containing the flitered versions of the input image\n",
        "    # input is a 4d tensor. dim: (minibatch=1, filter_kernels, height, width)\n",
        "    def __call__(self, input):\n",
        "\n",
        "        # if input.dim() == 3:\n",
        "        #     input2 = torch.unsqueeze(input, 0)\n",
        "        input.unsqueeze_(0)\n",
        "        output = fn.conv2d(input, self.kernels, padding = self.padding).float()\n",
        "        if not(self.thresholds is None):\n",
        "            output = torch.where(output < self.thresholds, torch.tensor(0.0, device=output.device), output)\n",
        "        if self.use_abs:\n",
        "            torch.abs_(output)\n",
        "        return output.squeeze(0)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ywXyWP0I83Au"
      },
      "source": [
        "# Design network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8bZpJmlrJDa9"
      },
      "source": [
        "compute_size = lambda inp_size, k, s: int((inp_size-k)/s) + 1\n",
        "\n",
        "class LCNet(Network):\n",
        "    def __init__(\n",
        "        self,\n",
        "        n_classes: int,\n",
        "        neuron_per_class: int,\n",
        "        in_channels : int,\n",
        "        n_channels1: int,\n",
        "        n_channels2: int,\n",
        "        filter_size1: int,\n",
        "        filter_size2: int,\n",
        "        stride1: int,\n",
        "        stride2: int,\n",
        "        maxPool1: bool,\n",
        "        maxPool2: bool,\n",
        "        online: bool,\n",
        "        deep: bool,\n",
        "        time: int,\n",
        "        reward_fn,\n",
        "        n_neurons: int,\n",
        "        pre_observation: bool,\n",
        "        has_decision_period: bool,\n",
        "        local_rewarding: bool,\n",
        "        nu_LC: Union[float, Tuple[float, float]],\n",
        "        nu_LC2: Union[float, Tuple[float, float]],\n",
        "        nu_Output: float,\n",
        "        dt: float,\n",
        "        crop_size:int ,\n",
        "        nu_inh_LC: float,\n",
        "        nu_inh: float,\n",
        "        inh_type,\n",
        "        inh_LC: bool,\n",
        "        inh_LC2: bool,\n",
        "        inh_factor_LC: float,\n",
        "        inh_factor_LC2: float,\n",
        "        inh_factor:float,\n",
        "        single_output_layer:bool,\n",
        "        NodesType_LC,\n",
        "        NodesType_Output, \n",
        "        update_rule_LC,\n",
        "        update_rule_LC2,\n",
        "        update_rule_Output,\n",
        "        update_rule_inh,\n",
        "        update_rule_inh_LC,\n",
        "        wmin: float,\n",
        "        wmax: float ,\n",
        "        soft_bound,\n",
        "        theta_plus: float,\n",
        "        tc_theta_decay: float,\n",
        "        tc_trace:int,\n",
        "        normal_init:bool,\n",
        "        mu: float,\n",
        "        std:float,\n",
        "        norm_factor_inh_LC: bool,\n",
        "        norm_factor_LC,\n",
        "        norm_factor_LC2,\n",
        "        norm_factor_out,\n",
        "        norm_factor_inh,\n",
        "        trace_additive,\n",
        "        load_path,\n",
        "        save_path,\n",
        "        LC_weights_path,\n",
        "        LC2_weights_path,\n",
        "        confusion_matrix,\n",
        "        lc_weights_vis,\n",
        "        out_weights_vis,\n",
        "        lc_convergence_vis,\n",
        "        out_convergence_vis,\n",
        "        thresh_LC,\n",
        "        thresh_FC,\n",
        "        **kwargs,\n",
        "    ) -> None:\n",
        "        # language=rst\n",
        "        \"\"\"\n",
        "        Constructor for class ``BioLCNet``.\n",
        "\n",
        "        :param n_inpt: Number of input neurons. Matches the 1D size of the input data.\n",
        "        :param n_neurons: Number of excitatory, inhibitory neurons.\n",
        "        :param exc: Strength of synapse weights from excitatory to inhibitory layer.\n",
        "        :param inh: Strength of synapse weights from inhibitory to excitatory layer.\n",
        "        :param dt: Simulation time step.\n",
        "        :param nu: Single or pair of learning rates for pre- and post-synaptic events,\n",
        "            respectively.\n",
        "        :param reduction: Method for reducing parameter updates along the minibatch\n",
        "            dimension.\n",
        "        :param wmin: Minimum allowed weight on input to excitatory synapses.\n",
        "        :param wmax: Maximum allowed weight on input to excitatory synapses.\n",
        "        :param norm: Input to excitatory layer connection weights normalization\n",
        "            constant.\n",
        "        :param theta_plus: On-spike increment of ``(adaptive)LIFNodes`` membrane\n",
        "            threshold potential.\n",
        "        :param tc_theta_decay: Time constant of ``(adaptive)LIFNodes`` threshold\n",
        "            potential decay.\n",
        "        :param inpt_shape: The dimensionality of the input layer.\n",
        "        \"\"\"\n",
        "        manual_seed(SEED)\n",
        "        super().__init__(dt=dt, reward_fn = None, online=online)\n",
        "        kwargs['single_output_layer'] = single_output_layer\n",
        "        kwargs['dt'] = dt\n",
        "        kwargs['n_labels'] = n_classes\n",
        "        kwargs['neuron_per_class'] = neuron_per_class\n",
        "        \n",
        "        self.dt = dt\n",
        "        self.reward_fn = reward_fn(**kwargs)\n",
        "        self.reward_fn.network = self\n",
        "        self.reward_fn.dt = self.dt\n",
        "        self.n_classes = n_classes\n",
        "        self.neuron_per_class = neuron_per_class\n",
        "        self.save_path = save_path\n",
        "        self.load_path = load_path\n",
        "        self.deep = deep\n",
        "        self.maxPool1 = maxPool1\n",
        "        self.maxPool2 = maxPool2\n",
        "        self.time = time\n",
        "        self.crop_size = crop_size\n",
        "        self.filter_size1 = filter_size1\n",
        "        self.filter_size2 = filter_size2\n",
        "        self.clamp_intensity = kwargs.get('clamp_intensity',None)\n",
        "        self.single_output_layer = single_output_layer\n",
        "        self.pre_observation = pre_observation\n",
        "        self.has_decision_period = has_decision_period\n",
        "        self.local_rewarding = local_rewarding\n",
        "        self.soft_bound = soft_bound\n",
        "        self.confusion_matrix = confusion_matrix\n",
        "        self.lc_weights_vis = lc_weights_vis\n",
        "        self.out_weights_vis = out_weights_vis\n",
        "        self.lc_convergence_vis = lc_convergence_vis\n",
        "        self.out_convergence_vis = out_convergence_vis\n",
        "        self.in_channels = in_channels\n",
        "        self.n_channels1 = n_channels1\n",
        "        self.n_channels2 = n_channels2\n",
        "        self.convergences = {}\n",
        "        self.norm_factor_LC = norm_factor_LC\n",
        "        self.norm_factor_LC2 = norm_factor_LC2\n",
        "        self.norm_factor_out = norm_factor_out\n",
        "        self.wmin = wmin \n",
        "        self.wmax = wmax\n",
        "\n",
        "        if kwargs['variant'] == 'scalar':\n",
        "            assert self.has_decision_period == True, ''\n",
        "\n",
        "        if self.online == False:\n",
        "            assert self.has_decision_period == True, ''\n",
        "        \n",
        "        if self.has_decision_period == True:\n",
        "            assert self.online == False, \"Decision period is not compatible with online learning.\"\n",
        "            self.observation_period = kwargs['observation_period']\n",
        "            assert self.observation_period >= 0, \"\"\n",
        "            self.decision_period = kwargs['decision_period']\n",
        "            assert self.decision_period > 0, \"\"\n",
        "            self.learning_period = self.time - self.observation_period - self.decision_period\n",
        "\n",
        "        elif self.pre_observation == True:\n",
        "            self.observation_period = kwargs['observation_period']\n",
        "            assert self.observation_period >= 0, \"\"\n",
        "            self.learning_period = self.time - self.observation_period\n",
        "            self.decision_period = self.time - self.observation_period\n",
        "\n",
        "        else:\n",
        "            self.observation_period = 0\n",
        "            self.decision_period = self.time\n",
        "            self.learning_period = self.time\n",
        "\n",
        "        ### nodes\n",
        "        inp = Input(shape= [in_channels,crop_size,crop_size], traces=True, tc_trace=tc_trace,traces_additive = trace_additive)\n",
        "        self.add_layer(inp, name=\"input\")\n",
        "\n",
        "        ## First hidden layer\n",
        "        conv_size1 = compute_size(crop_size, filter_size1, stride1)\n",
        "        main1 = NodesType_LC(shape= [n_channels1, conv_size1, conv_size1], thresh = thresh_LC, traces=True, tc_trace=tc_trace,\n",
        "                             traces_additive = trace_additive,tc_theta_decay = tc_theta_decay, theta_plus = theta_plus)\n",
        "        \n",
        "        self.add_layer(main1, name=\"main1\")\n",
        "\n",
        "        ### connections \n",
        "        LC1 = LocalConnection(inp, main1, filter_size1, stride1, in_channels, n_channels1, nu = _pair(nu_LC), update_rule = update_rule_LC,wmin = wmin, wmax= wmax, soft_bound = soft_bound, norm = norm_factor_LC)\n",
        "\n",
        "        if LC_weights_path:\n",
        "            a = torch.load(LC_weights_path)\n",
        "            LC1.w.data = a['state_dict']['input_to_main1.w']\n",
        "            print(\"Weights loaded ...\")\n",
        "        \n",
        "        elif normal_init:\n",
        "            w_lc_init = torch.normal(mu,std, size = (in_channels, n_channels1 * compute_size(crop_size, filter_size1, stride1)**2, filter_size1**2))\n",
        "            LC1.w.data = w_lc_init\n",
        "       \n",
        "        self.add_connection(LC1, \"input\", \"main1\")\n",
        "        self.convergences['lc1'] = []\n",
        "\n",
        "        if inh_LC:\n",
        "            main_width = compute_size(crop_size, filter_size1, stride1)\n",
        "            w_inh_LC = torch.zeros(n_channels1,main_width,main_width,n_channels1,main_width,main_width)\n",
        "            for c in range(n_channels1):\n",
        "                for w1 in range(main_width):\n",
        "                    for w2 in range(main_width):\n",
        "                        w_inh_LC[c,w1,w2,:,w1,w2] = - inh_factor_LC\n",
        "                        w_inh_LC[c,w1,w2,c,w1,w2] = 0\n",
        "        \n",
        "            w_inh_LC = w_inh_LC.reshape(main1.n,main1.n)\n",
        "                                                             \n",
        "            LC_recurrent_inhibition = Connection(\n",
        "                source=main1,\n",
        "                target=main1,\n",
        "                w=w_inh_LC,\n",
        "            )\n",
        "            self.add_connection(LC_recurrent_inhibition, \"main1\", \"main1\")\n",
        "        \n",
        "        \n",
        "        self.final_connection_source_name = 'main1'\n",
        "        self.final_connection_source = main1\n",
        "\n",
        "        self.hidden2 = main1\n",
        "        self.hidden2_name = 'main1'\n",
        "        if maxPool1:\n",
        "            maxPool_kernel = 2\n",
        "            maxPool_stride = 2\n",
        "            \n",
