mlp_baseline.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "75ef11a9",
   "metadata": {},
   "source": [
    "### Acknowledgement\n",
    "\n",
    "Some of the code in this baseline model consulted the `discussion.ipynb` file provided. The file is originated from CSE 151B Discussion Week 7, Spring 2022."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "718c38cf",
   "metadata": {},
   "source": [
    "## Install the package dependencies before running this notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "16ac7530",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n    number of trajectories in each city\\n    # austin --  train: 43041 test: 6325 \\n    # miami -- train: 55029 test:7971\\n    # pittsburgh -- train: 43544 test: 6361\\n    # dearborn -- train: 24465 test: 3671\\n    # washington-dc -- train: 25744 test: 3829\\n    # palo-alto -- train:  11993 test:1686\\n\\n    trajectories sampled at 10HZ rate, input 5 seconds, output 6 seconds\\n    \\n'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import os, os.path \n",
    "import numpy \n",
    "import pickle\n",
    "from glob import glob\n",
    "import pandas as pd\n",
    "import torch\n",
    "from torch import nn\n",
    "import torch.nn.functional as F\n",
    "from torch.autograd import Variable\n",
    "from torch.optim.lr_scheduler import ReduceLROnPlateau\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "\"\"\"\n",
    "    number of trajectories in each city\n",
    "    # austin --  train: 43041 test: 6325 \n",
    "    # miami -- train: 55029 test:7971\n",
    "    # pittsburgh -- train: 43544 test: 6361\n",
    "    # dearborn -- train: 24465 test: 3671\n",
    "    # washington-dc -- train: 25744 test: 3829\n",
    "    # palo-alto -- train:  11993 test:1686\n",
    "\n",
    "    trajectories sampled at 10HZ rate, input 5 seconds, output 6 seconds\n",
    "    \n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b472cf2",
   "metadata": {},
   "source": [
    "## Create a Torch.Dataset class for the training dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "091abbb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from glob import glob\n",
    "import pickle\n",
    "import numpy as np\n",
    "\n",
    "ROOT_PATH = \"./\"\n",
    "\n",
    "cities = [\"austin\", \"miami\", \"pittsburgh\", \"dearborn\", \"washington-dc\", \"palo-alto\"]\n",
    "splits = [\"train\", \"test\"]\n",
    "\n",
    "def transform_data(np_data, bch_id):\n",
    "    df = pd.DataFrame(np_data[bch_id], columns = ['x','y'])\n",
    "    df['x_vel'] = np.gradient(df.x)\n",
    "    df['y_vel'] = np.gradient(df.y)\n",
    "    df['vel'] = np.sqrt(df.x_vel**2 + df.y_vel**2)\n",
    "    df['x_acc'] = np.gradient(df.x_vel)\n",
    "    df['y_acc'] = np.gradient(df.y_vel)\n",
    "    df['acc'] = np.gradient(df.vel)\n",
    "    tangent = np.array([1/df.vel]*2).T * np.array([df.x_vel, df.y_vel]).T\n",
    "    df['curvature'] = np.abs(df.x_acc * df.y_vel - df.x_vel * df.y_acc) / (df.vel)**3\n",
    "    out = df[['x', 'y', 'curvature']]\n",
    "    return out.to_numpy()\n",
    "\n",
    "\n",
    "def rotate(X, startpoint, endpoint, default_angle):\n",
    "    \n",
    "    # Find the slope of the path\n",
    "    dx = X[:, endpoint, 0] - X[:, startpoint, 0]\n",
    "    dy = X[:, endpoint, 1] - X[:, startpoint, 1]\n",
    "    \n",
    "    # Convert theta to degree in the range(0, 360)\n",
    "    theta = np.arctan2(dy, dx)\n",
    "    angle = np.degrees(theta)\n",
    "    angle[angle < 0] += 360\n",
    "    \n",
    "    # Generate the degree we want to rotate by and convert back to theta\n",
    "    rotate_degree = -1 * (angle - default_angle)\n",
    "    rotate_theta = np.deg2rad(rotate_degree)\n",
    "    \n",
    "    # Reshape the array from [4, batchsize] to [batchsize, 2, 2]\n",
    "    rot = np.array([np.cos(rotate_theta), -np.sin(rotate_theta),\n",
    "                np.sin(rotate_theta), np.cos(rotate_theta)])\n",
    "    rot = rot.T.reshape(-1, 2, 2)\n",
    "    \n",
    "    return rot\n",
    "\n",
    "\n",
    "def get_city_trajectories(city=\"palo-alto\", split=\"train\", normalized=False):\n",
    "    f_in = ROOT_PATH + split + \"/\" + city + \"_inputs\"\n",
    "    inputs = pickle.load(open(f_in, \"rb\"))\n",
    "    inputs = np.asarray(inputs)\n",
    "    \n",
    "    outputs = None\n",
    "    \n",
    "    if split==\"train\":\n",
    "        f_out = ROOT_PATH + split + \"/\" + city + \"_outputs\"\n",
    "        outputs = pickle.load(open(f_out, \"rb\"))\n",
    "        outputs = np.asarray(outputs)\n",
    "\n",
    "    return inputs, outputs\n",
    "\n",
    "class ArgoverseDataset(Dataset):\n",
    "    \"\"\"Dataset class for Argoverse\"\"\"\n",
    "    def __init__(self, city: str, split:str, transform=None, normalized=False):\n",
    "        super(ArgoverseDataset, self).__init__()\n",
    "        self.transform = transform\n",
    "        self.normalized = normalized\n",
    "        self.split = split\n",
    "\n",
    "        self.inputs, self.outputs = self.get_city_trajectories(city=city, split=split)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.inputs)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "\n",
    "        if self.split == 'train':\n",
    "        \n",
    "            data = (self.inputs[idx], self.outputs[idx])\n",
    "\n",
    "#             if self.transform:\n",
    "#                 data = self.transform(data)\n",
    "\n",
    "            return data\n",
    "        \n",
    "        return self.inputs[idx]\n",
    "    \n",
    "    def get_city_trajectories(self, city=\"palo-alto\", split=\"train\"):\n",
    "        assert city in cities and split in splits\n",
    "\n",
    "        # get input\n",
    "        f_in = ROOT_PATH + split + \"/\" + city + \"_inputs\"\n",
    "        inputs = pickle.load(open(f_in, \"rb\"))\n",
    "        inputs = np.asarray(inputs)\n",
    "\n",
    "        # store input starting positions and rotation matrix\n",
    "        start_pos = inputs[:, 0, :].copy()\n",
    "        rotate_factor = rotate(inputs, 0, 29, 30)\n",
    "\n",
    "        # normalize inputs (translation + rotation)\n",
    "        if self.normalized:\n",
    "            for i in range(len(inputs)):\n",
    "                inputs[i] -= start_pos[i, :]\n",
    "                \n",
    "            for i in range(len(inputs)):\n",
    "                inputs[i] = inputs[i] @ rotate_factor[i].T\n",
    "\n",
    "        # get output\n",
    "        outputs = None\n",
    "        if split == \"train\":  # get and normalize outputs\n",
    "            f_out = ROOT_PATH + split + \"/\" + city + \"_outputs\"\n",
    "            outputs = pickle.load(open(f_out, \"rb\"))\n",
    "            outputs = np.asarray(outputs)\n",
    "            if self.normalized:\n",
    "                for i in range(len(inputs)):\n",
    "                    outputs[i] -= start_pos[i, :]\n",
    "                    \n",
    "                for i in range(len(inputs)):\n",
    "                    outputs[i] = outputs[i] @ rotate_factor[i].T\n",
    "                    \n",
    "            # Adding curvature as features\n",
    "            if self.transform:\n",
    "#                 print(inputs.shape)\n",
    "#                 print(outputs.shape)\n",
    "                inputs = np.array([transform_data(inputs, i) for i in range(len(inputs))])\n",
    "                print(inputs.shape)\n",
    "\n",
    "        self.start_pos = start_pos\n",
    "        self.rotate_matrix = rotate_factor # np.linalg.inv(rot[i].T) to reverse back\n",
    "\n",
    "        return inputs, outputs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7cb20029",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11993"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# intialize a dataset\n",
    "city = 'palo-alto' \n",
    "split = 'train' #'test' #'train'\n",
    "train_dataset = ArgoverseDataset(city = city, split = split, transform=False, normalized=True)\n",
    "\n",
    "len(train_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2dc5f2b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11993"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# intialize a dataset\n",
    "city = 'palo-alto' \n",
    "split = 'train' #'test' #'train'\n",
    "train_orig = ArgoverseDataset(city = city, split = split, transform=False, normalized=False)\n",
    "\n",
    "len(train_orig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "058453cc",
