pyt_diff.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import seaborn as sns\n",
    "sns.set_style(\"white\")\n",
    "# from torchvision import datasets\n",
    "# from torchvision.transforms import ToTensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Dataset(torch.utils.data.Dataset):\n",
    "    def __init__(self, sample_file = \"./differential_expression_zscores.xlsx\", metadata_file=\"metadata.tsv\"):\n",
    "        # Read file containing samples, then index by gene name (so we can say for gene name x, these are the gene expression levels associated with that gene)\n",
    "        self.data = pd.read_excel(sample_file).fillna(0).set_index(\"Gene Name\").T\n",
    "        self.shape = (len(self.data.index), len(self.data.iloc[0]))\n",
    "\n",
    "        # Use metadata to get which samples are healthy and not healthy.\n",
    "        # Read metadata file, then index by \"Sample\" (the sample names, ex GSM260895)\n",
    "        self.metadata = pd.read_csv(open(metadata_file, \"r\"), delimiter=\"\\t\").set_index(\"Sample\")\n",
    "        self.class_list = self.data.index\n",
    "\n",
    "    # Return number of samples\n",
    "    def __len__(self):\n",
    "        return len(self.data.index)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        # Given an index, find the index within self.data, then find the corresponding index name,\n",
    "        # Look it up in self.metadata, and return the value\n",
    "        data_sample = self.data.iloc[idx]\n",
    "        options = [\"Healthy\", \"SLE\"]\n",
    "        categorical = np.zeros(len(options))\n",
    "        \n",
    "        categorical[options.index(self.metadata.loc[data_sample.name][\"Condition\"])] = 1\n",
    "        return torch.from_numpy(data_sample.to_numpy()).float(), torch.from_numpy(categorical).float()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<__main__.Dataset at 0x7f5310164ee0>,\n",
       " (tensor([ 0.5511,  0.4965,  0.7744,  ...,  0.5926, -0.8119, -0.1592]),\n",
       "  tensor([1., 0.])),\n",
       " (1023, 4911))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load data (this can take a while)\n",
    "data = Dataset()\n",
    "data, data.__getitem__(1), data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sort the data by lupus or no lupus\n",
    "sorted_data = data.copy()\n",
    "sorted_data.loc[\"f\"] = metadata.loc[data.columns][\"Condition\"] #.drop('f', axis=1)\n",
    "sorted_data = sorted_data.sort_values(by=\"f\", axis=1)\n",
    "\n",
    "data_rem_names = sorted_data.rename(columns = { col: sorted_data.loc[\"f\"][col] for i, col in enumerate(sorted_data.columns) }).drop(\"f\")\n",
    "data_rem_names.index.rename(\"Genes\", inplace=True)\n",
    "data_rem_names = data_rem_names.astype(\"float32\")\n",
    "\n",
    "plot = sns.heatmap(data_rem_names, cmap=\"mako\")\n",
    "plot.set(title= \"Gene Expression Profiles of Samples\", xlabel=\"SLE Diagnosis\")\n",
    "plot.get_figure().savefig(\"gene_expression.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1023, 4911)\n"
     ]
    }
   ],
   "source": [
    "# Load data into PyTorch\n",
    "loader = torch.utils.data.DataLoader(data,\n",
    "                                    batch_size=64,\n",
    "                                    shuffle=True)\n",
    "\n",
    "size = loader.dataset.shape\n",
    "print(size)\n",
    "train_ds, test_ds = torch.utils.data.random_split(loader.dataset, (size[0] - 100, 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using cpu device\n",
      "NeuralNetwork(\n",
      "  (linear_relu_stack): Sequential(\n",
      "    (0): Linear(in_features=4911, out_features=10, bias=False)\n",
      "    (1): ReLU()\n",
      "    (2): Linear(in_features=10, out_features=20, bias=True)\n",
      "    (3): ReLU()\n",
      "    (4): Linear(in_features=20, out_features=2, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# Get cpu or gpu device for training.\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
    "print(f\"Using {device} device\")\n",
    "\n",
    "# Define model\n",
    "class NeuralNetwork(nn.Module):\n",
    "    def __init__(self):\n",
    "        # Initialize module\n",
    "        super().__init__()\n",
    "        # self.flatten = nn.Flatten()\n",
    "        self.linear_relu_stack = nn.Sequential(\n",
    "            nn.Linear(in_features=data.shape[1], out_features=10, bias=False),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(10, 20),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(20, 2)\n",
    "            # nn.Softmax(dim=0)\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        # x = self.flatten(x)\n",
    "        logits = self.linear_relu_stack(x)\n",
    "        \n",
    "        return logits\n",
    "\n",
    "model = NeuralNetwork().to(device)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(dataloader, model, loss_fn, optimizer):\n",
    "    size = len(dataloader.dataset)\n",
    "    model.train()\n",
    "    for batch, (X, y) in enumerate(dataloader):\n",
    "        X, y = X.to(device), y.to(device)\n",
    "\n",
    "        # Compute prediction error\n",
    "        pred = model(X)\n",
    "        loss = loss_fn(pred, y)\n",
    "\n",
    "        # Backpropagation\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        if batch % 100 == 0:\n",
    "            loss, current = loss.item(), batch\n",
    "            print(f\"loss: {loss:>7f}  [{current:>5d} /{size:>5d}]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test(name, dataloader, model, loss_fn, best_correct = None):\n",
    "    size = 0\n",
    "    num_batches = len(dataloader)\n",
    "    model.eval()\n",
    "    test_loss, correct = 0, 0\n",
    "    with torch.no_grad():\n",
    "        for X, y in dataloader:\n",
    "            X, y = X.to(device), y.to(device)\n",
    "            pred = model(X)\n",
    "            test_loss += loss_fn(pred, y).item()\n",
    "            # print((abs(pred[0] - y[0]) + abs(pred[1] - y[1])).sum().item(), pred, y)\n",
    "            # wrong += (abs(pred[0] - y[0]) + abs(pred[1] - y[1])).sum().item() / 2.0\n",
    "            correct += (pred.argmax() == y.argmax()).sum().item()\n",
    "            size += 1\n",
    "    test_loss /= num_batches\n",
    "    if best_correct is not None:\n",
    "        if correct / size > best_correct:\n",
    "            print(F\"Better accuracy: {correct / size}, saving to disk...\")\n",
    "            torch.save(model.state_dict(), \"model_best.pth\")\n",
    "    print(f\"{name} Error: \\n Accuracy: {((correct / size) * 100):>0.1f}%, Avg loss: {test_loss:>8f} \\n\")\n",
    "    return max(correct / size, best_correct if best_correct is not None else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1\n",
      "-------------------------------\n",
      "loss: 0.000117  [    0 / 1023]\n",
      "loss: 0.011022  [  100 / 1023]\n",
      "loss: 0.004884  [  200 / 1023]\n",
      "loss: 0.001285  [  300 / 1023]\n",
      "loss: 0.439273  [  400 / 1023]\n",
      "loss: 0.025079  [  500 / 1023]\n",
      "loss: 0.350079  [  600 / 1023]\n",
      "loss: 0.510049  [  700 / 1023]\n",
      "loss: 0.456909  [  800 / 1023]\n",
      "loss: 0.000907  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 80.0%, Avg loss: 0.360350 \n",
      "\n",
      "Better accuracy: 0.8255687973997833, saving to disk...\n",
      "Train Error: \n",
      " Accuracy: 82.6%, Avg loss: 0.352626 \n",
      "\n",
      "Epoch 2\n",
      "-------------------------------\n",
      "loss: 0.000188  [    0 / 1023]\n",
      "loss: 0.000491  [  100 / 1023]\n",
      "loss: 0.000210  [  200 / 1023]\n",
      "loss: 0.001583  [  300 / 1023]\n",
      "loss: 0.197789  [  400 / 1023]\n",
      "loss: 0.002630  [  500 / 1023]\n",
      "loss: 0.373627  [  600 / 1023]\n",
      "loss: 0.159983  [  700 / 1023]\n",
      "loss: 0.213361  [  800 / 1023]\n",
      "loss: 0.000078  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 79.0%, Avg loss: 0.674903 \n",
      "\n",
      "Better accuracy: 0.8342361863488624, saving to disk...\n",
      "Train Error: \n",
      " Accuracy: 83.4%, Avg loss: 0.541461 \n",
      "\n",
      "Epoch 3\n",
      "-------------------------------\n",
      "loss: 0.000000  [    0 / 1023]\n",
      "loss: 0.000708  [  100 / 1023]\n",
      "loss: 0.001122  [  200 / 1023]\n",
      "loss: 0.002434  [  300 / 1023]\n",
      "loss: 0.696847  [  400 / 1023]\n",
      "loss: 0.008784  [  500 / 1023]\n",
      "loss: 0.166041  [  600 / 1023]\n",
      "loss: 0.037191  [  700 / 1023]\n",
      "loss: 0.131811  [  800 / 1023]\n",
      "loss: 0.000311  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 84.0%, Avg loss: 0.265139 \n",
      "\n",
      "Better accuracy: 0.8624052004333694, saving to disk...\n",
      "Train Error: \n",
      " Accuracy: 86.2%, Avg loss: 0.281494 \n",
      "\n",
      "Epoch 4\n",
      "-------------------------------\n",
      "loss: 0.000044  [    0 / 1023]\n",
      "loss: 0.000005  [  100 / 1023]\n",
      "loss: 0.000214  [  200 / 1023]\n",
      "loss: 0.000088  [  300 / 1023]\n",
      "loss: 0.280479  [  400 / 1023]\n",
      "loss: 0.000450  [  500 / 1023]\n",
      "loss: 0.165765  [  600 / 1023]\n",
      "loss: 0.179479  [  700 / 1023]\n",
      "loss: 0.181548  [  800 / 1023]\n",
      "loss: 0.000110  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 88.0%, Avg loss: 0.296860 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 86.2%, Avg loss: 0.272631 \n",
      "\n",
      "Epoch 5\n",
      "-------------------------------\n",
      "loss: 0.000041  [    0 / 1023]\n",
      "loss: 0.000021  [  100 / 1023]\n",
      "loss: 0.001152  [  200 / 1023]\n",
      "loss: -0.000000  [  300 / 1023]\n",
      "loss: 0.370277  [  400 / 1023]\n",
      "loss: 0.005046  [  500 / 1023]\n",
      "loss: 0.153004  [  600 / 1023]\n",
      "loss: 0.024831  [  700 / 1023]\n",
      "loss: 0.088314  [  800 / 1023]\n",
