testing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from copy import copy\n",
    "from six import iteritems\n",
    "from matplotlib import pyplot as plt\n",
    "from pygenn import genn_model, genn_wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load MNIST data (pre-normalized and coverted to numpy format)\n",
    "testing_images = np.load(\"testing_images.npy\")\n",
    "testing_labels = np.load(\"testing_labels.npy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 784)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fe534d93828>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(testing_images.shape)\n",
    "assert np.allclose(np.sum(testing_images, axis=1), 1.0)\n",
    "\n",
    "fig, axis = plt.subplots()\n",
    "axis.imshow(np.reshape(testing_images[0], (28, 28)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "DT = 0.1\n",
    "INPUT_SCALE = 80.0\n",
    "NUM_PN = 784\n",
    "NUM_MBON = 10\n",
    "NUM_KC = 20000\n",
    "PRESENT_TIME_MS = 20.0\n",
    "\n",
    "# Standard LIF neurons parameters\n",
    "LIF_PARAMS = {\n",
    "    \"C\": 0.2,\n",
    "    \"TauM\": 20.0,\n",
    "    \"Vrest\": -60.0,\n",
    "    \"Vreset\": -60.0,\n",
    "    \"Vthresh\": -50.0,\n",
    "    \"Ioffset\": 0.0,\n",
    "    \"TauRefrac\": 2.0}\n",
    "\n",
    "# We only want PNs to spike once\n",
    "PN_PARAMS = copy(LIF_PARAMS)\n",
    "PN_PARAMS[\"TauRefrac\"] = 100.0\n",
    "\n",
    "# We will use weights of 1.0 for KC->GGN connections and  \n",
    "# want the GGN to inhibit the KCs after 200 spikes\n",
    "GGN_PARAMS = {\n",
    "    \"Vthresh\": 200.0}\n",
    "\n",
    "PN_KC_WEIGHT = 0.2\n",
    "PN_KC_TAU_SYN = 3.0\n",
    "PN_KC_FAN_IN = 20\n",
    "\n",
    "KC_MBON_TAU_SYN = 3.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Current source model, allowing current to be injected into neuron from variable\n",
    "cs_model = genn_model.create_custom_current_source_class(\n",
    "    \"cs_model\",\n",
    "    var_name_types=[(\"magnitude\", \"scalar\")],\n",
    "    injection_code=\"$(injectCurrent, $(magnitude));\")\n",
    "\n",
    "# Minimal integrate and fire neuron model\n",
    "if_model = genn_model.create_custom_neuron_class(\n",
    "    \"IF\",\n",
    "    param_names=[\"Vthresh\"],\n",
    "    var_name_types=[(\"V\", \"scalar\")],\n",
    "    sim_code=\n",
    "    \"\"\"\n",
    "    $(V)+= $(Isyn);\n",
    "    \"\"\",\n",
    "    threshold_condition_code=\n",
    "    \"\"\"\n",
    "    $(V) >= $(Vthresh)\n",
    "    \"\"\",\n",
    "    reset_code=\n",
    "    \"\"\"\n",
    "    $(V)= 0.0;\n",
    "    \"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model definition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create model\n",
    "model = genn_model.GeNNModel(\"float\", \"mnist_mb_testing\")\n",
    "model.dT = DT\n",
    "model._model.set_seed(1337)\n",
    "\n",
    "# Create neuron populations\n",
    "lif_init = {\"V\": PN_PARAMS[\"Vreset\"], \"RefracTime\": 0.0}\n",
    "if_init = {\"V\": 0.0}\n",
    "pn = model.add_neuron_population(\"pn\", NUM_PN, \"LIF\", PN_PARAMS, lif_init)\n",
    "kc = model.add_neuron_population(\"kc\", NUM_KC, \"LIF\", LIF_PARAMS, lif_init)\n",
    "ggn = model.add_neuron_population(\"ggn\", 1, if_model, GGN_PARAMS, if_init)\n",
