From dc39d219792b275a53632e3c14f33c9f2bcbb98b Mon Sep 17 00:00:00 2001
From: Kade Heckel <51248168+kmheckel@users.noreply.github.com>
Date: Wed, 24 Jan 2024 10:52:54 +0000
Subject: [PATCH] Delete research/benchmarking/SHD_comparison.ipynb

---
 research/benchmarking/SHD_comparison.ipynb | 521 ---------------------
 1 file changed, 521 deletions(-)
 delete mode 100644 research/benchmarking/SHD_comparison.ipynb

diff --git a/research/benchmarking/SHD_comparison.ipynb b/research/benchmarking/SHD_comparison.ipynb
deleted file mode 100644
index 57eab91..0000000
--- a/research/benchmarking/SHD_comparison.ipynb
+++ /dev/null
@@ -1,521 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "6793a986",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.environ[\"XLA_PYTHON_CLIENT_MEM_FRACTION\"] = \".40\"\n",
-    "\n",
-    "import numpy as np\n",
-    "\n",
-    "import jax\n",
-    "import jax.numpy as jnp\n",
-    "import spyx\n",
-    "import haiku as hk\n",
-    "import optax\n",
-    "from jax_tqdm import scan_tqdm\n",
-    "\n",
-    "import torch\n",
-    "import snntorch"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "da30d5e6",
-   "metadata": {},
-   "source": [
-    "## SHD Dataloading"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "ab70fd4b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "shd_dl = spyx.data.SHD_loader(256,128,128)\n",
-    "\n",
-    "key = jax.random.PRNGKey(0)\n",
-    "x, y = shd_dl.train_epoch(key)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "89d88f02",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"mps\") if torch.backends.mps.is_available() else torch.device(\"cpu\")\n",
-    "j2t_data = lambda data: torch.from_numpy(np.array(jnp.unpackbits(data, axis=1))).to(device)\n",
-    "j2t_targets = lambda tgt: torch.from_numpy(np.array(tgt)).to(device)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1f415eb8",
-   "metadata": {},
-   "source": [
-    "## Spyx SHD"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "ddc797e3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def shd_snn(x):\n",
-    "        \n",
-    "    core = hk.DeepRNN([\n",
-    "        hk.Linear(64, with_bias=False),\n",
-    "        spyx.nn.LIF((64,), activation=spyx.axn.Axon(spyx.axn.triangular())),\n",
-    "        hk.Linear(64, with_bias=False),\n",
-    "        spyx.nn.LIF((64,), activation=spyx.axn.Axon(spyx.axn.triangular())),\n",
-    "        hk.Linear(20, with_bias=False),\n",
-    "        spyx.nn.LI((20,))\n",
-    "    ])\n",
-    "    \n",
-    "    # static unroll for maximum performance\n",
-    "    spikes, V = hk.dynamic_unroll(core, x, core.initial_state(x.shape[0]), time_major=False, unroll=32)\n",
-    "    \n",
-    "    return spikes, V"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "77b65a1d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "jaxlib.xla_extension.ArrayImpl"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "344ed98f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "key = jax.random.PRNGKey(0)\n",
-    "# Since there's nothing stochastic about the network, we can avoid using an RNG as a param!\n",
-    "SNN = hk.without_apply_rng(hk.transform(shd_snn))\n",
-    "params = SNN.init(rng=key, x=jnp.float16(x[0]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "9f7422a0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def gd(SNN, params, dl, epochs=300):\n",
-    "        \n",
-    "    opt = optax.adam(learning_rate=5e-4)\n",
-    "    \n",
-    "    # create and initialize the optimizer\n",
-    "    opt_state = opt.init(params)\n",
-    "    grad_params = params\n",
-    "        \n",
-    "    # define and compile our eval function that computes the loss for our SNN\n",
-    "    @jax.jit\n",
-    "    def net_eval(weights, events, targets):\n",
-    "        readout = SNN.apply(weights, events)\n",
-    "        traces, V_f = readout\n",
-    "        return spyx.fn.integral_crossentropy(traces, targets)\n",
-    "        \n",
-    "    # Use JAX to create a function that calculates the loss and the gradient!\n",
-    "    surrogate_grad = jax.value_and_grad(net_eval) \n",
-    "        \n",
-    "    rng = jax.random.PRNGKey(0)        \n",
-    "    \n",
-    "    # compile the meat of our training loop for speed\n",
-    "    @jax.jit\n",
-    "    def train_step(state, data):\n",
-    "        grad_params, opt_state = state\n",
-    "        events, targets = data # fix this\n",
-    "        events = jnp.unpackbits(events, axis=1) # decompress temporal axis\n",
-    "        # compute loss and gradient                    # need better augment rng\n",
-    "        loss, grads = surrogate_grad(grad_params, events, targets)\n",