      "loss: 0.000245  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 83.0%, Avg loss: 0.346559 \n",
      "\n",
      "Better accuracy: 0.8678223185265439, saving to disk...\n",
      "Train Error: \n",
      " Accuracy: 86.8%, Avg loss: 0.300533 \n",
      "\n",
      "Epoch 6\n",
      "-------------------------------\n",
      "loss: 0.000003  [    0 / 1023]\n",
      "loss: 0.000386  [  100 / 1023]\n",
      "loss: 0.000470  [  200 / 1023]\n",
      "loss: 0.000609  [  300 / 1023]\n",
      "loss: 0.445151  [  400 / 1023]\n",
      "loss: 0.004352  [  500 / 1023]\n",
      "loss: 0.240420  [  600 / 1023]\n",
      "loss: 0.062226  [  700 / 1023]\n",
      "loss: 0.101659  [  800 / 1023]\n",
      "loss: 0.000028  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 85.0%, Avg loss: 0.294281 \n",
      "\n",
      "Better accuracy: 0.8775731310942578, saving to disk...\n",
      "Train Error: \n",
      " Accuracy: 87.8%, Avg loss: 0.246745 \n",
      "\n",
      "Epoch 7\n",
      "-------------------------------\n",
      "loss: 0.000010  [    0 / 1023]\n",
      "loss: 0.000084  [  100 / 1023]\n",
      "loss: 0.001512  [  200 / 1023]\n",
      "loss: 0.000021  [  300 / 1023]\n",
      "loss: 0.481479  [  400 / 1023]\n",
      "loss: 0.001454  [  500 / 1023]\n",
      "loss: 0.148806  [  600 / 1023]\n",
      "loss: 0.016458  [  700 / 1023]\n",
      "loss: 0.069286  [  800 / 1023]\n",
      "loss: 0.000007  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 87.0%, Avg loss: 0.278049 \n",
      "\n",
      "Better accuracy: 0.8938244853737811, saving to disk...\n",
      "Train Error: \n",
      " Accuracy: 89.4%, Avg loss: 0.214565 \n",
      "\n",
      "Epoch 8\n",
      "-------------------------------\n",
      "loss: 0.000128  [    0 / 1023]\n",
      "loss: 0.000105  [  100 / 1023]\n",
      "loss: 0.000718  [  200 / 1023]\n",
      "loss: 0.000239  [  300 / 1023]\n",
      "loss: 0.303382  [  400 / 1023]\n",
      "loss: 0.001269  [  500 / 1023]\n",
      "loss: 0.142959  [  600 / 1023]\n",
      "loss: 0.009729  [  700 / 1023]\n",
      "loss: 0.102299  [  800 / 1023]\n",
      "loss: 0.000001  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 85.0%, Avg loss: 0.314486 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 88.3%, Avg loss: 0.242685 \n",
      "\n",
      "Epoch 9\n",
      "-------------------------------\n",
      "loss: 0.000000  [    0 / 1023]\n",
      "loss: 0.000021  [  100 / 1023]\n",
      "loss: 0.000072  [  200 / 1023]\n",
      "loss: 0.000000  [  300 / 1023]\n",
      "loss: 0.381904  [  400 / 1023]\n",
      "loss: 0.000022  [  500 / 1023]\n",
      "loss: 0.116148  [  600 / 1023]\n",
      "loss: 0.011337  [  700 / 1023]\n",
      "loss: 0.113884  [  800 / 1023]\n",
      "loss: 0.000001  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 90.0%, Avg loss: 0.275525 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 88.8%, Avg loss: 0.239178 \n",
      "\n",
      "Epoch 10\n",
      "-------------------------------\n",
      "loss: 0.000000  [    0 / 1023]\n",
      "loss: 0.000526  [  100 / 1023]\n",
      "loss: 0.000778  [  200 / 1023]\n",
      "loss: 0.000001  [  300 / 1023]\n",
      "loss: 0.627727  [  400 / 1023]\n",
      "loss: 0.000228  [  500 / 1023]\n",
      "loss: 0.291663  [  600 / 1023]\n",
      "loss: 0.003718  [  700 / 1023]\n",
      "loss: 0.076677  [  800 / 1023]\n",
      "loss: 0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 84.0%, Avg loss: 0.444573 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 87.6%, Avg loss: 0.317574 \n",
      "\n",
      "Epoch 11\n",
      "-------------------------------\n",
      "loss: 0.000003  [    0 / 1023]\n",
      "loss: 0.000007  [  100 / 1023]\n",
      "loss: 0.000613  [  200 / 1023]\n",
      "loss: 0.000041  [  300 / 1023]\n",
      "loss: 0.129194  [  400 / 1023]\n",
      "loss: 0.000519  [  500 / 1023]\n",
      "loss: 0.167868  [  600 / 1023]\n",
      "loss: 0.013016  [  700 / 1023]\n",
      "loss: 0.102799  [  800 / 1023]\n",
      "loss: 0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 88.0%, Avg loss: 0.261834 \n",
      "\n",
      "Better accuracy: 0.8981581798483207, saving to disk...\n",
      "Train Error: \n",
      " Accuracy: 89.8%, Avg loss: 0.199829 \n",
      "\n",
      "Epoch 12\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: 0.000001  [  100 / 1023]\n",
      "loss: 0.000595  [  200 / 1023]\n",
      "loss: 0.000001  [  300 / 1023]\n",
      "loss: 0.116689  [  400 / 1023]\n",
      "loss: 0.000356  [  500 / 1023]\n",
      "loss: 0.137069  [  600 / 1023]\n",
      "loss: 0.015591  [  700 / 1023]\n",
      "loss: 0.132857  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 85.0%, Avg loss: 0.307526 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 88.8%, Avg loss: 0.220703 \n",
      "\n",
      "Epoch 13\n",
      "-------------------------------\n",
      "loss: 0.000146  [    0 / 1023]\n",
      "loss: 0.000001  [  100 / 1023]\n",
      "loss: 0.000504  [  200 / 1023]\n",
      "loss: 0.000035  [  300 / 1023]\n",
      "loss: 0.470545  [  400 / 1023]\n",
      "loss: 0.000456  [  500 / 1023]\n",
      "loss: 0.347243  [  600 / 1023]\n",
      "loss: 0.032235  [  700 / 1023]\n",
      "loss: 0.139355  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 91.0%, Avg loss: 0.198392 \n",
      "\n",
      "Better accuracy: 0.9219934994582882, saving to disk...\n",
      "Train Error: \n",
      " Accuracy: 92.2%, Avg loss: 0.174958 \n",
      "\n",
      "Epoch 14\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: 0.000000  [  100 / 1023]\n",
      "loss: 0.000026  [  200 / 1023]\n",
      "loss: 0.000005  [  300 / 1023]\n",
      "loss: 0.008981  [  400 / 1023]\n",
      "loss: 0.000002  [  500 / 1023]\n",
      "loss: 0.384784  [  600 / 1023]\n",
      "loss: 0.030439  [  700 / 1023]\n",
      "loss: 0.093050  [  800 / 1023]\n",
      "loss: 0.000003  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 89.0%, Avg loss: 0.364548 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 90.8%, Avg loss: 0.267852 \n",
      "\n",
      "Epoch 15\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: 0.000002  [  100 / 1023]\n",
      "loss: 0.000824  [  200 / 1023]\n",
      "loss: 0.000020  [  300 / 1023]\n",
      "loss: 0.884573  [  400 / 1023]\n",
      "loss: 0.000165  [  500 / 1023]\n",
      "loss: 0.283411  [  600 / 1023]\n",
      "loss: 0.021855  [  700 / 1023]\n",
      "loss: 0.072861  [  800 / 1023]\n",
      "loss: 0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 89.0%, Avg loss: 0.255399 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 91.5%, Avg loss: 0.161059 \n",
      "\n",
      "Epoch 16\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: 0.000000  [  100 / 1023]\n",
      "loss: 0.000057  [  200 / 1023]\n",
      "loss: 0.000001  [  300 / 1023]\n",
      "loss: 0.043866  [  400 / 1023]\n",
      "loss: 0.000010  [  500 / 1023]\n",
      "loss: 0.067938  [  600 / 1023]\n",
      "loss: 0.013284  [  700 / 1023]\n",
      "loss: 0.058397  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 91.0%, Avg loss: 0.218475 \n",
      "\n",
      "Better accuracy: 0.9295774647887324, saving to disk...\n",
      "Train Error: \n",
      " Accuracy: 93.0%, Avg loss: 0.167973 \n",
      "\n",
      "Epoch 17\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: 0.000006  [  100 / 1023]\n",
      "loss: 0.000176  [  200 / 1023]\n",
      "loss: 0.000000  [  300 / 1023]\n",
      "loss: 0.009322  [  400 / 1023]\n",
      "loss: 0.000007  [  500 / 1023]\n",
      "loss: 0.132168  [  600 / 1023]\n",
      "loss: 0.008668  [  700 / 1023]\n",
      "loss: 0.063748  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 89.0%, Avg loss: 0.206241 \n",
      "\n",
      "Better accuracy: 0.9328277356446371, saving to disk...\n",
      "Train Error: \n",
      " Accuracy: 93.3%, Avg loss: 0.133020 \n",
      "\n",
      "Epoch 18\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: 0.000000  [  100 / 1023]\n",
      "loss: 0.000007  [  200 / 1023]\n",
      "loss: 0.000017  [  300 / 1023]\n",
      "loss: 0.018165  [  400 / 1023]\n",
      "loss: 0.000015  [  500 / 1023]\n",
      "loss: 0.164899  [  600 / 1023]\n",
      "loss: 0.041831  [  700 / 1023]\n",
      "loss: 0.078461  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 90.0%, Avg loss: 0.271476 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 91.7%, Avg loss: 0.240536 \n",
      "\n",
      "Epoch 19\n",
      "-------------------------------\n",
      "loss: 0.000004  [    0 / 1023]\n",
      "loss: 0.000000  [  100 / 1023]\n",
      "loss: 0.000504  [  200 / 1023]\n",
      "loss: 0.000078  [  300 / 1023]\n",
      "loss: 0.004720  [  400 / 1023]\n",
      "loss: 0.000001  [  500 / 1023]\n",
      "loss: 0.097665  [  600 / 1023]\n",
      "loss: 0.047452  [  700 / 1023]\n",
      "loss: 0.072765  [  800 / 1023]\n",
      "loss: 0.000069  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 91.0%, Avg loss: 0.238149 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 92.4%, Avg loss: 0.156074 \n",
      "\n",
      "Epoch 20\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: 0.000000  [  100 / 1023]\n",
      "loss: 0.000000  [  200 / 1023]\n",
      "loss: 0.000421  [  300 / 1023]\n",
      "loss: 0.862002  [  400 / 1023]\n",
      "loss: 0.000002  [  500 / 1023]\n",
      "loss: 0.116313  [  600 / 1023]\n",
      "loss: 0.077376  [  700 / 1023]\n",
      "loss: 0.076567  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 89.0%, Avg loss: 0.266539 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 91.8%, Avg loss: 0.151365 \n",
      "\n",
      "Epoch 21\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: 0.000164  [  200 / 1023]\n",
      "loss: 0.000001  [  300 / 1023]\n",
      "loss: 0.000754  [  400 / 1023]\n",
      "loss: 0.000001  [  500 / 1023]\n",
      "loss: 0.112600  [  600 / 1023]\n",
