Train_Seqformer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IDT1Xwv9RZXb"
   },
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "qq-e52uORIVg"
   },
   "outputs": [],
   "source": [
    "import os, random\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import grelu.sequence.format as seqfmt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "qq-e52uORIVg"
   },
   "outputs": [],
   "source": [
    "from tqdm.notebook import tqdm\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "from Seqformer import *\n",
    "from lion_pytorch import Lion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "GEoNmg0aQVf9"
   },
   "outputs": [],
   "source": [
    "# Training settings\n",
    "minibatch_size = 4096\n",
    "batch_size = 4096\n",
    "epochs = 150\n",
    "lr = 0.0003\n",
    "criterion = nn.MSELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "mSO-6jwwlYzO"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    }
   ],
   "source": [
    "seed = 42\n",
    "random.seed(seed)\n",
    "os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "np.random.seed(seed)\n",
    "torch.manual_seed(seed)\n",
    "torch.cuda.manual_seed(seed)\n",
    "torch.cuda.manual_seed_all(seed)\n",
    "\n",
    "torch.set_default_dtype(torch.float)\n",
    "torch.set_num_threads(8)\n",
    "torch.set_float32_matmul_precision('high')\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prefer flash attention\n",
    "torch.backends.cuda.enable_flash_sdp(enabled=True)\n",
    "torch.backends.cuda.enable_mem_efficient_sdp(enabled=True)\n",
    "torch.backends.cuda.enable_math_sdp(enabled=True)\n",
    "torch.backends.cuda.enable_cudnn_sdp(enabled=True)\n",
    "torch.backends.cudnn.deterministic = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZEIyZaYXRkZF"
   },
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(40000, 2) 132\n"
     ]
    }
   ],
   "source": [
    "# Training dataset generated by sim_gene_seqs.ipynb\n",
    "traindata = pd.read_csv(\"./data/train.csv\", delimiter=\",\")\n",
    "trainX = traindata[\"seq\"][0    :20000].apply(str.upper)\n",
    "trainY = traindata[\"score\"][0    :20000]\n",
    "## use half for validation\n",
    "validX = traindata[\"seq\"][20001:24000].apply(str.upper)\n",
    "validY = traindata[\"score\"][20001:24000]\n",
    "print(traindata.shape, len(traindata[\"seq\"][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128\n"
     ]
    }
   ],
   "source": [
    "# feed input with offsets 0-3\n",
    "trainX = pd.concat([\n",
    "    trainX.str.slice(0, 128),\n",
    "    trainX.str.slice(1, 129),\n",
    "    trainX.str.slice(2, 130),\n",
    "    trainX.str.slice(3, 131),\n",
    "], ignore_index=True)\n",
    "validX = validX.str.slice(0,128)\n",
    "\n",
    "trainY = pd.concat([ trainY, trainY, trainY, trainY ], ignore_index=True)\n",
    "print(len(trainX[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "JiIcaG1zSZLA",
    "outputId": "bd4d6e91-6c28-4b42-f242-246250dc55f2"
   },
   "outputs": [],
   "source": [
    "trainX = seqfmt.strings_to_one_hot(trainX.to_list()).transpose(1, 2)\n",