   "metadata": {},
   "source": [
    "## Create a DataLoader class for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5c14f0e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_sz = 4  # batch size \n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_sz, drop_last=True)\n",
    "comparison_loader = DataLoader(train_orig, batch_size=batch_sz, drop_last=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5e7f2682",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2998, 2998)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_loader), len(comparison_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4e1ffd54",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([4, 50, 2])\n",
      "torch.Size([4, 60, 2])\n",
      "50\n",
      "60\n"
     ]
    }
   ],
   "source": [
    "for inp, out in train_loader:\n",
    "    print(inp.shape)\n",
    "    print(out.shape)\n",
    "    for bs in range(len(inp)):\n",
    "        print(len(inp[bs]))\n",
    "        print(len(out[bs]))\n",
    "        x = inp\n",
    "        y = out\n",
    "        break\n",
    "    break\n",
    "\n",
    "# # FOR TEST SET\n",
    "# for inp in train_loader:\n",
    "#     for bs in range(len(inp)):\n",
    "#         print(inp.shape)\n",
    "#         print(len(inp[bs]))\n",
    "#         print(inp[0][:5])\n",
    "#         break\n",
    "#     break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "47bb56ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "\n",
    "\n",
    "def show_sample_batch(sample_batch):\n",
    "    \"\"\"visualize the trajectory for a batch of samples\"\"\"\n",
    "    inp, out = sample_batch\n",
    "    batch_sz = inp.size(0)\n",
    "    agent_sz = inp.size(1)\n",
    "    \n",
    "    \n",
    "    fig, axs = plt.subplots(1,batch_sz, figsize=(15, 3), facecolor='w', edgecolor='k')\n",
    "    fig.subplots_adjust(hspace = .2, wspace=.5)\n",
    "    axs = axs.ravel()   \n",
    "    for i in range(4):\n",
    "        axs[i].xaxis.set_ticks([inp[i,0,0],\n",
    "                                   inp[i,-1,0], out[i,-1,0]])\n",
    "        axs[i].yaxis.set_ticks([inp[i,0,1],\n",
    "                                   inp[i,-1,1], out[i,-1,1]])\n",
    "\n",
    "        # first two feature dimensions are (x,y) positions\n",
    "        axs[i].scatter(inp[i,:,0], inp[i,:,1])\n",
    "        axs[i].scatter(out[i,:,0], out[i,:,1])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7126c7cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i_batch, sample_batch in enumerate(train_loader):\n",
    "    inp, out = sample_batch\n",
    "    show_sample_batch(sample_batch)\n",
    "    inp = inp.numpy()\n",
    "    out = out.numpy()\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ce71f7f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0.88875498,  0.45838259],\n",
       "        [-0.45838259,  0.88875498]],\n",
       "\n",
       "       [[-0.46170034,  0.88703596],\n",
       "        [-0.88703596, -0.46170034]],\n",
       "\n",
       "       [[-0.4281046 ,  0.90372919],\n",
       "        [-0.90372919, -0.4281046 ]],\n",
       "\n",
       "       [[-0.92272612, -0.38545624],\n",
       "        [ 0.38545624, -0.92272612]]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for inp, out in comparison_loader:\n",
    "    for bs in range(len(inp)):\n",
    "        x = inp.numpy()\n",
    "        y = out.numpy()\n",
    "        break\n",
    "    break\n",
    "\n",
    "rot = rotate(x, 0, 29, 30)\n",
    "rot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c5d4a3a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f2da4769be0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "after = (y[2]@rot[2].T)\n",
    "plt.scatter(after[:, 0], after[:, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d78d73c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f2c7d146d90>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "convert = after@np.linalg.inv(rot[2].T)\n",
    "plt.scatter(convert[:, 0], convert[:, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a20e694",
   "metadata": {},
   "source": [
    "## Sample a batch of data and visualize "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a840a958",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i_batch, sample_batch in enumerate(train_loader):\n",
    "    inp, out = sample_batch\n",
    "    \"\"\"\n",
    "    TODO:\n",
    "      implement your Deep learning model\n",
    "      implement training routine\n",
    "    \"\"\"\n",
    "    show_sample_batch(sample_batch)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "76da87f8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i_batch, sample_batch in enumerate(comparison_loader):\n",
    "    inp, out = sample_batch\n",
    "    \"\"\"\n",
    "    TODO:\n",
    "      implement your Deep learning model\n",
    "      implement training routine\n",
    "    \"\"\"\n",
    "    show_sample_batch(sample_batch)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d091e61",
   "metadata": {},
   "source": [
    "## Create a AutoEncoder Model for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "86c3f8a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MLP(nn.Module):\n",
    "    \"\"\"\n",
    "    Baseline Model: Fully-Connected Feed-Forward\n",
    "    Input: 50 x (x, y) = 50 x 2\n",
    "    Output: 60 x (x, y) = 60 x 2\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, hidden_dim=64, input_dim=2, output_dim=120, batch_size=16):\n",
    "        super(MLP, self).__init__()\n",
    "        self.input_dim = input_dim\n",
    "        self.hidden_dim = hidden_dim\n",
    "        self.output_dim = output_dim\n",
    "        self.batch_size = batch_size\n",
    "\n",
    "        self.encoder = nn.Sequential(\n",
    "            nn.Linear(input_dim, hidden_dim),  # encoder Shape: [batch, 50, hidden_dim]\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(hidden_dim, hidden_dim),  # interpreter [batch, 50, hidden_dim]\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(hidden_dim, hidden_dim), # [batch, 50, hidden_dim]\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(hidden_dim, hidden_dim), # [batch, 50, hidden_dim]\n",
    "            nn.ReLU()\n",
    "        )\n",
    "        \n",
    "        # After Reshape [batch, hidden_dim*50]\n",
    "\n",
    "        self.decode = nn.Sequential(\n",
    "            nn.Linear(hidden_dim*50, hidden_dim*20),  # decoder  [batch, hidden_dim]\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(hidden_dim*20, hidden_dim),  # interpreter [batch, hidden_dim]\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(hidden_dim, output_dim) # [batch, output_dim]\n",
    "        )\n",
    "        \n",
    "        self.init_weights()\n",
    "        \n",
    "#         nn.Linear(hidden_dim * 50, output_dim)\n",
    "    \n",
    "    def forward(self, X):\n",
    "        step = self.encoder(X)\n",
    "        step = step.reshape(self.batch_size, -1)\n",
    "        return self.decode(step)\n",
    "\n",
    "    def predict(self, X):\n",
    "        pass\n",
    "    \n",
    "    def init_weights(self):\n",
    "        for m in self.modules():\n",
    "#             print(m)\n",
    "            if isinstance(m, nn.Linear):\n",
    "                nn.init.xavier_uniform_(m.weight)\n",
    "                nn.init.constant_(m.bias, 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "19c45bad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "10a1a9c9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def fit(city, split, transform=False, normalized=True, batch_size=32,\n",
    "        hidden_dim=128, input_dim=2, output_dim=120, learning_rate=0.01, num_iters=50):\n",
    "    \n",
    "    # Create train test sets\n",
    "    train_dataset = ArgoverseDataset(city = city, split = split, transform=False, normalized=True)\n",
    "    train_sz = int(len(train_dataset) * 0.9)\n",
    "    val_sz = len(train_dataset) - train_sz\n",
    "    train_loader, val_loader = torch.utils.data.random_split(train_dataset, [train_sz, val_sz])\n",
    "    train_loader = DataLoader(train_loader, batch_size=batch_size, drop_last=True)\n",
    "    val_loader = DataLoader(val_loader, batch_size=batch_size, drop_last=True)\n",
    "\n",
    "    # initialize model\n",
    "    model = MLP(hidden_dim=hidden_dim, input_dim=input_dim, \n",
    "                output_dim=output_dim, batch_size=batch_size).to(device)\n",
    "    loss_function = nn.MSELoss()\n",