      "loss: 0.053527  [  700 / 1023]\n",
      "loss: 0.111419  [  800 / 1023]\n",
      "loss: 0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 87.0%, Avg loss: 0.281854 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 92.6%, Avg loss: 0.169669 \n",
      "\n",
      "Epoch 22\n",
      "-------------------------------\n",
      "loss: 0.000013  [    0 / 1023]\n",
      "loss: 0.000000  [  100 / 1023]\n",
      "loss: 0.000046  [  200 / 1023]\n",
      "loss: 0.000005  [  300 / 1023]\n",
      "loss: 0.001841  [  400 / 1023]\n",
      "loss: 0.000011  [  500 / 1023]\n",
      "loss: 0.149352  [  600 / 1023]\n",
      "loss: 0.023327  [  700 / 1023]\n",
      "loss: 0.070639  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 88.0%, Avg loss: 0.424655 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 90.0%, Avg loss: 0.189495 \n",
      "\n",
      "Epoch 23\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: 0.000087  [  200 / 1023]\n",
      "loss: 0.000040  [  300 / 1023]\n",
      "loss: 0.000480  [  400 / 1023]\n",
      "loss: 0.000000  [  500 / 1023]\n",
      "loss: 0.225071  [  600 / 1023]\n",
      "loss: 0.070284  [  700 / 1023]\n",
      "loss: 0.062634  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 87.0%, Avg loss: 0.387626 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 91.7%, Avg loss: 0.201970 \n",
      "\n",
      "Epoch 24\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: 0.000006  [  200 / 1023]\n",
      "loss: 0.000958  [  300 / 1023]\n",
      "loss: 0.000048  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.218078  [  600 / 1023]\n",
      "loss: 0.002620  [  700 / 1023]\n",
      "loss: 0.048409  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 90.0%, Avg loss: 0.197211 \n",
      "\n",
      "Better accuracy: 0.9360780065005417, saving to disk...\n",
      "Train Error: \n",
      " Accuracy: 93.6%, Avg loss: 0.135886 \n",
      "\n",
      "Epoch 25\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: 0.000000  [  100 / 1023]\n",
      "loss: 0.000015  [  200 / 1023]\n",
      "loss: 0.000000  [  300 / 1023]\n",
      "loss: 0.001700  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.130772  [  600 / 1023]\n",
      "loss: 0.011271  [  700 / 1023]\n",
      "loss: 0.032907  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 87.0%, Avg loss: 0.669060 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 92.7%, Avg loss: 0.277444 \n",
      "\n",
      "Epoch 26\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: 0.000006  [  300 / 1023]\n",
      "loss: 0.009026  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.176342  [  600 / 1023]\n",
      "loss: 0.004477  [  700 / 1023]\n",
      "loss: 0.057725  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 93.0%, Avg loss: 0.177243 \n",
      "\n",
      "Better accuracy: 0.9534127843986999, saving to disk...\n",
      "Train Error: \n",
      " Accuracy: 95.3%, Avg loss: 0.109586 \n",
      "\n",
      "Epoch 27\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: -0.000000  [  300 / 1023]\n",
      "loss: 0.046008  [  400 / 1023]\n",
      "loss: 0.000000  [  500 / 1023]\n",
      "loss: 0.115357  [  600 / 1023]\n",
      "loss: 0.077672  [  700 / 1023]\n",
      "loss: 0.022931  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 90.0%, Avg loss: 0.480153 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 91.9%, Avg loss: 0.321049 \n",
      "\n",
      "Epoch 28\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: 0.000000  [  200 / 1023]\n",
      "loss: 0.000185  [  300 / 1023]\n",
      "loss: 0.001313  [  400 / 1023]\n",
      "loss: 0.000001  [  500 / 1023]\n",
      "loss: 0.029396  [  600 / 1023]\n",
      "loss: 0.035808  [  700 / 1023]\n",
      "loss: 0.019924  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 93.0%, Avg loss: 0.187566 \n",
      "\n",
      "Better accuracy: 0.9609967497291441, saving to disk...\n",
      "Train Error: \n",
      " Accuracy: 96.1%, Avg loss: 0.078345 \n",
      "\n",
      "Epoch 29\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: 0.000000  [  200 / 1023]\n",
      "loss: -0.000000  [  300 / 1023]\n",
      "loss: 0.000643  [  400 / 1023]\n",
      "loss: 0.000005  [  500 / 1023]\n",
      "loss: 0.190084  [  600 / 1023]\n",
      "loss: 0.009625  [  700 / 1023]\n",
      "loss: 0.029896  [  800 / 1023]\n",
      "loss: 0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 89.0%, Avg loss: 0.349839 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 92.5%, Avg loss: 0.201694 \n",
      "\n",
      "Epoch 30\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: 0.000000  [  100 / 1023]\n",
      "loss: 0.000011  [  200 / 1023]\n",
      "loss: 0.000760  [  300 / 1023]\n",
      "loss: 0.011695  [  400 / 1023]\n",
      "loss: 0.000004  [  500 / 1023]\n",
      "loss: 0.046766  [  600 / 1023]\n",
      "loss: 0.003468  [  700 / 1023]\n",
      "loss: 0.004770  [  800 / 1023]\n",
      "loss: 0.000001  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 86.0%, Avg loss: 0.267520 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 94.1%, Avg loss: 0.114463 \n",
      "\n",
      "Epoch 31\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: 0.000000  [  100 / 1023]\n",
      "loss: 0.000834  [  200 / 1023]\n",
      "loss: -0.000000  [  300 / 1023]\n",
      "loss: 0.002950  [  400 / 1023]\n",
      "loss: 0.000000  [  500 / 1023]\n",
      "loss: 0.085004  [  600 / 1023]\n",
      "loss: 0.006458  [  700 / 1023]\n",
      "loss: 0.092847  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 92.0%, Avg loss: 0.188446 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 95.9%, Avg loss: 0.089632 \n",
      "\n",
      "Epoch 32\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: 0.000011  [  300 / 1023]\n",
      "loss: 0.001232  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.216656  [  600 / 1023]\n",
      "loss: 0.015029  [  700 / 1023]\n",
      "loss: 0.007088  [  800 / 1023]\n",
      "loss: 0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 89.0%, Avg loss: 0.265675 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 94.4%, Avg loss: 0.112687 \n",
      "\n",
      "Epoch 33\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: 0.000000  [  300 / 1023]\n",
      "loss: 0.000096  [  400 / 1023]\n",
      "loss: 0.000000  [  500 / 1023]\n",
      "loss: 0.123646  [  600 / 1023]\n",
      "loss: 0.006243  [  700 / 1023]\n",
      "loss: 0.047906  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 92.0%, Avg loss: 0.338044 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 95.0%, Avg loss: 0.133038 \n",
      "\n",
      "Epoch 34\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: 0.000012  [  200 / 1023]\n",
      "loss: 0.000083  [  300 / 1023]\n",
      "loss: 0.000461  [  400 / 1023]\n",
      "loss: 0.000319  [  500 / 1023]\n",
      "loss: 0.050034  [  600 / 1023]\n",
      "loss: 0.296778  [  700 / 1023]\n",
      "loss: 0.007295  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 87.0%, Avg loss: 0.371980 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 92.1%, Avg loss: 0.145186 \n",
      "\n",
      "Epoch 35\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: -0.000000  [  300 / 1023]\n",
      "loss: 0.001070  [  400 / 1023]\n",
      "loss: 0.000000  [  500 / 1023]\n",
      "loss: 0.371542  [  600 / 1023]\n",
      "loss: 0.009010  [  700 / 1023]\n",
      "loss: 0.018671  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 91.0%, Avg loss: 0.256990 \n",
      "\n",
      "Better accuracy: 0.9664138678223185, saving to disk...\n",
      "Train Error: \n",
      " Accuracy: 96.6%, Avg loss: 0.064364 \n",
      "\n",
      "Epoch 36\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: -0.000000  [  300 / 1023]\n",
      "loss: 0.000125  [  400 / 1023]\n",
      "loss: 0.000001  [  500 / 1023]\n",
      "loss: 0.011154  [  600 / 1023]\n",
      "loss: 0.003138  [  700 / 1023]\n",
      "loss: 0.012357  [  800 / 1023]\n",
      "loss: 0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 90.0%, Avg loss: 0.338912 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 95.2%, Avg loss: 0.109953 \n",
      "\n",
      "Epoch 37\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: -0.000000  [  300 / 1023]\n",
      "loss: 0.000441  [  400 / 1023]\n",
      "loss: 0.000000  [  500 / 1023]\n",
      "loss: 0.012588  [  600 / 1023]\n",
      "loss: 0.000157  [  700 / 1023]\n",
      "loss: 0.009482  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 82.0%, Avg loss: 0.787527 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 88.9%, Avg loss: 0.320727 \n",
      "\n",
      "Epoch 38\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: 0.000000  [  100 / 1023]\n",
      "loss: 0.000032  [  200 / 1023]\n",
      "loss: 0.002017  [  300 / 1023]\n",
      "loss: 0.002016  [  400 / 1023]\n",
      "loss: 0.000000  [  500 / 1023]\n",
      "loss: 0.042104  [  600 / 1023]\n",
      "loss: 0.021392  [  700 / 1023]\n",
      "loss: 0.015033  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 87.0%, Avg loss: 0.492412 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 93.6%, Avg loss: 0.185808 \n",
      "\n",
      "Epoch 39\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: 0.000005  [  300 / 1023]\n",
      "loss: 0.000471  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.033400  [  600 / 1023]\n",
      "loss: 0.111026  [  700 / 1023]\n",
      "loss: 0.006609  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 90.0%, Avg loss: 0.232992 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 96.1%, Avg loss: 0.072294 \n",
      "\n",
      "Epoch 40\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: 0.000255  [  200 / 1023]\n",
      "loss: 0.001036  [  300 / 1023]\n",
      "loss: 0.000751  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.007921  [  600 / 1023]\n",
      "loss: 0.026824  [  700 / 1023]\n",