-    "        # generate updates based on the gradients and optimizer\n",
-    "        updates, opt_state = opt.update(grads, opt_state, grad_params)\n",
-    "        # return the updated parameters\n",
-    "        new_state = [optax.apply_updates(grad_params, updates), opt_state]\n",
-    "        return new_state, loss\n",
-    "    \n",
-    "    # For validation epochs, do the same as before but compute the\n",
-    "    # accuracy, predictions and losses (no gradients needed)\n",
-    "    @jax.jit\n",
-    "    def eval_step(grad_params, data):\n",
-    "        events, targets = data # fix\n",
-    "        events = jnp.unpackbits(events, axis=1)\n",
-    "        readout = SNN.apply(grad_params, events)\n",
-    "        traces, V_f = readout\n",
-    "        acc, pred = spyx.fn.integral_accuracy(traces, targets)\n",
-    "        loss = spyx.fn.integral_crossentropy(traces, targets)\n",
-    "        return grad_params, jnp.array([acc, loss])\n",
-    "        \n",
-    "    \n",
-    "    val_data = dl.val_epoch()\n",
-    "    \n",
-    "    # Here's the start of our training loop!\n",
-    "    @scan_tqdm(epochs)\n",
-    "    def epoch(epoch_state, epoch_num):\n",
-    "        curr_params, curr_opt_state = epoch_state\n",
-    "        \n",
-    "        shuffle_rng = jax.random.fold_in(rng, epoch_num)\n",
-    "        train_data = dl.train_epoch(shuffle_rng)\n",
-    "        \n",
-    "        # train epoch\n",
-    "        end_state, train_loss = jax.lax.scan(\n",
-    "            train_step,# func\n",
-    "            [curr_params, curr_opt_state],# init\n",
-    "            train_data,# xs\n",
-    "            train_data.obs.shape[0]# len\n",
-    "        )\n",
-    "        \n",
-    "        new_params, _ = end_state\n",
-    "            \n",
-    "        # val epoch\n",
-    "        _, val_metrics = jax.lax.scan(\n",
-    "            eval_step,# func\n",
-    "            new_params,# init\n",
-    "            val_data,# xs\n",
-    "            val_data.obs.shape[0]# len\n",
-    "        )\n",
-    "\n",
-    "        \n",
-    "        return end_state, jnp.concatenate([jnp.expand_dims(jnp.mean(train_loss),0), jnp.mean(val_metrics, axis=0)])\n",
-    "    # end epoch\n",
-    "    \n",
-    "    # epoch loop\n",
-    "    final_state, metrics = jax.lax.scan(\n",
-    "        epoch,\n",
-    "        [grad_params, opt_state], # metric arrays\n",
-    "        jnp.arange(epochs), # \n",
-    "        epochs # len of loop\n",
-    "    )\n",
-    "    \n",
-    "    final_params, _ = final_state\n",
-    "    \n",
-    "                \n",
-    "    # return our final, optimized network.       \n",
-    "    return final_params, metrics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "5a5cb1c4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def test_gd(SNN, params, dl):\n",
-    "\n",
-    "    @jax.jit\n",
-    "    def test_step(params, data):\n",
-    "        events, targets = data\n",
-    "        events = jnp.unpackbits(events, axis=1)\n",
-    "        readout = SNN.apply(params, events)\n",
-    "        traces, V_f = readout\n",
-    "        acc, pred = spyx.fn.integral_accuracy(traces, targets)\n",
-    "        loss = spyx.fn.integral_crossentropy(traces, targets)\n",
-    "        return params, [acc, loss, pred, targets]\n",
-    "    \n",
-    "    test_data = dl.test_epoch()\n",
-    "    \n",
-    "    _, test_metrics = jax.lax.scan(\n",
-    "            test_step,# func\n",
-    "            params,# init\n",
-    "            test_data,# xs\n",
-    "            test_data.obs.shape[0]# len\n",
-    "    )\n",
-    "    \n",
-    "    acc = jnp.mean(test_metrics[0])\n",
-    "    loss = jnp.mean(test_metrics[1])\n",
-    "    preds = jnp.array(test_metrics[2]).flatten()\n",
-    "    tgts = jnp.array(test_metrics[3]).flatten()\n",
-    "    return acc, loss, preds, tgts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "dd08a737",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4bb910e2154b443c9b38409fef6d79e6",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/300 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "grad_params, metrics = gd(SNN, params, shd_dl, epochs=300)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "b7a3a0dd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Performance: train_loss=1.735216736793518, val_acc=0.8795573115348816, val_loss=1.7838948965072632\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Performance: train_loss={}, val_acc={}, val_loss={}\".format(*metrics[-1]))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "8553879b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy: 0.7519531 Loss: 1.9914175\n"
-     ]
-    }
