Custom_Spiking_MNIST.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyP5234OyP2GwKVONSPkEg3Q",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/atlaie/Case-Studies/blob/main/Custom_Spiking_MNIST.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yUSum8jH_PNs"
      },
      "outputs": [],
      "source": [
        "!pip install nengo-dl[docs]\n",
        "\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "import nengo\n",
        "from nengo.utils.ensemble import sorted_neurons\n",
        "from nengo.utils.matplotlib import rasterplot\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import nengo_dl\n",
        "from nengo_dl.graph_optimizer import noop_planner\n",
        "\n",
        "# In this example, a population of LIF neurons is recurrently connected.\n",
        "# The connection strength (weight) for any two neurons is given by a Laplacian distribution. \n",
        "# Moreover, with a fixed probability, some neurons are set to be inhibitory.\n",
        "\n",
        "rng = np.random.RandomState(seed=100)\n",
        "inhibitory_pre = 0.2  # 20% inhibitory connections\n",
        "sparsity_pre = 0.3  # 80% probability of being connected\n",
        "# inhibitory_post = 0.1  # 20% inhibitory connections\n",
        "# sparsity_post = 0.6  # 80% probability of being connected\n",
        "n_neurons = 100\n",
        "maxRate = 100\n",
        "tau = 0.01  # synaptic time constant on recurrent connection\n",
        "t_int = 0.02  # length of time for input presentation\n",
        "\n",
        "with nengo.Network() as model:\n",
        "    nengo_dl.configure_settings(planner=noop_planner)\n",
        "    # set some default parameters for the neurons that will make\n",
        "    # the training progress more smoothly\n",
        "    model.config[nengo.Ensemble].max_rates = nengo.dists.Choice([maxRate])\n",
        "    model.config[nengo.Ensemble].intercepts = nengo.dists.Choice([0])\n",
        "    # model.config[nengo.Ensemble].gain = nengo.dists.Choice([1])\n",
        "    model.config[nengo.Connection].synapse = None\n",
        "\n",
        "    # this is an optimization to improve the training speed,\n",
        "    # since we won't require stateful behaviour in this example\n",
        "    nengo_dl.configure_settings(stateful=False)\n",
        "\n",
        "    # the input node that will be used to feed in input images\n",
        "    inp = nengo.Node(np.zeros(28 * 28))\n",
        "\n",
        "    # Neural ensemble.\n",
        "    ensA = nengo.Ensemble(n_neurons, 1, neuron_type = nengo.LIF(amplitude=1/maxRate))\n",
        "\n",
        "    nengo.Connection(inp, ensA.neurons, transform = nengo_dl.dists.Glorot())\n",
        "\n",
        "    # Specify connectivity and make the recurrence.\n",
        "    pre_post = np.random.laplace(size = (n_neurons, n_neurons))\n",
        "    pre_post[rng.rand(n_neurons, n_neurons) <= inhibitory_pre] *= -1\n",
        "    pre_post[rng.rand(n_neurons, n_neurons) <= sparsity_pre] *= 0\n",
        "\n",
        "    recurr = nengo.Connection(ensA.neurons, ensA.neurons, transform=pre_post, synapse=tau)\n",
        "\n",
        "    # dense linear readout\n",
        "    out = nengo.Node(size_in=10)\n",
        "    nengo.Connection(ensA, out, transform=nengo_dl.dists.Glorot(), synapse=None)\n",
        "\n",
        "    # we'll create two different output probes, one with a filter\n",
        "    # (for when we're simulating the network over time and\n",
        "    # accumulating spikes), and one without (for when we're\n",
        "    # training the network using a rate-based approximation)\n",
        "    out_p = nengo.Probe(out, label=\"out_p\")\n",
        "    out_p_filt = nengo.Probe(out, synapse=0.1, label=\"out_p_filt\")\n",
        "    ens_probe = nengo.Probe(ensA.neurons)\n",
        "\n",
        "\n",
        "minibatch_size = 256\n",
        "sim = nengo_dl.Simulator(model, minibatch_size=minibatch_size)\n",
        "weights_pre = sim.data[recurr].weights\n",
        "# weights_post = sim.data[conn_post].weights\n",
        "fig, ax = plt.subplots(figsize=(10,7))\n",
        "weights = weights_pre.ravel()\n",
        "sns.histplot(data = weights[weights!=0], stat = 'probability', kde = False, lw = 2)\n",
        "plt.xlabel('Weight')\n",
        "sns.despine()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 724
        },
        "id": "buccJRU7_Z7z",
        "outputId": "d1e2d67d-f747-4199-9a59-51f15dfb6daf"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "|#####################Building network (50%)                     | ETA: 0:00:00"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/nengo_dl/simulator.py:456: UserWarning: No GPU support detected. See https://www.nengo.ai/nengo-dl/installation.html#installing-tensorflow for instructions on setting up TensorFlow with GPU support.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Build finished in 0:00:00                                                      \n",
            "Optimization finished in 0:00:00                                               \n",
            "Construction finished in 0:00:01                                               \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from urllib.request import urlretrieve\n",
        "import tensorflow as tf\n",
        "(train_images, train_labels), (\n",
        "    test_images,\n",
        "    test_labels,\n",
        ") = tf.keras.datasets.mnist.load_data()\n",
        "\n",
        "# flatten images\n",