      "loss: 0.000443  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 90.0%, Avg loss: 0.281745 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 96.0%, Avg loss: 0.075122 \n",
      "\n",
      "Epoch 41\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: 0.000016  [  300 / 1023]\n",
      "loss: 0.003976  [  400 / 1023]\n",
      "loss: 0.000008  [  500 / 1023]\n",
      "loss: 0.017525  [  600 / 1023]\n",
      "loss: 0.009297  [  700 / 1023]\n",
      "loss: 0.000317  [  800 / 1023]\n",
      "loss: 0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 87.0%, Avg loss: 0.393986 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 94.3%, Avg loss: 0.112033 \n",
      "\n",
      "Epoch 42\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: 0.000000  [  200 / 1023]\n",
      "loss: 0.000473  [  300 / 1023]\n",
      "loss: 0.001410  [  400 / 1023]\n",
      "loss: 0.000001  [  500 / 1023]\n",
      "loss: 0.018388  [  600 / 1023]\n",
      "loss: 0.049313  [  700 / 1023]\n",
      "loss: 0.001034  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 90.0%, Avg loss: 0.222520 \n",
      "\n",
      "Better accuracy: 0.9685807150595883, saving to disk...\n",
      "Train Error: \n",
      " Accuracy: 96.9%, Avg loss: 0.055034 \n",
      "\n",
      "Epoch 43\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: 0.000001  [  300 / 1023]\n",
      "loss: 0.000651  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 1.015815  [  600 / 1023]\n",
      "loss: 0.129433  [  700 / 1023]\n",
      "loss: 0.000980  [  800 / 1023]\n",
      "loss: 0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 90.0%, Avg loss: 0.254599 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 95.8%, Avg loss: 0.078219 \n",
      "\n",
      "Epoch 44\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: 0.000057  [  100 / 1023]\n",
      "loss: 0.000037  [  200 / 1023]\n",
      "loss: 0.000041  [  300 / 1023]\n",
      "loss: 0.008285  [  400 / 1023]\n",
      "loss: 0.000064  [  500 / 1023]\n",
      "loss: 0.002096  [  600 / 1023]\n",
      "loss: 0.582027  [  700 / 1023]\n",
      "loss: 0.003990  [  800 / 1023]\n",
      "loss: 0.000009  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 91.0%, Avg loss: 0.196720 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 95.7%, Avg loss: 0.079757 \n",
      "\n",
      "Epoch 45\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: 0.000001  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: -0.000000  [  300 / 1023]\n",
      "loss: 0.001639  [  400 / 1023]\n",
      "loss: 0.000005  [  500 / 1023]\n",
      "loss: 0.001352  [  600 / 1023]\n",
      "loss: 0.012294  [  700 / 1023]\n",
      "loss: 0.000800  [  800 / 1023]\n",
      "loss: 0.000001  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 88.0%, Avg loss: 0.332679 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 95.4%, Avg loss: 0.100023 \n",
      "\n",
      "Epoch 46\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: -0.000000  [  300 / 1023]\n",
      "loss: 0.000648  [  400 / 1023]\n",
      "loss: 0.000002  [  500 / 1023]\n",
      "loss: 0.054063  [  600 / 1023]\n",
      "loss: 0.316729  [  700 / 1023]\n",
      "loss: 0.074123  [  800 / 1023]\n",
      "loss: 0.000018  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 92.0%, Avg loss: 0.191596 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 94.0%, Avg loss: 0.100993 \n",
      "\n",
      "Epoch 47\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: 0.000001  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: 0.000000  [  300 / 1023]\n",
      "loss: 0.000885  [  400 / 1023]\n",
      "loss: 0.000001  [  500 / 1023]\n",
      "loss: 0.036332  [  600 / 1023]\n",
      "loss: 0.006309  [  700 / 1023]\n",
      "loss: 0.002861  [  800 / 1023]\n",
      "loss: 0.000001  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 90.0%, Avg loss: 0.217936 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 96.5%, Avg loss: 0.062695 \n",
      "\n",
      "Epoch 48\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: 0.000007  [  300 / 1023]\n",
      "loss: 0.001496  [  400 / 1023]\n",
      "loss: 0.000001  [  500 / 1023]\n",
      "loss: 0.020767  [  600 / 1023]\n",
      "loss: 0.005389  [  700 / 1023]\n",
      "loss: 0.005955  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 89.0%, Avg loss: 0.353268 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 94.3%, Avg loss: 0.203903 \n",
      "\n",
      "Epoch 49\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: 0.000000  [  100 / 1023]\n",
      "loss: 0.000401  [  200 / 1023]\n",
      "loss: 0.000036  [  300 / 1023]\n",
      "loss: 0.001033  [  400 / 1023]\n",
      "loss: 0.000000  [  500 / 1023]\n",
      "loss: 0.000996  [  600 / 1023]\n",
      "loss: 0.004205  [  700 / 1023]\n",
      "loss: 0.000491  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 89.0%, Avg loss: 0.262396 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 95.2%, Avg loss: 0.080415 \n",
      "\n",
      "Epoch 50\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: 0.000022  [  300 / 1023]\n",
      "loss: 0.001738  [  400 / 1023]\n",
      "loss: 0.000000  [  500 / 1023]\n",
      "loss: 0.002298  [  600 / 1023]\n",
      "loss: 0.155556  [  700 / 1023]\n",
      "loss: 0.001396  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 87.0%, Avg loss: 0.355873 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 95.0%, Avg loss: 0.099695 \n",
      "\n",
      "Epoch 51\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: 0.000002  [  300 / 1023]\n",
      "loss: 0.000055  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.009636  [  600 / 1023]\n",
      "loss: 0.004783  [  700 / 1023]\n",
      "loss: 0.007201  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 91.0%, Avg loss: 0.189750 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 95.8%, Avg loss: 0.075060 \n",
      "\n",
      "Epoch 52\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: -0.000000  [  300 / 1023]\n",
      "loss: 0.000079  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.000891  [  600 / 1023]\n",
      "loss: 0.016620  [  700 / 1023]\n",
      "loss: 0.000390  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 90.0%, Avg loss: 0.223498 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 96.6%, Avg loss: 0.060123 \n",
      "\n",
      "Epoch 53\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: 0.001594  [  300 / 1023]\n",
      "loss: 0.075325  [  400 / 1023]\n",
      "loss: 0.001341  [  500 / 1023]\n",
      "loss: 0.158650  [  600 / 1023]\n",
      "loss: 0.016511  [  700 / 1023]\n",
      "loss: 0.007024  [  800 / 1023]\n",
      "loss: 0.000638  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 87.0%, Avg loss: 0.317638 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 95.3%, Avg loss: 0.092875 \n",
      "\n",
      "Epoch 54\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: 0.000001  [  100 / 1023]\n",
      "loss: 0.000000  [  200 / 1023]\n",
      "loss: 0.000000  [  300 / 1023]\n",
      "loss: 0.001016  [  400 / 1023]\n",
      "loss: 0.000000  [  500 / 1023]\n",
      "loss: 0.024147  [  600 / 1023]\n",
      "loss: 0.005312  [  700 / 1023]\n",
      "loss: 0.000570  [  800 / 1023]\n",
      "loss: 0.000001  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 88.0%, Avg loss: 0.354309 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 94.0%, Avg loss: 0.170104 \n",
      "\n",
      "Epoch 55\n",
      "-------------------------------\n",
      "loss: 0.000001  [    0 / 1023]\n",
      "loss: 0.000008  [  100 / 1023]\n",
      "loss: 0.000015  [  200 / 1023]\n",
      "loss: 0.000295  [  300 / 1023]\n",
      "loss: 0.007255  [  400 / 1023]\n",
      "loss: 0.000007  [  500 / 1023]\n",
      "loss: 0.270015  [  600 / 1023]\n",
      "loss: 0.090872  [  700 / 1023]\n",
      "loss: 0.003002  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 88.0%, Avg loss: 0.311731 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 95.6%, Avg loss: 0.068203 \n",
      "\n",
      "Epoch 56\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: 0.000000  [  300 / 1023]\n",
      "loss: 0.000176  [  400 / 1023]\n",
      "loss: 0.000000  [  500 / 1023]\n",
      "loss: 0.000928  [  600 / 1023]\n",
      "loss: 0.001268  [  700 / 1023]\n",
      "loss: 0.000798  [  800 / 1023]\n",
      "loss: 0.000204  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 89.0%, Avg loss: 0.260224 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 94.8%, Avg loss: 0.095579 \n",
      "\n",
      "Epoch 57\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: 0.000001  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: 0.000000  [  300 / 1023]\n",
      "loss: 0.001500  [  400 / 1023]\n",
      "loss: 0.000000  [  500 / 1023]\n",
      "loss: 0.015921  [  600 / 1023]\n",
      "loss: 0.104649  [  700 / 1023]\n",
      "loss: 0.002832  [  800 / 1023]\n",
      "loss: 0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 90.0%, Avg loss: 0.250844 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 96.9%, Avg loss: 0.051459 \n",
      "\n",
      "Epoch 58\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: -0.000000  [  300 / 1023]\n",
      "loss: 0.000321  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.013158  [  600 / 1023]\n",
      "loss: 0.017590  [  700 / 1023]\n",
      "loss: 0.002894  [  800 / 1023]\n",
      "loss: 0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 89.0%, Avg loss: 0.317885 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 96.2%, Avg loss: 0.071630 \n",
      "\n",
      "Epoch 59\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: -0.000000  [  300 / 1023]\n",
      "loss: 0.004100  [  400 / 1023]\n",
      "loss: 0.000000  [  500 / 1023]\n",
      "loss: 0.037085  [  600 / 1023]\n",
      "loss: 0.045464  [  700 / 1023]\n",