    "    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
    "    scheduler = ReduceLROnPlateau(optimizer, mode='min', patience=5)\n",
    "    \n",
    "    # initiate trackers\n",
    "    avg_train_loss, avg_val_loss = [], []\n",
    "    train_time, elapsed_time = [], []\n",
    "    # prev_avg_val, best_val_score = float('inf'), float('inf')\n",
    "    best_train_score = float('inf')\n",
    "    val_increase_num = 0\n",
    "\n",
    "    # train epoch\n",
    "    for epoch in tqdm(list(range(num_iters))):\n",
    "        print(f'Epoch {epoch+1}')\n",
    "        print('Training & Validating ', end='')\n",
    "\n",
    "        start_time = time.time()\n",
    "        train_loss, val_loss = [], []\n",
    "\n",
    "        # train a batch\n",
    "        for batches, (X, y) in enumerate(train_loader):\n",
    "            X = X.to(device)\n",
    "            y = y.to(device)\n",
    "\n",
    "            # track progress\n",
    "            if (batches + 1) % 20 == 0:\n",
    "                print('-', end='')\n",
    "\n",
    "            # train\n",
    "            optimizer.zero_grad()                 # clear gradient\n",
    "            output = model(X.float())                     # forward pass\n",
    "\n",
    "    #         print(y.shape)\n",
    "    #         print(output.shape)\n",
    "\n",
    "            loss = loss_function(output, y.reshape(batch_size, -1).float())   # compute loss\n",
    "            loss.backward()                       # backward pass: gradient\n",
    "            optimizer.step()                      # update parameter\n",
    "            train_loss.append(loss.item())        # loss value for 1 batch\n",
    "\n",
    "        print()\n",
    "        avg_train = np.mean(train_loss)\n",
    "        avg_train_loss.append(avg_train)\n",
    "\n",
    "        end_train_time = time.time()\n",
    "        train_time.append(end_train_time - start_time)\n",
    "\n",
    "        # save better model\n",
    "#         if avg_train < best_train_score:\n",
    "#             best_train_score = avg_train\n",
    "#             torch.save(model, 'best_overfit.pt')\n",
    "#             print('- Model Saved')\n",
    "\n",
    "        # validation\n",
    "        with torch.no_grad():\n",
    "\n",
    "            for batches, (X, y) in enumerate(val_loader):\n",
    "                X = X.to(device)\n",
    "                y = y.to(device)\n",
    "            \n",
    "                output = model(X.float())\n",
    "                loss = loss_function(output, y.reshape(batch_size, -1).float())\n",
    "                val_loss.append(loss.item())\n",
    "\n",
    "            avg_val = np.mean(val_loss)\n",
    "            avg_val_loss.append(avg_val)\n",
    "\n",
    "    # #     # save better model\n",
    "    # #     if avg_val < best_val_score:\n",
    "    # #         best_val_score = avg_val\n",
    "    # #         torch.save(model.state_dict(), 'best_overfit.pt')\n",
    "\n",
    "        end_time = time.time()\n",
    "        elapsed_time.append(end_time - start_time)\n",
    "\n",
    "        print(f'- Training Loss: {avg_train}\\n- Validation Loss: {avg_val}')\n",
    "        print(f'- Train Time: {sum(train_time)}\\n- Elapsed Time: {sum(elapsed_time)}\\n')\n",
    "        \n",
    "        scheduler.step(avg_val)\n",
    "\n",
    "#         # early stop\n",
    "#         if avg_val > prev_avg_val:\n",
    "#             val_increase_num += 1\n",
    "#         else:\n",
    "#             val_increase_num = 0\n",
    "#         prev_avg_val = avg_val\n",
    "#         if val_increase_num >= 5:\n",
    "#             print('EARLY STOPPING INITIATED...')\n",
    "#             break\n",
    "    \n",
    "    return model, (avg_train_loss, avg_val_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fc51a40c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 181.70109817323768\n",
      "- Validation Loss: 127.70834804225612\n",
      "- Train Time: 1.25958251953125\n",
      "- Elapsed Time: 1.2970771789550781\n",
      "\n",
      "Epoch 2\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 105.7579814899569\n",
      "- Validation Loss: 122.46295413455448\n",
      "- Train Time: 2.801421642303467\n",
      "- Elapsed Time: 2.875170946121216\n",
      "\n",
      "Epoch 3\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 103.3312622885322\n",
      "- Validation Loss: 109.97482093604836\n",
      "- Train Time: 4.480900049209595\n",
      "- Elapsed Time: 4.624350309371948\n",
      "\n",
      "Epoch 4\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 71.03532915087058\n",
      "- Validation Loss: 67.12677991712415\n",
      "- Train Time: 6.017111539840698\n",
      "- Elapsed Time: 6.197236061096191\n",
      "\n",
      "Epoch 5\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 50.888037395760286\n",
      "- Validation Loss: 46.516218391624655\n",
      "- Train Time: 7.549373149871826\n",
      "- Elapsed Time: 7.767030715942383\n",
      "\n",
      "Epoch 6\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 43.31293873263396\n",
      "- Validation Loss: 57.2069352639688\n",
      "- Train Time: 9.044995307922363\n",
      "- Elapsed Time: 9.299788236618042\n",
      "\n",
      "Epoch 7\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 49.65525901918949\n",
      "- Validation Loss: 44.34601170307881\n",
      "- Train Time: 10.560123920440674\n",
      "- Elapsed Time: 10.852122783660889\n",
      "\n",
      "Epoch 8\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 42.953918564567225\n",
      "- Validation Loss: 58.10926262108055\n",
      "- Train Time: 12.101399183273315\n",
      "- Elapsed Time: 12.431010007858276\n",
      "\n",
      "Epoch 9\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 40.92747969584932\n",
      "- Validation Loss: 61.82267854020402\n",
      "- Train Time: 13.565239667892456\n",
      "- Elapsed Time: 13.93115758895874\n",
      "\n",
      "Epoch 10\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 43.91436699660666\n",
      "- Validation Loss: 45.50181801254685\n",
      "- Train Time: 14.790862560272217\n",
      "- Elapsed Time: 15.194146633148193\n",
      "\n",
      "Epoch 11\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 39.64889928212152\n",
      "- Validation Loss: 46.06434507627745\n",
      "- Train Time: 16.269163608551025\n",
      "- Elapsed Time: 16.70898461341858\n",
      "\n",
      "Epoch 12\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 40.95927325908084\n",
      "- Validation Loss: 37.68492018209921\n",
      "- Train Time: 17.778031587600708\n",
      "- Elapsed Time: 18.254342079162598\n",
      "\n",
      "Epoch 13\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 37.713401732175925\n",
      "- Validation Loss: 40.27183939959552\n",
      "- Train Time: 19.3092360496521\n",
      "- Elapsed Time: 19.82241988182068\n",
      "\n",
      "Epoch 14\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 42.465971242072676\n",
      "- Validation Loss: 60.7785573392301\n",
      "- Train Time: 20.8377845287323\n",
      "- Elapsed Time: 21.38749408721924\n",
      "\n",
      "Epoch 15\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 37.11443767773999\n",
      "- Validation Loss: 34.35300038311932\n",
      "- Train Time: 22.305419206619263\n",
      "- Elapsed Time: 22.89181900024414\n",
      "\n",
      "Epoch 16\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 35.68993691803794\n",
      "- Validation Loss: 33.40642980627111\n",
      "- Train Time: 23.855006456375122\n",
      "- Elapsed Time: 24.477938652038574\n",
      "\n",
      "Epoch 17\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 36.87339138913933\n",
      "- Validation Loss: 31.09843197384396\n",
      "- Train Time: 25.382883310317993\n",
      "- Elapsed Time: 26.042496919631958\n",
      "\n",
      "Epoch 18\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 35.43250120994953\n",
      "- Validation Loss: 33.43574039356129\n",
      "- Train Time: 26.91437315940857\n",
      "- Elapsed Time: 27.610513925552368\n",
      "\n",
      "Epoch 19\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 34.850083866175986\n",
      "- Validation Loss: 31.177436416213578\n",
      "- Train Time: 28.439961671829224\n",
      "- Elapsed Time: 29.172612190246582\n",
      "\n",
      "Epoch 20\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 35.919278886268685\n",
      "- Validation Loss: 32.349544473596524\n",
      "- Train Time: 30.731913089752197\n",