        "            conv_size1 =compute_size(conv_size1, maxPool_kernel, maxPool_stride)\n",
        "            self.final_connection_source_name = 'maxpool1'\n",
        "            \n",
        "            maxpool1 = LIFNodes(shape= [self.n_channels1, conv_size1, conv_size1], refrac = 0)\n",
        "            self.add_layer(maxpool1, name=\"maxpool1\")\n",
        "            self.final_connection_source = maxpool1\n",
        "            \n",
        "            maxPoolConnection = MaxPool2dLocalConnection(main1, maxpool1, maxPool_kernel, maxPool_stride)\n",
        "            self.add_connection(maxPoolConnection, \"main1\", 'maxpool1')\n",
        "            \n",
        "            self.hidden2 = maxpool1\n",
        "            self.hidden2_name = 'maxpool1'\n",
        "\n",
        "        if deep:\n",
        "            # # Second hidden layer\n",
        "            conv_size2 = compute_size(conv_size1, filter_size2, stride2)\n",
        "\n",
        "            main2 = NodesType_LC(shape= [n_channels2, conv_size2, conv_size2],traces=True, tc_trace=tc_trace,traces_additive = trace_additive,\n",
        "                                            tc_theta_decay = tc_theta_decay, theta_plus = theta_plus)\n",
        "            \n",
        "            self.add_layer(main2, name=\"main2\")\n",
        "\n",
        "            ### connections \n",
        "            lc2_input_shape = (conv_size1,conv_size1)\n",
        "            LC2 = LocalConnection(self.hidden2, main2, filter_size2, stride2, n_channels1, n_channels2, input_shape= lc2_input_shape,\n",
        "            nu = _pair(nu_LC2), update_rule = update_rule_LC2, wmin = wmin, wmax= wmax, soft_bound = soft_bound, norm = norm_factor_LC2)\n",
        "\n",
        "            self.add_connection(LC2,  self.hidden2_name, \"main2\")\n",
        "            self.convergences['lc2'] = []\n",
        "            if LC2_weights_path:\n",
        "                a = torch.load(LC2_weights_path)\n",
        "                LC2.w.data = a['state_dict']['main1_to_main2.w']\n",
        "                print(\"Weights loaded ...\")\n",
        "            \n",
        "            elif normal_init:\n",
        "                w_lc_init = torch.normal(mu,std, size = (n_channels1, n_channels2 * compute_size(conv_size1, filter_size2, stride2)**2, filter_size2**2))\n",
        "                LC2.w.data = w_lc_init\n",
        "\n",
        "            self.final_connection_source_name = 'main2'\n",
        "            self.final_connection_source = main2\n",
        "\n",
        "            if inh_LC2:\n",
        "                main_width = conv_size2\n",
        "                w_inh_LC2 = torch.zeros(n_channels2,main_width,main_width,n_channels2,main_width,main_width)\n",
        "                for c in range(n_channels2):\n",
        "                    for w1 in range(main_width):\n",
        "                        for w2 in range(main_width):\n",
        "                            w_inh_LC2[c,w1,w2,:,w1,w2] = - inh_factor_LC2\n",
        "                            w_inh_LC2[c,w1,w2,c,w1,w2] = 0\n",
        "            \n",
        "                w_inh_LC2 = w_inh_LC2.reshape(main2.n,main2.n)\n",
        "                                                                \n",
        "                LC_recurrent_inhibition2 = Connection(\n",
        "                    source=main2,\n",
        "                    target=main2,\n",
        "                    w=w_inh_LC2,\n",
        "                )\n",
        "                self.add_connection(LC_recurrent_inhibition2, \"main2\", \"main2\")\n",
        "\n",
        "\n",
        "            if maxPool2:\n",
        "                maxPool_kernel = 2\n",
        "                maxPool_stride = 2\n",
        "                conv_size2 =compute_size(conv_size2, maxPool_kernel, maxPool_stride)\n",
        "                self.final_connection_source_name = 'maxpool2'\n",
        "                maxpool2 = LIFNodes(shape= [self.n_channels2, conv_size2, conv_size2], refrac = 0)\n",
        "                self.final_connection_source = maxpool2\n",
        "                maxPoolConnection2 = MaxPool2dLocalConnection(main2, maxpool2, maxPool_kernel, maxPool_stride)\n",
        "\n",
        "                self.add_layer(maxpool2, name=\"maxpool2\")\n",
        "                self.add_connection(maxPoolConnection2, \"main2\", 'maxpool2')\n",
        "\n",
        "\n",
        "        ### main2 to output\n",
        "        out = NodesType_Output(n= n_neurons, traces=True,traces_additive = trace_additive, thresh=thresh_FC, tc_trace=tc_trace, tc_theta_decay = tc_theta_decay, theta_plus = theta_plus)\n",
        "\n",
        "        self.add_layer(out, \"output\")\n",
        "\n",
        "        last_main_out = Connection(self.final_connection_source, out, nu = nu_Output, update_rule = update_rule_Output, wmin = wmin, wmax= wmax, norm = norm_factor_out)\n",
        "\n",
        "        if normal_init:\n",
        "            w_last_main_init = torch.normal(mu,std,size = (self.final_connection_source.n,out.n)) \n",
        "            last_main_out.w.data = w_last_main_init\n",
        "        print(torch.mean(last_main_out.w),torch.std(last_main_out.w))    \n",
        "        self.add_connection(last_main_out, self.final_connection_source_name, \"output\")\n",
        "        self.convergences['last_main_out'] = []\n",
        "        ### Inhibitory:\n",
        "        if inh_type == 'between_layers':\n",
        "            w = -inh_factor * torch.ones(out.n, out.n)\n",
        "            for c in range(n_classes):\n",
        "                ind = slice(c*neuron_per_class,(c+1)*neuron_per_class)\n",
        "                w[ind, ind] = 0\n",
        "\n",
        "            out_recurrent_inhibition = Connection(\n",
        "                source=out,\n",
        "                target=out,\n",
        "                w=w,\n",
        "                update_rule = update_rule_inh,\n",
        "                wmin=-inh_factor,\n",
        "                wmax=0,\n",
        "                nu = nu_inh,\n",
        "                norm = norm_factor_inh,\n",
        "            )\n",
        "            self.add_connection(out_recurrent_inhibition, \"output\", \"output\")\n",
        "        elif inh_type == 'one_2_all':\n",
        "            w = -inh_factor * (torch.ones(out.n, out.n) - torch.eye(out.n, out.n))\n",
        "            out_recurrent_inhibition = Connection(\n",
        "                source=out,\n",
        "                target=out,\n",
        "                w=w,\n",
        "                update_rule = update_rule_inh,\n",
        "                wmin=-inh_factor,\n",
        "                wmax=0,\n",
        "                nu = nu_inh,\n",
        "                norm = norm_factor_inh,\n",
        "            )\n",
        "            self.add_connection(out_recurrent_inhibition, \"output\", \"output\")\n",
        "        # Diehl and Cook\n",
        "        elif inh_type == 'DC':\n",
        "            raise NotImplementedError('Diehl and cook not implemented yet fo r 10 classes')\n",
        "\n",
        "        # Directs network to GPU\n",
        "        if gpu:\n",
        "            self.to(\"cuda\")\n",
        "\n",
        "\n",
        "    def fit(\n",
        "        self,\n",
        "        dataloader,\n",
        "        val_loader,\n",
        "        reward_hparams,\n",
        "        label = None,\n",
        "        hparams = None,\n",
        "        online_validate = True,\n",
        "        n_train = 2000,\n",
        "        n_test = 250,\n",
        "        n_val = 250,\n",
        "        val_interval = 250,\n",
        "        running_window_length = 250,\n",
        "        verbose = True,\n",
        "    ):\n",
        "        manual_seed(SEED)\n",
        "        self.verbose = verbose\n",
        "        self.label = label\n",
        "        # add Monitors\n",
        "        #main_monitor = Monitor(self.layers[\"main\"], [\"v\"], time=None, device=device)\n",
        "        reward_monitor = RewardMonitor(time =self.time)\n",
        "        #Plot_et = PlotET(i = 0, j = 0, source = self.layers[\"main\"], target = self.layers[\"output\"], connection = self.connections[(\"main\",\"output\")])\n",
        "        #tensorboard = TensorBoardMonitor(self, time = self.time)\n",
        "        #self.add_monitor(main_monitor, name=\"main\")\n",
        "        self.add_monitor(reward_monitor, name=\"reward\")\n",
        "        #self.add_monitor(Plot_et, name=\"Plot_et\")\n",
        "        #self.add_monitor(tensorboard, name=\"tensorboard\")\n",
        "\n",
        "            \n",
        "        acc_hist = collections.deque([], running_window_length)\n",
        "\n",
        "        #if self.single_output_layer:\n",
        "        self.spikes = {}\n",
        "        for layer in set(self.layers):\n",
        "            self.spikes[layer] = Monitor(self.layers[layer], state_vars=[\"s\"], time=None)\n",
        "            self.add_monitor(self.spikes[layer], name=\"%s_spikes\" % layer)\n",
        "            self.dopaminergic_layers = self.layers[\"output\"]\n",
        "        # else:\n",
        "        #     output_layers = set([layer for layer in self.layers if layer.startswith('output')])\n",
        "        #     self.output_spikes = {}\n",
        "        #     for layer in output_layers:\n",
        "        #         self.output_spikes[layer] = Monitor(self.layers[layer], state_vars=[\"s\"], time=self.time)\n",
        "        #         self.add_monitor(self.output_spikes[layer], name=\"%s_spikes\" % layer)\n",
        "        #         self.dopaminergic_layers = {name: layer for name, layer in self.layers.items() if name.startswith('output')}\n",
        "\n",
        "        val_acc = 0.0\n",
        "        acc = 0.0\n",
        "\n",
        "        reward_history = []\n",
        "        if self.load_path:\n",
        "            # try:\n",
        "            self.model_params = torch.load(self.load_path)\n",
        "            self.load_state_dict(torch.load(self.load_path)['state_dict'])\n",
        "            iteration =  self.model_params['iteration']\n",
        "            hparams = self.model_params['hparams']\n",
        "            train_accs = self.model_params['train_accs']\n",
        "            val_accs = self.model_params['val_accs']\n",
        "            acc_rewards = self.model_params['acc_rewards']\n",
        "            print(f'Previous model loaded! Resuming training from iteration {iteration}..., last running training accuracy: {train_accs[-1]}, last validation accuracy: {val_accs[-1]}\\n') if self.verbose else None\n",
        "        else:\n",
        "            print(f'Previous model not found! Training from the beginning...\\n') if self.verbose else None\n",
        "            val_accs = []\n",
        "            train_accs = []\n",
        "            acc_rewards = []\n",
        "            # except:\n",
        "            #     pass\n",
        "        pbar = tqdm(total=n_train)\n",
        "        self.reset_state_variables()\n",
        "\n",
        "\n",
        "\n",
        "        for (i, datum) in enumerate(dataloader):\n",
        "            if self.load_path:\n",
        "                #try:\n",
        "                if i <= iteration:\n",
        "                    n_train += 1\n",
        "                    continue\n",
        "                # except:\n",
        "                #     pass\n",
        "            if i > n_train:\n",
        "                break\n",
        "\n",
        "            image = datum[\"encoded_image\"]\n",
        "            if self.label is None : label = datum[\"label\"]\n",
        "\n",
        "            # Run the network on the input.\n",
        "            if gpu:\n",