-   ],
-   "source": [
-    "acc, loss, preds, tgts = test_gd(SNN, grad_params, shd_dl)\n",
-    "print(\"Accuracy:\", acc, \"Loss:\", loss)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e1befb9f",
-   "metadata": {},
-   "source": [
-    "## snnTorch SHD"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "33059d64",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "num_hidden = 64\n",
-    "beta = 0.8\n",
-    "# Define Network\n",
-    "class Net(torch.nn.Module):\n",
-    "    def __init__(self):\n",
-    "        super().__init__()\n",
-    "\n",
-    "        # Initialize layers\n",
-    "        self.fc1 = torch.nn.Linear(128, num_hidden)\n",
-    "        self.lif1 = snntorch.Leaky(beta=beta)\n",
-    "        self.fc2 = torch.nn.Linear(num_hidden, num_hidden)\n",
-    "        self.lif2 = snntorch.Leaky(beta=beta)\n",
-    "        self.fc3 = torch.nn.Linear(num_hidden, 20)\n",
-    "        self.lif3 = snntorch.Leaky(beta=beta, threshold=10e6)\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "\n",
-    "        # Initialize hidden states at t=0\n",
-    "        mem1 = self.lif1.init_leaky()\n",
-    "        mem2 = self.lif2.init_leaky()\n",
-    "        mem3 = self.lif3.init_leaky()\n",
-    "        \n",
-    "        # Record the final layer\n",
-    "        V = []\n",
-    "\n",
-    "        for step in range(num_steps):\n",
-    "            cur1 = self.fc1(x)\n",
-    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
-    "            cur2 = self.fc2(spk1)\n",
-    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
-    "            cur3 = self.fc2(spk2)\n",
-    "            spk3, mem3 = self.lif2(cur3, mem3)\n",
-    "            \n",
-    "            V.append(mem3)\n",
-    "\n",
-    "        return torch.stack(V, dim=0)\n",
-    "        \n",
-    "# Load the network onto CUDA if available\n",
-    "net = Net().to(device)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "30b450cf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loss = torch.nn.CrossEntropyLoss()\n",
-    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "09b7f675",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "OutOfMemoryError",
-     "evalue": "CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 5.80 GiB total capacity; 2.31 GiB already allocated; 367.69 MiB free; 2.90 GiB allowed; 2.89 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mOutOfMemoryError\u001b[0m                          Traceback (most recent call last)",
-      "\u001b[0;32m/tmp/ipykernel_21888/2264808485.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     24\u001b[0m         \u001b[0;31m# forward pass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m         \u001b[0mnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\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---> 26\u001b[0;31m         \u001b[0mspk_rec\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmem_rec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\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     27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m         \u001b[0;31m# initialize the loss & sum over time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.local/lib/python3.10/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1502\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\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[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/tmp/ipykernel_21888/1276384039.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     30\u001b[0m             \u001b[0mspk2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmem2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlif2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcur2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmem2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m             \u001b[0mcur3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfc2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspk2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m             \u001b[0mspk3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmem3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlif2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcur3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmem3\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     33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m             \u001b[0mV\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmem3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.local/lib/python3.10/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1502\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\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[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.local/lib/python3.10/site-packages/snntorch/_neurons/leaky.