diff --git a/VRC_PonderRNN.ipynb b/VRC_PonderRNN.ipynb
index e6c2393..8ef7d13 100644
--- a/VRC_PonderRNN.ipynb
+++ b/VRC_PonderRNN.ipynb
@@ -52,12 +52,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 206,
    "source": [
     "# Setup and imports\n",
     "import torch\n",
     "from torch import nn\n",
     "import torch.nn.functional as F\n",
+    "from torch.utils.data import TensorDataset, DataLoader, random_split\n",
     "from torch.utils.tensorboard import SummaryWriter\n",
     "\n",
     "from tqdm import tqdm\n",
@@ -80,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 202,
    "source": [
     "# produce a tarin of spikes and store timestamps of each spike in `spike_timestamps`.\n",
     "\n",
@@ -115,7 +116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 203,
    "source": [
     "\n",
     "\n",
@@ -158,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 204,
    "source": [
     "# mock data parameters\n",
     "n_subjects = 1\n",
@@ -172,10 +173,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 210,
    "source": [
     "class PonderRNN(nn.Module):\n",
-    "  def __init__(self, n_inputs, n_channels, n_outputs, halting_prob_prior=0.0):\n",
+    "  def __init__(self, n_inputs, n_channels, n_outputs, halting_prob_prior=0.2):\n",
     "    super(PonderRNN, self).__init__()\n",
     "    self.encode = nn.Sequential(  # encode: x -> sent_msg\n",
     "      nn.Linear(n_inputs, n_channels, bias=False),\n",
@@ -189,7 +190,7 @@
     "    )\n",
     "\n",
     "    # \\lambda_p\n",
-    "    self.halting_prob_prior = halting_prob_prior\n",
+    "    self.halting_prob = halting_prob_prior\n",
     "\n",
     "  def forward(self, x):\n",
     "    # x: one stimulus category, output: y[1..N] + halting_prob[1..N]\n",
@@ -203,16 +204,18 @@
     "    msg = self.decode(msg)\n",
     "    y = msg.squeeze()\n",
     "\n",
-    "    # TODO\n",
-    "    halting_prob = 0.\n",
-    "    # lambda_n = ...\n",
-    "    # halting_prob = torch.distributions.Geometric(self.halting_prob_prior)\n",
+    "    halting_n = torch.distributions.Geometric(self.halting_prob).sample().detach()\n",
     "    # self.halting_dist = torch.distributions.Geometric(prob)\n",
     "    # self.halting_probs = torch.cat(halting_prob, halting_prob)\n",
     "\n",
-    "    return y, halting_prob\n",
+    "    return y, halting_n\n",
     "\n",
     "\n",
+    "# split params\n",
+    "train_size = int(n_trials * .8)\n",
+    "test_size = n_trials - train_size\n",
+    "\n",
+    "# training parrms\n",
     "n_epoches = 1500\n",
     "\n",
     "logs = SummaryWriter()\n",
@@ -221,33 +224,53 @@
     "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
     "criterion = torch.nn.CrossEntropyLoss()\n",
     "\n",
-    "X_train = torch.tensor(X)\n",
-    "y_train = torch.tensor(y) - 1\n",
+    "dataset = TensorDataset(torch.tensor(X), torch.tensor(y)-1)\n",
+    "train_subset, test_subset = random_split(dataset, lengths=(train_size,test_size))\n",
+    "\n",
+    "X_test, y_test = dataset[test_subset.indices]\n",
+    "\n",
+    "print(X_test)\n",
+    "\n",
+    "xx\n",
     "\n",
     "for epoch in tqdm(range(n_epoches), desc='Epochs'):\n",
-    "  model.train()\n",
-    "  optimizer.zero_grad()\n",
-    "  y_pred, _ = model(X_train)\n",
+    "\n",
+    "  for X_batch, y_batch in DataLoader(train_subset, batch_size=1):\n",
+    "    model.train()\n",
+    "    optimizer.zero_grad()\n",
+    "    y_pred, _ = model(X_batch)\n",
     "\n",
     "  # logs.add_embedding(h.reshape(n_trials,n_stimuli), global_step=epoch, tag='embedding')\n",
     "\n",
-    "  model_accuracy = accuracy_score(y_train.squeeze(), torch.argmax(y_pred.detach(),dim=1))\n",
+    "  model_accuracy = accuracy_score(y_batch.squeeze(), torch.argmax(y_pred.detach(),dim=1))\n",
     "  logs.add_scalar('accurracy/train', model_accuracy, epoch)  \n",
     "\n",
-    "  loss = criterion(y_pred, y_train.squeeze())\n",
-    "\n",
+    "  loss = criterion(y_pred, y_batch.squeeze())\n",
     "  logs.add_scalar('loss/train', loss, epoch)\n",
+    "\n",
     "  loss.backward()\n",
     "  optimizer.step()\n",
     "\n",
+    "  model.eval()\n",
+    "  with torch.no_grad():\n",
+    "    y_pred = model(X_test)\n",
+    "    loss = criterion(y_test, y_pred)\n",
+    "    logger.add_scalar('loss/test', loss.detach(), epoch)\n",
+    "\n",
+    "\n",
     "# tensorboard --logdir=runs"
    ],
    "outputs": [
     {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "Epochs: 100%|██████████| 1500/1500 [00:02<00:00, 659.68it/s]\n"
+     "output_type": "error",
+     "ename": "ValueError",
+     "evalue": "Sum of input lengths does not equal the length of the input dataset!",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-210-d7a3afc60ba2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTensorDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m \u001b[0mtrain_subset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_subset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrandom_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlengths\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtest_size\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     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtest_subset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindices\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/py3/lib/python3.9/site-packages/torch/utils/data/dataset.py\u001b[0m in \u001b[0;36mrandom_split\u001b[0;34m(dataset, lengths, generator)\u001b[0m\n\u001b[1;32m    349\u001b[0m     \u001b[0;31m# Cannot verify that dataset is Sized\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    350\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlengths\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 351\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Sum of input lengths does not equal the length of the input dataset!\"\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    352\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    353\u001b[0m     \u001b[0mindices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrandperm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlengths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgenerator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgenerator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\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;31mValueError\u001b[0m: Sum of input lengths does not equal the length of the input dataset!"
      ]
     }
    ],
@@ -279,20 +302,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 199,
    "source": [
-    "torch.distributions.Geometric(.01).sample()"
+    "halting_prob = .1\n",
+    "x = torch.distributions.Geometric(halting_prob).sample().item()\n",
+    "type(x)\n",
+    "# .2*.2*.2*.8"
    ],
    "outputs": [
     {
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "tensor(56.)"
+       "float"
       ]
      },
      "metadata": {},
-     "execution_count": 107
+     "execution_count": 199
     }
    ],
    "metadata": {}