        "                inputs = {\"input\": image.cuda().view(self.time, 1, self.in_channels, self.crop_size, self.crop_size)}\n",
        "            else:\n",
        "                inputs = {\"input\": image.view(self.time, 1, self.in_channels, self.crop_size, self.crop_size)}\n",
        "            #print(self.spikes['output'].get('s'))\n",
        "\n",
        "            ### Spike clamping (baseline activity)\n",
        "\n",
        "            clamp = {}\n",
        "            if self.clamp_intensity is not None:\n",
        "                encoder = PoissonEncoder(time = self.time, dt = self.dt)\n",
        "                clamp['output'] = encoder.enc(datum = torch.rand(self.layers['output'].n)*self.clamp_intensity,time = self.time, dt = self.dt)\n",
        "\n",
        "            self.run(inputs=inputs, \n",
        "                    time=self.time, \n",
        "                    **reward_hparams,\n",
        "                    one_step=True,\n",
        "                    true_label = label.int().item(),\n",
        "                    dopaminergic_layers= self.dopaminergic_layers,\n",
        "                    clamp = clamp\n",
        "                     )\n",
        "\n",
        "            # Get voltage recording.\n",
        "            #main_voltage = main_monitor.get(\"v\")\n",
        "            reward_history.append(reward_monitor.get())\n",
        "            #tensorboard.update(step= i)\n",
        "\n",
        "            # Add to spikes recording.\n",
        "            #if self.single_output_layer:\n",
        "            lc_spikes1 = self.spikes['main1'].get('s')\n",
        "            #lc_spikes2 = self.spikes['main2'].get('s')\n",
        "            out_spikes = self.spikes[\"output\"].get(\"s\").view(self.time, n_classes, neuron_per_class)\n",
        "            sum_spikes = out_spikes[self.observation_period:self.observation_period+self.decision_period,:,:].sum(0).sum(1)\n",
        "            predicted_label = torch.argmax(sum_spikes)\n",
        "            \n",
        "            # else:\n",
        "            #     spikes_record = torch.zeros(self.n_classes, self.time, self.neuron_per_class)\n",
        "            #     for c in range(self.n_classes):\n",
        "            #         spikes_record[c] = self.output_spikes[f\"output_{c}\"].get(\"s\").squeeze(1)\n",
        "            #     sum_spikes = spikes_record.sum(1).sum(1)\n",
        "            #     predicted_label = torch.argmax(sum_spikes)    \n",
        "\n",
        "            if predicted_label == label:\n",
        "                acc_hist.append(1)\n",
        "            else:\n",
        "                acc_hist.append(0)\n",
        "  \n",
        "            w_lc1 = self.connections[('input', 'main1')].w\n",
        "            w_last_main_out = self.connections[(self.final_connection_source_name,'output')].w\n",
        "            #w_lc2 = self.connections[('main1', 'main2')].w\n",
        "            #w_inh = self.connections[('output','output')].w\n",
        "            convg_lc1=  1 -  torch.mean((w_lc1 - self.wmin)*(self.wmax-w_lc1))\n",
        "            convg_out= 1 -  torch.mean((w_last_main_out - self.wmin)*(self.wmax-w_last_main_out))\n",
        "            if self.norm_factor_LC is not None:\n",
        "                mean_norm_factor_lc = self.norm_factor_LC / w_lc1.shape[-1]\n",
        "                convg_lc1=  1 - ( torch.mean((w_lc1 - self.wmin)*(self.wmax-w_lc1)) / ((mean_norm_factor_lc - self.wmin)*(self.wmax - mean_norm_factor_lc)) )\n",
        "            if self.norm_factor_out is not None:    \n",
        "                mean_norm_factor_out = self.norm_factor_out / w_last_main_out.shape[-2]\n",
        "                convg_out= 1 - ( torch.mean((w_last_main_out - self.wmin)*(self.wmax-w_last_main_out)) / ((mean_norm_factor_out - self.wmin)*(self.wmax - mean_norm_factor_out)) )\n",
        "           \n",
        "            self.convergences['lc1'].append((convg_lc1 * 10**4).round() / (10**4))\n",
        "            self.convergences['last_main_out'].append((convg_out * 10**4).round() / (10**4)) \n",
        "\n",
        "            print(\"\\routput\", sum_spikes, 'pred_label:',\n",
        "                predicted_label.item(), 'GT:', label.item(),\n",
        "                ', Acc Rew:', round(sum(reward_monitor.get()).item(),4),\n",
        "                f\"Pos dps: {self.reward_fn.dps:.5f}, Neg dps: {self.reward_fn.neg_dps:.5f}, Rew base: {self.reward_fn.rew_base:.5f}, Pun base: {self.reward_fn.punish_base:.5f}, RPe: {self.reward_fn.reward_predict_episode:.3f}\",\n",
        "                f\"input_mean_fire_freq: {torch.mean(image.float())*1000:.1f},main_mean_fire_freq:{torch.mean(lc_spikes1.float())*1000:.1f}\",#\" main2_mean_fire_freq:{torch.mean(lc_spikes2.float())*1000:.1f}\",\n",
        "                f\"output_mean_fire_freq:{torch.mean(out_spikes.float())*1000:.1f}\",\n",
        "                f\"mean_lc1_w: {torch.mean(w_lc1):.5f}, mean_fc_w:{torch.mean(w_last_main_out):.5f}\",\n",
        "                f\"std_lc1_w: {torch.std(w_lc1):.5f}, std_fc_w:{torch.std(w_last_main_out):.5f}\",\n",
        "                f\"convergence_lc1: {convg_lc1:.5f}, convergence_fc: {convg_out:.5f}\",\n",
        "                end = '')           \n",
        "            \n",
        "            acc = 100 * sum(acc_hist)/len(acc_hist)\n",
        "            self.reward_fn.update(accumulated_reward= sum(reward_monitor.get()), ema_window = reward_hparams['ema_window']) \n",
        "\n",
        "            if online_validate and i % val_interval == 0 and i!=0:\n",
        "                self.reset_state_variables()\n",
        "                val_acc = self.evaluate(val_loader, n_val, val_interval, running_window_length)\n",
        "                #tensorboard.writer.add_scalars(\"accuracy\", {\"train\": acc, \"val\" : val_acc}, i)\n",
        "                train_accs.append(acc)\n",
        "                val_accs.append(val_acc)\n",
        "                #acc_rewards.append(sum(reward_monitor.get()))\n",
        "                if self.save_path is not None:\n",
        "                    model_params = {'state_dict': self.state_dict(), 'hparams': hparams, 'iteration': i, 'val_accs': val_accs, 'train_accs': train_accs, 'acc_rewards': acc_rewards}\n",
        "                    torch.save(model_params, self.save_path)\n",
        "            # else:\n",
        "            #     pass\n",
        "                #tensorboard.writer.add_scalars(\"accuracy\", {\"train\": acc}, i)\n",
        "            #tensorboard.writer.add_scalar(\"reward\", sum(reward_monitor.get()), i)\n",
        "\n",
        "            # if  i % val_interval == 0 and i!=0:\n",
        "            #     fig = create_plot(self.output_spikes, reward_monitor.get(), label)\n",
        "            #     tensorboard.writer.add_figure('reward', fig, i)\n",
        "            \n",
        "            #Plot_et.plot()    \n",
        "            self.reset_state_variables()  # Reset state variables.\n",
        "            \n",
        "            pbar.set_description_str(\"Running accuracy: \" + \"{:.2f}\".format(acc) + \"%, \" + \"Current val accuracy: \" + \"{:.2f}\".format(val_acc) + \"%, \")\n",
        "            pbar.update()\n",
        "\n",
        "        result_metrics = {'train_acc': acc, 'val_acc': val_acc}\n",
        "        # tensorboard.writer.add_hparams(\n",
        "        #     {k:(v if type(v) in (int, float, bool, str, torch.Tensor) else str(v)) for k,v in {**train_hparams, **data_hparams, **network_hparams, **reward_hparams}.items() },\n",
        "        #     result_metrics\n",
        "        # )\n",
        "\n",
        "\n",
        "\n",
        "    def evaluate(self, val_loader, n_val, val_interval, running_window_length):\n",
        "        manual_seed(SEED)\n",
        "        acc_hist_val = collections.deque([], running_window_length)\n",
        "\n",
        "        spikes_val = {}\n",
        "\n",
        "        self.train(False)\n",
        "        self.learning = False\n",
        "\n",
        "        GT, y_pred = [], []\n",
        "        for (i, datum) in enumerate(val_loader):\n",
        "            if i > n_val:\n",
        "                break\n",
        "\n",
        "            image = datum[\"encoded_image\"]\n",
        "            if self.label is None : \n",
        "              label = datum[\"label\"]\n",
        "            else :\n",
        "              label = self.label\n",
        "\n",
        "            # Run the network on the input.\n",
        "            if gpu:\n",
        "                inputs = {\"input\": image.cuda().view(self.time, 1, self.in_channels, self.crop_size, self.crop_size)}\n",
        "            else:\n",
        "                inputs = {\"input\": image.view(self.time, 1, self.in_channels, self.crop_size, self.crop_size)}\n",
        "\n",
        "            self.run(inputs=inputs, \n",
        "                    time=self.time, \n",
        "                    **reward_hparams,\n",
        "                    one_step = True,\n",
        "                    true_label = label.int().item(),\n",
        "                    dopaminergic_layers= self.dopaminergic_layers,\n",
        "                     )\n",
        "            # Add to spikes recording.\n",
        "            #if self.single_output_layer:\n",
        "            out_spikes = self.spikes[\"output\"].get(\"s\").view(self.time, n_classes, neuron_per_class)\n",
        "            sum_spikes = out_spikes[self.observation_period:self.observation_period+self.decision_period,:,:].sum(0).sum(1)\n",
        "            predicted_label = torch.argmax(sum_spikes)\n",
        "            # else:\n",
        "            #     spikes_record = torch.zeros(self.n_classes, self.time, self.neuron_per_class)\n",
        "            #     for c in range(self.n_classes):\n",
        "            #         spikes_record[c] = self.output_spikes[f\"output_{c}\"].get(\"s\").squeeze(1)\n",
        "            #     sum_spikes = spikes_record.sum(1).sum(1)\n",
        "            #     predicted_label = torch.argmax(sum_spikes)\n",
        "\n",
        "            if predicted_label == label:\n",
        "                acc_hist_val.append(1)\n",
        "            else:\n",
        "                acc_hist_val.append(0)\n",
        "            \n",
        "            GT.append(label)\n",
        "            y_pred.append(predicted_label)\n",
        "            \n",
        "            print(\"\\r*validation: output\",sum_spikes,\n",
        "                'predicted_label:', predicted_label.item(), 'GT:', label.item(),\n",
        "                end = '') if self.verbose else None\n",
        "            \n",
        "            self.reset_state_variables()  # Reset state variables.\n",
        "\n",
        "        self.train(True)\n",
        "        self.learning = True\n",
        "        \n",
        "        if self.confusion_matrix:\n",
        "            self.plot_confusion_matrix(GT, y_pred)\n",
        "\n",
        "        if self.lc_weights_vis:\n",
        "            plot_locally_connected_weights_meh(self.connections[('input','main1')].w,self.n_channels1,self.in_channels,0,self.crop_size,self.filter_size1,self.layers['main1'].shape[1])\n",
        "            plt.show()\n",
        "            if self.deep:\n",
        "                plot_locally_connected_weights_meh(self.connections[(self.hidden2_name,'main1')].w,self.n_channels2,self.n_channels1,0,self.layers[self.hidden2_name].shape[1],self.filter_size2,self.layers['main2'].shape[1])                \n",
        "                plt.show()\n",
        "        \n",
        "        if self.lc_convergence_vis: \n",
        "            plot_convergence_and_histogram(self.connections[('input','main1')].w,self.convergences['lc1'])\n",
        "            plt.show()\n",