    "validX = seqfmt.strings_to_one_hot(validX.to_list()).transpose(1, 2)\n",
    "trainY = torch.tensor(trainY.to_list()).to(torch.float)\n",
    "validY = torch.tensor(validY.to_list()).to(torch.float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "JiIcaG1zSZLA",
    "outputId": "bd4d6e91-6c28-4b42-f242-246250dc55f2"
   },
   "outputs": [],
   "source": [
    "train_loader = DataLoader(\n",
    "    SeqDataset(trainX, trainY),\n",
    "    batch_size=minibatch_size,\n",
    "    shuffle=True\n",
    ")\n",
    "\n",
    "valid_loader = DataLoader(\n",
    "    SeqDataset(validX, validY),\n",
    "    batch_size=minibatch_size,\n",
    "    shuffle=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TF9yMaRrSvmv"
   },
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seqformer(\n",
      "  (to_patch_embedding): Sequential(\n",
      "    (0): Rearrange('b (n w) c -> b n (w c)', w=4)\n",
      "    (1): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n",
      "    (2): Linear(in_features=16, out_features=64, bias=False)\n",
      "    (3): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "  )\n",
      "  (dropout): Dropout(p=0.2, inplace=False)\n",
      "  (transformer): Transformer(\n",
      "    (layers): ModuleList(\n",
      "      (0-3): 4 x ModuleList(\n",
      "        (0): Attention(\n",
      "          (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (to_q): Linear(in_features=64, out_features=256, bias=False)\n",
      "          (to_kv): Linear(in_features=64, out_features=512, bias=False)\n",
      "          (to_out): Sequential(\n",
      "            (0): Linear(in_features=256, out_features=64, bias=False)\n",
      "            (1): Dropout(p=0.0, inplace=False)\n",
      "          )\n",
      "        )\n",
      "        (1): FeedForward(\n",
      "          (net): Sequential(\n",
      "            (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "            (1): Linear(in_features=64, out_features=256, bias=True)\n",
      "            (2): GELU(approximate='none')\n",
      "            (3): Dropout(p=0.2, inplace=False)\n",
      "            (4): Linear(in_features=256, out_features=64, bias=True)\n",
      "            (5): Dropout(p=0.2, inplace=False)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (head): Sequential(\n",
      "    (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "    (1): Linear(in_features=64, out_features=1, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "## Transformer model - my_model.py\n",
    "## Original code adapted from\n",
    "## https://github.com/lucidrains/vit-pytorch/blob/main/vit_pytorch/vit_1d.py\n",
    "model = Seqformer(\n",
    "    dim=64,\n",
    "    hidden_dim=256,\n",
    "    head_dim=64,\n",
    "    heads=4,\n",
    "    depth=4,\n",
    "    dropout=0.2,\n",
    "    emb_dropout=0.2,\n",
    "    word_len=4,\n",
    "    num_classes=1,\n",
    "    channels=4,\n",
    "    seq_len=len(trainX[0])\n",
    ")\n",
    "print(model)\n",
    "model = torch.compile(model)\n",