      "loss: 0.003140  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 83.0%, Avg loss: 0.875409 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 91.1%, Avg loss: 0.346885 \n",
      "\n",
      "Epoch 60\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: 0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: 0.000076  [  300 / 1023]\n",
      "loss: 0.010771  [  400 / 1023]\n",
      "loss: 0.000002  [  500 / 1023]\n",
      "loss: 0.008282  [  600 / 1023]\n",
      "loss: 0.003732  [  700 / 1023]\n",
      "loss: 0.006124  [  800 / 1023]\n",
      "loss: 0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 91.0%, Avg loss: 0.319857 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 93.7%, Avg loss: 0.148219 \n",
      "\n",
      "Epoch 61\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: 0.000000  [  300 / 1023]\n",
      "loss: 0.000835  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.002384  [  600 / 1023]\n",
      "loss: 0.009521  [  700 / 1023]\n",
      "loss: 0.002524  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 90.0%, Avg loss: 0.274044 \n",
      "\n",
      "Better accuracy: 0.9707475622968581, saving to disk...\n",
      "Train Error: \n",
      " Accuracy: 97.1%, Avg loss: 0.049024 \n",
      "\n",
      "Epoch 62\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: -0.000000  [  300 / 1023]\n",
      "loss: 0.000035  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.058659  [  600 / 1023]\n",
      "loss: 0.046529  [  700 / 1023]\n",
      "loss: 0.021301  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 91.0%, Avg loss: 0.211104 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 97.0%, Avg loss: 0.054119 \n",
      "\n",
      "Epoch 63\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: -0.000000  [  300 / 1023]\n",
      "loss: 0.000075  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.008211  [  600 / 1023]\n",
      "loss: 0.041516  [  700 / 1023]\n",
      "loss: 0.029077  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 92.0%, Avg loss: 0.202233 \n",
      "\n",
      "Better accuracy: 0.9750812567713976, saving to disk...\n",
      "Train Error: \n",
      " Accuracy: 97.5%, Avg loss: 0.042469 \n",
      "\n",
      "Epoch 64\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: -0.000000  [  300 / 1023]\n",
      "loss: 0.000058  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.003678  [  600 / 1023]\n",
      "loss: 0.192109  [  700 / 1023]\n",
      "loss: 0.179313  [  800 / 1023]\n",
      "loss: 0.000013  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 93.0%, Avg loss: 0.223058 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 94.4%, Avg loss: 0.124304 \n",
      "\n",
      "Epoch 65\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: 0.000000  [  100 / 1023]\n",
      "loss: 0.000000  [  200 / 1023]\n",
      "loss: -0.000000  [  300 / 1023]\n",
      "loss: 0.001541  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.037107  [  600 / 1023]\n",
      "loss: 0.184317  [  700 / 1023]\n",
      "loss: 0.000213  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 91.0%, Avg loss: 0.234010 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 97.4%, Avg loss: 0.043775 \n",
      "\n",
      "Epoch 66\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: -0.000000  [  300 / 1023]\n",
      "loss: 0.000036  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.001336  [  600 / 1023]\n",
      "loss: 0.006698  [  700 / 1023]\n",
      "loss: 0.000013  [  800 / 1023]\n",
      "loss: 0.000002  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 92.0%, Avg loss: 0.218213 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 94.9%, Avg loss: 0.102206 \n",
      "\n",
      "Epoch 67\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: -0.000000  [  300 / 1023]\n",
      "loss: 0.000047  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.000731  [  600 / 1023]\n",
      "loss: 0.000007  [  700 / 1023]\n",
      "loss: 0.040044  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 89.0%, Avg loss: 0.317401 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 96.3%, Avg loss: 0.088361 \n",
      "\n",
      "Epoch 68\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: 0.000116  [  300 / 1023]\n",
      "loss: 0.000426  [  400 / 1023]\n",
      "loss: 0.000000  [  500 / 1023]\n",
      "loss: 0.008419  [  600 / 1023]\n",
      "loss: 0.000819  [  700 / 1023]\n",
      "loss: 0.000441  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 91.0%, Avg loss: 0.225097 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 97.3%, Avg loss: 0.046517 \n",
      "\n",
      "Epoch 69\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: 0.000000  [  300 / 1023]\n",
      "loss: 0.000614  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.020035  [  600 / 1023]\n",
      "loss: 0.023464  [  700 / 1023]\n",
      "loss: 0.005618  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 92.0%, Avg loss: 0.162796 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 97.4%, Avg loss: 0.045863 \n",
      "\n",
      "Epoch 70\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: -0.000000  [  300 / 1023]\n",
      "loss: 0.000017  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.001824  [  600 / 1023]\n",
      "loss: 0.005382  [  700 / 1023]\n",
      "loss: 0.001382  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 92.0%, Avg loss: 0.170330 \n",
      "\n",
      "Better accuracy: 0.9761646803900325, saving to disk...\n",
      "Train Error: \n",
      " Accuracy: 97.6%, Avg loss: 0.039342 \n",
      "\n",
      "Epoch 71\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: -0.000000  [  300 / 1023]\n",
      "loss: 0.000001  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.000922  [  600 / 1023]\n",
      "loss: 0.031767  [  700 / 1023]\n",
      "loss: 1.059862  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 90.0%, Avg loss: 0.324741 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 96.4%, Avg loss: 0.078577 \n",
      "\n",
      "Epoch 72\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: -0.000000  [  300 / 1023]\n",
      "loss: 0.000285  [  400 / 1023]\n",
      "loss: 0.000000  [  500 / 1023]\n",
      "loss: 0.004108  [  600 / 1023]\n",
      "loss: 0.037412  [  700 / 1023]\n",
      "loss: 0.000533  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 85.0%, Avg loss: 0.712218 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 91.8%, Avg loss: 0.310196 \n",
      "\n",
      "Epoch 73\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: 0.000000  [  300 / 1023]\n",
      "loss: 0.000133  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.034422  [  600 / 1023]\n",
      "loss: 0.054856  [  700 / 1023]\n",
      "loss: 0.008375  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 91.0%, Avg loss: 0.156093 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 97.4%, Avg loss: 0.047073 \n",
      "\n",
      "Epoch 74\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: 0.000000  [  300 / 1023]\n",
      "loss: 0.000004  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.003320  [  600 / 1023]\n",
      "loss: 0.009142  [  700 / 1023]\n",
      "loss: 0.004873  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 91.0%, Avg loss: 0.185503 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 97.1%, Avg loss: 0.050893 \n",
      "\n",
      "Epoch 75\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: 0.000001  [  300 / 1023]\n",
      "loss: 0.000672  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.002027  [  600 / 1023]\n",
      "loss: 0.000708  [  700 / 1023]\n",
      "loss: 0.000439  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 91.0%, Avg loss: 0.229713 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 97.2%, Avg loss: 0.044758 \n",
      "\n",
      "Epoch 76\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: -0.000000  [  300 / 1023]\n",
      "loss: 0.000032  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.000948  [  600 / 1023]\n",
      "loss: 0.076778  [  700 / 1023]\n",
      "loss: 0.000474  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 90.0%, Avg loss: 0.269576 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 96.7%, Avg loss: 0.075150 \n",
      "\n",
      "Epoch 77\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: -0.000000  [  300 / 1023]\n",
      "loss: 0.000070  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.004295  [  600 / 1023]\n",
      "loss: 0.021297  [  700 / 1023]\n",
      "loss: 0.004688  [  800 / 1023]\n",
      "loss: 0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 89.0%, Avg loss: 0.264746 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 96.2%, Avg loss: 0.098891 \n",
      "\n",
      "Epoch 78\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: 0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: 0.000002  [  300 / 1023]\n",
      "loss: 0.000997  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.011995  [  600 / 1023]\n",
      "loss: 0.017619  [  700 / 1023]\n",
      "loss: 0.006749  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 91.0%, Avg loss: 0.188977 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 97.1%, Avg loss: 0.046996 \n",
      "\n",
      "Epoch 79\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: 0.000008  [  300 / 1023]\n",
      "loss: 0.000327  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.008034  [  600 / 1023]\n",
      "loss: 0.005063  [  700 / 1023]\n",
      "loss: 0.001782  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 91.0%, Avg loss: 0.200690 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 97.2%, Avg loss: 0.044272 \n",
      "\n",
      "Epoch 80\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: 0.000000  [  300 / 1023]\n",
      "loss: 0.000075  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.002257  [  600 / 1023]\n",
      "loss: 0.000422  [  700 / 1023]\n",