        "        if self.out_convergence_vis:\n",
        "            plot_convergence_and_histogram(self.connections[(self.final_connection_source_name,'output')].w,self.convergences['last_main_out'])\n",
        "            plt.show() \n",
        "\n",
        "        if self.out_weights_vis:\n",
        "            plot_locally_connected_weights_meh3(self.connections[('input','main1')].w,self.connections[(self.final_connection_source_name,'output')].w,\\\n",
        "                                                0,0,self.neuron_per_class,self.n_channels1,self.in_channels,0,self.crop_size,self.filter_size1,self.layers['main1'].shape[1]) \n",
        "            plt.show()\n",
        "            plot_locally_connected_weights_meh3(self.connections[('input','main1')].w,self.connections[(self.final_connection_source_name,'output')].w,\\\n",
        "                                                0,1,self.neuron_per_class,self.n_channels1,self.in_channels,0,self.crop_size,self.filter_size1,self.layers['main1'].shape[1])           \n",
        "            plt.show()\n",
        "        val_acc = 100 * sum(acc_hist_val)/len(acc_hist_val)\n",
        "        return val_acc\n",
        "\n",
        "    @staticmethod\n",
        "    def plot_confusion_matrix(GT, y_predicted):\n",
        "        cm = confusion_matrix(GT, y_predicted)\n",
        "        plt.figure(figsize = (10,7))\n",
        "        sn.heatmap(cm, annot=True)\n",
        "        plt.xlabel('Predicted')\n",
        "        plt.ylabel('Truth')\n",
        "        plt.show()\n",
        "\n",
        "    \n",
        "    def one_step(self,datum,label = None):\n",
        "        self.reset_state_variables()\n",
        "        \n",
        "        image = datum[\"encoded_image\"]\n",
        "        if label is None : label = datum[\"label\"]\n",
        "\n",
        "        if gpu:\n",
        "            inputs = {\"input\": image.cuda().view(self.time, 1, self.in_channels, self.crop_size, self.crop_size)}\n",
        "        else:\n",
        "            inputs = {\"input\": image.view(self.time, 1, self.in_channels, self.crop_size, self.crop_size)}\n",
        "\n",
        "        clamp = {}\n",
        "        if self.clamp_intensity is not None:\n",
        "            encoder = PoissonEncoder(time = self.time, dt = self.dt)\n",
        "            clamp['output'] = encoder.enc(datum = torch.rand(self.layers['output'].n)*self.clamp_intensity,time = self.time, dt = self.dt)\n",
        "\n",
        "        self.run(inputs=inputs, \n",
        "                time=self.time, \n",
        "                **reward_hparams,\n",
        "                one_step = True,\n",
        "                true_label = label.int().item(),\n",
        "                dopaminergic_layers= self.dopaminergic_layers,\n",
        "                )\n",
        "    "
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6zuUBU9pU3vE"
      },
      "source": [
        "# Load Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2DW7dB11jdqi"
      },
      "source": [
        "class ClassSelector(torch.utils.data.sampler.Sampler):\n",
        "    \"\"\"Select target classes from the dataset\"\"\"\n",
        "    def __init__(self, target_classes, data_source, mask = None):\n",
        "        if mask is not None:\n",
        "            self.mask = mask\n",
        "        else:\n",
        "            self.mask = torch.tensor([1 if data_source[i]['label'] in target_classes else 0 for i in range(len(data_source))])\n",
        "        self.data_source = data_source\n",
        "\n",
        "    def __iter__(self):\n",
        "        return iter([i.item() for i in torch.nonzero(self.mask)])\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.data_source)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DlCXBY0DU3Mc"
      },
      "source": [
        "kernels = [DoGKernel(7,1,2),\n",
        "\t\t\tDoGKernel(7,2,1),]\n",
        "filter = Filter(kernels, padding = 3, thresholds = 50/255)\n",
        "# Load MNIST data.\n",
        "\n",
        "def load_datasets(network_hparams, data_hparams, mask=None, test_mask=None):\n",
        "    manual_seed(SEED)\n",
        "    dataset = MNIST(\n",
        "        PoissonEncoder(time=network_hparams['time'], dt=network_hparams['dt']),\n",
        "        None,\n",
        "        root=os.path.join(\"..\", \"..\", \"data\", \"MNIST\"),\n",
        "        download=True,\n",
        "        transform=transforms.Compose(\n",
        "            [transforms.ToTensor(),\n",
        "            # filter,\n",
        "            transforms.Lambda(lambda x: (\n",
        "                x.round() if data_hparams['round_input'] else x\n",
        "            ) * data_hparams['intensity']),\n",
        "            transforms.CenterCrop(data_hparams['crop_size'])]\n",
        "        ),\n",
        "    )\n",
        "\n",
        "    # Create a dataloader to iterate and batch data\n",
        "    dataloader = torch.utils.data.DataLoader(dataset, batch_size=1,\n",
        "                                            sampler = ClassSelector(\n",
        "                                                    target_classes = target_classes,\n",
        "                                                    data_source = dataset,\n",
        "                                                    mask = mask,\n",
        "                                                    ) if target_classes else None\n",
        "                                            )\n",
        "\n",
        "    # Load test dataset\n",
        "    test_dataset = MNIST(   \n",
        "        PoissonEncoder(time=network_hparams['time'], dt=network_hparams['dt']),\n",
        "        None,\n",
        "        root=os.path.join(\"..\", \"..\", \"data\", \"MNIST\"),\n",
        "        download=True,\n",
        "        train=False,\n",
        "        transform=transforms.Compose(\n",
        "            [transforms.ToTensor(),\n",
        "            # filter,\n",
        "            transforms.Lambda(lambda x: (\n",
        "                x.round() if data_hparams['round_input'] else x\n",
        "            ) * data_hparams['intensity']),\n",
        "            transforms.CenterCrop(data_hparams['crop_size'])]\n",
        "        ),\n",
        "    )\n",
        "\n",
        "    val_loader = torch.utils.data.DataLoader(test_dataset, batch_size=1,\n",
        "                                            sampler = ClassSelector(\n",
        "                                                    target_classes = target_classes,\n",
        "                                                    data_source = test_dataset,\n",
        "                                                    mask = mask_test,\n",
        "                                                    ) if target_classes else None\n",
        "                                            )\n",
        "    \n",
        "\n",
        "    return dataloader, val_loader"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kCqAFucAUDb8"
      },
      "source": [
        "# Set up hyper-parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I0Wh_eJIFkO-"
      },
      "source": [
        "train_hparams = {\n",
        "    'n_train' : 59999,\n",
        "    'n_test' : 9999,\n",
        "    'n_val' : 1,\n",
        "    'val_interval' : 250,\n",
        "    'running_window_length': 500,\n",
        "}"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MVOqxcYtFd5T"
      },
      "source": [
        "# Dataset Hyperparameters\n",
        "target_classes = None #(0,1)\n",
        "if target_classes:\n",
        "    npz_file = np.load(f'bindsnet/mask_{\"_\".join([str(i) for i in target_classes])}.npz')\n",
        "    # npz_file = np.load('bindsnet/mask_0_1.npz') ##### KESAFAT KARI !!!\n",
        "    mask, mask_test = torch.from_numpy(npz_file['arr_0']), torch.from_numpy(npz_file['arr_1'])\n",
        "    n_classes = len(target_classes)\n",
        "    \n",
        "else:\n",
        "    mask = None\n",
        "    mask_test = None\n",
        "    n_classes = 10\n",
        "\n",
        "data_hparams = { \n",
        "    'intensity': 128,\n",
        "    'crop_size': 22,\n",
        "    'round_input': False,\n",
        "}"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3TerGeJoFdzg"
      },
      "source": [
        "n_neurons = 100 #100\n",
        "neuron_per_class = int(n_neurons/n_classes)\n",
        "single_output_layer = True\n",
        "thresh_LC = -52\n",
        "thresh_FC = -52\n",
        "\n",
        "network_hparams = {\n",
        "    # net structure\n",
        "    'crop_size': 22,\n",
        "    'neuron_per_class': neuron_per_class,\n",
        "    'deep': False,\n",
        "    'maxPool1': False,\n",
        "    'maxPool2': False,\n",
        "    'in_channels':1,\n",
        "    'n_channels1': 500,\n",
        "    'n_channels2': 64,\n",
        "    'filter_size1': 15,\n",
        "    'filter_size2': 5,\n",
        "    'stride1': 4,\n",
        "    'stride2': 1,\n",
        "    'n_neurons' : n_neurons,\n",
        "    'n_classes': n_classes,\n",
        "    'single_output_layer': single_output_layer,\n",
        "    \n",
        "    # time & Phase\n",
        "    'dt' : 1,\n",
        "    'pre_observation': True,\n",
        "    'has_decision_period': True,\n",
        "    'observation_period': 256,\n",
        "    'decision_period': 256,\n",
        "    'online': False,\n",
        "    'local_rewarding': False,\n",
        "     \n",
        "    # Nodes\n",
        "    'NodesType_LC': AdaptiveLIFNodes,\n",
        "    'NodesType_Output': LIFNodes, \n",
        "    'theta_plus': 0.05,\n",
        "    'tc_theta_decay': 1e6,\n",
        "    'tc_trace':20,\n",
        "    'trace_additive' : False,\n",
        "    \n",
        "    # Learning\n",
        "    'update_rule_LC': None,\n",
        "    'update_rule_LC2': None,\n",
        "    'update_rule_Output': MSTDP,\n",
        "    'update_rule_inh': None,\n",
        "    'update_rule_inh_LC' : None,\n",
        "    'nu_LC': (0.00,0.0),\n",
        "    'nu_LC2': (0.0,0.0),\n",
        "    'nu_Output':0.1,\n",
        "    'nu_inh': 0.0,\n",
        "    'nu_inh_LC': 0.0,\n",
        "    'soft_bound': True,\n",
        "    'thresh_LC': thresh_LC,\n",
        "    'thresh_FC': thresh_FC,\n",
        "\n",
        "    # weights\n",
        "    'normal_init': False,\n",
        "    'mu' : 0.8,\n",
        "    'std' : 0.02,\n",
        "    'wmin': 0.0,\n",
        "    'wmax': 1.0,\n",
        "    \n",
        "    # Inhibition\n",
        "    'inh_type': 'between_layers',\n",
        "    'inh_factor': 100,\n",
        "    'inh_LC': True,\n",
        "    'inh_factor_LC': 100,\n",
        "    'inh_LC2': False,\n",
        "    'inh_factor_LC2': 0,\n",
        "    \n",
        "    # Normalization\n",
        "    'norm_factor_LC': 0.25*15*15,\n",
        "    'norm_factor_LC2': None,\n",
        "    'norm_factor_out': None,\n",
        "    'norm_factor_inh': None,\n",
        "    'norm_factor_inh_LC': None,\n",
        "    \n",
        "    # clamp\n",
        "    'clamp_intensity': None,#1000,\n",
        "\n",
        "    # Save\n",
        "    'save_path': '/content/drive/My Drive/LCNet/BioLCNet_layer1_Shallow_f15_s4_inh100_norm25_ch500_tr_n100.pth',\n",
        "    'load_path': '/content/drive/My Drive/LCNet/BioLCNet_layer1_Shallow_f15_s4_inh100_norm25_ch500_tr_n100.pth',\n",
        "    'LC_weights_path': '/content/drive/My Drive/LCNet/BioLCNet_layer1_Shallow_f15_s4_inh100_norm25_c500.pth',#'/content/drive/My Drive/LCNet/LCNet_ch81_f13_22_2norm_Adapt_fc_test2.pth',\n",
        "    'LC2_weights_path': None,#'/content/drive/My Drive/LCNet/DeepLCNet_layer2_ch64_f5_s2_norm3.pth',\n",
        "\n",
        "    # Plot:\n",
        "    'confusion_matrix' : False,\n",
        "    'lc_weights_vis': False,\n",