    "model = model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#optimizer = optim.AdamW(model.parameters(), lr=lr)\n",
    "optimizer = Lion(model.parameters(), lr=lr)\n",
    "\n",
    "# normalization factor for gradient accumulation\n",
    "normf = minibatch_size / batch_size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
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    "id": "KDBDOcxeU1eR",
    "outputId": "ef6d4d62-e705-4490-90a7-85036ff6ce62",
    "scrolled": true
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[01] Loss: 52.40\tAcc: 0.002\tVloss: 47.22\tVacc: 0.053\n",
      "[02] Loss: 50.10\tAcc: 0.069\tVloss: 46.80\tVacc: 0.102\n",
      "[03] Loss: 49.40\tAcc: 0.125\tVloss: 46.49\tVacc: 0.173\n",
      "[04] Loss: 48.76\tAcc: 0.181\tVloss: 45.39\tVacc: 0.187\n",
      "[05] Loss: 47.45\tAcc: 0.216\tVloss: 44.55\tVacc: 0.250\n",
      "[06] Loss: 46.88\tAcc: 0.265\tVloss: 43.64\tVacc: 0.287\n",
      "[07] Loss: 46.08\tAcc: 0.289\tVloss: 43.11\tVacc: 0.304\n",
      "[08] Loss: 45.71\tAcc: 0.310\tVloss: 42.74\tVacc: 0.305\n",
      "[09] Loss: 44.79\tAcc: 0.326\tVloss: 41.97\tVacc: 0.328\n",
      "[10] Loss: 44.24\tAcc: 0.345\tVloss: 41.34\tVacc: 0.349\n",
      "[11] Loss: 43.83\tAcc: 0.361\tVloss: 41.17\tVacc: 0.351\n",
      "[12] Loss: 43.27\tAcc: 0.373\tVloss: 40.53\tVacc: 0.386\n",
      "[13] Loss: 43.10\tAcc: 0.386\tVloss: 40.37\tVacc: 0.384\n",
      "[14] Loss: 42.05\tAcc: 0.403\tVloss: 40.50\tVacc: 0.387\n",
      "[15] Loss: 41.70\tAcc: 0.415\tVloss: 39.85\tVacc: 0.399\n",
      "[16] Loss: 41.56\tAcc: 0.424\tVloss: 39.70\tVacc: 0.405\n",
      "[17] Loss: 41.23\tAcc: 0.432\tVloss: 39.33\tVacc: 0.405\n",
      "[18] Loss: 40.70\tAcc: 0.442\tVloss: 39.39\tVacc: 0.401\n",
      "[19] Loss: 40.29\tAcc: 0.450\tVloss: 38.28\tVacc: 0.431\n",
      "[20] Loss: 40.01\tAcc: 0.459\tVloss: 38.99\tVacc: 0.412\n",
      "[21] Loss: 39.59\tAcc: 0.463\tVloss: 37.79\tVacc: 0.455\n",
      "[22] Loss: 39.41\tAcc: 0.472\tVloss: 37.81\tVacc: 0.459\n",
      "[23] Loss: 38.96\tAcc: 0.481\tVloss: 38.16\tVacc: 0.444\n",
      "[24] Loss: 38.78\tAcc: 0.488\tVloss: 37.87\tVacc: 0.440\n",
      "[25] Loss: 38.10\tAcc: 0.493\tVloss: 37.99\tVacc: 0.436\n",
      "[26] Loss: 37.68\tAcc: 0.498\tVloss: 37.16\tVacc: 0.457\n",
      "[27] Loss: 37.52\tAcc: 0.505\tVloss: 37.12\tVacc: 0.458\n",
      "[28] Loss: 37.33\tAcc: 0.515\tVloss: 38.08\tVacc: 0.439\n",
      "[29] Loss: 36.76\tAcc: 0.516\tVloss: 37.69\tVacc: 0.450\n",
      "[30] Loss: 36.68\tAcc: 0.522\tVloss: 38.12\tVacc: 0.446\n",
      "[31] Loss: 36.41\tAcc: 0.530\tVloss: 36.94\tVacc: 0.463\n",
      "[32] Loss: 36.07\tAcc: 0.540\tVloss: 37.60\tVacc: 0.449\n",
      "[33] Loss: 35.50\tAcc: 0.550\tVloss: 37.11\tVacc: 0.464\n",
      "[34] Loss: 35.07\tAcc: 0.553\tVloss: 36.78\tVacc: 0.469\n",
      "[35] Loss: 34.59\tAcc: 0.563\tVloss: 36.94\tVacc: 0.464\n",
      "[36] Loss: 34.21\tAcc: 0.572\tVloss: 35.79\tVacc: 0.487\n",
      "[37] Loss: 33.89\tAcc: 0.580\tVloss: 34.66\tVacc: 0.512\n",
      "[38] Loss: 32.91\tAcc: 0.595\tVloss: 35.22\tVacc: 0.505\n",
      "[39] Loss: 32.25\tAcc: 0.608\tVloss: 34.55\tVacc: 0.514\n",