      "- Elapsed Time: 31.525738954544067\n",
      "\n",
      "Epoch 21\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 37.30754626469371\n",
      "- Validation Loss: 45.38447653280722\n",
      "- Train Time: 32.83607292175293\n",
      "- Elapsed Time: 33.67670464515686\n",
      "\n",
      "Epoch 22\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 34.112585503552715\n",
      "- Validation Loss: 38.59022578677616\n",
      "- Train Time: 34.3018536567688\n",
      "- Elapsed Time: 35.176079988479614\n",
      "\n",
      "Epoch 23\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 35.02702459454183\n",
      "- Validation Loss: 35.82777446025127\n",
      "- Train Time: 35.77651643753052\n",
      "- Elapsed Time: 36.68415427207947\n",
      "\n",
      "Epoch 24\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 26.33234682564212\n",
      "- Validation Loss: 27.44649314880371\n",
      "- Train Time: 37.725399017333984\n",
      "- Elapsed Time: 38.704575300216675\n",
      "\n",
      "Epoch 25\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 25.520503304478677\n",
      "- Validation Loss: 27.131592776324297\n",
      "- Train Time: 39.8194854259491\n",
      "- Elapsed Time: 40.86006140708923\n",
      "\n",
      "Epoch 26\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 25.192364678538517\n",
      "- Validation Loss: 27.029483795166016\n",
      "- Train Time: 41.92195749282837\n",
      "- Elapsed Time: 43.034592390060425\n",
      "\n",
      "Epoch 27\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 24.977438072068754\n",
      "- Validation Loss: 26.90502764727618\n",
      "- Train Time: 44.03718280792236\n",
      "- Elapsed Time: 45.21079134941101\n",
      "\n",
      "Epoch 28\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 24.80741668667213\n",
      "- Validation Loss: 26.68791508030247\n",
      "- Train Time: 46.14694142341614\n",
      "- Elapsed Time: 47.36549472808838\n",
      "\n",
      "Epoch 29\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 24.68971146320377\n",
      "- Validation Loss: 26.2829732121648\n",
      "- Train Time: 48.285059213638306\n",
      "- Elapsed Time: 49.570865631103516\n",
      "\n",
      "Epoch 30\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 24.598823131368846\n",
      "- Validation Loss: 26.099580403920765\n",
      "- Train Time: 50.34864068031311\n",
      "- Elapsed Time: 51.681097984313965\n",
      "\n",
      "Epoch 31\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 24.479931146525487\n",
      "- Validation Loss: 26.115769901791133\n",
      "- Train Time: 52.05857229232788\n",
      "- Elapsed Time: 53.42559576034546\n",
      "\n",
      "Epoch 32\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 24.38385380481754\n",
      "- Validation Loss: 26.20294424005457\n",
      "- Train Time: 53.76449203491211\n",
      "- Elapsed Time: 55.196995973587036\n",
      "\n",
      "Epoch 33\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 24.311548968804697\n",
      "- Validation Loss: 26.111259383124274\n",
      "- Train Time: 56.005839586257935\n",
      "- Elapsed Time: 57.49997925758362\n",
      "\n",
      "Epoch 34\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 24.186099400506176\n",
      "- Validation Loss: 26.175345163087588\n",
      "- Train Time: 57.66119408607483\n",
      "- Elapsed Time: 59.215150117874146\n",
      "\n",
      "Epoch 35\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 24.127377651565507\n",
      "- Validation Loss: 25.793649209512246\n",
      "- Train Time: 59.767335653305054\n",
      "- Elapsed Time: 61.3956401348114\n",
      "\n",
      "Epoch 36\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 23.993744521890974\n",
      "- Validation Loss: 25.950414683367754\n",
      "- Train Time: 61.92367696762085\n",
      "- Elapsed Time: 63.612361907958984\n",
      "\n",
      "Epoch 37\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 23.935832920697038\n",
      "- Validation Loss: 25.840611251624853\n",
      "- Train Time: 63.41961407661438\n",
      "- Elapsed Time: 65.14217209815979\n",
      "\n",
      "Epoch 38\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 23.816963778758968\n",
      "- Validation Loss: 25.9251685271392\n",
      "- Train Time: 64.95226097106934\n",
      "- Elapsed Time: 66.71000409126282\n",
      "\n",
      "Epoch 39\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 23.761745266107848\n",
      "- Validation Loss: 25.666333533622122\n",
      "- Train Time: 66.50135660171509\n",
      "- Elapsed Time: 68.29853773117065\n",
      "\n",
      "Epoch 40\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 23.70580125350273\n",
      "- Validation Loss: 25.674237199731774\n",
      "- Train Time: 68.07578682899475\n",
      "- Elapsed Time: 69.91513299942017\n",
      "\n",
      "Epoch 41\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 23.614855868172576\n",
      "- Validation Loss: 25.830907512355495\n",
      "- Train Time: 70.45843768119812\n",
      "- Elapsed Time: 72.36631751060486\n",
      "\n",
      "Epoch 42\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 23.432167159343685\n",
      "- Validation Loss: 25.58099873001511\n",
      "- Train Time: 72.17631888389587\n",
      "- Elapsed Time: 74.12366199493408\n",
      "\n",
      "Epoch 43\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 23.391793655712455\n",
      "- Validation Loss: 25.476654542459023\n",
      "- Train Time: 73.78810405731201\n",
      "- Elapsed Time: 75.774001121521\n",
      "\n",
      "Epoch 44\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 23.314739617820308\n",
      "- Validation Loss: 25.505312352567106\n",
      "- Train Time: 75.36021399497986\n",
      "- Elapsed Time: 77.38589429855347\n",
      "\n",
      "Epoch 45\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 23.211156862072137\n",
      "- Validation Loss: 25.93903234842661\n",
      "- Train Time: 77.27552652359009\n",
      "- Elapsed Time: 79.36748957633972\n",
      "\n",
      "Epoch 46\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 23.130972116566554\n",
      "- Validation Loss: 25.48691631007839\n",
      "- Train Time: 78.52222537994385\n",
      "- Elapsed Time: 80.64778590202332\n",
      "\n",
      "Epoch 47\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 23.079291936551783\n",
      "- Validation Loss: 25.687679703171188\n",
      "- Train Time: 79.80261850357056\n",
      "- Elapsed Time: 81.96682024002075\n",
      "\n",
      "Epoch 48\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 22.929591057208597\n",
      "- Validation Loss: 25.55445686546532\n",
      "- Train Time: 81.07959747314453\n",
      "- Elapsed Time: 83.28041195869446\n",
      "\n",
      "Epoch 49\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 22.87571772906476\n",
      "- Validation Loss: 25.451826662630648\n",
      "- Train Time: 83.07071423530579\n",
      "- Elapsed Time: 85.33487582206726\n",
      "\n",
      "Epoch 50\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 22.770500788702808\n",
      "- Validation Loss: 25.605690182866276\n",
      "- Train Time: 85.19415545463562\n",
      "- Elapsed Time: 87.5167145729065\n",
      "\n",
      "Epoch 51\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 22.830926681484595\n",
      "- Validation Loss: 25.569264360376305\n",
      "- Train Time: 86.80434942245483\n",
      "- Elapsed Time: 89.16950607299805\n",
      "\n",
      "Epoch 52\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 22.65243998762408\n",
      "- Validation Loss: 25.616633518322093\n",
      "- Train Time: 88.94525790214539\n",
      "- Elapsed Time: 91.35286927223206\n",
      "\n",
      "Epoch 53\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 22.56341052692085\n",
      "- Validation Loss: 25.449212950629157\n",
      "- Train Time: 90.76167941093445\n",
      "- Elapsed Time: 93.23663973808289\n",
      "\n",
      "Epoch 54\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 22.42355227258156\n",
      "- Validation Loss: 25.49797666395033\n",
      "- Train Time: 93.04483008384705\n",
      "- Elapsed Time: 95.59133195877075\n",
      "\n",
      "Epoch 55\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 22.368239596618743\n",
      "- Validation Loss: 25.680267024684596\n",
      "- Train Time: 95.15508890151978\n",
      "- Elapsed Time: 97.763662815094\n",
      "\n",
      "Epoch 56\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 22.238895290329477\n",