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_, mem)\u001b[0m\n\u001b[1;32m    178\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minit_hidden\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmem_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmem\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    181\u001b[0m             \u001b[0mmem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_build_state_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.local/lib/python3.10/site-packages/snntorch/_neurons/neurons.py\u001b[0m in \u001b[0;36mmem_reset\u001b[0;34m(self, mem)\u001b[0m\n\u001b[1;32m    105\u001b[0m         Returns reset.\"\"\"\n\u001b[1;32m    106\u001b[0m         \u001b[0mmem_shift\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmem\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthreshold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 107\u001b[0;31m         \u001b[0mreset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspike_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmem_shift\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\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    108\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    109\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mreset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mOutOfMemoryError\u001b[0m: CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 5.80 GiB total capacity; 2.31 GiB already allocated; 367.69 MiB free; 2.90 GiB allowed; 2.89 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF"
-     ]
-    }
-   ],
-   "source": [
-    "num_epochs = 10\n",
-    "loss_hist = []\n",
-    "test_loss_hist = []\n",
-    "counter = 0\n",
-    "batch_size=256\n",
-    "num_steps=128\n",
-    "\n",
-    "rng = jax.random.PRNGKey(0)\n",
-    "\n",
-    "# Outer training loop\n",
-    "for epoch in range(num_epochs):\n",
-    "    iter_counter = 0\n",
-    "    \n",
-    "    shuffle_rng = jax.random.fold_in(rng, iter_counter)\n",
-    "    train_batch = shd_dl.train_epoch(shuffle_rng)\n",
-    "    train_data, targets = train_batch\n",
-    "    \n",
-    "    \n",
-    "    # Minibatch training loop\n",
-    "    for data, targets in zip(train_data, targets):\n",
-    "\n",
-    "        data = j2t_data(data).to(dtype=torch.float32)\n",
-    "        targets = j2t_targets(targets)\n",
-    "        # forward pass\n",
-    "        net.train()\n",
-    "        spk_rec, mem_rec = net(data)\n",
-    "\n",
-    "        # initialize the loss & sum over time\n",
-    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
-    "        for step in range(num_steps):\n",
-    "            loss_val += loss(mem_rec[step], targets)\n",
-    "\n",
-    "        # Gradient calculation + weight update\n",
-    "        optimizer.zero_grad()\n",
-    "        loss_val.backward()\n",
-    "        optimizer.step()\n",
-    "\n",
-    "        # Store loss history for future plotting\n",
-    "        loss_hist.append(loss_val.item())\n",
-    "\n",
-    "        ## Test set\n",
-    "        #with torch.no_grad():\n",
-    "        #    net.eval()\n",
-    "        #    test_data, test_targets = next(iter(test_loader))\n",
-    "        #    test_data = test_data.to(device)\n",
-    "        #    test_targets = test_targets.to(device)\n",
-    "\n",
-    "        #    # Test set forward pass\n",
-    "        #    test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
-    "\n",
-    "        #    # Test set loss\n",
-    "        #    test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
-    "        #    for step in range(num_steps):\n",
-    "        #        test_loss += loss(test_mem[step], test_targets)\n",
-    "        #    test_loss_hist.append(test_loss.item())\n",
-    "\n",
-    "        #    # Print train/test loss/accuracy\n",
-    "        #    if counter % 50 == 0:\n",
-    "        #        train_printer(\n",
-    "        #            data, targets, epoch,\n",
-    "        #            counter, iter_counter,\n",
-    "        #            loss_hist, test_loss_hist,\n",
-    "        #            test_data, test_targets)\n",
-    "        counter += 1\n",
-    "        iter_counter +=1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0bda8291",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6ff94871",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
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-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
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