        "train_images = train_images.reshape((train_images.shape[0], -1))\n",
        "test_images = test_images.reshape((test_images.shape[0], -1))\n",
        "\n",
        "# add single timestep to training data\n",
        "train_images = train_images[:, None, :]\n",
        "train_labels = train_labels[:, None, None]\n",
        "\n",
        "# when testing our network with spiking neurons we will need to run it\n",
        "# over time, so we repeat the input/target data for a number of\n",
        "# timesteps.\n",
        "n_steps = 10\n",
        "test_images = np.tile(test_images[:, None, :], (1, n_steps, 1))\n",
        "test_labels = np.tile(test_labels[:, None, None], (1, n_steps, 1))\n",
        "\n",
        "def classification_accuracy(y_true, y_pred):\n",
        "    return tf.metrics.sparse_categorical_accuracy(y_true[:, -1], y_pred[:, -1])\n",
        "\n",
        "\n",
        "# note that we use `out_p_filt` when testing (to reduce the spike noise)\n",
        "sim.compile(loss={out_p_filt: classification_accuracy})\n",
        "print(\"Accuracy before training:\",\n",
        "    sim.evaluate(test_images, {out_p_filt: test_labels}, verbose=0)[\"loss\"],)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xAtZuK6g_b_2",
        "outputId": "9c72c4e0-968a-4134-aecd-9e09d1c0a45f"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 0s 0us/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/nengo_dl/simulator.py:1892: UserWarning: Number of elements in input data (10000) is not evenly divisible by Simulator.minibatch_size (256); input data will be truncated.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy before training: 0.10196314007043839\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# run training\n",
        "sim.compile(\n",
        "    optimizer=tf.optimizers.Adam(),\n",
        "    loss={out_p: tf.losses.SparseCategoricalCrossentropy(from_logits=True)},\n",
        ")\n",
        "sim.fit(train_images, {out_p: train_labels}, epochs=10)\n",
        "\n",
        "sim.compile(loss={out_p_filt: classification_accuracy})\n",
        "print(\"Accuracy after training:\",\n",
        "    sim.evaluate(test_images, {out_p_filt: test_labels}, verbose=0)[\"loss\"],)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fGoQV33U_dvo",
        "outputId": "20f3eda3-b543-4d85-85ba-f87cef5fc2e4"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/nengo_dl/simulator.py:1892: UserWarning: Number of elements in input data (60000) is not evenly divisible by Simulator.minibatch_size (256); input data will be truncated.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/nengo_dl/simulator.py:1003: UserWarning: Running for one timestep, but the network contains synaptic filters (which will introduce at least a one-timestep delay); did you mean to set synapse=None?\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "234/234 [==============================] - 13s 20ms/step - loss: 0.5298 - out_p_loss: 0.5298\n",
            "Epoch 2/10\n",
            "234/234 [==============================] - 5s 22ms/step - loss: 0.2569 - out_p_loss: 0.2569\n",
            "Epoch 3/10\n",
            "234/234 [==============================] - 6s 25ms/step - loss: 0.2190 - out_p_loss: 0.2190\n",
            "Epoch 4/10\n",
            "234/234 [==============================] - 5s 21ms/step - loss: 0.1962 - out_p_loss: 0.1962\n",
            "Epoch 5/10\n",
            "234/234 [==============================] - 6s 24ms/step - loss: 0.1844 - out_p_loss: 0.1844\n",
            "Epoch 6/10\n",
            "234/234 [==============================] - 6s 24ms/step - loss: 0.1759 - out_p_loss: 0.1759\n",
            "Epoch 7/10\n",
            "234/234 [==============================] - 5s 20ms/step - loss: 0.1632 - out_p_loss: 0.1632\n",
            "Epoch 8/10\n",
            "234/234 [==============================] - 4s 19ms/step - loss: 0.1525 - out_p_loss: 0.1525\n",
            "Epoch 9/10\n",
            "234/234 [==============================] - 5s 23ms/step - loss: 0.1441 - out_p_loss: 0.1441\n",
            "Epoch 10/10\n",
            "234/234 [==============================] - 5s 20ms/step - loss: 0.1443 - out_p_loss: 0.1443\n",
            "Accuracy after training: 0.9494190812110901\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = sim.predict(test_images[:minibatch_size])\n",
        "fig, ax = plt.subplots(nrows = 2, ncols = 3, figsize=(12,6))\n",
        "\n",
        "for ii in range(3):\n",
        "    axi = ax[0, ii]\n",
        "    axi.imshow(test_images[ii, 0].reshape((28, 28)), cmap=\"gray\")\n",
        "    axi.axis(\"off\")\n",
        "\n",
        "    axi = ax[1, ii]\n",
        "    axi.plot(tf.nn.softmax(data[out_p_filt][ii]), marker = 'o', lw = 1)\n",
        "    axi.set_ylim([0, 0.35])\n",
        "    if ii == 0:\n",
        "        axi.set_xlabel(\"Timesteps\")\n",
        "        axi.set_ylabel(\"Probability\")\n",
        "        axi.set_yticks([0, 0.3])\n",
        "        axi.set_xticks(np.arange(0, 11, 5))\n",
        "        axi.set_xticklabels(['1','5','10'])\n",
        "        axi.legend([str(i) for i in range(10)], ncol = 5, handlelength = 0, labelcolor = 'linecolor', loc = 'upper left')\n",
        "\n",
        "    else:\n",
        "        axi.set_yticks([])\n",
        "        axi.set_xticks([])\n",
        "sns.despine()\n",
        "plt.tight_layout()\n",
        "sim.close()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "DogK1wjn_dyf",
        "outputId": "fec5af1c-3bc5-4369-e586-fdb16a4baad3"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}