      "[40] Loss: 31.81\tAcc: 0.618\tVloss: 34.30\tVacc: 0.521\n",
      "[41] Loss: 30.99\tAcc: 0.632\tVloss: 34.93\tVacc: 0.511\n",
      "[42] Loss: 30.14\tAcc: 0.649\tVloss: 32.51\tVacc: 0.555\n",
      "[43] Loss: 29.49\tAcc: 0.650\tVloss: 32.81\tVacc: 0.552\n",
      "[44] Loss: 29.28\tAcc: 0.655\tVloss: 32.06\tVacc: 0.564\n",
      "[45] Loss: 28.30\tAcc: 0.677\tVloss: 31.24\tVacc: 0.578\n",
      "[46] Loss: 27.72\tAcc: 0.680\tVloss: 30.99\tVacc: 0.589\n",
      "[47] Loss: 26.72\tAcc: 0.700\tVloss: 28.68\tVacc: 0.625\n",
      "[48] Loss: 25.48\tAcc: 0.715\tVloss: 27.82\tVacc: 0.640\n",
      "[49] Loss: 25.12\tAcc: 0.720\tVloss: 31.35\tVacc: 0.578\n",
      "[50] Loss: 24.10\tAcc: 0.734\tVloss: 26.45\tVacc: 0.667\n",
      "[51] Loss: 23.62\tAcc: 0.742\tVloss: 27.38\tVacc: 0.649\n",
      "[52] Loss: 22.78\tAcc: 0.748\tVloss: 27.47\tVacc: 0.649\n",
      "[53] Loss: 22.01\tAcc: 0.762\tVloss: 24.84\tVacc: 0.692\n",
      "[54] Loss: 20.83\tAcc: 0.780\tVloss: 23.49\tVacc: 0.708\n",
      "[55] Loss: 20.32\tAcc: 0.786\tVloss: 24.56\tVacc: 0.699\n",
      "[56] Loss: 19.43\tAcc: 0.797\tVloss: 25.03\tVacc: 0.685\n",
      "[57] Loss: 19.11\tAcc: 0.802\tVloss: 21.35\tVacc: 0.741\n",
      "[58] Loss: 18.37\tAcc: 0.810\tVloss: 21.80\tVacc: 0.743\n",
      "[59] Loss: 17.77\tAcc: 0.817\tVloss: 22.95\tVacc: 0.715\n",
      "[60] Loss: 16.99\tAcc: 0.823\tVloss: 20.96\tVacc: 0.748\n",
      "[61] Loss: 16.38\tAcc: 0.833\tVloss: 19.49\tVacc: 0.768\n",
      "[62] Loss: 15.90\tAcc: 0.839\tVloss: 18.82\tVacc: 0.775\n",
      "[63] Loss: 15.28\tAcc: 0.844\tVloss: 16.45\tVacc: 0.808\n",
      "[64] Loss: 14.33\tAcc: 0.854\tVloss: 17.23\tVacc: 0.812\n",
      "[65] Loss: 14.43\tAcc: 0.853\tVloss: 15.59\tVacc: 0.822\n",
      "[66] Loss: 13.89\tAcc: 0.859\tVloss: 14.27\tVacc: 0.842\n",
      "[67] Loss: 13.14\tAcc: 0.866\tVloss: 15.27\tVacc: 0.827\n",
      "[68] Loss: 12.73\tAcc: 0.872\tVloss: 15.38\tVacc: 0.822\n",
      "[69] Loss: 12.73\tAcc: 0.871\tVloss: 14.31\tVacc: 0.835\n",
      "[70] Loss: 12.59\tAcc: 0.870\tVloss: 12.09\tVacc: 0.865\n",
      "[71] Loss: 12.44\tAcc: 0.873\tVloss: 13.55\tVacc: 0.844\n",
      "[72] Loss: 11.96\tAcc: 0.879\tVloss: 12.85\tVacc: 0.853\n",
      "[73] Loss: 11.53\tAcc: 0.883\tVloss: 12.76\tVacc: 0.855\n",
      "[74] Loss: 11.33\tAcc: 0.887\tVloss: 13.89\tVacc: 0.840\n",
      "[75] Loss: 10.92\tAcc: 0.891\tVloss: 14.56\tVacc: 0.834\n",
      "[76] Loss: 10.85\tAcc: 0.891\tVloss: 13.13\tVacc: 0.850\n",
      "[77] Loss: 10.79\tAcc: 0.890\tVloss: 12.63\tVacc: 0.856\n",
      "[78] Loss: 10.25\tAcc: 0.896\tVloss: 13.56\tVacc: 0.844\n",
      "[79] Loss: 10.23\tAcc: 0.896\tVloss: 11.68\tVacc: 0.868\n",
      "[80] Loss: 10.41\tAcc: 0.894\tVloss: 14.05\tVacc: 0.838\n",
      "[81] Loss: 9.80\tAcc: 0.900\tVloss: 13.06\tVacc: 0.858\n",
      "[82] Loss: 9.99\tAcc: 0.900\tVloss: 10.25\tVacc: 0.885\n",
      "[83] Loss: 9.84\tAcc: 0.902\tVloss: 10.46\tVacc: 0.885\n",
      "[84] Loss: 9.48\tAcc: 0.905\tVloss: 11.53\tVacc: 0.872\n",
      "[85] Loss: 9.28\tAcc: 0.906\tVloss: 13.47\tVacc: 0.850\n",
      "[86] Loss: 9.15\tAcc: 0.909\tVloss: 11.01\tVacc: 0.879\n",