      "loss: 0.000195  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 91.0%, Avg loss: 0.226167 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 97.6%, Avg loss: 0.038899 \n",
      "\n",
      "Epoch 81\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: -0.000000  [  300 / 1023]\n",
      "loss: 0.000006  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.000481  [  600 / 1023]\n",
      "loss: 0.000584  [  700 / 1023]\n",
      "loss: 0.000459  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 85.0%, Avg loss: 0.692705 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 92.8%, Avg loss: 0.241842 \n",
      "\n",
      "Epoch 82\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: 0.000001  [  300 / 1023]\n",
      "loss: 0.040940  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.273631  [  600 / 1023]\n",
      "loss: 0.006293  [  700 / 1023]\n",
      "loss: 0.001526  [  800 / 1023]\n",
      "loss: 0.000005  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 89.0%, Avg loss: 0.276661 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 95.2%, Avg loss: 0.095257 \n",
      "\n",
      "Epoch 83\n",
      "-------------------------------\n",
      "loss: 0.000007  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: 0.000013  [  200 / 1023]\n",
      "loss: 0.000078  [  300 / 1023]\n",
      "loss: 0.007711  [  400 / 1023]\n",
      "loss: 0.000003  [  500 / 1023]\n",
      "loss: 0.030271  [  600 / 1023]\n",
      "loss: 0.009164  [  700 / 1023]\n",
      "loss: 0.001113  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 91.0%, Avg loss: 0.203091 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 97.5%, Avg loss: 0.043470 \n",
      "\n",
      "Epoch 84\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: -0.000000  [  300 / 1023]\n",
      "loss: 0.000310  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.003505  [  600 / 1023]\n",
      "loss: 0.002066  [  700 / 1023]\n",
      "loss: 0.000064  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 90.0%, Avg loss: 0.302078 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 96.5%, Avg loss: 0.058948 \n",
      "\n",
      "Epoch 85\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: 0.000000  [  300 / 1023]\n",
      "loss: 0.000072  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.001308  [  600 / 1023]\n",
      "loss: 0.000418  [  700 / 1023]\n",
      "loss: 0.000053  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 92.0%, Avg loss: 0.258387 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 97.6%, Avg loss: 0.039314 \n",
      "\n",
      "Epoch 86\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: -0.000000  [  300 / 1023]\n",
      "loss: 0.009827  [  400 / 1023]\n",
      "loss: 0.000008  [  500 / 1023]\n",
      "loss: 0.024905  [  600 / 1023]\n",
      "loss: 0.082314  [  700 / 1023]\n",
      "loss: 0.001020  [  800 / 1023]\n",
      "loss: 0.000003  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 91.0%, Avg loss: 0.205036 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 97.6%, Avg loss: 0.048789 \n",
      "\n",
      "Epoch 87\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: 0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: 0.000000  [  300 / 1023]\n",
      "loss: 0.001270  [  400 / 1023]\n",
      "loss: 0.000000  [  500 / 1023]\n",
      "loss: 0.005620  [  600 / 1023]\n",
      "loss: 0.018759  [  700 / 1023]\n",
      "loss: 0.000511  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 90.0%, Avg loss: 0.228826 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 96.1%, Avg loss: 0.068464 \n",
      "\n",
      "Epoch 88\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: -0.000000  [  300 / 1023]\n",
      "loss: 0.000107  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.004384  [  600 / 1023]\n",
      "loss: 0.003826  [  700 / 1023]\n",
      "loss: 0.002609  [  800 / 1023]\n",
      "loss: 0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 90.0%, Avg loss: 0.191349 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 97.6%, Avg loss: 0.040075 \n",
      "\n",
      "Epoch 89\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: -0.000000  [  300 / 1023]\n",
      "loss: 0.000002  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.000121  [  600 / 1023]\n",
      "loss: 0.000351  [  700 / 1023]\n",
      "loss: 0.000034  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 89.0%, Avg loss: 0.737895 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 93.6%, Avg loss: 0.529703 \n",
      "\n",
      "Epoch 90\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: 0.000005  [  200 / 1023]\n",
      "loss: 0.001671  [  300 / 1023]\n",
      "loss: 0.000817  [  400 / 1023]\n",
      "loss: 0.000001  [  500 / 1023]\n",
      "loss: 0.011360  [  600 / 1023]\n",
      "loss: 0.075510  [  700 / 1023]\n",
      "loss: 0.004244  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 90.0%, Avg loss: 0.220201 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 96.1%, Avg loss: 0.091203 \n",
      "\n",
      "Epoch 91\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: 0.000021  [  300 / 1023]\n",
      "loss: 0.001672  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.007270  [  600 / 1023]\n",
      "loss: 0.008700  [  700 / 1023]\n",
      "loss: 0.000477  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 90.0%, Avg loss: 0.266042 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 96.3%, Avg loss: 0.068448 \n",
      "\n",
      "Epoch 92\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: -0.000000  [  300 / 1023]\n",
      "loss: 0.000093  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.002310  [  600 / 1023]\n",
      "loss: 0.002359  [  700 / 1023]\n",
      "loss: 0.000218  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 89.0%, Avg loss: 0.272521 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 95.4%, Avg loss: 0.109168 \n",
      "\n",
      "Epoch 93\n",
      "-------------------------------\n",
      "loss: 0.000000  [    0 / 1023]\n",
      "loss: 0.000018  [  100 / 1023]\n",
      "loss: 0.000000  [  200 / 1023]\n",
      "loss: 0.000062  [  300 / 1023]\n",
      "loss: 0.001118  [  400 / 1023]\n",
      "loss: 0.000000  [  500 / 1023]\n",
      "loss: 0.012117  [  600 / 1023]\n",
      "loss: 0.000666  [  700 / 1023]\n",
      "loss: 0.000046  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 92.0%, Avg loss: 0.246460 \n",
      "\n",
      "Better accuracy: 0.9772481040086674, saving to disk...\n",
      "Train Error: \n",
      " Accuracy: 97.7%, Avg loss: 0.037610 \n",
      "\n",
      "Epoch 94\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: 0.000002  [  300 / 1023]\n",
      "loss: 0.000164  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.001687  [  600 / 1023]\n",
      "loss: 0.000328  [  700 / 1023]\n",
      "loss: 0.000013  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 92.0%, Avg loss: 0.249244 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 97.7%, Avg loss: 0.034234 \n",
      "\n",
      "Epoch 95\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: 0.000000  [  300 / 1023]\n",
      "loss: 0.000030  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.000425  [  600 / 1023]\n",
      "loss: 0.000088  [  700 / 1023]\n",
      "loss: 0.000004  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 91.0%, Avg loss: 0.277495 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 97.7%, Avg loss: 0.033650 \n",
      "\n",
      "Epoch 96\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: 0.000002  [  300 / 1023]\n",
      "loss: 0.000067  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.004487  [  600 / 1023]\n",
      "loss: 0.000133  [  700 / 1023]\n",
      "loss: 0.000004  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 91.0%, Avg loss: 0.291604 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 97.7%, Avg loss: 0.036459 \n",
      "\n",
      "Epoch 97\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: -0.000000  [  300 / 1023]\n",
      "loss: 0.000004  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.000012  [  600 / 1023]\n",
      "loss: 0.000002  [  700 / 1023]\n",
      "loss: 0.000049  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 86.0%, Avg loss: 0.538522 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 91.8%, Avg loss: 0.260173 \n",
      "\n",
      "Epoch 98\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: 0.000029  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: -0.000000  [  300 / 1023]\n",
      "loss: 0.023942  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.000043  [  600 / 1023]\n",
      "loss: 0.000208  [  700 / 1023]\n",
      "loss: 0.000001  [  800 / 1023]\n",
      "loss: 0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 90.0%, Avg loss: 0.281621 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 97.7%, Avg loss: 0.038667 \n",
      "\n",
      "Epoch 99\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: 0.000000  [  300 / 1023]\n",
      "loss: 0.000046  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.000021  [  600 / 1023]\n",
      "loss: 0.000079  [  700 / 1023]\n",
      "loss: 0.000001  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 90.0%, Avg loss: 0.367920 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 97.7%, Avg loss: 0.035887 \n",
      "\n",
      "Epoch 100\n",
      "-------------------------------\n",
      "loss: -0.000000  [    0 / 1023]\n",
      "loss: -0.000000  [  100 / 1023]\n",
      "loss: -0.000000  [  200 / 1023]\n",
      "loss: -0.000000  [  300 / 1023]\n",
      "loss: 0.000011  [  400 / 1023]\n",
      "loss: -0.000000  [  500 / 1023]\n",
      "loss: 0.000006  [  600 / 1023]\n",
      "loss: 0.000175  [  700 / 1023]\n",
      "loss: 0.000001  [  800 / 1023]\n",
      "loss: -0.000000  [  900 / 1023]\n",
      "Test Error: \n",
      " Accuracy: 91.0%, Avg loss: 0.364635 \n",
      "\n",
      "Train Error: \n",