        "    'out_weights_vis': False,\n",
        "    'lc_convergence_vis': False,\n",
        "    'out_convergence_vis': False,\n",
        "\n",
        "}\n",
        "\n",
        "\n",
        "\n",
        "reward_hparams= {\n",
        "    'n_labels': n_classes,\n",
        "    'neuron_per_class': neuron_per_class,\n",
        "    \n",
        "    'variant': 'scalar',  #true_pred, #pure_per_spike (Just in phase I, online : True) , and #scalar #per_spike\n",
        "    'tc_reward':0,\n",
        "    'dopamine_base': 0.0,\n",
        "    'reward_base': 1.,\n",
        "    'punishment_base': 1.,\n",
        "    \n",
        "\n",
        "    'sub_variant': 'static', #static, #RPE, #pred_decay\n",
        "    'td_nu': 0.0001,  #RPE\n",
        "    'ema_window': 10, #RPE\n",
        "    'tc_dps': 20,     #pred_decay\n",
        "    'dps_factor': 20, #pred_decay, #RPE\n",
        "    }\n"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Rz1wmKKYuVg"
      },
      "source": [
        "time = 256+256+256 #320\n",
        "network_hparams.update(\n",
        "    {\n",
        "     'time': time,\n",
        "    }\n",
        ")\n",
        "dataloader, val_loader = load_datasets(network_hparams, data_hparams, mask, mask_test)"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SokdidkrV2Z5"
      },
      "source": [
        "# Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Venb2KhSYrT_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "be01d959-096d-4721-a6f3-44c7abc0881e"
      },
      "source": [
        "from google.colab import drive\n",
        "if network_hparams['save_path'] or network_hparams['LC_weights_path']:    \n",
        "    drive.mount('/content/drive')"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gRruWKq0kHbz"
      },
      "source": [
        "### Hyperparameters 1 "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IavVVHrvYVoI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 157,
          "referenced_widgets": [
            "c3eb34ca0b5843199d529cf568906589",
            "05734697da134b90a978c298a6a2bda1",
            "7917d8f9b04c47029d8a1713bdcb28d6",
            "42dc6c7a97f14d719f8f21d61eaf9c4e",
            "9eba8cdb28134fbaa419e18e402931d6",
            "5e03a22222fc4d2995caba40da823eda",
            "d003e4d965ac4eca895831fc011ddc25",
            "1897caa4e873499db1bb11b3aba4cd71",
            "3dbb93b2d9bc46888fcbe752b46b432b",
            "48c5bf13a5c447a4a06bac4a2de92599",
            "7374c630d6084298a7b116ef7b2fc144"
          ]
        },
        "outputId": "74d997aa-3ce2-4149-db33-695c04cc4265"
      },
      "source": [
        "manual_seed(SEED)\n",
        "hparams = {**reward_hparams, **network_hparams, **train_hparams, **data_hparams}\n",
        "net = LCNet(**hparams, reward_fn = DynamicDopamineInjection)\n",
        "net.fit(dataloader = dataloader, val_loader = val_loader, reward_hparams = reward_hparams, **train_hparams)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Weights loaded ...\n",
            "tensor(0.4992) tensor(0.2886)\n",
            "Previous model loaded! Resuming training from iteration 250..., last running training accuracy: 16.4, last validation accuracy: 0.0\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c3eb34ca0b5843199d529cf568906589",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/59999 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "output tensor([230, 230, 230, 230, 230, 230, 230, 230, 230, 207]) pred_label: 0 GT: 2 , Acc Rew: -256.0 Pos dps: 1.00000, Neg dps: 1.00000, Rew base: 1.00000, Pun base: 1.00000, RPe: 25.600 input_mean_fire_freq: 29.6,main_mean_fire_freq:5.1 output_mean_fire_freq:87.8 mean_lc1_w: 0.25000, mean_fc_w:0.56083 std_lc1_w: 0.22623, std_fc_w:0.32982 convergence_lc1: 0.27296, convergence_fc: 0.86248"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SfTcVmMNCSUo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "outputId": "510404bb-39f9-4d94-a35a-faeb9ba2aeb2"
      },
      "source": [
        "plot_locally_connected_weights_meh(net.connections[('input', 'main1')].w,100,1,0,22,15,2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f4177e9c810>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lqb0Ph5AYdRv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "78bae690-ed4b-4d07-c6c0-e1b79a6a5031"
      },
      "source": [
        "dataloader_iterator = iter(dataloader)\n",
        "for i in range(10):\n",
        "    datum = next(dataloader_iterator)\n",
        "    plt.imshow(datum['encoded_image'].squeeze().sum(0))\n",
        "    plt.show()\n",
        "    net.one_step(datum)\n",
        "    spikes = net.spikes[\"main1\"].get(\"s\").sum(0).squeeze().view(30,30)\n",
        "    print(torch.max(spikes))\n",
        "    plot_locally_connected_weights_meh2(net.connections[('input', 'main1')].w,spikes,100,1,0,20,12,3)\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(21)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(23)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(18)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(21)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(23)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(22)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JSgPo8zxvzji+fwGw0vO8240xpwJ3G2Nme573QbUvaA8rdTKADDbuoCiiGy+2LputU6enZr3E5ILYhZRjtUzzl7lcAiwA8DzvKWNMExIE/0a1jeogXMZYeM9CFt6zMOlmICJhs5xZ8ShOxrfo+yH8H2a7MGZSxJiQ/VngWGPMkcaYicgg2wOhz7wKzAMwxvwe0plvjrRRvcxmjHf2vZN0EwgKONpSVC2U30Lnn3j6IXw66ulZD3FYlZ7nDRljrkQqsh4IfN/zvM3GmBuBLs/zHgCuBv7JGPOXyIDcUs/zvJG2qz2s1MEAYv3aQaPDKPcDK/XhWr16atZDnDPhPM9bB6wLLfu68/pF4PSxbFNdEMoYGUTqAPb4r1sRd1hxKlPHg72DcF06St7Ry6wyBoaQqIeN/ut2JNOZLdCq1E/Y+tVTs16yNBVZezljnDPjnIT2bKdYbySoBTjTf+4f4Xv5JN5+GETiq1tQS3hsjDMOuOGoAGeMa067JsG9dxPcHs9CXA8lxB8MRYpdjb8fXJdEkaqpjA/NhqbkhCGgC5nhNgURW1tQ0xa3tNEPByM+4MnoIVUvtiKxO6HFPgZRSzif6NmSMeaunAvAY0sfi3lPj8OD80WDLwBm/AhYUuFzJYZXE84/8fSDimwUqAtCyTi9wO1S2PgjwIyJaJSDkgXUBaFknEHgZ/DyQ5JKciHAbPz6QoqSelSAlYwyBDwOfX8K9yJZT084Cokt19tjJf1oFISSYfqAqyTt9NvAH4H4fU9EBVhRokcFOGMsnrU4pi0PAg/Cj1+EXwNfAj56G1LPDVSAy4mvH5Txoi4IJTauOOmKmLb8ErzzJ/Acktqh8wIC14NNkahY4usHZTxkbRAuS+4SBdi7by979+2NeKsvAJfAz5BqxhcB/DUivruBxylSprNaiKcflCiIoyZcXKgFnDHOvkcKrUYXf9oHfBt+8xw8AxwDHL4IeBIJPXseSb4Trr5SbKLvB6WIqAAXnueBB+EnyL3bmSDREN+Hd56TkOAjP4xUM1aUdJM1F4QKcOHx0x8eBcwBTjgZcT2cApNegInvIVEQkxNso6LUTpb8qirAhecsYAfMrbRuudSCzTQ2n0LWUjzaQc+stTtZ1AJWlP2CV+/6qNvS6H2Ol3A9uKy0Ox2oACuxsbRzadJNGIVqZYlsKXu7vrUBbQnvMzprMr5+sNnPXMsdNA47n6gAZ4zsCLCbShHnuREC7O43njp18fSDG28d/h+VWtCpyEqs9O3tA6CtOa3ZyfpJVjziF1+Iox/cdoOcmuH/UU/XWlAXhBIb5686H0hz/Km9fa5EaYR1UeDevtv9lYhjFl88/VByntUPXA86CKcUHDt12cUeZnFbw2HxrdSWtFPpoqH+37yiAqxEjBW+8IBXCZnOHNeU5vDte1i0snCo2/9MKySPB/UBKwWmCRGPJspFMGydRolbP829AFRyR6QVV3zD1m+a250u1AWhFJxqFXxthV+I57ALC20WIwcq+X31FB0ragErsXH5nMuTbkIdWEvUtY6jphR6tq8rLR8/8faD/Z+UsaIWsBIrS2ZXqkycdpqQcvZxUWK45W0P7WoW+fiIpx/U4i0a2tsZY+eenQBMnzw94ZYUG+2HdKIWsBIrF//kYiDNccDFQPshvagPWFEUJQGyZgFn6WKhKIqSK9QCVhQlV2TJAlYBVhQlN2g2NCVWrj716qSboKD9kGbUAlZiY9HMRUk3QUH7Ia1kzQLOUlsVoLuvm+6+7qSbUXi0H5QoGJMF3L27m7kr55YtWzxrMVecdAV79+3l7HvOHvadpZ1LWdq5lL69fftzqLpcPudylsxews49O/fHVrpcferVLJq5iO6+bi5be9mw9defcT3zj5rPpt5NLHt42bD1t8y7hdOmn8aGnRu4bv11w9bfseAOOts7eXTbo9z8xM3D1t91zl3MbJvJmu413P7U7cPW3/3Zu5k+eTr3vnAvd3bdOWz9fYvvo625jZWbVrJy08ph69dduI7mCc0sf3Y5qzavGrbexpnetuE21m5Zy6beTQB0tncyacIkHrrwIQBuevwm1r+yvuy7U5qnsHrxagCuffRanup5qmz9tEOn8cPP/RCAZQ8v279ty4wpM1ixaAUAl665lC27t5St72zv5I4FdwBw0f0X0fNWT9n6U6edyq3zbwXgvFXnsXvv7rL1846cxw1n3gDAwnsW8s6+d8rWnzPjHK457RqAYccdJHvsberdxBGtR/D8nz5fmGPPpbHH3p+wZfd/la3vbD+ROxZ8C/CGtVtdEIqiKOPGTbD/vrPcA95FEjyVC3DW4oCN5w2/glRjzpw5XldXV4zNUUbDWoI6AytZtB8aga2R55ZjKi9UOmfO6XR1bTT2G53GeOW2eO20wUbP8+bU3dw6UAtYUZSUYtNzugn+w2lHTeWvZgQVYEVRUopblLRELdnisuaCUAHOGNefcX3STVDQfmgsbpWQkXNJqwArsTL/qPlJN0FB+6FxuL7fQWpJ5p+l2FoV4IzhhqEpyaH90Ais7zdcJ686agErsWLjTXX0PVm0HxpB/iuE5PvXKYqSYVz3Q+2oC0JRFCUS3MkYo9f3UxeEoigOQ8BLBCLiTiSwTANagX5/XZPzXSiPh22rcb+PAD3+NvH3H0xggBOBTn+/tfyGfud9E3EVO628b+uKqK2atgqwoig+fcC3kWmz4YEkKyqfBk4BuoHJiLhZwR5Ept32A4cBc6hN/P4CXt4Cvf7b3zqrfhc44WLgKkSER2MQeB6RtjakwnWjBLg20S2jXh/EB3V+bxyoAGeMW+bdknQTFMbSD9Z6LCHWZpu/rMdfNg2xRluBo/3nEoFgD/rf7wd2+8trEb8BeA84mCB6y+p/K4jgt9f4GyytQEeN+1dqQQU4Y5w2/bSkm6Awln5oAmYh4nWK//4ZRIBBrNpj/NfubK8WAtdDL/A6la3oavSK+E4BJgA7/cdExALe36ZaKCGWeQepF9/xOIHVAlZGY8PODYAKcdLU3g8l4DjEyj0O2ARsRoS1HfgEQaxr2M9pT892/ztQe0RAKxzeD/wB0AMvbhNh+jBQ+hIwm7Gd/m0EFwXbDrctNmY3BdQrwPsibUVNpOQfU2rF5pXV+NNkqb0fSsBMRHxBBsd6EcGaiQih/Vy10zGchKYWvolYywPALbAXaAY+sQg4j7EJph38s66QAf+5heCiYQW6Dp9tlGSsJIYKsKLESguBi6EP8eNa6/d0anMD1HOatgHbgR/Ak2+JP/gPAP4aGcgbi1AOAi8gbbeDgyBuESvAvYiLoqOOthYXFWBFiZ0WxGq0vt8S4vutdRCsx//+WGaGDQKbYcuLYv12AJN+ytjFF0Rw7QBgOCSs5O9rj78+BWQoDk0FWFFix0Y9PIKIVAdwFrUJ8CBiyYKIXq3iOQR8TwbeJgOzbgDmMb5T3g7A2VC5IQLRrXVwMGYyNhMjQ94SRckqA8BGxG9qoyKOGfEbQj8yaGdv9Q+j9iiE7fDkBzKyf3Iz8BnG55+1Vu8Uyt0m7nThsU0Zjo0D6nyMgjFmgTGm2xiz1Rjz1SqfWWyMedEYs9kY8y+jbVMt4Ixhi2AqyVJ7P1jrtxuxEtuRiReVfL9uhEEfYvm+7r9vQyznWk/ZW2TyRTvA1WP8biVKwEH+6wECwX2XYJZdwgNwEJsFbIw5EPgO8CmkQ581xjzged6LzmeOBa4FTvc87zfGmI+Otl0V4IyRfPrDIYZbO265mKROwnB7wsujPdRr74chgrCzEwncDlv951YCMbY+Vjf37WH+czsiwrX+jnmwYCMy0PdFxhe/W0Is3/C+3eOgxNgndmSKk4GtnudtAzDG/Ag4F3jR+cz/Ar7jed5vADzPe2O0jaoAZ4xHtz0KJJ0Q3C2WCIHoWgsvicPKtscNrwpXUoiuXbX3g/2vzkRmug0i1u3LiIthGiKSLZRbxbVOkqjGA+P8vksTtU1ZTgn1O1bbjDFu1eEVnuet8F9PRTzqlh5kZo3LDABjzJOIHf53nuc9PNIOVYAzxs1P3AwkLcDuTC0IRCYNlKq8jpax9UMrcATlFmIJ8Qv3Iv9dymeYZYXxuSD6xlkVuQQcC8xFrqxPGGM+5nle/0hfUJQxUGn2U+0VC5KhWhayRlBCjKd2yq3wVv/9WKYXKzURTxTELmC6836av8ylB3jG87x9wCvGmC2IID9bbaMaBaGMkabQw52llZJR8P0MVXndSJqQc9XOGnOxF4aqBpKSHp4FjjXGHGmMmQh8nuF+np8i1i/GmDbEJbFtpI2qBazUgXvYWAsurYdSktYv/n5HGzxL24Urw8Q0FdnzvCFjzJVIMPeBwPc9z9tsjLkR6PI87wF/3f8wxrwIvA/8led5u0fablrPGiUTWOvNipxNpZgE1hKvFvWQ5KFead/1JFlXaiKmiRie560D1oWWfd157SFJlq+qdZsqwBnjrnPuSroJDlZ87SDSeEfux4Mb/uYKsVtRITrG1w+DiLvQCvC0SNqkkLmZcCrAGWNm28wE9+6Kms2M5VpxSQfih/3RrjUc7aFefz8MUT7BYmpUTVIsGRrZUgHOGGu61wCwaOaiBu/Zjf11XQ+20kMawqjcCSEQl/UL4+mHfiQO2N41dETaLiVbqABnjNufuh1IQoAtYfFNUSLuYa6H8Lro2llfP/Qirgc3JWWuZ481HnVBKPnEnXxhfb1WfN2KDpZeYD1BJi+bktHytYjbFx58cy3hNGDb0oK4HdzYYCVS1AWh5A97qNQyYu9XAn7vVtHhA4FDkKTge/2PHP43RHv4VdpWmg5vmytBLd5YyZgFnKFrhZINhigT3wlIPplDD4e2Q+XkeD/RBipKakiTiaDkghuB2+FNRHx/51AkZ8m78M4TYgVPTLJ9Sq7JmAWsApwx7v7s3Uk3YRS6YNdesXIPAcmidR4wCO8/IQnCDxrp+9kg/f1QYDJ0X68CnDGmT54++ocS5Xmxcg8BDr0cKQIJcCn8GrGK224g64de+vuhoKgFrMTJvS/cC8CS2UsSbkk1BoLKNcxCBuTuh76fwz78fFJfTK55EZH+figwKsBKXNzZdSeQ5hN/NnxkA5j/iUyxfR3eulWiHyYDE79BHiYfpL8flCygAqxEzN9D6WUkVvgw4Csyb+NAoO2zwB+jh50SGzFlQ4sLPROUiGlDJhu0I7XQ/NpnUw9AxFczfykxoy4Ipbgch8x46wcehHd+LQNvLEHC0fSQU2JELWCl2LgJe46GSV+CSYPAFf56W/k3zim44SnIYz3Mh6heKshu+33SbWqFc2KUQs9KGtDeyBj3Lb4v6SbUSBvicrjQf+9mUYu7hLlbBcOtkAy1HfL9BDksILhYWGEe4r7F30TKfaUZm6s5jYmTYiTN18UQBeiNfNHWnHYfqnuSWwtyiPJMZXEnoClRLr62DbUe7rZSsS0lZFNugoTV9dDWPIH0+7PDF6ICnO4aB6zEycpNKwFY2rk00XZUx62Q7LojGkm1EkC1Hu6DBDmOXRG29LNy0xrghRT3g/3vLQURYFAfsBIf6RfgcOJ2FyuCcZVhDwtOpeW10EIgvmFrXba7ctNa4JAU90O8FUFSi1rASrGxAlyJJuKziCtt14r9WPdXrcTSAJJQfQ/ZSemmg29pRntFiZhK1YjDy6IWYNftEdU+wq6UfgLfMIiplRUKlvRdLWCluLRQbnW5FTMGSK5KxVgiANy6beHKGk0k9xvqoWDiq3HASrGxpYuqLY/L/2v3EYU42ouFu10rZHH/hihx7z4GqXy650wC1AesxMm6C9cl3YRRGKSy1eXGpMbFSNseSxREGxIFYYXWCrB99LLuwluBM+psZyOoVCG60iClSkCS6L+fMZonNCfdhFFwY0/dZf00xnKsFv0wFuGf5j/bC4Zr0Ysl2TyhBchCX7jPlkpFVHOEuiCUuFj+7HIArjjpilE+mRTVrN+mKuuioNLU5vG4I+xpUcmVIhESy5+9F/hVRvqh2tTsHJ7+6oJQ4mTV5lVAmgW40iHViIGgRvk3ZVbcqs0/B7LSDwU7zVWAFUVREiBjURAZaqqiKEq+UAtYUZR8oS4IRVGUBNBBOCVOHlv6WNJNUNB+SDUZcqxmqKmKoij5Qi3gjGHJ2BsAABt3SURBVHHbhtsAuOa0axJuSbHRfkgpGXNBqAWcMdZuWcvaLWuTbkbh0X5IMQfU+UgAtYAVRckPGbOAVYAVpSJu+sxKNdU+QG8gU4oKsKJkHZuE3eaxcPMcDwFeQu1S8oQKcMaYNGFS0k0oAG5dOzcVZZDEZtKEg8lWVYyCkLGpyCrAGeOhCx9KugkFwIqvFd3hLoiHLnw4kZYpNaAuCEXJMm7yeJuSUk+VTKCDcEqc3PT4TQDccOYNCbckz7i+3n6COnCt+9dpP6SYDLkgMtRUBWD9K+tZ/8r6pJuRc1zXA0g0RB+wHRFk7QclGtQCVpRhNBHUhLOhaNYt0UcQBaGDcKlDXRCKkgfC0Q8DBGFpA0gccIbO9CKRoft6FWBFqYj1+0J5hWFbXFTjgFOJWsBKnExpnpJ0EwqEW9izPAxtSnMrcFACbVLyhApwxli9eHXSTSgY7ikSzIZbvfg+GlNsVBkTagErSl6xPmEl1agPWImLax+9FoBb59+acEuKjfZDSlELWImTp3qeSroJCtoPqSZDApwhY11RFCVfqAWsKEp+0GxoiqIoCZIhF4QKcMaYdui0pJugoP2QWtQCVuLkh5/7YdJNUNB+KCLGmAXAPyI29nc9z/tGlc+dB9wHnOR5XtdI21QBVhQlX8TggjDGHAh8B/gU0AM8a4x5wPO8F0OfOwT4CvBMLdvNkLGuACx7eBnLHl6WdDMKj/ZDSrFxwPU8RuZkYKvneds8z3sP+BFwboXP3QT8PZIwZFQaZAG7FQYsUcwqcrfZMtIHc8Om3k1JN0FB+yHV1G9WthljXJfBCs/zVvivpwI7nXU9wCnul40xHweme573oDHmr2rZYQME2KbvCwswjE+A7XbHux1FUXLD+GbC9XmeN6eu3RpzAPAtYOlYvhezCyIsvm6O1fGK5sDoH1EURYmGXcB05/00f5nlEGA28JgxZjvwSeABY8yIgh6jBWzdDmHxDTJK1Y9rTZenCVQUpeDEY1Y+CxxrjDkSEd7PA1+wKz3P2wO02ffGmMeAaxKMgrDWb9TiC+X+7WKJ74wpM5JugoL2Q2qJKRmP53lDxpgrgUf8PXzf87zNxpgbgS7P8x6oZ7sxC7DdRdTia7dttxv2LeeXFYtWjP4hJXa0H1JKjNnQPM9bB6wLLft6lc/OrWWbMfqAS4g4unW1oiAcSTFEkQRYUZT8EPP9u/XPRhWl4IqtbXpN4Xa54dI1lwJqgSWN9kOKydDshgY4UCvtwg7QjTV21xVgt2hicdiye0vSTVDQfkgtmpDdMpLVW28IWTXBLdZAnKIoI6ACPBLW+o1iFpxtfvEsYUVRKqDZ0KoxAGwCdiOuhyOA1jFuww7qpZkBoJdyK7+Dsf/WJOijsk99CAlxLMZ072Lxj0A70re2/weQeQan++v0DjMuGpgLogd4HuhHxOhA4Jg6tpXmg2EIEd8e4H3gYGd5NHS2d0a2rQDbvj6Ghw/aO5YsXEAaRzz9kAS7kAlcRwCTgfuR43cXMAsR4IyhLogwQ8jJ3Y+c1K3k84R2xfcgxPK1scrRcMeCOyLbVkAJ6SN3YNPGVxcryqRW4umHJPgMIsCDQDfBRdj2e5oNngqoCyKMtX43+7trQ66seRNga/2+SyC+bWTnAHYtXghyeEA0uTuUdHIcctw+gpyjtt+PIJPWL6gFHGCvpN2I9WtP8l3x77rhuCFy1sIvMTxueXxcdP9FQNQVGWzbS85rN9Oc/S2KJZ5+SIKXENfgRuQcteGhxxCkNohi0FypRAPOqq3+w0Yt9CIdfPBIX8ogNu8FBBNQhgjcLtG4IXre6olkO+W4+ZptAiV3XT/RTSXPB/H0QxIsZ7ibqQM4Een7rYhb4g8a26x60ThglyHE2rXWlCtMb8e764bj/pWvE1xwBhFrIq0RBNbi7SfoJ2sNW6woqxWUP6xxMIgcAx2IX9i6H8LHQgZQH7BlK3KLA9LRxwGHAXvIX+xuC3KrPoQMwu0mG3HKbjpP99mKbaVE+kp+uJDABwxwJnKeZvRiqxawZRDY4b+2PqUTEStrd3y7TYwSYj20IL/RWpNZiCIoEVw8IBh0sz5BJb/MQuLz+xGf74mUDx63kbkBcxVgCESoCRGmU5CO7EV8Sm1Vv5ldWgjCtwaQgzpaX+Gp006NdHuCFeAwmmmuGvH0QxLYAbgSMvGig3JZaEUvwvExJgHu3t3N3JVzy5YtnrWYK066gr379nL2PWc7az4A3mZp51ks7TyXvr1DnL/qjxDfbz/QDPwDl8+5nCWzl7Bzz04u/snFw/Z59alXs2jmIrr7urls7WXD1l9/xvXMP2o+m3o3VaxSe8u8Wzht+mls2LmB69ZfN2z9HQvuoLO9k0e3PcrNT9w8bP1d59zFzLaZrOlew+1P3T5s/d2fvZvpk6dz7wv3cmfXnc4aD3iP+xb/LW3NJVZuWsnKTSuHfX/dhetontDM8meXs2rzqmHrH1v6GAC3bbiNtVvW7l8+d+VcJk2YxEMXPgTATY/fxPpX1pd9d0rzFFYvXg3AtY9ey1M9T5Wtn3botP2j+MseXhYqNOkxY8rvsmLR1wC4dM1fsmX3K2Xf72zv3B8Pe9H9Fw0bmDp12qncOv9WAM5bdR6795bf+cw7ch43nHkDAAvvWcg7+94pW3/OjHO45rRr9v/eMNWPPWFp51KWdi6lb28f5686f9j6KI49IIXHnnDf4vtoa24b5dh7kuXPvsiqzdZYuHv/ejn2erhtw3LWbnmu7LvxHnuS8N5mmrt0zaXDkh+5x14ZGgdsOQCJdDgSmdbY5y+bBEwgU/cJdWGQ33kcmbuFA6T9zYhFBPJblPzxBeQu5xfAxArr24EPNbRF4yZD0mI8z6v5w3PmzPG6ukYscaTEzHmrzgPYb10oyaD9kA7mzJlDV1eX2f/+eON13T3SN6pj5rCx3qrI9ZKx+BIlfBuvJIP2gxIFKsCKouSLDLkgVIAVRckPGgesKIqSIBoFocTFvCPnJd0EBe2H1KIWsBInNm5WSRbtByUKVIAVRckPagErcbLwnoUA+2chKcmg/ZBi1AesxEV4uq6SDNoPKUUtYCV7DCJTxaE8Z7PNadyC5O/oJ6gdZysnhJMPPYBk2Po0weHVBPwMnvw87AQ+B0ysfQamouSVDBnrSnxYwbUZ7KzYuoUZB5BE803++2kEde+sCL8ObPe/Y8Xb5pX9NlwHPA5MnBHnj1GKzgF1PhJABVihPB2lm4KyiUBg3QKdrf7yJufz9tEGPEOQyN23ftdvEH3+GMBfxPtzlOJiXRD1PBJAXRAZ45wZ58S4dbeIqH1vD5EWAuvXJuweQFwXfUjpqSGkmu7LwNf99S3AZrgTSa62ECD7MbTx9oMyLtQHrMSFzY8bLeFCnO5yWxPM+n1bnXV9iNha18UQcBnwHYK6eEPAdngTycx55CIyW+7cIZ5+UMaN5gNWsoUtyFmp9psdiGtCKpnsJhDg7UjJKfs9K9Ql4GvONnrhjd+Rj50KcC7pLVCqKI1FBThj2MoQtlLG+AmXorduBrdCsn3uRUbRrKvCfdhoifAh1Q//7r+cD1KaKvuHXfT9oESGuiCU7FBp0M0KsluS3Fq3A877Ied1KzDFX9bjfP/rUnC3Hb+4Rj9SLfuYmH6PUmg0DljJLtbPa/257iBcq//o99+7vuE24DD/+SVgs/+5XbDrx/Bb4Hhg0kcRC7oNFWAlNtQHrGQLa8mG3Q7u4THNf+5z1rc6DyvW6/3PDABd4n74iP8RpjHcz6woxUUFWKHcp+suayKYSNGCWK3TnM9YfzHO9+3suF3Ay1IEeyowE+CLwJlks0ipkgnUBaGMTj/Bbb6d2NA04jcsi2ctjrgtLQSVj12fr6UUel0tgsFawPMIZtV9Gf70Zf99KzIAl48IiOj7ISnsXY89/rb6y0rIcZHBi6W6IJTqWB+rO8239m644qQrYmhTKfQ8HqzAtiLW8uwItpk+4umHpLDH4HYk0uV9ZEA1g/KgFrAyMq74NhEIVj+1WBt79+0FoHlCcwxtU2olP/3Qh8iAnVjzPnAQwRT0jKECrFTH3ppb3FlldtruyF1y9j1nAxp/mjT56YceAt//+4h6tZOH2YpZQAW4oYTF14Z9uZnHFKWRhKNSmsi8+KoPWKmOG+IVnkmmKElyIOXTzzOKqdcH8X6kzagFPesbivt323y5avkqSeK6vWxkTj+1uMPSiaH+dqsA5xwbN2t9weHYW0VpNO2IO2wAiYJ4F4mEaCGbrojxCPC7UTakJlSAG04TgS947Nbv0s6lUTZGqZP89EM75aWj3kaEaKDqN5ToUAFuKDZpjVtJwl03enfk58TPNvnpBzfhvrWEszwmMR4LuPFkp6W5wZ3eGxbg0enbK8Uz25rbIm2VMjby2Q/WGnZLVGWR7MhazC21I6p2yu3YZn3FyxASeO5W/m30wMPY93X+qvOBPMSfZpt89kMT5bk+ksaem1B+rgw468LVtbNlAccYMWdvsd1KC2n6Y2wnumkX09Q+RSk61c7L/AxeN8ACDueVTQNuPgZbbDKD0y4VJdfUohmmwvs0ac3INKClaXToW8vcXhgyHHSuKIVjJOtXBdjBdT2k5U+x4mtxB8UURUk3tQxcZycbT4yq6DrP0+SvcQU4e+J7+ZzLk26CgvZDcoTHbcKoBVxh82maXx4uQhluk21rOn3CS2YvSboJCtoPyWDHbiAvrsOYBdid9ZWWpDNhazw8G822N50CvHPPTgCmT56ecEuKjfZDI7FGkVvEoNr5qRZwaPNWhN0/z5L0FczO+nFJw0WiOhf/5GIgb/Gn2UP7oREMOs+uAWeLwFYiPgE2xiwA/hFxMn/X87xvhNZfBfyJ39A3gS97nrdjpG02IHOmndxgbx8GkGxLSfmFw4Uk3c51XyuKkixuuKgV39EmS1kBrucxwlaNORD4DrAQOB64wBhzfOhj/wHM8TzvBOA+4Juj/cIGCLCb/wCST7/YhJRbsVMt3dsbt0imoijJ4hpDNldFYnfNJwNbPc/b5nnee8CPgHPdD3ie9wvP8/b6b5+mhmmFMd1ru35fu5twAnJbwjwJ7OBbJaFNr/tBUYpFvXk26j6H24wxXc77FZ7nrfBfTwV2Out6kDLf1bgEeGi0HcakNi2Uh3u1UG75utOTk0TFVlHyxbh8wH2e580ZdwuMuQiYA5w52mdjjgN2LVybghFqrQCsDOfqU69OugkK2g/pJbZBuF2AG/IyzV9Wvndj5gNfA870PG/UDO8NNAHdAa40WL/ZZNHMRUk3QUH7Ib3EJsDPAscaY45EhPfzwBfK9mzM7wN3AQs8z3ujlo02uH6orTel1Et3Xzfdfd1JN6PwaD8UC8/zhoArgUeA/wRWeZ632RhzozHmM/7H/gG57f+xMWaTMeaB0bbbYCdoWny/2eWytZcBGn+aNNoPaSW+OGDP89YB60LLvu68nj/WbTZIgCvlBk56EoaiKPkkO4PrDWqpG4Jm32fnT1IUJStkaypyA33A4TSQiqIoxabBLghFUZQ4yZYFnFBL1f9bL9efcX3STVDQfkgvKsBVdtNO+dTCOHfdAzwD/AW8/Bp8GPjQUcjMwX+Jcb/xM/+oMQ+0NphwEVabiAnKM80lMQ3dhkH+DNgBTEam83cAXcDjSHz9eYiREB40Du7k5h/1SdKasrTYqACnZHdPwn+/Bv8MfAz4w23Qth34QYPbES2bejcB0NnemXBLRqOa28kmPEqCQeTCvNlZ9iAiui8BuxExtrjVXNyMXLCp91dAcwb6oWioAKeAErAV/jfQC7yGhE8f+wH8TaINGzfLHl4GpDn+1E3CX42kDruXgCcJhHQQeN1/2OnxI10cbLubWPbwdUCa+0HJAjkV4EEYWAPbgH3AUcBFwAkTye1PThVu3g9rRboJ+ZPqg26CmZgtwEHA+86yyQQCXM2Cd/NJK+lEi3ImTA/8AjnnzwGu+kMkN/LsRFtVHMIiOxhalyQl4Gjg0/7rbsQq7vPXH+Y/hwU43ZVSFIu6IBJmANghmTsnAGcBfAsR3xz+3NQTHshKMgKmFZiFDLy1I217mcAv3YkcJ+H6hSq+2UEFOGH8ChzTkTuRt0FGvJv857OSa1rhcAspQvKH21RgJiK+IO3pRy7aRwCfIHCZuKVqNIeJEg9JnxExUAKOCLwNq4CTr0JGup9GFDm7P/uWebck3YQaCd/Cj1bLqxEcR7kF3oMMzJUQ8Q1XYCg5z/a3iDhnpx+KhlrAKaAdjvwGzP+qFAW5Zhs0b5M70KuSbtv4OG36aUk3oQYqJV5Kw6Hmiu8gEo62m8D94BIW3XKy0Q9FJQ3HWm00OB9wI7CVOP4YrjocLgC2Ay8AhyTZrmjYsHMDG3ZuSLoZo5CFxEv9wFZEiI+mvEKLW6g17H6Q35KNfigi8VRFjos0nhkR0IT4+Z6BL2+HL7+M3G62kvWffN36tMefVhKstP3nQ4j120/gfnBxrV9L+eBh+vuhqKgLIkW0+49PJt2QHGAtRneSRYv/mEYwLTcLeT5KSL3E0/334TZn4TeMRi/BYCNULwem06mTJOcCrERHHyK+7/vvD6rwmSyFa6XRMo+SVqS/BpB+GaD8djsNg6JxoBawkltc8W2jPJwL5CTXatfpoAe5a6lk9VoBbgKOaXC74kYFWMkl9iQ+CDlp20PrB5GTPunJFoowwPACCOHkQnk9/bPzu7LTUgWAOxbckeDeD2R4WlGQk7qPIlW8TrYfaiXsZimPZdbTP3m0BzJGsukPrdshfFL3I4M+7ybRqERIfxrKJuc5PACXZ/FVF4QSI49uexRIIjG7dS24+RHs4E4vsKfB7UmW5PqhVtxp1OGE+E3k11WkAqzEyM1P3AwkceLbcCV3ksIA5a6HyRTlkEquH2rF9k84JtsKbyv5DEFTAVZyiSu81u0wiOTWOBCYgsQD6yGVDiq5HFpCjzz2lQqwkkusn9fihqS1I+Kbx1varDLNeW0tXze5kJIGtCeUGhlCBtnsIXMgchvbRvnJrqQD18It2mmend8bY0vt7ep2gjn3LcgJa4PA1WLKDrOJvqKIO1PL5umwkwdAjpMkJnY8DWz02/Iu5TP8DvZfb6e8gGfaKOq5pS4IZ9O20oA9iY5BTqpB5Ha2I77d55S7zrkr6SbEgPUp29eQnPiCWPQvI8U6hxA3ix20kqKjd51zNvCVhNqnVMegNeH2b7op9LCjrluR6hQd8e0+p8xsm5l0EyKkiWBGljtg1MTwyR6NpBXJkNaCDC7OJoin7QI2M7OtDamuoSj1E7Otbq1dCKyZAaRGvPoN62FN9xoAFs1clHBLoiQcKpV0PokSUj3jmNAykAvDAGu6/xNYk7N+yAPqgggRtmS6kFs7FeB6uP2p24G8CLCbr8BaweGCmEmwnfIUm5ZB5M5tkNuf+iXwZk76IU+oAI9AL1ICHIo7SKAIdgAOAhdVWnJJ9CFi69aQG0Dqx72EDMyZZJqm1IAKcAUGgWeQwP0m4LDG7VpJIa74tjI8c1eSDCF3aSBW8ID//gWkhhzksppXLlALuAovIQfwEFIeXF0Qxca6GextvivISZ9AexBr3D6sq8TGQR8ETEysdUp+aNCR3g88jxzYTVQuAa4UD+v3dXMWpCFTVy/ihrCTTyAIqTwYOXYrVQRRkkct4Aq8hMRVAhyBjC6rD7ge7v7s3Uk3ISLCuSUgPZNzbI4L96IwmaDG4Inc/dkzSEdblXJUgEMMINbvIHLAziL5MKPsMn3y9KSbEBGVYn/tRIekOZHyqslWeA9DYoJLTJ+cXOuUkVABDtECXBb/bhKlj2B0vAtYDkOvwj5/9aSVwB9Hsqd7X7gXgCWzl0SyveS4DrbcDR8BPrQQ+BYieJfAL5+Akz8MLAFuRA7THoKZaVMI8lDEcTGf6z+qk59+yCMqwAXDTh7oQy44R0OpBKVpiM8wXD+tfu7suhPI+ok/BNwD3cjf86EO5H/7Z3j1CWd86zz/uQ/YhdxNHeF/tp0k89nmox+UpFEBjoRWgttVsLOlgumrRyfUrhQz8EGQ0ZIe4J+BW+DX+BPQPoFEy1iXhJ2gsQOZAqyHrlIJdUEUlA6C4P1BRJBtroM8Vh4YJy0z4JAtfvjvDuBx+PleCS5oOQC5iG1G/tcS4nawg3ab/ecOgixqigIqwIWjj8BK246IQhOB2yHpabVppQVOAg49FOiDX70FbyHXLzqQSTsA5xK4HJoQP3A/ElUziJjLbeh/rAgqwAXDWrt9SJxzGyK6NiG2vXVOw+h+WigBn4FDv4gI6pMw+JpkEXwf5D+dBczzP9tGMGOuFXFZ7Pa/W3I+oyjZQgV43NgcBo8TzJLqcNbZgbloBPi+xfdFsp3kuQwRze3Aerl2dQAnXIBEjBxG+WzJFue5xf/eDiRed4BGC3B++iFvqAVcMFoJBtzWAw8CnUgs6TQkPO0UYE4ke2trzoulZ100LfDOL+E9oHMicAsj54m21u4gYgGHK/82hvz0Qx7Jjqxlp6WpxQbqzwMeBO8XYJ6kPE61I7K9rdy0EoClnUsj22ZyDAGPSzgaANdRW8jeoPOAJA7jfPVDnlALuIA0IYNBl4DZDEOvweAbwBt+bpm+yPaUrxO/C7hHvAhHgbglwq6aSjmD+xHVHkRC/BofZZKvfsgTKsAFpQk4C/gWlNZDSw/i2Cyhmd8qYcPJdsiMweOguh/XirDrdrDhfbZWm6JkDxXgSGlFps/q7KjRaQIukcfckUL17KCbotSCFuVUlDGih6ESJdk5nrLTUkVRlFFRH7ASI+suXJd0ExS0H5RoUAHOGM0TmpNugoL2Q3pRC1iJkeXPLgfgipOuSLglxUb7Ia1kS4C1tGvGWLV5Fas2r0q6GYVH+yGtWAGu59F4snOpUBRFGRW1gBVFUXKHMWaBMabbGLPVGPPVCusPMsbc669/xhjTMdo2VYAVRckZ0bsgjDEHAt8BFgLHAxcYY44PfewS4Dee5x0D/B/g70drqQqwoig5IjYf8MnAVs/ztnme9x7wI6RagMu5SG0tgPuAecYYM9JGx+Qs2bhxY58xZsdYvqPEg/nSiP2qNAjth8Q5wn2zcePGR4wx9eYKbTLGdDnvV3iet8J/PRXY6azrQfLMuuz/jOd5Q8aYPUgtrarZuMYkwJ7nfWQsn1cURWkknuctSLoNY0FdEIqiKKOzC5juvJ/mL6v4GWNMCZiM1M6qigqwoijK6DwLHGuMOdIYMxH4PPBA6DMPIPW0AM4H/t3zPG+kjWYnYE5RFCUhfJ/ulcAjSL7L73uet9kYcyPQ5XneA8D3gLuNMVuBXyMiPSJmFIFWFEVRYkJdEIqiKAmhAqwoipIQKsCKoigJoQKsKIqSECrAiqIoCaECrCiKkhAqwIqiKAnx/wG16O0RdeYwcgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(22)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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G1xa4I3XrERXgwKyavsp3CILyEFuBfRJOBTgwlWWVvkMQlIdYC+jIVkChCsCmpk1satrkO4yipzxIIagDDsyDOx4EYEblDM+RFDflIaY0ghAR8Sigv+tVgEUkOQLrgAN6rxARSRZ1wCKSHIF1wCrAgVlz3RrfIQjKQ6wF9He9CnBgKs6vyP4kiZzyEFPqgCVK615aB8DssbM9R1LclIcYUwGWqKxsWAnoF9835UEKQQVYRJIjorOhRUUFWESSRSMIEREPAuuAAwpVRCRZ1AEHZv2s9b5DEJSHWNMIQqJSVlrmOwRBeYgtrQOWKNU21gJQU1XjNY5ipzzEWECDVRXgwOgXPx6Uh5gKrAMO6L1CRCRZ1AGLSLIE1AGrAItIcgS2DlgFWESSI7AZsApwYOrn1PsOQVAepDBUgANT2q/UdwiC8hBrGkFIVFbsXAHAgssWeI6kuCkPMRXYCCKg9woBqNtVR92uOt9hFD3lIcb65nnzQB2wiCRHYKsgAgpVRCRZ1AGLSLIENANWARaR5AjsIJwKcGCernnadwiC8hBrAQ1WAwpVRCRZ1AEHZtn2ZQDcNf4uz5EUN+UhpgIbQagDDszmPZvZvGez7zCKnvIQY33yvHmgDlhEkiOwDlgFWESSJaACrBGEiIgn6oADM6DfAN8hCMpDbAX2UWQV4MBsmbPFdwiC8hBrAY0gVIBFJDl0EE6idN8z9wGwZMISz5EUN+UhxgIaQQQUqgBs27+Nbfu3+Q6j6CkPUgjqgEUkOTSCEBHxKKC/61WARSQ51AFLlIaUDvEdgqA8SGGoAAdmw6wNvkMQlIfYUgcsIuKRZsASlUVbFwHwwOQHPEdS3JSHmFIHLFHa0bzDdwiC8hBrARXggJp1EZFkUQcsIsmhs6GJiHgU0AhCBTgw5eeV+w5BUB5iSx2wROnR6x/1HYKgPBQjY8wU4Ae4HvtvrbXfPsPzZgLrgcustQ3d7VMFWESSJYIRhDGmL/AQcDXQDOw0xmy01u7u9LxzgW8Az/dkvwE16wKw8MmFLHxyoe8wip7yEFPpdcD53Lp3ObDXWrvPWnsUeAy49jTPuw/4DtDWk3DVAQem8WCj7xAE5SHW8m8ry4wxmSOD1dba1amvhwEHMrY1A1dkvtgY83mgwlr7hDHmmz35hirAIpIcZ/dJuBZrbXVe39aYPsD3gJpcXqcRhIhIdm8AFRn3y1OPpZ0LjAWeNsa8BlwJbDTGdFvQ1QGLSLJE01buBEYZY0bgCu+NwJfTG6217wFl6fvGmKeBu7QKImFGDxntOwRBeYitiE7GY61tN8bcDjyV+g4PW2t3GWPuBRqstRvz2a8KcGBWz1id/UkSOeUhpiI8G5q1th6o7/TYPWd47sSe7FMzYBERT9QBB2bepnmAOjDflIcYC6itVAEOzJ7De3yHICgPsaUTsouIeKQCLCLiQWBnQwsoVBGRZFEHHJiqoVW+QxCUh1jTCEKisnzKct8hCMpDbAU2glABFpFkCagDDui9QgDmPj6XuY/P9R1G0VMepBDUAQem+f1m3yEIykNsaR2wiIhHAf1drwIsIsmhDlhExCMVYInKuPJxvkMQlAcpjJwKcNPhJibWTuzw2Kwxs1hw2QJaj7Uybe20Lq+pqaqhpqqGltYWbqi7ocv2+dXzmT12NgfeO8DNP7m5y/Y7x93JjMoZNLU0cdvm27psX3zVYiZfNJnGg42nvUrt0klLGV8xnu0HtnP3tru7bF8+ZTlVQ6vYum8r9z97f5ftq6avorKskk1Nm3hwx4Ndtq+5bg0V51ew7qV1rGxY2WX7+lnrKSsto7axltrG2i7b6+fUU9qvlBU7V1C3q67L9qdrngZg2fZlbN6z+eTjE2snMqDfALbM2QLAfc/cx7b92zq8dkjpEDbM2gDAoq2L2NG8o8P28vPKefT6RwF3ld/OF5ocPWT0ybN9zds0r8sJaKqGVp1cDzv38bldDkyNKx/HA5MfAGBm3UwOtx7usH3SiEksmbAEgKlrp/LRsY86bJ8+ejp3jb/r5H9vZ3H42QOK7mcPiNXPXgdaBywi4lFAIwhjre3xk6urq21DQ7eXOJKIzaybCXCyuxA/lId4qK6upqGhwZy8f6mxDWvy25ep5oV8r4qcL3XAgen8Z7z4oTxIIagAi0iyBDSCUAEWkeTQOmAREY+0CkKiMmnEJN8hCMpDbKkDliil182KX8qDFIIKsIgkhzpgidLUtVMBTn4KSfxQHmJMM2CJSueP64ofykNMBdYBB/RekQQHgWHwAwOLDLxggE+CNfCcAX7mOT4R6U3qgHtVCdAOj8Orz8LQb8PAPzkEnwWeB774luf4RBIgoLZSBbjXtKduc+Ci73PwWXgKGPg3cCEwsQ/wyBGvEYoEL7ARhApwr2oHPgfz4Ys/g757YTvwNvDWCZjNwax7mD56etRBSg8oDzGmAixdlQCDgXK4/HJY9AuuXA9X9gOG4yoxQ7PuJX1+XPFLeYgpnQ9YzmwQ8AVgKdyyFm55AmiBV0/AVICRXqMTkd6lAtzrBgMTgIHAbOCgq7sjnwDKs746fWWI9NUKxA/lIcY0gpDsqoFmYAhwMe5QXJnXiESCp4Nwkl0b0B/Ylvr3LeAZ4DO4MYWI5E0zYOneEeCV1L/NwHHcQuA2n0GJSC9TAe416XXAJbjC+1zq3+PAXlwhbvcWnUgiaAQhp1fCqf+7y3EH4NKO4Lrf4Vn3MmvMrEIHJnlQHnrDX+B+Z/4M9zvSAtwDh9bAucCAGcCHXV+mEYR0rz89WfN7OgsuW1DYUCQvykNv+BD409TXR4C/Ap6C3bjFRBdvAi7q+BJ1wBKl1mOtAJT2K/UcSXFTHnrDLbi/Ft8FVgFPAEdch/shqUMmxzq+JLACHFCzLgDT1k5j2tppvsMoespDb7gYV2WfB+pwXfAg+EPcH5HneAytQFSARSSmGoFJcHQKtOzGFeBr4Z9Tm8+7DujX9WV98rx5oBGEiMTU38PL2+HXqbtTBgFPwDvp7e2ctgCbfGcQx/N8Xf5UgEUkph5xKzQBPglQArvedDPeIeCOxHVuXQ35lzUVYBER58j7UIGrsUOA3x5yB9/OBUaBO6dKY6cXnU0B/l2er8ufCnBgaqpqfIcgKA+9YtCn4OI33cG2c/q4swYeI3X1gj8DZgIrvYZ4tlSAA6Nf/HhQHnrDahjUglvyAIx8AUbuBa7Bdb+DT/Oas+mAe184kQoALa0tAJSV6sxpPikPveFLGV+3AWNwR+TOp/uTVoVT1sKJVAC4oe4GIMbnof2NgSbc7G7kOcAcXMdyITABao+6UyH/8TGy//jtBSrh9RNuf+bTqX19CbgC1xm14M6r8ZUo/mvOKPZ5SJz+uLMFDqfjx/o7C6sD1jpgKaxW4OPABQB3AktxRfM5+NFR2IErpj3SDJtOuGVHH4D7+Paf4grwUE79IupipsWhBNf59iekItudZPxXSHyM/AS0HIJ/9wlgEu4k88/Ar+5y1727ELiylJ796B13K4M+Dpw3GvgOMPY0z+tfmNglAcLqgMOJVALxGSg7AvwP3IGSI8AC+Dvc+s3rwH2stIcGAsNKcecFqOq0cRDuPAE6ib2kqQBLUXum0/2H4K/3uPWb1wGXfBVXmHvoY+DGDcM5/VHvd4GDecQpyRXO2XhUgAMzv3q+7xBy8Aq0L4Z/wc19rwGYR8871r6pefEt6Rd30gI8nPq3d4WVh2KiDlgiNHvs7OxPioU2YCn8ELd4/g+Bkj+n6xghi6HgTl6f2f22464g8kN4+ftwyeizDTZn4eRB4kyrIAJz4L0DHHjvgO8weuAZ+MUa+FdcEb3687hCmssBs+OwD2A18BqnCu8TwDzY/314E3zMgMPJQ7FJd8D53HqfOuDA3PyTm4G4rz9tARbAPwAngPkAD+HWceboNaDfd2H0I6nXN8P7+9w1Td8BPgturNG7wshDMYpuBGGMmQL8ADdk/ltr7bc7bb8D+BquU3gbuMVa++suO8qgAiwF1g48AbX74C2gGhi5DLgyv919gDsfd8Mh6HPIPXYMd0KWPwDKvk3HT0xJcYumABtj+uK6iKtxf4btNMZstNbuznjaL4Fqa22rMWY+7hpK3c6qNIKQAjsCLTWuaJ4P3NEH92m4fAxxH3hLXz6vDdd7fBb4z9dB2XrcCVm0DE0idzmw11q7z1p7FHgMuDbzCdban1prW1N3f467nlK3EtwBt6Vu4OaOWqzfOwZD2cuwsgX34zWcfC9ACmNhxH4YcQTXWZPaZ/qipv0zHhNJy/vnocwY05Bxf7W1dnXq62FA5tC/GdcenMmtwJZs3zChP7ntuOLbjs8Be/HKY9Z7WukCLtJTZzWCaLHWVp91BMbMxQ3fsi54L4LKlC7A7alb2J3wnePu9B2CoDzEV2QH4d6g41lMylOPdfzuxkzGnax4grU26xneE1qA08UW3H9i53FEuGZUzvAdgqA8xFdkBXgnMMoYMwJXeG8EvtzhOxvz+8AqYIq19lBPdprQg3CZxTdzHBG+ppYmmlqafIdR9JSH4mKtbQduB54CXgbqrLW7jDH3GmP+U+pp38UdEf6xMabRGLMx234T2gFnShfh9Knswnbb5tsArT/1TXmIq+jWAVtr64H6To/dk/H15Fz3WQQFGEIfO4hILsIpa+FEmpPMA29tnCrAbSShCxaRMwnrZDwJnQFnJiBdhDMPxImI+BfOW0VO0ov1OxdcjSJEki2sDjicSHOWzE+/Lb5qse8QBOUhvlSAJUKTL8r5QKtEQHmIKxVgiVDjwUYAqobmeGJzKSjlIa5UgCVCC59cCGj9qW/KgxSCCrCIJIwuyiki4oFGECIinoRVgBP6QQwRkfgL561CAFg6aanvEATlIb7C6oDDiVQAGF8x3ncIgvIQb+GUtXAiFQC2H9gOqAD4pjzElTpgidDd2+4GtP7UN+UhrsIqwDoIJyLiSThvFSIiWYXVAYcTqYhIVirAIiIehVPWwolUAFg+ZbnvEATlQQpDBTgwOv1hPCgPcaURhERo676tgE4I7pvyEFcqwBKh+5+9H9Avvm/KQ1ypAIuIeBJWAdYHMUREPAnnrUJEpEfCKWvqgAuqBfgZsBa4BxgJ+w1wh9eoRIpHegSRz633hfNWEXvtwF5gIxz6Lvwz8B5QCowo3HdZNX1V4XYmeVMe4sqga8IVpXbgcdjzXWgCfgecm7oxqGDfpbKssmD7kvwpD1IIKsAF0w40QitwAVCB634HAnyyYN9lU9MmAGZUzijYPiV3ykNchbUKIpxIY68EmARVLwJHgNuAK1K38oJ9lwd3PAjoF9835SGuVICLTBuu+x0EfAUYjPu/dQ7Q32NcIsUqnLIWTqSx9C7wSurrcmAopwpv+v/axtT9z/R6dCLFRx1wEXkJ+AauC/4CMBOYhOuI24FngLuBL6MCLCKdqQCflSZ44V/gTaByN4x+ATfzbQda4P0t8Dxw9QSvUYoUD3XARWSQW+nwLtAA/Ntu6L8bjuFubal/C2jNdWsKu0PJi/IQVyrARaQSLvljqPgx/BR4BzgOnAN8DLcGeDzAlwr2HSvOryjYviR/ykNcqQAXkSrgRzDoRzCj/QzPKezHHNe9tA6A2WNnF2yfkjvlIc7CKWvhRBpbJZ3+jdbKhpWAfvF9Ux6kEFSARSRBNIIQEfFEBVhExJOwCrDOBywi4kk4bxUCwPpZ632HICgP8RVWBxxOpAJAWWmZ7xAE5SHewilr4UQqANQ21gJQU1XjNY5ipzzElTpgiZB+8eNBeYirsAqwDsKJiHgSzltFUXgN2AAcxl1h+Yi7PbwJ9uFuo4ARV3mLUCTewroopzrgWBmOO6H7MNxJ3N+FX2xyp7t8DXgD+ADAeopPJAS6LL3kbSjuenKNwBMwcAt8CvgQd3a1weDe5UWkq7BmwOFEWlSOABcD34Axg2DM3fDGURhWSv2xamCL5/ikfk697xAkAVSAY+kgbgTRBgwCexSGXQB8ndJ+A3FngRefSvspB/EUVgesGXAsDcYVYYAjcABgAvBHrNj5r6zYucJbZOKs2LlCeYildAEOYwasAhxL/XFD31eB59wBOD4HDKdu1y+p21XnMTYBqNtVpzzEUlgFOJxevagMBoYA2+xHUhMAAAGjSURBVOCNn7hVEPRP3fSeKXJmGkFIQQwHBsM/4db/ci/wX4A9HmMSKV7GmCnGmCZjzF5jzLdOs/1jxph1qe3PG2OGZ9unCnBs9QeucaPfBmBuK3x1C7x8FDjhNzSRWCv8CMIY0xd4CJgKXArcZIy5tNPTbgV+a629GPg+8J1skaoAx9pgGHET/AfcFZf3UvDL3IskS2Qz4MuBvdbafdbao8BjwLWdnnMt8Ejq6/XAJGNMt4v2jbU9/1SVMeZt4Nc9foGISLQutNZ+PH3HGPMkkO+5QtNrP9NWW2tXp/Z7AzDFWvu11P2bgSustbdnfO+XUs9pTt1/NfWcljN9w5ym1Zn/oSIicWOtneI7hlxoBCEikt0bQEXG/fLUY6d9jjGmBDgfd2atM1IBFhHJbicwyhgzwhhzDnAjsLHTczYCX0l9fQPw/2yWGW84C+ZERDyx1rYbY24HnsKd7/Jha+0uY8y9QIO1diPwd8AaY8xe3GHzG7PtN6eDcCIiUjgaQYiIeKICLCLiiQqwiIgnKsAiIp6oAIuIeKICLCLiiQqwiIgn/x+VoNfFiY4zowAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(25)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GPg+ct8hj0hxVdSPw+JzF5wFXdNNXAG9Y0EFNwJDjWtL2hVA4Fnho1vzWbtlSUMD1Sb6VZN1iD2YeHFVVj3TT32em6fBScXGSb3eXF/vcZdGz2RdCYSk7rapexsyl0TuT/N5iD2i+1Mxv30vl9++PAScAa4BHgA8v7nAma18IhW3Aqlnzx3XL9nlVta173Q5sZOZSaSl5NMkxAN3r9kUez0RU1aNVtauqdgOfZIl9bvtCKNwCrE7y4iQrgPOBqxd5TGNLcnCSQ/dMA2cCdz77u/Y5VwMXdtMXAl9exLFMzJ6g67yRJfa5TX2HqKrameRi4DpgGbChqu5a5GFNwlHAxiQw8zl8rqquXdwhjS7JlcDpwJFJtgKXAh8C/iPJRcz8KfybF2+EoxlyXKcnWcPM5dADwNsXbYDzwNucJTX2hcsHSQvIUJDUMBQkNQwFSQ1DQVLDUJDUMBQkNf4fWaswdA2jtrIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(18)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(22)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iz4jl4_2jso-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "outputId": "5fe83b99-d1f5-410b-d445-0eeb6ba1c02a"
      },
      "source": [
        "plot_locally_connected_weights_meh3(net.connections[('input','main1')].w,net.connections[(net.final_connection_source_name,'output')].w,\\\n",
        "                                    0,5,net.neuron_per_class,net.n_channels1,net.in_channels,0,net.crop_size,net.filter_size1,net.layers['main1'].shape[1]) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7efeec714f90>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vkz7skWaiWj7"
      },
      "source": [
        "## Tensorboard"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nAKGbShj7kOM"
      },
      "source": [
        "# Load the TensorBoard notebook extension\n",
        "%load_ext tensorboard"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EXv13w317oW7"
      },
      "source": [
        "%tensorboard --logdir '/content/runs'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IGe49P4-q9zp"
      },
      "source": [
        "## Save/Load Sessions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Ow_r8C5qzwu"
      },
      "source": [
        "Save tensorBoard Session "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XdHDClhIqUlW"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "!cp -a /content/runs/. /content/drive/MyDrive/LCNet/logs/"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6NOgZCamq4p-"
      },
      "source": [
        "Read Saved Sessions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DlItthjkq4S5"
      },
      "source": [
        "%tensorboard --logdir /content/drive/MyDrive/LCNet/logs/"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QZtdOQm4oAu1"
      },
      "source": [
        "## Optuna"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hcLDxcrwithj"
      },
      "source": [
        "install and import optuna"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CGik9_MeQfaI"
      },
      "source": [
        "!pip install optuna\n",
        "import optuna"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bsM-Y9CZiv15"
      },
      "source": [
        "Define objective function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yj_n_rwWDrdV"
      },
      "source": [
        "STUDY_NAME  = ''\n",
        "DATA_PATH = ''\n",
        "N_TRIALS = ''\n",
        "\n",
        "def objective(trial):\n",
        "    ### Suggest parameters: \n",
        "    num_layers = trial.suggest_int('Number of Layers', 1, 4)\n",
        "    dropout_rate  = trial.suggest_float('Dropout', 0, .99)\n",
        "    activation = trial.suggest_categorical('activation', ['relu', 'selu', 'sigmoid', 'elu'])\n",
        "    lr = trial.suggest_float('Learning rate', 1e-6, 1)\n",
        "    network_hparams.update({\n",
        "        \n",
        "    })\n",
        "    ###Define your model\n",
        "    manual_seed(SEED)\n",
        "    hparams = {**reward_hparams, **network_hparams, **train_hparams, **data_hparams}\n",
        "    net = LCNet(**hparams, reward_fn = DynamicDopamineInjection)\n",
        "    va_acc = net.fit(dataloader = dataloader, val_loader = val_loader, reward_hparams = reward_hparams, **train_hparams)\n",
        "    ### Define objective value\n",
        "    objective_value = min(va_acc)\n",
        "\n",
        "    \n",
        "    return objective_value\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ywzhxB84jthL"
      },
      "source": [
        "Run the study"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IDFttXEkjv4f"
      },
      "source": [
        "study = optuna.create_study(study_name = STUDY_NAME , storage=f\"sqlite:////content/drive/MyDrive/LCNet/optuna/optuna_study.db\", load_if_exists=True)\n",
        "study.optimize(objective, n_trials=N_TRIALS )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Azp25HveO77U",
        "outputId": "5fdaa0eb-5888-4acd-acdb-d059bfcd5b87"
      },
      "source": [
        "study.best_params"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Dropout': 0.09142336347778651,\n",
              " 'Layer 1': 82,\n",
              " 'Layer 2': 508,\n",
              " 'Layer 3': 286,\n",
              " 'Learning rate': 0.008771184760927113,\n",
              " 'Number of Layers': 3,\n",
              " 'activation': 'relu',\n",
              " 'l1': None}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "epsdhrYujy4f"
      },
      "source": [
        "Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lYgaOQZBaPJk"
      },
      "source": [
        "plot_parallel_coordinate(study, params=[\"Learning rate\", \"Number of Layers\", \"Dropout\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ya3ZmsSUqtd3"
      },
      "source": [
        "## Izhikevich 2007 - Task 2 | Pavlovian Conditioning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jw9OfurBUytc"
      },
      "source": [
        "train_hparams = {\n",
        "    'n_train' : 4000,\n",
        "    'n_test' : 500,\n",
        "    'n_val' : 50,\n",
        "    'val_interval' : 250,\n",
        "    'running_window_length': 250,\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dK13AFE7Uytc"
      },
      "source": [
        "# Dataset Hyperparameters\n",
        "target_classes = (8,9)\n",
        "if target_classes:\n",
        "    #npz_file = np.load(f'bindsnet/mask_{\"_\".join([str(i) for i in target_classes])}.npz')\n",
        "    npz_file = np.load('bindsnet/mask_5.npz') ##### KESAFAT KARI !!!\n",
        "    mask, mask_test = torch.from_numpy(npz_file['arr_0']), torch.from_numpy(npz_file['arr_1'])\n",
        "    n_classes = len(target_classes)\n",
        "    \n",
        "else:\n",
        "    mask = None\n",
        "    mask_test = None\n",
        "    n_classes = 10\n",
        "\n",
        "data_hparams = { \n",
        "    'intensity': 128,\n",
        "    'crop_size': 22,\n",
        "    'round_input': False,\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rqj7BASfUytd"
      },
      "source": [
        "n_neurons = 2 #100\n",
        "neuron_per_class = int(n_neurons/n_classes)\n",
        "single_output_layer = True\n",
        "\n",
        "network_hparams = {\n",
        "    # net structure\n",
        "    'crop_size': 22,\n",
        "    'neuron_per_class': neuron_per_class,\n",
        "    'deep': False,\n",
        "    'maxPool1': False,\n",
        "    'maxPool2': False,\n",
        "    'in_channels':1,\n",
        "    'n_channels1': 25,\n",
        "    'n_channels2': 64,\n",
        "    'filter_size1': 13,\n",
        "    'filter_size2': 5,\n",
        "    'stride1': 3,\n",
        "    'stride2': 1,\n",
        "    'n_neurons' : n_neurons,\n",
        "    'n_classes': n_classes,\n",
        "    'single_output_layer': single_output_layer,\n",
        "    \n",
        "    # time & Phase\n",
        "    'dt' : 1,\n",
        "    'pre_observation': False,\n",
        "    'has_decision_period': True,\n",
        "    'observation_period': 0,\n",
        "    'decision_period': 256,\n",
        "    'online': False,\n",
        "    'local_rewarding': False,\n",
        "     \n",
        "    # Nodes\n",
        "    'NodesType_LC': AdaptiveLIFNodes,\n",
        "    'NodesType_Output': LIFNodes, \n",
        "    'theta_plus': 0.05,\n",
        "    'tc_theta_decay': 1e6,\n",
        "    'tc_trace':20,\n",
        "    'trace_additive' : False,\n",
        "    \n",
        "    # Learning\n",
        "    'update_rule_LC': None,\n",
        "    'update_rule_LC2': None,\n",
        "    'update_rule_Output': MSTDP,\n",
        "    'update_rule_inh': None,\n",
        "    'update_rule_inh_LC' : None,\n",
        "    'nu_LC': (0.00,0.0),\n",
        "    'nu_LC2': (0.0,0.0),\n",
        "    'nu_Output':0.01,\n",
        "    'nu_inh': 0.0,\n",