      "- Validation Loss: 25.510206815358753\n",
      "- Train Time: 96.67209529876709\n",
      "- Elapsed Time: 99.31885123252869\n",
      "\n",
      "Epoch 57\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 22.21938272753526\n",
      "- Validation Loss: 25.64114320600355\n",
      "- Train Time: 98.27355337142944\n",
      "- Elapsed Time: 100.96057438850403\n",
      "\n",
      "Epoch 58\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 22.129875777383234\n",
      "- Validation Loss: 25.66082237862252\n",
      "- Train Time: 100.268967628479\n",
      "- Elapsed Time: 103.03574562072754\n",
      "\n",
      "Epoch 59\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 22.043945987429748\n",
      "- Validation Loss: 25.728535213985957\n",
      "- Train Time: 102.49203443527222\n",
      "- Elapsed Time: 105.3384017944336\n",
      "\n",
      "Epoch 60\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 21.05057293158964\n",
      "- Validation Loss: 24.85484471192231\n",
      "- Train Time: 104.58958721160889\n",
      "- Elapsed Time: 107.49714994430542\n",
      "\n",
      "Epoch 61\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.826934981416876\n",
      "- Validation Loss: 24.818675814448177\n",
      "- Train Time: 106.25765371322632\n",
      "- Elapsed Time: 109.21756887435913\n",
      "\n",
      "Epoch 62\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.780704344061785\n",
      "- Validation Loss: 24.800450634311986\n",
      "- Train Time: 107.83840942382812\n",
      "- Elapsed Time: 110.83563613891602\n",
      "\n",
      "Epoch 63\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.75275981320118\n",
      "- Validation Loss: 24.78760562071929\n",
      "- Train Time: 109.89013528823853\n",
      "- Elapsed Time: 112.93819952011108\n",
      "\n",
      "Epoch 64\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.722025421323693\n",
      "- Validation Loss: 24.782120446901065\n",
      "- Train Time: 112.00766658782959\n",
      "- Elapsed Time: 115.12002968788147\n",
      "\n",
      "Epoch 65\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.699245517968425\n",
      "- Validation Loss: 24.782025285669274\n",
      "- Train Time: 114.1445677280426\n",
      "- Elapsed Time: 117.30110287666321\n",
      "\n",
      "Epoch 66\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.680097612499836\n",
      "- Validation Loss: 24.761073189812738\n",
      "- Train Time: 115.71752047538757\n",
      "- Elapsed Time: 118.93940234184265\n",
      "\n",
      "Epoch 67\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.666157337607544\n",
      "- Validation Loss: 24.74812920029099\n",
      "- Train Time: 117.32668781280518\n",
      "- Elapsed Time: 120.58316612243652\n",
      "\n",
      "Epoch 68\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.647607739672107\n",
      "- Validation Loss: 24.74141904470083\n",
      "- Train Time: 118.83615231513977\n",
      "- Elapsed Time: 122.12779879570007\n",
      "\n",
      "Epoch 69\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.630002668420357\n",
      "- Validation Loss: 24.733020292746055\n",
      "- Train Time: 120.8909764289856\n",
      "- Elapsed Time: 124.23713541030884\n",
      "\n",
      "Epoch 70\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.612937805560648\n",
      "- Validation Loss: 24.71936037733748\n",
      "- Train Time: 122.51783967018127\n",
      "- Elapsed Time: 125.92891311645508\n",
      "\n",
      "Epoch 71\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.59599218877911\n",
      "- Validation Loss: 24.711940352981156\n",
      "- Train Time: 124.64893317222595\n",
      "- Elapsed Time: 128.1048333644867\n",
      "\n",
      "Epoch 72\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.58038624811597\n",
      "- Validation Loss: 24.699758658537995\n",
      "- Train Time: 126.20228052139282\n",
      "- Elapsed Time: 129.72616863250732\n",
      "\n",
      "Epoch 73\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.564080177499562\n",
      "- Validation Loss: 24.686897071632178\n",
      "- Train Time: 128.7449905872345\n",
      "- Elapsed Time: 132.3394660949707\n",
      "\n",
      "Epoch 74\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.551637740092744\n",
      "- Validation Loss: 24.684584282539987\n",
      "- Train Time: 131.11887526512146\n",
      "- Elapsed Time: 134.78796529769897\n",
      "\n",
      "Epoch 75\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.536085083505874\n",
      "- Validation Loss: 24.6779599576383\n",
      "- Train Time: 132.53674292564392\n",
      "- Elapsed Time: 136.25334978103638\n",
      "\n",
      "Epoch 76\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.51955318875412\n",
      "- Validation Loss: 24.668594231476654\n",
      "- Train Time: 134.02789950370789\n",
      "- Elapsed Time: 137.7782905101776\n",
      "\n",
      "Epoch 77\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.509599988467617\n",
      "- Validation Loss: 24.666806762282913\n",
      "- Train Time: 135.54646968841553\n",
      "- Elapsed Time: 139.33050751686096\n",
      "\n",
      "Epoch 78\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.491392988833194\n",
      "- Validation Loss: 24.66204323639741\n",
      "- Train Time: 137.30294036865234\n",
      "- Elapsed Time: 141.14827728271484\n",
      "\n",
      "Epoch 79\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.47785771423702\n",
      "- Validation Loss: 24.659220154220993\n",
      "- Train Time: 139.64028930664062\n",
      "- Elapsed Time: 143.5473392009735\n",
      "\n",
      "Epoch 80\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.4630450282677\n",
      "- Validation Loss: 24.656337815362054\n",
      "- Train Time: 141.767094373703\n",
      "- Elapsed Time: 145.721257686615\n",
      "\n",
      "Epoch 81\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.45079570566511\n",
      "- Validation Loss: 24.647492769602184\n",
      "- Train Time: 143.2735092639923\n",
      "- Elapsed Time: 147.26120972633362\n",
      "\n",
      "Epoch 82\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.438488455130013\n",
      "- Validation Loss: 24.64110941500277\n",
      "- Train Time: 144.69974303245544\n",
      "- Elapsed Time: 148.72106099128723\n",
      "\n",
      "Epoch 83\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.42206859022644\n",
      "- Validation Loss: 24.64948878417144\n",
      "- Train Time: 146.64673447608948\n",
      "- Elapsed Time: 150.73444485664368\n",
      "\n",
      "Epoch 84\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.41000469785062\n",
      "- Validation Loss: 24.64496017146755\n",
      "- Train Time: 148.76222038269043\n",
      "- Elapsed Time: 152.90530800819397\n",
      "\n",
      "Epoch 85\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.397464411195024\n",
      "- Validation Loss: 24.641300871565534\n",
      "- Train Time: 150.81552410125732\n",
      "- Elapsed Time: 155.0221631526947\n",
      "\n",
      "Epoch 86\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.384546833151525\n",
      "- Validation Loss: 24.64519789412215\n",
      "- Train Time: 153.0473780632019\n",
      "- Elapsed Time: 157.31824398040771\n",
      "\n",
      "Epoch 87\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.371652982355226\n",
      "- Validation Loss: 24.642427032058304\n",
      "- Train Time: 154.5678677558899\n",
      "- Elapsed Time: 158.87582635879517\n",
      "\n",
      "Epoch 88\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.360232182001855\n",
      "- Validation Loss: 24.629077602077174\n",
      "- Train Time: 156.82516479492188\n",
      "- Elapsed Time: 161.1930980682373\n",
      "\n",
      "Epoch 89\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.346115109474795\n",
      "- Validation Loss: 24.63178335653769\n",
      "- Train Time: 158.40669965744019\n",
      "- Elapsed Time: 162.8082137107849\n",
      "\n",
      "Epoch 90\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.33588624495781\n",
      "- Validation Loss: 24.63004071003682\n",
      "- Train Time: 159.9854118824005\n",
      "- Elapsed Time: 164.42037749290466\n",
      "\n",
      "Epoch 91\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.32091383127501\n",
      "- Validation Loss: 24.624797821044922\n",
      "- Train Time: 162.31124567985535\n",
      "- Elapsed Time: 166.82133388519287\n",