      "[87] Loss: 8.72\tAcc: 0.912\tVloss: 12.70\tVacc: 0.859\n",
      "[88] Loss: 8.42\tAcc: 0.915\tVloss: 12.48\tVacc: 0.859\n",
      "[89] Loss: 8.50\tAcc: 0.914\tVloss: 10.51\tVacc: 0.881\n",
      "[90] Loss: 8.48\tAcc: 0.915\tVloss: 11.21\tVacc: 0.875\n",
      "[91] Loss: 8.38\tAcc: 0.916\tVloss: 12.39\tVacc: 0.867\n",
      "[92] Loss: 8.45\tAcc: 0.914\tVloss: 12.82\tVacc: 0.856\n",
      "[93] Loss: 8.19\tAcc: 0.918\tVloss: 11.08\tVacc: 0.882\n",
      "[94] Loss: 7.79\tAcc: 0.921\tVloss: 12.27\tVacc: 0.863\n",
      "[95] Loss: 8.02\tAcc: 0.920\tVloss: 10.97\tVacc: 0.878\n",
      "[96] Loss: 7.92\tAcc: 0.921\tVloss: 13.05\tVacc: 0.852\n",
      "[97] Loss: 7.74\tAcc: 0.922\tVloss: 10.79\tVacc: 0.880\n",
      "[98] Loss: 7.86\tAcc: 0.921\tVloss: 10.95\tVacc: 0.878\n",
      "[99] Loss: 7.44\tAcc: 0.926\tVloss: 12.29\tVacc: 0.862\n",
      "[100] Loss: 7.64\tAcc: 0.923\tVloss: 11.63\tVacc: 0.871\n",
      "[101] Loss: 7.31\tAcc: 0.927\tVloss: 11.99\tVacc: 0.866\n",
      "[102] Loss: 7.30\tAcc: 0.927\tVloss: 9.97\tVacc: 0.890\n",
      "[103] Loss: 7.18\tAcc: 0.929\tVloss: 11.27\tVacc: 0.877\n",
      "[104] Loss: 6.95\tAcc: 0.931\tVloss: 11.11\tVacc: 0.876\n",
      "[105] Loss: 7.43\tAcc: 0.925\tVloss: 10.15\tVacc: 0.886\n",
      "[106] Loss: 6.75\tAcc: 0.933\tVloss: 10.23\tVacc: 0.887\n",
      "[107] Loss: 6.67\tAcc: 0.934\tVloss: 10.99\tVacc: 0.878\n",
      "[108] Loss: 6.69\tAcc: 0.933\tVloss: 10.12\tVacc: 0.889\n",
      "[109] Loss: 6.90\tAcc: 0.931\tVloss: 12.21\tVacc: 0.861\n",
      "[110] Loss: 6.43\tAcc: 0.936\tVloss: 11.69\tVacc: 0.869\n",
      "[111] Loss: 6.39\tAcc: 0.937\tVloss: 10.99\tVacc: 0.877\n",
      "[112] Loss: 6.41\tAcc: 0.936\tVloss: 9.88\tVacc: 0.891\n",
      "[113] Loss: 6.29\tAcc: 0.936\tVloss: 11.15\tVacc: 0.876\n",
      "[114] Loss: 6.34\tAcc: 0.937\tVloss: 9.50\tVacc: 0.895\n",
      "[115] Loss: 6.62\tAcc: 0.935\tVloss: 9.86\tVacc: 0.890\n",
      "[116] Loss: 6.29\tAcc: 0.937\tVloss: 10.82\tVacc: 0.877\n",
      "[117] Loss: 6.11\tAcc: 0.940\tVloss: 10.48\tVacc: 0.884\n",
      "[118] Loss: 6.07\tAcc: 0.939\tVloss: 10.05\tVacc: 0.889\n",
      "[119] Loss: 6.26\tAcc: 0.938\tVloss: 9.57\tVacc: 0.894\n",
      "[120] Loss: 5.98\tAcc: 0.941\tVloss: 10.49\tVacc: 0.883\n",
      "[121] Loss: 5.86\tAcc: 0.942\tVloss: 9.64\tVacc: 0.893\n",
      "[122] Loss: 5.70\tAcc: 0.943\tVloss: 9.97\tVacc: 0.889\n",
      "[123] Loss: 6.19\tAcc: 0.939\tVloss: 9.81\tVacc: 0.895\n",
      "[124] Loss: 6.00\tAcc: 0.941\tVloss: 10.69\tVacc: 0.882\n",
      "[125] Loss: 5.86\tAcc: 0.941\tVloss: 9.33\tVacc: 0.896\n",
      "[126] Loss: 5.94\tAcc: 0.939\tVloss: 10.11\tVacc: 0.889\n",
      "[127] Loss: 5.81\tAcc: 0.943\tVloss: 9.34\tVacc: 0.896\n",
      "[128] Loss: 6.30\tAcc: 0.937\tVloss: 10.02\tVacc: 0.889\n",
      "[129] Loss: 5.92\tAcc: 0.941\tVloss: 10.30\tVacc: 0.885\n",
      "[130] Loss: 5.69\tAcc: 0.943\tVloss: 10.96\tVacc: 0.880\n",
      "[131] Loss: 5.70\tAcc: 0.944\tVloss: 10.54\tVacc: 0.887\n",
      "[132] Loss: 5.64\tAcc: 0.944\tVloss: 8.30\tVacc: 0.909\n",