        "    'nu_inh_LC': 0.0,\n",
        "    'soft_bound': False,\n",
        "\n",
        "    # weights\n",
        "    'normal_init': False,\n",
        "    'mu' : 0.8,\n",
        "    'std' : 0.05,\n",
        "    'wmin': 0.0,\n",
        "    'wmax': 1.0,\n",
        "    \n",
        "    # Inhibition\n",
        "    'inh_type': 'between_layers',\n",
        "    'inh_factor': 1,\n",
        "    'inh_LC': True,\n",
        "    'inh_factor_LC': 100,\n",
        "    'inh_LC2': False,\n",
        "    'inh_factor_LC2': 0,\n",
        "    \n",
        "    # Normalization\n",
        "    'norm_factor_LC': 0.25*13*13,\n",
        "    'norm_factor_LC2': None,\n",
        "    'norm_factor_out': None,\n",
        "    'norm_factor_inh': None,\n",
        "    'norm_factor_inh_LC': None,\n",
        "    \n",
        "    # clamp\n",
        "    'clamp_intensity': None,#1000,\n",
        "\n",
        "    # Save\n",
        "    'save_path': None,#'/content/drive/My Drive/LCNet/LCNet_phase2_baseline2_gpu.pth',\n",
        "    'load_path': None,#'/content/drive/My Drive/LCNet/LCNet_phase2_baseline2_gpu.pth',\n",
        "    'LC_weights_path': '/content/drive/My Drive/LCNet/BioLCNet_layer1_Shallow_f13_s3_inh100_norm3.pth',#'/content/drive/My Drive/LCNet/LCNet_ch81_f13_22_2norm_Adapt_fc_test2.pth',\n",
        "    'LC2_weights_path': None,#'/content/drive/My Drive/LCNet/DeepLCNet_layer2_ch64_f5_s2_norm3.pth',\n",
        "\n",
        "    # Plot:\n",
        "    'confusion_matrix' : False,\n",
        "    'lc_weights_vis': False,\n",
        "    'out_weights_vis': True,\n",
        "    'lc_convergence_vis': False,\n",
        "    'out_convergence_vis': True,\n",
        "\n",
        "}\n",
        "\n",
        "\n",
        "\n",
        "reward_hparams= {\n",
        "    'n_labels': n_classes,\n",
        "    'neuron_per_class': neuron_per_class,\n",
        "    \n",
        "    'variant': 'scalar',  #true_pred, #pure_per_spike (Just in phase I, online : True) , and #scalar #per_spike\n",
        "    'tc_reward':0,\n",
        "    'dopamine_base': 0.0,\n",
        "    'reward_base': 2.5,\n",
        "    'punishment_base': 2.5,\n",
        "    \n",
        "\n",
        "    'sub_variant': 'static', #static, #RPE, #pred_decay\n",
        "    'td_nu': 0.0001,  #RPE\n",
        "    'ema_window': 10, #RPE\n",
        "    'tc_dps': 20,     #pred_decay\n",
        "    'dps_factor': 20, #pred_decay, #RPE\n",
        "    }\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T2iFc_v_Uytd"
      },
      "source": [
        "time = 356 #320\n",
        "network_hparams.update(\n",
        "    {\n",
        "     'time': time,\n",
        "    }\n",
        ")\n",
        "dataloader, val_loader = load_datasets(network_hparams, data_hparams, mask, mask_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vGsvklk7U7_r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b3cebe3b-f481-4d7c-eea0-06a2a6a21f46"
      },
      "source": [
        "from google.colab import drive\n",
        "if network_hparams['save_path'] or network_hparams['LC_weights_path']:    \n",
        "    drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vaBPo8BJU7_s"
      },
      "source": [
        "manual_seed(SEED)\n",
        "hparams = {**reward_hparams, **network_hparams, **train_hparams, **data_hparams}\n",
        "net = LCNet(**hparams, reward_fn = DynamicDopamineInjection)\n",
        "net.fit(dataloader = dataloader, label = torch.tensor([1]),val_loader = val_loader, reward_hparams = reward_hparams, **train_hparams)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qy7-iAETvuPH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439,
          "referenced_widgets": [
            "661194ee80564effb4e50a9317318c7a",
            "92194849783e4c0b960c23ca86006a10",
            "98fc35d9ecca410f8b515b11c7260b43",
            "fdda91ea19b3465a9ccf453cc2a36c15",
            "7a08fbda807c40c79b41433ccc759803",
            "672be7dc1f1041ecbe4d8e6d2e6554e3",
            "ea4a9292abf5464ca25e407a262b7854",
            "db144e915cb14c418d82b2f9107cef01",
            "b69ab972c8a842f9aacebba80d5554b7",
            "6bf1b8ee9ecc4330a35777de7a491df5",
            "9042967f47494e5a92663c30648fcb74"
          ]
        },
        "outputId": "42a308c1-e219-4b2f-fae6-b02090072a5b"
      },
      "source": [
        "net.fit(dataloader = dataloader, val_loader = val_loader, reward_hparams = reward_hparams, hparams = hparams, **train_hparams)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Begin training.\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "661194ee80564effb4e50a9317318c7a",
              "version_minor": 0,
              "version_major": 2
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "output tensor([878,  95]) pred_label: 0 GT: 0 , Acc Rew: 220.4388 Pos dps: 1.00000, Neg dps: 1.00000, Rew base: 0.88175, Pun base: 1.11825, RPe: 135.512 input_mean_fire_freq: 34.3,main_mean_fire_freq:105.3 output_mean_fire_freq:38.4 mean_lc_w: 0.97072, mean_fc_w:0.23497 std_lc_w: 0.10048,std_fc_w:0.32059"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-42-22a2e2b2dc29>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mdataloader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_loader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_datasets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnetwork_hparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_hparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataloader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataloader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_loader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mval_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreward_hparams\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreward_hparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhparams\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mtrain_hparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-8-3ee30e0b7704>\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, dataloader, val_loader, reward_hparams, hparams, online_validate, n_train, n_test, n_val, val_interval, running_window_length, verbose)\u001b[0m\n\u001b[1;32m    358\u001b[0m             \u001b[0mmain_voltage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmain_monitor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"v\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    359\u001b[0m             \u001b[0mreward_history\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreward_monitor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 360\u001b[0;31m             \u001b[0mtensorboard\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mi\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    361\u001b[0m             \u001b[0;31m# Add to spikes recording.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    362\u001b[0m             \u001b[0;31m#if self.single_output_layer:\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/bindsnet/network/monitors.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, step)\u001b[0m\n\u001b[1;32m    518\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    519\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_scalers\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--> 520\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_grids\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    521\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    522\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/bindsnet/network/monitors.py\u001b[0m in \u001b[0;36m_add_grids\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    495\u001b[0m                         \u001b[0mgrid\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    496\u001b[0m                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 497\u001b[0;31m                         \u001b[0mdataformats\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;34m'HW'\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    498\u001b[0m                         )\n\u001b[1;32m    499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/tensorboard/writer.py\u001b[0m in \u001b[0;36madd_image\u001b[0;34m(self, tag, img_tensor, global_step, walltime, dataformats)\u001b[0m\n\u001b[1;32m    547\u001b[0m             \u001b[0mimg_tensor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mworkspace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFetchBlob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    548\u001b[0m         self._get_file_writer().add_summary(\n\u001b[0;32m--> 549\u001b[0;31m             image(tag, img_tensor, dataformats=dataformats), global_step, walltime)\n\u001b[0m\u001b[1;32m    550\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    551\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0madd_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtag\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_tensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mglobal_step\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwalltime\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataformats\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'NCHW'\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/torch/utils/tensorboard/summary.py\u001b[0m in \u001b[0;36mimage\u001b[0;34m(tag, tensor, rescale, dataformats)\u001b[0m\n\u001b[1;32m    392\u001b[0m     \u001b[0;31m# Do not assume that user passes in values in [0, 255], use data type to detect\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    393\u001b[0m     \u001b[0mscale_factor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_calc_scale_factor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 394\u001b[0;31m     \u001b[0mtensor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\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    395\u001b[0m     \u001b[0mtensor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mscale_factor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muint8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    396\u001b[0m     \u001b[0mimage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrescale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrescale\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KrbB8W8g7JbQ"
      },
      "source": [
        "for i in range(20):\n",
        "  net.one_step()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lkmj9tDazbm3"
      },
      "source": [
        "## Plotting the feature maps"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zSdD8hPAzm5P",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "98c2cf40-d558-43e5-94f3-c370d603f37f"
      },
      "source": [
        "n_filts = (int((network_hparams['crop_size']-network_hparams['filter_size'])/network_hparams['stride'])+1)\n",
        "n_filts"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 444
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kYAtejQAzoku",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52685884-8bad-4b58-b492-333f1f708a19"
      },
      "source": [
        "\n",
        "\n",
        "print(net.connections[('input', 'main')].w.shape)\n",
        "\n",
        "in_chans = 2\n",
        "n_chans = 100\n",
        "k = 12\n",
        "\n",
        "reshaped_w = net.connections[('input', 'main')].w.view(in_chans, n_chans, n_filts, n_filts, k, k)\n",
        "\n",
        "print(reshaped_w.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([2, 900, 144])\n",
            "torch.Size([2, 100, 3, 3, 12, 12])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ut7wA1Emzq4J"
      },
      "source": [
        "chan_idx = 20 # 0 to N_channels \n",
        "fig, axs = plt.subplots(n_filts, n_filts)\n",
        "for i in range(n_filts):\n",
        "    for j in range(n_filts):\n",
        "        axs[i][j].imshow(reshaped_w[0,chan_idx,i,j].view(k, k).cpu(), cmap='Greys')\n",
        "        axs[i][j].axis('off')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}