      "\n",
      "Epoch 92\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.309195318986117\n",
      "- Validation Loss: 24.62006816348514\n",
      "- Train Time: 164.52398920059204\n",
      "- Elapsed Time: 169.09788703918457\n",
      "\n",
      "Epoch 93\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.295581279947072\n",
      "- Validation Loss: 24.620780403549606\n",
      "- Train Time: 167.01413106918335\n",
      "- Elapsed Time: 171.65950059890747\n",
      "\n",
      "Epoch 94\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.283099311749375\n",
      "- Validation Loss: 24.613664652850176\n",
      "- Train Time: 169.37732887268066\n",
      "- Elapsed Time: 174.09049463272095\n",
      "\n",
      "Epoch 95\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.27166257626225\n",
      "- Validation Loss: 24.613090154286976\n",
      "- Train Time: 171.50716137886047\n",
      "- Elapsed Time: 176.28517746925354\n",
      "\n",
      "Epoch 96\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.26039118016863\n",
      "- Validation Loss: 24.62279054280874\n",
      "- Train Time: 173.84759306907654\n",
      "- Elapsed Time: 178.6926040649414\n",
      "\n",
      "Epoch 97\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.248041147294312\n",
      "- Validation Loss: 24.62524648614832\n",
      "- Train Time: 175.7883858680725\n",
      "- Elapsed Time: 180.6699571609497\n",
      "\n",
      "Epoch 98\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.23586502697772\n",
      "- Validation Loss: 24.621514294598555\n",
      "- Train Time: 177.6152467727661\n",
      "- Elapsed Time: 182.5358760356903\n",
      "\n",
      "Epoch 99\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.219772614781863\n",
      "- Validation Loss: 24.615881842535895\n",
      "- Train Time: 179.00972843170166\n",
      "- Elapsed Time: 183.96384143829346\n",
      "\n",
      "Epoch 100\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 20.208908143312357\n",
      "- Validation Loss: 24.61377089732402\n",
      "- Train Time: 180.45909881591797\n",
      "- Elapsed Time: 185.44688940048218\n",
      "\n"
     ]
    }
   ],
   "source": [
    "city = 'palo-alto' \n",
    "split = 'train' #'test' #'train'\n",
    "batch_size = 32\n",
    "hidden_dim = 64\n",
    "num_iters = 100\n",
    "palo_train_mod, losses = fit(city=city, split=split, batch_size=batch_size,\n",
    "                            hidden_dim=hidden_dim, num_iters=num_iters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9fb00d97",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_loss(start_from, city, split, losses):\n",
    "\n",
    "    avg_train_loss, avg_val_loss = losses\n",
    "    plt.plot(np.sqrt(avg_train_loss[start_from:]), label='train_loss')\n",
    "    plt.plot(np.sqrt(avg_val_loss[start_from:]), label='validation_loss')\n",
    "    plt.title(f'{city} RMSE {split} Loss vs. Epoch')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8dbface1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(2, 'Palo-Alto', 'train', losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4aedcfee",
   "metadata": {},
   "outputs": [],
   "source": [
    "def vis_results(city, split, batch_size, model, idx):\n",
    "    train_dataset = ArgoverseDataset(city = city, split = split, transform=False, normalized=True)\n",
    "    train_loader = DataLoader(train_dataset, batch_size=batch_size)\n",
    "    model.cpu()\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for (X, y) in train_loader:\n",
    "            X = X.cpu()\n",
    "            y = y.cpu()\n",
    "            output = model(X.float().cpu()).reshape(batch_size, -1, 2).cpu()\n",
    "            break\n",
    "    \n",
    "    print(X.shape)\n",
    "    print(y.shape)\n",
    "    print(output.shape)\n",
    "    \n",
    "    x_jump = train_dataset.start_pos[idx, 0]\n",
    "    y_jump = train_dataset.start_pos[idx, 0]\n",
    "    rot = train_dataset.rotate_matrix[idx].T\n",
    "    X = X[idx] @ np.linalg.inv(rot) + train_dataset.start_pos[idx]\n",
    "    y = y[idx] @ np.linalg.inv(rot) + train_dataset.start_pos[idx]\n",
    "    output = output[idx] @ np.linalg.inv(rot) + train_dataset.start_pos[idx]\n",
    "    \n",
    "    plt.scatter(X[:, 0], X[:, 1], label='seed')\n",
    "    plt.scatter(y[:, 0], y[:, 1], label='ground truth')\n",
    "    plt.scatter(output[:, 0], output[:, 1], label='prediction')\n",
    "    plt.title(f'Random Sample From {city}_{split} Projectile Visualization')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "175597ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 50, 2])\n",
      "torch.Size([32, 60, 2])\n",
      "torch.Size([32, 60, 2])\n"
     ]
    },
    {
     "data": {
      "image/png": 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EjjnmGMaMGcOKFSsA+PTTTznhhBM49thjufHGG5t8jiKSQaI1Yo//BbRpV3/bNu285SmWsQ3ciXpmyRdc+/T77K72HqP7ReVurn36fQDOHHZIo9J84YUX6NmzJ+Xl5QBs3bqVU089lWeffZbu3bvz+OOPc/3113P//fdz6aWXcu+993L44Yfz9ttvc8UVV/DKK69QUlLC5ZdfzkUXXcQ999yTnJMVkfSXSCP2y7d4VU+FvbxAkeLGbciA51mMGDHChU8k+NFHH3HkkUcmtP+oma/wReXu/ZYf0rkdb874TqPytHLlSk455RTOO+88Jk2aRJcuXTjxxBPp1897dnpNTQ1FRUU8/fTTdO/enf79+9ft+8033/DRRx/RtWtXNmzYQJs2bdi2bRs9e/Zkx44djcpPKjXksxARvJLE1rX7Ly/sDf+xPGmHMbN3nHMjkpVe1pcs1kUIFLGWJ+KII47gnXfe4W9/+xvXXnstJ598MkcffTRvvfVWve22bdtG586dWbp0acR01PVUJAelcSN2LFnfZtGzc7sGLU/EunXraN++PRdccAFXXnklb7/9Nhs3bqwLFtXV1XzwwQd06tSJvn378sQTTwDeqOf33nsPgFGjRvHYY48B8PDDDzc6LyKSYdK4ETuWrA8WV53Sn3Zt8uota9cmj6tO6R9lj/jef/99jjvuOIYOHcptt93GLbfcwpNPPsk111zDkCFDGDp0KAsXLgS8QHDfffcxZMgQjj76aJ599lkAysrKuOeeezj22GPZunVr409QRNJLvBHYadyIHUvWt1mA18h9x4sfs65yNz07t+OqU/o3unFb6lObhUhAeOM1eIHgtLvrN1Ivm9Psjdhqs2iEM4cdouAgIs0v1gjsYDAYfF5a9HBqiLjVUGZ2v5l9ZWbLA8vuMLMVZrbMzOaaWeewfQ41sx1mdqX/vr2Zlfv7fGBmM5N+JiIiqZahjdeJSKTNYjYQ/tDvl4CBzrnBwErg2rD1dwF/D1t2p3NuADAMGGVmpzY8uyIiaSxDG68TETdYOOfmA1vCls1zzu313y4C6q6EmZ0JrAY+CGy/yzn3qv96D/BucB8RkayQoY3XiUhGb6jp+KUIMysArgFujraxX2V1GvByjG0uNbPFZrZ448aNSciiiEgLGHye15hd2Bsw73d443aGalIDt5ldD+wFQgMFbgbucs7tiDTgzMxaA48CdzvnVkdL1zk3C5gFXm+opuRRRCQpEu3BlIGN14lodMnCzKYCk4Dz3b7+t8cDt5vZGuDnwHVm9pPAbrOAfznnftfY44qntLSUO++8c7/lzzzzDB9++GGD01uzZg2PPPJI3fvZs2fzk5/8JMYeIjkk1CV261rA7ZvPKU2eYtcSGhUszGwiXnXT6c65XaHlzrkxzrk+zrk+wO+AXznnfu/vcytQiBdEcsLevXvjb5RksYJFrPyEBwsRCUjjhxK1lES6zj4KvAX0N7MKM/sR8HugI/CSmS01s3vjpNELuB44CnjX3+ffm579BDXDM21/+ctfMmDAAE4++WSmTJlS9y1/3LhxXHfddYwdO5aysjJefvllhg0bxqBBg5g+fTrffPMNAH369GHTpk0ALF68mHHjxgFeiWH69OmMGzeOfv36cffdd9cd87bbbqN///5897vf5eOPP94vTwsXLuS5557jqquuYujQoaxatWq//EybNo0nn3yybp/QQ5dmzJjBG2+8wdChQ7nrrrsAb1qTiRMncvjhh3P11Vc3+ZqJZKws7hKbqLhtFs65KREW35fAfqWB1xVAambNa4Zn2i5evJinnnqKJUuWsHfvXoYPH84xxxxTt76yspLXX3+dqqoqDj/8cF5++WWOOOIILrroIv74xz/y85//PGb6K1as4NVXX2X79u3079+fyy+/nGXLlvHYY49FPSbAiSeeyOmnn86kSZM455xz9ssPwLRp0yIec+bMmdx555389a9/BbxqqKVLl7JkyRLatm1L//79+elPf0rv3r0bccVEMlxhrygzxeZOp86snxuqOYqPCxYs4IwzzqBdu3Z07NiR0047rd76yZMnA/Dxxx/Tt29fjjjiCACmTp3K/Pnz46ZfXFxM27Zt6datGwcddBBffvklb7zxBmeddRbt27enU6dOnH766QnnN5Sfhho/fjyFhYXk5+dz1FFH8dlnnzUqHZGMl8VdYhOV/cGiGYqP8ebTKigoiLtd69atqa2tBaCqqqreurZt29a9zsvLq2traOyU5qH8hB/XOceePXui7hctHyI5J4u7xCYq+4NFM4yoHD16NM8//zxVVVXs2LGj7ol54QYMGMCaNWv45JNPAHjooYcYO3Ys4LVZvPPOOwA89dRTcY950kknMXfuXHbv3s327dt5/vnnI27XsWNHtm/fHjWd4HGfffZZqqurE9pPJOcNPs97OFFppfc7hwIF5EKwaIbi47HHHsvpp5/OkCFDOPvssxkxYgSFhYX7bZefn8+f//xnzj33XAYNGkSrVq247LLLALjpppsoKSlhzJgx5OXl7bdvuOHDhzN58mSGDh3K97//fcaMGRNxux/84AfccccdDBs2jFWrVu23/pJLLuH111/nuOOO4+23364rdQwePJjWrVszZMiQugZukazWDB1fsllOTFHeHNMB79ixgw4dOrBr1y5OOukkZs2axfDhw5uUZibSFOWSkRKdSjyDaYryxmiGEZWXXnopH374IVVVVUydOjUnA4VIxkp0KnGpkxvBohloAJtIBtO4iQbL/jYLEZFwWTyVeHNRsBCR3KNxEw2mYCEiuUfjJhpMbRYikpuydCrx5qKSRRp47bXXmDRpEgDPPfccM2dGf0R5ZWUlf/jDH+rer1u3rt48UCI5SWMmmp2CRTOqqalp8D6nn346M2bMiLo+PFj07Nmz3iyyIjlHz5poETkRLMpXlzPhyQkMfmAwE56cQPnqyNNzNMSaNWsYMGAAU6dOZfDgwZxzzjns2rWLPn36cMsttzB69GieeOIJ5s2bxwknnMDw4cM599xz2bFjBwAvvPACAwYMYPTo0Tz99NN16QYfOvTll19y1llnMWTIEIYMGcLChQuZMWMGq1atYujQoVx11VWsWbOGgQMHAt4cUxdffDGDBg1i2LBhvPrqq3Vpnn322ZpuXLKTnjXRIrK+zaJ8dTmlC0upqvEm61u/cz2lC0sBKO5X3KS0P/74Y+677z5GjRrF9OnT677x5+fns2DBAjZt2sTZZ5/NP/7xDwoKCvjNb37Db3/7W66++mouueQSXnnlFb797W9HnRX2Zz/7GWPHjmXu3LnU1NSwY8cOZs6cyfLly1m6dCngBa2Qe+65B4D333+fFStWMGHCBFauXAmg6cYle2nMRIvI+pJF2btldYEipKqmirJ3y5qcdu/evRk1ahQAF1xwAQsWLAD2TQm+aNEiPvzwQ0aNGsXQoUN54IEH+Oyzz1ixYgV9+/bl8MMPx8y44IILIqb/yiuvcPnllwPerK+R5p8KWrBgARdeeCHgTWJ42GGH1QULTTcuWUtjJlpE1pcsNuzc0KDlDRE+ZXjofXCK8pNPPplHH3203nZLly5t9HTjscSa50vTjUvWGv+LyPM8acxEUmV9yaJHQY8GLW+Izz//nLfeeguARx99lNGjR9dbP3LkSN588826Kcp37drFypUrGTBgAJ9++mndrLDhwSRk/Pjx/PGPfwS8xvJt27bFnEr8pJNO4uGHHwZg5cqVfP755/Tv37/J5ymS1jRmokVkfbAoGV5Cfl5+vWX5efmUDC9pctpHHnkkDzzwAIMHD2bLli11VUYh3bt3Z/bs2UyZMoXBgwczcuRIVqxYQX5+PrNmzaK4uJjRo0dz2GGHRUy/rKyMV199lUGDBnHMMcfwwQcf0LVrV0aNGsXAgQO56qqr6m1/xRVXUFNTw6BBg5g8eTKzZ8+uV6IQyVo5/qyJlpATU5SXry6n7N0yNuzcQI+CHpQML2ly4/aaNWuYNGkSy5cvb1I6mU5TlIukJ01R3gjF/YqbHBxEpAU0w7NnJDlyIlg0hz59+uR8qUIkqcIfSBQaXAcKGGkgY9ss0r36LBfoM5Ck0uC6tJaRwSI/P5/Nmzfrn1UKOefYvHkz+fn58TcWSYQG16W1jKyG6tWrFxUVFWzcuDHVWclp+fn59OqlgU+SJIW9/PmdIiyXlMvIYNGmTRv69u2b6myISDJpcF1ay8hqKBHJQhpcl9YysmQhIllKDyRKWypZiIhIXAoWIpJ8enJd1lE1lIgklwbXZSWVLEQkuTS4LispWIhIcmlwXVaKGyzM7H4z+8rMlgeW3WFmK8xsmZnNNbPOYfscamY7zOzKwLLbzGytme1I6hmISHrRk+uyUiIli9nAxLBlLwEDnXODgZXAtWHr7wL+HrbseeC4RuRRRDLJ+F94g+mCNLgu48UNFs65+cCWsGXznHOh53IuAuq+MpjZmcBq4IOwfRY559Y3NcMikuY0uC4rJaM31HTgcQAzKwCuAU4Groy1UyxmdilwKcChhx6ahCyKSIvS4Lqs06QGbjO7HtgLPOwvuhm4yznXpHYJ59ws59wI59yI7t27NyUpERFJgkaXLMxsKjAJGO/2zRV+PHCOmd0OdAZqzazKOff7JudURFJDT68TGhkszGwiXnXTWOfcrtBy59yYwDalwA4FCpEMpgF24kuk6+yjwFtAfzOrMLMfAb8HOgIvmdlSM7s3gXRuN7MKoL2fTmkT8y4izU0D7MQXt2ThnJsSYfF9CexXGvb+auDqhHMmIqmnAXbi0whuEYlOA+zEp2AhItFpgJ34FCxEJDoNsBOfpigXkdg0wE5QyUIk9+jBRNIIKlmI5BKNm5BGUslCJJdo3IQ0koKFSC7RuAlpJAULkVyicRPSSAoWIrlE4yakkRQsRHKJxk1II6k3lEiu0bgJaQSVLESyhcZPSDNSyUIkG2j8hDQzlSxEsoHGT0gzU7AQyQYaPyHNTMFCJBto/IQ0MwULkWyg8RPSzBQsRDJBvJ5OGj8hzUy9oUTSXaI9nTR+QpqRShYi6U49nSQNKFiIpDv1dJI0oGAhku7U00nSgIKFSLqI1oitnk6SBtTALZIOEmnEfvkWr+qpsJcXKNSYLS1IwUIkHcRqxA71clJwkBRSNZRIOlAjtqQ5BQuRlhStXUKN2JLmFCxEWkqoXWLrWsDta5dYNkeN2JL2FCxEWkq8dglN1yFpTA3cIi0lXruEGrEljalkIdJS1C4hGSxusDCz+83sKzNbHlh2h5mtMLNlZjbXzDqH7XOome0wsysDy44xs/fN7BMzu9vMLKlnIpJOIjVkq11CMlgiJYvZwMSwZS8BA51zg4GVwLVh6+8C/h627I/ApcDh/k94miLZIVpDNqhdQjJW3DYL59x8M+sTtmxe4O0i4JzQGzM7E1gN7AwsKwI6Oefe8t8/CJzJ/gFFJPPFasj+j+UKDpKRktFmMR3/n76ZFQDXADeHbXMIEGzdq/CXRWRml5rZYjNbvHHjxiRkUaQFaYCdZKEm9YYys+uBvcDD/qKbgbucczvCmiQitU+4aOk652YBswBGjBgRdTuRtLBsTv15m9p1gd1b9t9ODdmSwRodLMxsKjAJGO+cC/1DPx44x8xuBzoDtWZWBTwFBP9SegHrGntskbQRaQLAVm0g7wCo2bNvOzVkS4ZrVLAws4l41U1jnXO7Qsudc2MC25QCO5xzv/ffbzezkcDbwEXAfzUh3yLpIVL7RG01tDsQDijQLLGSNeIGCzN7FBgHdDOzCuAmvN5PbYGX/OqmRc65y+IkdTlez6p2eG0catyWzBetHWL313DNpy2bF5FmlEhvqCkRFt+XwH6lYe8XAwMTzplIuglvmxj/C+/31rX7b6v2CckyGsEtkohoYycOn6CBdpITFCxEEhFt7MS/5mmgneQETSQokohYYyc0AaDkAAULkUg0dkKkHgULkXAaOyGyH7VZiISLNnbigA5qm5CcpZKFSHiVU6SusKCxE5LTFCwkt0WqcsKIOHWZ2ickh6kaSnJbpConHPvNfan2CclxKllIbkm0ygnntUtobicRQMFCckmDqpx6ew8qEhFA1VCSS1TlJNJoKllI9lKVk0jSKFhIdlKVk0hSqRpKspOqnESSSiULyR7Baqeoj3hXlZNIYyhYSHYIr3aKRlVOIo2iaijJDhGrncKoykmk0VSykMyUcE8n8Cb+U5WTSFMoWEjmUU8nkRanaijJPOrpJNLiVLKQ9KfBdSIpp2Ah6U1VTiJpQdVQkt5U5SSSFhQsJL1trYiywukRpyItSNVQkl7C2yfadYHdW/bfTlVOIi1KwULSR6T2iVZtIO8AqNmzbztVOYm0OFVDSfqI1D5RWw0HdFCVk0iKqWQhqRFe3TT+F9HbJ3Z/Ddd82rL5E5F6FCyk5UWqbnr+ZzHaJ3q1bP5EZD+qhpKWF6m6KfS+Tbv6y9U+IZIWFCyk5cWqbjrtbrVPiKShuNVQZnY/MAn4yjk30F92B3AasAdYBVzsnKs0s+OAWaFdgVLn3Fx/n8nA9UAeUO6cuzrZJyNpJlK7xODzok/ZUdjLW6/gIJJ2EilZzAYmhi17CRjonBsMrASu9ZcvB0Y454b6+/y3mbU2s67AHcB459zRwMFmNj4J+Zd0FWqX2LoWcPvaJZbN8YKGqptEMkrcYOGcmw9sCVs2zzm313+7COjlL98VWJ7Pvgl8+gErnXMb/ff/AL7fxLxLOovWLvHyLV7JQdVNIhklGb2hpgOPh96Y2fHA/cBhwIXOub1m9gkwwMz6ABXAmcAB0RI0s0uBSwEOPfTQJGRRWly0donQclU3iWSUJjVwm9n1wF7g4dAy59zbflXTscC1ZpbvnPsauBwvqLwBrPH3i8g5N8s5N8I5N6J79+5NyaKkSrTuruoGK5KRGh0szGwqXsP3+c65/eaLds59BOwEBvrvn3fOHe+cOwH4GPhXY48taWTZHLhrIJR29n4vm+MtV7uESFZpVDWUmU0ErgHGOud2BZb3Bdb6VU+HAf3xShGY2UHOua/MrAtwBaA6iEwXbXAd7KtiitQbSkQyTiJdZx8FxgHdzKwCuAmv91Nb4CUzA1jknLsMGA3MMLNqoBa4wjm3yU+qzMyG+K9vcc6tTOqZSMuL14itdgmRrBE3WDjnpkRYfF+UbR8CHmpAOpLJ4jVii0jW0AhuiS1amwSoEVskhyhYSHSxBtaBGrFFcoiChUQXq00CGjW4rnx1OROenMDgBwYz4ckJlK8ub778i0jSWIRer2llxIgRbvHixanORm4q7cy+QfhBBqWVDU7u1kW38vjHj0dcV1RQRMnwEor7FTc4XRHZn5m945wbkaz09DwLiS7WhH9xlK8up+zdMjbs3ECPgh6c1OukqIECYP3O9ZQuLAVQwBBJQwoWuS7KzLDlq8spO7gzG7pAj701lHxdSfHOXfu1SZSvLmfmP2dS+U0lAIUHFDKx70Se/eRZqmqqAC8QxAoUIVU1VZS9W6ZgIZKGFCxyWZRBdeVb3qe04gXvn70Z69u0prTbgdC+K8Vj9g2sK19dzo1v3kh1bXVdklv3bE0oMESzYeeGJp2SiDQPBYtcFqUBu2z1XKryrN7iqlatuK5jK+hQQOh7f9m7ZfUCRTL0KOiR1PREJDnUGyqXRRk8tyHKXVHraildWFrXg2n9zvVJzU5+Xj4lw0uSmqaIJIeCRS6L0lDdozb6LqF2hYZ2ec3Py2dy/8m0ssi3XCtrRemJpWqvEElTChY5IOrYhiiD6kr6nRUzvQ07N1D2blnU9a2tNZP7T6aooAjDKCooovTEUm4YeQO/Gv0r8vPy622fn5fPr0b/SoFCJI1pnEU2WzaH8jduobS9o6rVvu8FbVq1oX3r9mzbs40ebTp5PZ021u8NNeiBQVGTLSooYsPODbiIYzBg5piZMf/xh3er1fgKkeTTOAtJjN/TqezgLlS1qv8xV9dWs3XPVgDWV2+ltLAdnPpwvX/YRQVFUdskSoaXUPZuWcT1RQVFcf/xF/crVnAQyTCqhspWfk+n9a3z4m4aaoeAfVVW0QLF5P6TKe5XTMnwkojVSWqgFslOKllkgVsX3coTK5+g1tXSylpx7hHncoPf06kV3oNF4tmwcwPlq8spXVhaN5guKHw6jtBvVSeJ5Aa1WWS4aPMtTa5y3LB+LYP69AazCHvWV1RQBETuDltUUMS8c+Y1PbMi0mKS3WahaqgMVf7ajUy4fyCPr3gs4von8ltBm3YU7a2Jm1ao+ija6GmNqhYRBYs0FG8a7/LXbuSGT+eyPs+ilhpqcXDa3ZR8kwcxSo+hbq3F/Yqjjp7WqGoRUbBIM6F2g/U71+NwdbOxBgPGrz+dy95WsauWWlkrGHwexf9nOSOLToi4zeT+k5l3zry6dgY1WotINGrgTgPBcQdmRq2r3yQdPhvr1vhNEJx7xLl1r/90yp8iN4KPvKHePmq0FpFo1MCdYuWry7lhwQ3sdXvjbhvqkTRj/jWRq5+co1WrvIiBQERyiwblZZhIDwGaXzG/7n1lVWVCgQL2PSCofV5bdtXu2W9959bteeOCfyb7FEREFCyaU/i4hfCHADVm1taqmioKDyikTXUN1W5fT6c2lseME29qeqZFRCJQA3czKnu3LOIAt6batmcbvxx9W72J+n45+ja1LYhIs1HJohk11/iEHgU9NL+SiLQolSyaUXOMT1BXVhFJBQWLZhRp3EK4/Lx8RvYYGTsh58C5egPoRERakqqhkizY+6mwbSFG/S6u7fLa0RbYuncXPfbWULJjN8WDT6b88DP9/dbTqaYWw7G1VStvm227KP7uHTD4vNSclIjkPAWLJArv/VT5TeV+2+yu2Y2rdfx602aKd+7yFj7/M4pPu5vi0GR9y+Z4U4xv9R9IpEAhIimmQXlJNOaxMREDRCRF1XuZV7Fu34LC3vAfy5snYyKSczTrbJoqX12ecKAA2BD+UCL/+RMiIulIwaIBYs0GO/OfMxuUVo/wqcMLeyUjiyIizSJum4WZ3Q9MAr5yzg30l90BnAbsAVYBFzvnKs3sOGBWaFeg1Dk3199nCnAd4IB1wAXOuU1JPp8mC5+eIzSRXvhDhkJTb4Q0pFSRX+so+TqwfZt2MP4XTc+8iEgzidtmYWYnATuABwPBYgLwinNur5n9BsA5d42ZtQf2+MuLgPeAnn5S64CjnHObzOx2YJdzrjReBluizSIUIKJNv9Eurx27a3ZHXBfrCXN1nKt7vGlRh56UdDue4iVz9zVgj/+FGrBFJKlafCJB59x8M+sTtiz4jM1FwDn+8l2B5fl4pQjwShkGFJjZZqAT8Enjs508sZ47HRItUEACo7SdY+bGUM8ng1K/EXvcLxuRWxGR1EhGm8V04O+hN2Z2vJl9ALwPXOac2+ucqwYu95etA44C7kvCsZusqfM39WjTiR41tTG3qesiq3YJEclQTQoWZnY9sBd4OLTMOfe2c+5o4FjgWjPLN7M2eMFiGF611DLg2hjpXmpmi81s8caNG5uSxXoiNVA3df6mkg1rKdm8OeqjS+uega12CRHJYI0OFmY2Fa/h+3wXoeHDOfcRsBMYCAz1l63yt50DnBgtbefcLOfcCOfciO7duzc2i/VEe1xpYdvCRqc5ucpRvK2S4p27mLxt+34BI985Sr7e6o2hOO1utUuISMZq1AhuM5sIXAOMDbZTmFlfYK3fwH0Y0B9YAxwAHGVm3Z1zG4GTgY+amvlExGq8rqqpalIV1A3r942NuGFLJcO+2UNZl85saJ1Hjw499UhSEckaiXSdfRQYB3QzswrgJrwqpLbAS+Y93nORc+4yYDQww8yq8Tr/XBHqHmtmNwPz/XWfAdOSfjZhGvLI0qDJ/ScD1OsqG66oxkG7LrB7S92y4p27vPaJwt4wbV7UfUVEMk0ivaGmRFgcsXHaOfcQ8FCUdfcC9zYod40UrytsPPMr5sdcn19bS8nmLfBNNeQdADWBR5yqbUJEslDWTSSYSFfYeGI2ejtH6aYt+3o4tTsQDijQmAkRyWpZFyyS8SjTHgU9YM8u1ldv3W9d0d6afYECYPfXcM2nTTqeiEi6y7q5oZLxKNOSL9dR8sUq8mvrj5/Ir62tP00HaOyEiOSErCtZ9Cjo0ei2CgCco3jj2rq3db2b9tZQ8nVl/VKF2idEJEdkXbAoGV7CjW/eSHVtdaP2LwrMBlvXuynI8sDVqn1CRHJK1lVDFfcrpn3r9o3aN2I1U1CbdnDWvVBa6T2oSIFCRHJE1gULgG17tjV4n1bhvZzCaRS2iOSwrKuGgoa3W+TX1sYIFAZnz1KQEJGclpUli5LhJTHX5+8eTe2ezphzFFXvjV2iGDFdgUJEcl5WliyK+xWz5KslEafraLX9RDZWTAIm8eYBV9DTKvFmLImgQxFM+m2z5lVEJBNkZckC4IaRNzBzzEyKCoowjKKCItpsPp+tFafXbTNqzx9Y5zrjHPv90G0AXLkidScgIpJG4j5WNdWS9VjV8//0Fm+u2hJ/Q6BT2zyW3TyxyccUEUmVZD9WNWtLFuESDRSAAoWISJicCRaJ6NQ2jzUz9fwJEZFwChYBKlGIiESmYCEiInHlTLDIz4vWP9Yz6lsHtlBOREQyT84EixW3fS9qwBj1rQN5+JITWjhHIiKZIysH5UWz4rbvpToLIiIZKWdKFiIi0ngKFiIiEpeChYiIxKVgISIicSlYiIhIXGk/kaCZbQQ+A7oBm1KcnaZQ/lNL+U+dTM47ZGb+NwE455I2LUXaB4sQM1uczBkUW5ryn1rKf+pkct4h8/OfLKqGEhGRuBQsREQkrkwKFrNSnYEmUv5TS/lPnUzOO2R+/pMiY9osREQkdTKpZCEiIimiYCEiInG1eLAws3PN7AMzqzWzEYHlJ5vZO2b2vv/7O4F1L5jZe/5+95pZnr/8MDN72cyWmdlrZtYryjGP8dP9xMzuNrPYD7doufwfamavmtkS/xwiTovrn9vHZrbU/zkow/Kfrtf/rsA1XWlmlVGOma7XP9H8p+X199edZ2Yf+useiXLMtLz+Dch/0q5/SjnnWvQHOBLoD7wGjAgsHwb09F8PBL4IrOvk/zbgKeAH/vsngKn+6+8AD0U55j+BE/z9/w6cmib5nwVc7r8+ClgT5Zj1jpVG1z/R/Kfl9Q9L96fA/Zl0/RuQ/7S8/sDhwBKgi//+oEy6/g3If9Kufyp/Wrxk4Zz7yDn3cYTlS5xz6/y3HwD5ZtbWX7fNX94aOAAItcofBbzsv34VOCM8XTMrwvuw33LeJ/cgcGaa5N8BnfzXhUBo/2bT0vlP8+sfNAV4tLH5SlRL5z/Nr/8lwD3Oua/97b5qbL4S1dL5T/b1T6V0bbP4PrDEOfdNaIGZvQh8BWwHnvQXv+dvC3AW0NHMuoaldQhQEXhf4S9rTonmvxS4wMwqgL/hfTuM5s9+EfzGFijGJjP/6Xz9Q+sOA/oCr8RIMx2vf2hdrPyn8/U/AjjCzN40s0VmFmtqinS8/onkPxXXv1k0S7Aws3+Y2fIIP/t984+w79HAb4AfB5c7504BioC2eFVOAFcCY81sCTAW+ALYG55khMPE7C/cgvmfAsx2zvUCvgc8ZGaRPpPznXODgDH+z4UZlP90vv4hPwCedM7VREk2Xa9/IvlP5+vfGq8qZxzevfQ/ZtY5QrLpev0TyX+Dr3/aSlX9FxHqIYFewEpgVIz9pgK/j7C8A1ARYXkRsCLwfgrw3+mQf7zibu/AutVEqfcMbDMt0vmna/7T+foHli0BTkzwuGlz/RPJfzpff+BeYFpg3cvAsZly/RPJf3Nd/1T8pE01lB+Ry4FrnXNvBpZ38Ov9MLPWeN9gV/jvuwW+yV4L3B+ernNuPbDdzEb6xdeLgGfTIf/A58B4f92RQD6wMSzd1mbWzX/dBpgELM+U/Kf59cfM+gNdgLeipJvO1z9u/tP8+j8D/Ju/rhtetc7qsHTT+frHzX9LXf8W0dLRCa9toQL4BvgSeNFffgOwE1ga+DkIOBj4X2AZ3jfZ/wJa+/ucA/wL79vA/wBtA8dZGng9Au8GWwX8Hn/kehrk/yjgTby2l6XAhPD8AwXAO4H9y4C8TMl/Ol9/f79SYGaE46T99U8k/+l8/fGqaH4LfAi8T6CXVyZc/0Tyn+zrn8ofTfchIiJxpU01lIiIpC8FCxERiUvBQkRE4lKwEBGRuBQsREQkLgULERGJS8FCRETi+v/HNokWYA2NsgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis_results('palo-alto', 'train', 32, palo_train_mod, 16)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca12906f",