      " Accuracy: 97.7%, Avg loss: 0.035300 \n",
      "\n",
      "Done!\n",
      "Saved PyTorch Model State to model.pth\n"
     ]
    }
   ],
   "source": [
    "epochs = 100\n",
    "best_correct = 0\n",
    "model = model.float()\n",
    "for t in range(epochs):\n",
    "    print(f\"Epoch {t+1}\\n-------------------------------\")\n",
    "    train(train_ds, model, loss_fn, optimizer)\n",
    "    test(\"Test\", test_ds, model, loss_fn)\n",
    "    best_correct = test(\"Train\", train_ds, model, loss_fn, best_correct)\n",
    "print(\"Done!\")\n",
    "\n",
    "torch.save(model.state_dict(), \"model.pth\")\n",
    "print(\"Saved PyTorch Model State to model.pth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = NeuralNetwork()\n",
    "model.load_state_dict(torch.load(\"model.pth\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.eval()\n",
    "\n",
    "with torch.no_grad():\n",
    "    # Go through each thing and make it super big, then super small, then detect its impact on our predictions\n",
    "    size = data.shape[1]\n",
    "    input_val = np.zeros([size, size])\n",
    "    for pos, _ in enumerate(input_val):\n",
    "        input_val[pos][pos] = 3\n",
    "    inp_t = torch.from_numpy(input_val).float()\n",
    "    pred = nn.Softmax(dim=1)(model(inp_t)).numpy()\n",
    "    \n",
    "    # Now find the largest predictions\n",
    "    genes = data.data.index\n",
    "    new_arr = np.zeros([size, 2])\n",
    "    new_arr[:,0] = pred[:,1]\n",
    "\n",
    "    for pos, _ in enumerate(input_val):\n",
    "        input_val[pos][pos] = -3\n",
    "    inp_t = torch.from_numpy(input_val).float()\n",
    "    pred = nn.Softmax(dim=1)(model(inp_t)).numpy()\n",
    "    \n",
    "    new_arr[:,1] = pred[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_ds = pd.DataFrame(new_arr, index=data.data.columns, columns=[\"Upregulated\", \"Downregulated\"])\n",
    "\n",
    "# Go through, sort, and create new column\n",
    "pd_ds[\"Max\"] = pd_ds[[\"Upregulated\", \"Downregulated\"]].max(axis=1)\n",
    "\n",
    "vals = pd_ds.sort_values(by=[\"Max\"], ascending=False)\n",
    "vals.to_csv(\"interesting.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Upregulated</th>\n",
       "      <th>Downregulated</th>\n",
       "      <th>Max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gene Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>USP18</th>\n",
       "      <td>0.807227</td>\n",
       "      <td>0.234586</td>\n",
       "      <td>0.807227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XIST</th>\n",
       "      <td>0.803813</td>\n",
       "      <td>0.049959</td>\n",
       "      <td>0.803813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALMS1</th>\n",
       "      <td>0.209829</td>\n",
       "      <td>0.798347</td>\n",
       "      <td>0.798347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVIL</th>\n",
       "      <td>0.215426</td>\n",
       "      <td>0.793529</td>\n",
       "      <td>0.793529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUTS2</th>\n",
       "      <td>0.203965</td>\n",
       "      <td>0.788753</td>\n",
       "      <td>0.788753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ISG15</th>\n",
       "      <td>0.782489</td>\n",
       "      <td>0.313925</td>\n",
       "      <td>0.782489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HERC5</th>\n",
       "      <td>0.781336</td>\n",
       "      <td>0.358409</td>\n",
       "      <td>0.781336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TUBA1A</th>\n",
       "      <td>0.425195</td>\n",
       "      <td>0.777822</td>\n",
       "      <td>0.777822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IFITM3</th>\n",
       "      <td>0.769035</td>\n",
       "      <td>0.304145</td>\n",
       "      <td>0.769035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SCRN1</th>\n",
       "      <td>0.238471</td>\n",
       "      <td>0.766853</td>\n",
       "      <td>0.766853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBE2J1</th>\n",
       "      <td>0.764786</td>\n",
       "      <td>0.411271</td>\n",
       "      <td>0.764786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MX1</th>\n",
       "      <td>0.763767</td>\n",
       "      <td>0.337357</td>\n",
       "      <td>0.763767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TYMS</th>\n",
       "      <td>0.753593</td>\n",
       "      <td>0.480073</td>\n",
       "      <td>0.753593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MMP9</th>\n",
       "      <td>0.745355</td>\n",
       "      <td>0.391105</td>\n",
       "      <td>0.745355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HSPA1B</th>\n",
       "      <td>0.233657</td>\n",
       "      <td>0.741162</td>\n",
       "      <td>0.741162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CD47</th>\n",
       "      <td>0.443180</td>\n",
       "      <td>0.734502</td>\n",
       "      <td>0.734502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RSAD2</th>\n",
       "      <td>0.733731</td>\n",
       "      <td>0.486058</td>\n",
       "      <td>0.733731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ITM2A</th>\n",
       "      <td>0.728887</td>\n",
       "      <td>0.335530</td>\n",
       "      <td>0.728887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANXA3</th>\n",
       "      <td>0.727040</td>\n",
       "      <td>0.374201</td>\n",
       "      <td>0.727040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IFITM2</th>\n",
       "      <td>0.722684</td>\n",
       "      <td>0.371887</td>\n",
       "      <td>0.722684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT1E</th>\n",
       "      <td>0.719447</td>\n",
       "      <td>0.412483</td>\n",
       "      <td>0.719447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLEK2</th>\n",
       "      <td>0.514225</td>\n",
       "      <td>0.717874</td>\n",
       "      <td>0.717874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCZ1B</th>\n",
       "      <td>0.282295</td>\n",
       "      <td>0.714992</td>\n",
       "      <td>0.714992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KIAA1324</th>\n",
       "      <td>0.373107</td>\n",
       "      <td>0.713620</td>\n",
       "      <td>0.713620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RRM2</th>\n",
       "      <td>0.713476</td>\n",
       "      <td>0.484821</td>\n",
       "      <td>0.713476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FOS</th>\n",
       "      <td>0.712022</td>\n",
       "      <td>0.412483</td>\n",
       "      <td>0.712022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SGK1</th>\n",
       "      <td>0.710193</td>\n",
       "      <td>0.393490</td>\n",
       "      <td>0.710193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRAK2</th>\n",
       "      <td>0.709501</td>\n",
       "      <td>0.354591</td>\n",
       "      <td>0.709501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YWHAB</th>\n",
       "      <td>0.495307</td>\n",
       "      <td>0.706648</td>\n",
       "      <td>0.706648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGALS3BP</th>\n",
       "      <td>0.705889</td>\n",
       "      <td>0.382123</td>\n",
       "      <td>0.705889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARG1</th>\n",
       "      <td>0.702860</td>\n",
       "      <td>0.541625</td>\n",
       "      <td>0.702860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACAP1</th>\n",
       "      <td>0.702574</td>\n",
       "      <td>0.420915</td>\n",
       "      <td>0.702574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SLPI</th>\n",
       "      <td>0.700236</td>\n",
       "      <td>0.489809</td>\n",
       "      <td>0.700236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTTG1</th>\n",
       "      <td>0.699003</td>\n",
       "      <td>0.489698</td>\n",
       "      <td>0.699003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSMA6</th>\n",
       "      <td>0.698070</td>\n",
       "      <td>0.451536</td>\n",
       "      <td>0.698070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MMUT</th>\n",
       "      <td>0.697145</td>\n",
       "      <td>0.356164</td>\n",
       "      <td>0.697145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EIF2AK2</th>\n",
       "      <td>0.696500</td>\n",
       "      <td>0.477419</td>\n",
       "      <td>0.696500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRDM1</th>\n",
       "      <td>0.696181</td>\n",
       "      <td>0.517914</td>\n",
       "      <td>0.696181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CDKN1C</th>\n",
       "      <td>0.462441</td>\n",
       "      <td>0.695610</td>\n",
       "      <td>0.695610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MYLIP</th>\n",
       "      <td>0.694991</td>\n",
       "      <td>0.536341</td>\n",
       "      <td>0.694991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SCO2</th>\n",
       "      <td>0.694302</td>\n",
       "      <td>0.541055</td>\n",
       "      <td>0.694302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OAS3</th>\n",
       "      <td>0.692195</td>\n",
       "      <td>0.455369</td>\n",
       "      <td>0.692195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRAPPC10</th>\n",
       "      <td>0.377685</td>\n",
       "      <td>0.689504</td>\n",
       "      <td>0.689504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPATS2L</th>\n",
       "      <td>0.689488</td>\n",
       "      <td>0.521333</td>\n",
       "      <td>0.689488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GAPDH</th>\n",
       "      <td>0.689178</td>\n",
       "      <td>0.429462</td>\n",
       "      <td>0.689178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OASL</th>\n",
       "      <td>0.689077</td>\n",
       "      <td>0.503942</td>\n",
       "      <td>0.689077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRBD1</th>\n",