    "mbon = model.add_neuron_population(\"mbon\", NUM_MBON, \"LIF\", LIF_PARAMS, lif_init)\n",
    "\n",
    "# Turn on spike recording\n",
    "pn.spike_recording_enabled = True\n",
    "kc.spike_recording_enabled = True\n",
    "mbon.spike_recording_enabled = True\n",
    "\n",
    "# Create current sources to deliver input and supervision to network\n",
    "pn_input = model.add_current_source(\"pn_input\", cs_model, pn , {}, {\"magnitude\": 0.0})\n",
    "mbon_input = model.add_current_source(\"mbon_input\", cs_model, mbon , {}, {\"magnitude\": 0.0})\n",
    "\n",
    "# Create synapse populations\n",
    "pn_kc = model.add_synapse_population(\"pn_kc\", \"SPARSE_GLOBALG\", genn_wrapper.NO_DELAY,\n",
    "                                     pn, kc,\n",
    "                                     \"StaticPulse\", {}, {\"g\": PN_KC_WEIGHT}, {}, {},\n",
    "                                     \"ExpCurr\", {\"tau\": PN_KC_TAU_SYN}, {},\n",
    "                                     genn_model.init_connectivity(\"FixedNumberPreWithReplacement\", {\"colLength\": PN_KC_FAN_IN}))\n",
    "\n",
    "kc_ggn = model.add_synapse_population(\"kc_ggn\", \"DENSE_GLOBALG\", genn_wrapper.NO_DELAY, \n",
    "                                      kc, ggn, \n",
    "                                      \"StaticPulse\", {}, {\"g\": 1.0}, {}, {}, \n",
    "                                      \"DeltaCurr\", {}, {})\n",
    "\n",
    "ggn_kc = model.add_synapse_population(\"ggn_kc\", \"DENSE_GLOBALG\", genn_wrapper.NO_DELAY, \n",
    "                                      ggn, kc,\n",
    "                                      \"StaticPulse\", {}, {\"g\": -5.0}, {}, {}, \n",
    "                                      \"ExpCurr\", {\"tau\": 5.0}, {})\n",
    "\n",
    "kc_mbon = model.add_synapse_population(\"kc_mbon\", \"DENSE_INDIVIDUALG\", genn_wrapper.NO_DELAY,\n",
    "                                       kc, mbon,\n",
    "                                       \"StaticPulse\", {}, {\"g\": np.load(\"kc_mbon.npy\")}, {}, {},\n",
    "                                       \"ExpCurr\", {\"tau\": KC_MBON_TAU_SYN}, {})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert present time into timesteps\n",
    "present_timesteps = int(round(PRESENT_TIME_MS / DT))\n",
    "\n",
    "# Build model and load it\n",
    "model.build()\n",
    "model.load(num_recording_timesteps=present_timesteps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def reset_neuron(pop, var_init):\n",
    "    # Reset variables\n",
    "    for var_name, var_val in iteritems(var_init):\n",
    "        pop.vars[var_name].view[:] = var_val\n",
    "    \n",
    "    # Push the state to GPU\n",
    "    pop.push_state_to_device()\n",
    "\n",
    "def reset_in_syn(pop):\n",
    "    pop.in_syn[:] = 0.0\n",
    "    pop.push_in_syn_to_device()\n",
    "\n",
    "for s in range(4):\n",
    "    # Set training image\n",
    "    pn_input.vars[\"magnitude\"].view[:] = testing_images[s] * INPUT_SCALE\n",
    "    pn_input.push_var_to_device(\"magnitude\")\n",
    "    \n",
    "    # Simulate present timesteps\n",
    "    for i in range(present_timesteps):\n",
    "        model.step_time()\n",
    "    \n",
    "    # Reset neuron state for next stimuli\n",
    "    reset_neuron(pn, lif_init)\n",