      "[133] Loss: 5.35\tAcc: 0.947\tVloss: 12.06\tVacc: 0.867\n",
      "[134] Loss: 5.72\tAcc: 0.942\tVloss: 10.70\tVacc: 0.882\n",
      "[135] Loss: 5.40\tAcc: 0.946\tVloss: 10.33\tVacc: 0.886\n",
      "[136] Loss: 5.40\tAcc: 0.946\tVloss: 10.38\tVacc: 0.892\n",
      "[137] Loss: 5.16\tAcc: 0.949\tVloss: 10.01\tVacc: 0.889\n",
      "[138] Loss: 5.31\tAcc: 0.948\tVloss: 8.85\tVacc: 0.902\n",
      "[139] Loss: 5.21\tAcc: 0.949\tVloss: 9.84\tVacc: 0.891\n",
      "[140] Loss: 5.27\tAcc: 0.948\tVloss: 9.09\tVacc: 0.899\n",
      "[141] Loss: 5.23\tAcc: 0.948\tVloss: 9.17\tVacc: 0.898\n",
      "[142] Loss: 5.21\tAcc: 0.948\tVloss: 10.82\tVacc: 0.882\n",
      "[143] Loss: 5.34\tAcc: 0.946\tVloss: 8.90\tVacc: 0.902\n",
      "[144] Loss: 5.09\tAcc: 0.950\tVloss: 10.27\tVacc: 0.887\n",
      "[145] Loss: 5.13\tAcc: 0.949\tVloss: 7.79\tVacc: 0.916\n",
      "[146] Loss: 5.47\tAcc: 0.945\tVloss: 8.12\tVacc: 0.912\n",
      "[147] Loss: 5.08\tAcc: 0.950\tVloss: 9.32\tVacc: 0.896\n",
      "[148] Loss: 5.17\tAcc: 0.949\tVloss: 7.83\tVacc: 0.916\n",
      "[149] Loss: 5.09\tAcc: 0.950\tVloss: 8.96\tVacc: 0.900\n",
      "[150] Loss: 5.14\tAcc: 0.949\tVloss: 8.09\tVacc: 0.911\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(epochs):\n",
    "    epoch_loss = 0\n",
    "    epoch_accuracy = 0\n",
    "    count = 0\n",
    "\n",
    "    for data, label in train_loader:\n",
    "        data = data.to(device)\n",
    "        label = label.to(device)\n",
    "\n",
    "        output = model(data).squeeze()\n",
    "        loss = criterion(output, label) * normf\n",
    "        loss.backward()\n",
    "\n",
    "        count += minibatch_size\n",
    "        if count >= batch_size:\n",
    "            optimizer.step()\n",
    "            optimizer.zero_grad()\n",
    "            count=0\n",
    "\n",
    "        acc = np.corrcoef(x=output.detach().cpu(), y=label.detach().cpu())[0,1]\n",
    "        epoch_accuracy += acc / len(train_loader)\n",
    "        epoch_loss += loss / len(train_loader)\n",
    "\n",
    "    optimizer.step()\n",
    "    optimizer.zero_grad()\n",
    "    with torch.no_grad():\n",
    "        epoch_val_accuracy = 0\n",
    "        epoch_val_loss = 0\n",
    "        for data, label in valid_loader:\n",
    "            data = data.to(device)\n",
    "            label = label.to(device)\n",
    "\n",
    "            val_output = model(data).squeeze()\n",
    "            val_loss = criterion(val_output, label)\n",
    "\n",
    "            acc = np.corrcoef(x=val_output.cpu(), y=label.cpu())[0,1]\n",
    "            epoch_val_accuracy += acc / len(valid_loader)\n",
    "            epoch_val_loss += val_loss / len(valid_loader)\n",
    "\n",
    "    if epoch_val_accuracy >= 0.93:\n",
    "        torch.save( model, f\"./models/simtest_v0_e{epoch}.pkl\" )\n",
    "\n",
    "    print(\n",
    "        f\"[{epoch+1:02d}] Loss: {epoch_loss:.2f}\\tAcc: {epoch_accuracy:.3f}\\tVloss: {epoch_val_loss:.2f}\\tVacc: {epoch_val_accuracy:.3f}\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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