       "      <td>0.688654</td>\n",
       "      <td>0.365472</td>\n",
       "      <td>0.688654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HSPH1</th>\n",
       "      <td>0.480984</td>\n",
       "      <td>0.687914</td>\n",
       "      <td>0.687914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PDZK1IP1</th>\n",
       "      <td>0.472619</td>\n",
       "      <td>0.686345</td>\n",
       "      <td>0.686345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DBT</th>\n",
       "      <td>0.541055</td>\n",
       "      <td>0.686198</td>\n",
       "      <td>0.686198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IFIT1</th>\n",
       "      <td>0.685982</td>\n",
       "      <td>0.511462</td>\n",
       "      <td>0.685982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZC2HC1A</th>\n",
       "      <td>0.481405</td>\n",
       "      <td>0.685965</td>\n",
       "      <td>0.685965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LY6E</th>\n",
       "      <td>0.685824</td>\n",
       "      <td>0.518043</td>\n",
       "      <td>0.685824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMC2</th>\n",
       "      <td>0.685169</td>\n",
       "      <td>0.522384</td>\n",
       "      <td>0.685169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COX16</th>\n",
       "      <td>0.684396</td>\n",
       "      <td>0.460755</td>\n",
       "      <td>0.684396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GATAD1</th>\n",
       "      <td>0.347622</td>\n",
       "      <td>0.683330</td>\n",
       "      <td>0.683330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HBS1L</th>\n",
       "      <td>0.683275</td>\n",
       "      <td>0.353460</td>\n",
       "      <td>0.683275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DDX60</th>\n",
       "      <td>0.682243</td>\n",
       "      <td>0.474626</td>\n",
       "      <td>0.682243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TUT4</th>\n",
       "      <td>0.480245</td>\n",
       "      <td>0.681354</td>\n",
       "      <td>0.681354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DR1</th>\n",
       "      <td>0.408887</td>\n",
       "      <td>0.681203</td>\n",
       "      <td>0.681203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TSPYL1</th>\n",
       "      <td>0.492360</td>\n",
       "      <td>0.681009</td>\n",
       "      <td>0.681009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAG3</th>\n",
       "      <td>0.426171</td>\n",
       "      <td>0.680850</td>\n",
       "      <td>0.680850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PNP</th>\n",
       "      <td>0.680290</td>\n",
       "      <td>0.426306</td>\n",
       "      <td>0.680290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PJA1</th>\n",
       "      <td>0.443888</td>\n",
       "      <td>0.680275</td>\n",
       "      <td>0.680275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZCCHC2</th>\n",
       "      <td>0.680249</td>\n",
       "      <td>0.334129</td>\n",
       "      <td>0.680249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZFP36</th>\n",
       "      <td>0.679507</td>\n",
       "      <td>0.487305</td>\n",
       "      <td>0.679507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FBXO7</th>\n",
       "      <td>0.541055</td>\n",
       "      <td>0.679464</td>\n",
       "      <td>0.679464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANKH</th>\n",
       "      <td>0.532154</td>\n",
       "      <td>0.679450</td>\n",
       "      <td>0.679450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CBFA2T2</th>\n",
       "      <td>0.473064</td>\n",
       "      <td>0.678064</td>\n",
       "      <td>0.678064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RPL23</th>\n",
       "      <td>0.677701</td>\n",
       "      <td>0.392780</td>\n",
       "      <td>0.677701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SP140</th>\n",
       "      <td>0.675117</td>\n",
       "      <td>0.489832</td>\n",
       "      <td>0.675117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RBM4</th>\n",
       "      <td>0.480695</td>\n",
       "      <td>0.674795</td>\n",
       "      <td>0.674795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SLC6A8</th>\n",
       "      <td>0.458296</td>\n",
       "      <td>0.674768</td>\n",
       "      <td>0.674768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PURA</th>\n",
       "      <td>0.541055</td>\n",
       "      <td>0.674433</td>\n",
       "      <td>0.674433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAM</th>\n",
       "      <td>0.674105</td>\n",
       "      <td>0.350791</td>\n",
       "      <td>0.674105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HIST1H1C</th>\n",
       "      <td>0.672817</td>\n",
       "      <td>0.489975</td>\n",
       "      <td>0.672817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SLC39A8</th>\n",
       "      <td>0.672671</td>\n",
       "      <td>0.384391</td>\n",
       "      <td>0.672671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMGB2</th>\n",
       "      <td>0.672206</td>\n",
       "      <td>0.438469</td>\n",
       "      <td>0.672206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABCA1</th>\n",
       "      <td>0.671734</td>\n",
       "      <td>0.367860</td>\n",
       "      <td>0.671734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MRPL19</th>\n",
       "      <td>0.469452</td>\n",
       "      <td>0.670889</td>\n",
       "      <td>0.670889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAT</th>\n",
       "      <td>0.281335</td>\n",
       "      <td>0.669854</td>\n",
       "      <td>0.669854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTGS2</th>\n",
       "      <td>0.669519</td>\n",
       "      <td>0.503662</td>\n",
       "      <td>0.669519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAPRE2</th>\n",
       "      <td>0.531223</td>\n",
       "      <td>0.669457</td>\n",
       "      <td>0.669457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GFM1</th>\n",
       "      <td>0.493836</td>\n",
       "      <td>0.669279</td>\n",
       "      <td>0.669279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POGK</th>\n",
       "      <td>0.450264</td>\n",
       "      <td>0.668967</td>\n",
       "      <td>0.668967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BCL2L1</th>\n",
       "      <td>0.525275</td>\n",
       "      <td>0.668867</td>\n",
       "      <td>0.668867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOB1</th>\n",
       "      <td>0.668728</td>\n",
       "      <td>0.497042</td>\n",
       "      <td>0.668728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GLYR1</th>\n",
       "      <td>0.517799</td>\n",
       "      <td>0.668616</td>\n",
       "      <td>0.668616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COMMD4</th>\n",
       "      <td>0.667783</td>\n",
       "      <td>0.484179</td>\n",
       "      <td>0.667783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAP30</th>\n",
       "      <td>0.667406</td>\n",
       "      <td>0.541055</td>\n",
       "      <td>0.667406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BCL11A</th>\n",
       "      <td>0.486920</td>\n",
       "      <td>0.667271</td>\n",
       "      <td>0.667271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATP7A</th>\n",
       "      <td>0.378097</td>\n",
       "      <td>0.667110</td>\n",
       "      <td>0.667110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DDIT4</th>\n",
       "      <td>0.666976</td>\n",
       "      <td>0.532479</td>\n",
       "      <td>0.666976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZNHIT1</th>\n",
       "      <td>0.666269</td>\n",
       "      <td>0.468561</td>\n",
       "      <td>0.666269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRNP</th>\n",
       "      <td>0.439157</td>\n",
       "      <td>0.665691</td>\n",
       "      <td>0.665691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AHCYL1</th>\n",
       "      <td>0.487810</td>\n",
       "      <td>0.665050</td>\n",
       "      <td>0.665050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S100A8</th>\n",
       "      <td>0.665044</td>\n",
       "      <td>0.426825</td>\n",
       "      <td>0.665044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMTA1</th>\n",
       "      <td>0.371623</td>\n",
       "      <td>0.664961</td>\n",
       "      <td>0.664961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOCS2</th>\n",
       "      <td>0.664881</td>\n",
       "      <td>0.483517</td>\n",
       "      <td>0.664881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NFKBIA</th>\n",
       "      <td>0.664802</td>\n",
       "      <td>0.507411</td>\n",
       "      <td>0.664802</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_rows = 1000\n",
    "pd.options.display.max_seq_items = 1000\n",
    "\n",
    "from IPython.display import HTML\n",
    "HTML(vals[:100].to_html())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now save everything > 60% into a lil folder\n",
    "downreg_list = []\n",
    "upreg_list = []\n",
    "for i in vals.index:\n",
    "    loc_pos = vals.loc[i]\n",
    "    if loc_pos[\"Max\"] > 0.65:\n",
    "        # Save into either down or upregulated list\n",
    "        if loc_pos[\"Upregulated\"] > loc_pos[\"Downregulated\"]:\n",
    "            upreg_list.append(i)\n",
    "        else:\n",
    "            downreg_list.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save to disk\n",
    "u = open(\"upregulated_list.csv\", \"w\")\n",
    "u.write(\"\\n\".join(upreg_list))\n",
    "u.close()\n",
    "\n",
    "d = open(\"downregulated_list.csv\", \"w\")\n",
    "d.write(\"\\n\".join(downreg_list))\n",
    "d.close()"
   ]
  }
 ],
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