    "    reset_neuron(kc, lif_init)\n",
    "    reset_neuron(ggn, if_init)\n",
    "    reset_neuron(mbon, lif_init)\n",
    "\n",
    "    # Reset synapse state\n",
    "    reset_in_syn(pn_kc)\n",
    "    reset_in_syn(ggn_kc)\n",
    "    \n",
    "    # Download spikes from GPU\n",
    "    model.pull_recording_buffers_from_device();\n",
    "    \n",
    "    # Plot PN, KC and MBON spikes\n",
    "    fig, axes = plt.subplots(3, sharex=True)\n",
    "    pn_spike_times, pn_spike_ids = pn.spike_recording_data\n",
    "    kc_spike_times, kc_spike_ids = kc.spike_recording_data\n",
    "    mbon_spike_times, mbon_spike_ids = mbon.spike_recording_data\n",
    "    \n",
    "    \n",
    "    axes[0].scatter(pn_spike_times, pn_spike_ids, s=1)\n",
    "    axes[0].set_ylabel(\"PN\")\n",
    "    axes[1].scatter(kc_spike_times, kc_spike_ids, s=1)\n",
    "    axes[1].set_ylabel(\"KC\")\n",
    "    axes[2].scatter(mbon_spike_times, mbon_spike_ids, s=2)\n",
    "    axes[2].axhline(testing_labels[s], linestyle=\"--\", color=\"green\", alpha=0.3)\n",
    "    axes[2].set_ylim((-0.5, 10.5))\n",
    "    \n",
    "    if len(mbon_spike_times) > 0:\n",
    "        classification = mbon_spike_ids[np.argmin(mbon_spike_times)]\n",
    "        axes[2].axhline(classification, linestyle=\"--\", color=\"red\", alpha=0.3)\n",
    "    axes[2].set_ylabel(\"MBON\")\n",
    "    \n",
    "    axes[2].set_xlabel(\"Time [ms]\")\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0/10000\n",
      "1000/10000\n",
      "2000/10000\n",
      "3000/10000\n",
      "4000/10000\n",
      "5000/10000\n",
      "6000/10000\n",
      "7000/10000\n",
      "8000/10000\n",
      "9000/10000\n",
      "7571/10000 correct (75.710000 %)\n"
     ]
    }
   ],
   "source": [
    "num_correct = 0\n",
    "for s in range(testing_images.shape[0]):\n",
    "    # Show progress\n",
    "    if (s % 1000) == 0:\n",
    "        print(\"%u/%u\" % (s, testing_images.shape[0]))\n",
    "        \n",
    "    # Set training image\n",
    "    pn_input.vars[\"magnitude\"].view[:] = testing_images[s] * INPUT_SCALE\n",
    "    pn_input.push_var_to_device(\"magnitude\")\n",
    "    \n",
    "    # Simulate present timesteps\n",
    "    for i in range(present_timesteps):\n",
    "        model.step_time()\n",
    "    \n",
    "    # Reset neuron state for next stimuli\n",
    "    reset_neuron(pn, lif_init)\n",
    "    reset_neuron(kc, lif_init)\n",
    "    reset_neuron(ggn, if_init)\n",
    "    reset_neuron(mbon, lif_init)\n",
    "\n",
    "    # Reset synapse state\n",
    "    reset_in_syn(pn_kc)\n",
    "    reset_in_syn(ggn_kc)\n",
    "    reset_in_syn(kc_mbon)\n",
    "    \n",
    "    # Download spikes from GPU\n",
    "    model.pull_recording_buffers_from_device();\n",
    "    \n",
    "    # Determine the classification and count correct \n",
    "    mbon_spike_times, mbon_spike_ids = mbon.spike_recording_data\n",
    "    if len(mbon_spike_times) > 0:\n",
    "        if mbon_spike_ids[np.argmin(mbon_spike_times)] == testing_labels[s]:\n",
    "            num_correct += 1\n",
    "\n",
    "print(\"%u/%u correct (%f %%)\" % (num_correct, testing_images.shape[0], (num_correct * 100.0) / testing_images.shape[0]))"
   ]
  }
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