   "metadata": {},
   "source": [
    "## Train models for other cities"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88d07e65",
   "metadata": {},
   "source": [
    "### austin\n",
    "- best KAGGLE val loss: 31.061992844538903\n",
    "- best val loss: 21.4315324826027"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7f144ddc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8df6e11256cd4dd1a98e7dff6b8229c4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 89.69602610690535\n",
      "- Validation Loss: 42.500753616219136\n",
      "- Train Time: 9.687225818634033\n",
      "- Elapsed Time: 9.86424994468689\n",
      "\n",
      "Epoch 2\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 42.96211491892161\n",
      "- Validation Loss: 37.37696472566519\n",
      "- Train Time: 17.131022930145264\n",
      "- Elapsed Time: 17.473789930343628\n",
      "\n",
      "Epoch 3\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 40.31700306648065\n",
      "- Validation Loss: 34.458844761350264\n",
      "- Train Time: 22.81644582748413\n",
      "- Elapsed Time: 23.278975009918213\n",
      "\n",
      "Epoch 4\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 37.16654974488188\n",
      "- Validation Loss: 35.11411406388923\n",
      "- Train Time: 28.514603853225708\n",
      "- Elapsed Time: 29.100958347320557\n",
      "\n",
      "Epoch 5\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 34.96541622256444\n",
      "- Validation Loss: 34.24248284012524\n",
      "- Train Time: 34.98444604873657\n",
      "- Elapsed Time: 35.730051040649414\n",
      "\n",
      "Epoch 6\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 33.39881450006784\n",
      "- Validation Loss: 32.91501771158247\n",
      "- Train Time: 42.77417063713074\n",
      "- Elapsed Time: 43.68348670005798\n",
      "\n",
      "Epoch 7\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 32.63022361156369\n",
      "- Validation Loss: 31.667196095879397\n",
      "- Train Time: 48.45096778869629\n",
      "- Elapsed Time: 49.5026741027832\n",
      "\n",
      "Epoch 8\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 31.944594824609677\n",
      "- Validation Loss: 29.413347023636547\n",
      "- Train Time: 55.02374291419983\n",
      "- Elapsed Time: 56.22971487045288\n",
      "\n",
      "Epoch 9\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 30.942066875962187\n",
      "- Validation Loss: 29.406644073884877\n",
      "- Train Time: 60.12123441696167\n",
      "- Elapsed Time: 61.448546409606934\n",
      "\n",
      "Epoch 10\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 30.88202463654447\n",
      "- Validation Loss: 31.008398155667887\n",
      "- Train Time: 65.68035936355591\n",
      "- Elapsed Time: 67.14165043830872\n",
      "\n",
      "Epoch 11\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 30.18432607729573\n",
      "- Validation Loss: 31.713667826866036\n",
      "- Train Time: 71.92774724960327\n",
      "- Elapsed Time: 73.51301169395447\n",
      "\n",
      "Epoch 12\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 29.826742269184965\n",
      "- Validation Loss: 28.961839576265707\n",
      "- Train Time: 78.34995532035828\n",
      "- Elapsed Time: 80.08716773986816\n",
      "\n",
      "Epoch 13\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 29.75086638080187\n",
      "- Validation Loss: 32.402113772150294\n",
      "- Train Time: 86.23838329315186\n",
      "- Elapsed Time: 88.16405081748962\n",
      "\n",
      "Epoch 14\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 29.42223835937248\n",
      "- Validation Loss: 31.64907321645253\n",
      "- Train Time: 92.09788942337036\n",
      "- Elapsed Time: 94.15595936775208\n",
      "\n",
      "Epoch 15\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 29.043150558156416\n",
      "- Validation Loss: 28.794953865791435\n",
      "- Train Time: 97.82419729232788\n",
      "- Elapsed Time: 100.00400495529175\n",
      "\n",
      "Epoch 16\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 28.828540024875608\n",
      "- Validation Loss: 26.80239217672775\n",
      "- Train Time: 104.32976531982422\n",
      "- Elapsed Time: 106.68157505989075\n",
      "\n",
      "Epoch 17\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 28.650515888939218\n",
      "- Validation Loss: 29.452761151897374\n",
      "- Train Time: 110.07357096672058\n",
      "- Elapsed Time: 112.54563164710999\n",
      "\n",
      "Epoch 18\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 28.84192549650334\n",
      "- Validation Loss: 27.059206564034987\n",
      "- Train Time: 115.35009121894836\n",
      "- Elapsed Time: 117.94240832328796\n",
      "\n",
      "Epoch 19\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 28.922775118016013\n",
      "- Validation Loss: 28.843238367963192\n",
      "- Train Time: 121.7410798072815\n",
      "- Elapsed Time: 124.50029993057251\n",
      "\n",
      "Epoch 20\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 28.34480652612103\n",
      "- Validation Loss: 29.887050820820367\n",
      "- Train Time: 127.5429961681366\n",
      "- Elapsed Time: 130.50877451896667\n",
      "\n",
      "Epoch 21\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 28.03750826859277\n",
      "- Validation Loss: 32.285604377291094\n",
      "- Train Time: 134.20220804214478\n",
      "- Elapsed Time: 137.3337664604187\n",
      "\n",
      "Epoch 22\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 28.22590786090567\n",
      "- Validation Loss: 26.645080943605794\n",
      "- Train Time: 139.179940700531\n",
      "- Elapsed Time: 142.4312059879303\n",
      "\n",
      "Epoch 23\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 28.32419259173811\n",
      "- Validation Loss: 29.735783235350652\n",
      "- Train Time: 144.94926381111145\n",
      "- Elapsed Time: 148.35194659233093\n",
      "\n",
      "Epoch 24\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 27.653048702902044\n",
      "- Validation Loss: 27.63936597197803\n",
      "- Train Time: 149.54640793800354\n",
      "- Elapsed Time: 153.0943603515625\n",
      "\n",
      "Epoch 25\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 27.86983047595694\n",
      "- Validation Loss: 28.81525712226754\n",
      "- Train Time: 154.56903433799744\n",
      "- Elapsed Time: 158.24468278884888\n",
      "\n",
      "Epoch 26\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 27.863723693800367\n",
      "- Validation Loss: 29.8604268244843\n",
      "- Train Time: 158.85544848442078\n",
      "- Elapsed Time: 162.65124940872192\n",
      "\n",
      "Epoch 27\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 27.580932184487335\n",
      "- Validation Loss: 27.57422252199543\n",
      "- Train Time: 163.4311604499817\n",
      "- Elapsed Time: 167.35185384750366\n",
      "\n",
      "Epoch 28\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 27.287091397056894\n",
      "- Validation Loss: 29.830789587390957\n",
      "- Train Time: 171.50598239898682\n",
      "- Elapsed Time: 175.6090009212494\n",
      "\n",
      "Epoch 29\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 22.949090748779046\n",
      "- Validation Loss: 23.843174813398676\n",
      "- Train Time: 177.22338700294495\n",
      "- Elapsed Time: 181.4491424560547\n",
      "\n",
      "Epoch 30\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 22.49100649179506\n",
      "- Validation Loss: 23.63659381866455\n",
      "- Train Time: 183.30852937698364\n",
      "- Elapsed Time: 187.71405625343323\n",
      "\n",
      "Epoch 31\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 22.361277162536116\n",
      "- Validation Loss: 23.305874141294566\n",
      "- Train Time: 188.45979142189026\n",
      "- Elapsed Time: 192.98868036270142\n",
      "\n",
      "Epoch 32\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 22.23658730924622\n",
      "- Validation Loss: 23.20763022864043\n",
      "- Train Time: 193.79173398017883\n",
      "- Elapsed Time: 198.49652433395386\n",
      "\n",
      "Epoch 33\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 22.182326092995886\n",
      "- Validation Loss: 23.19697254807202\n",
      "- Train Time: 199.2192964553833\n",
      "- Elapsed Time: 204.05624222755432\n",
      "\n",
      "Epoch 34\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 22.109142219133613\n",
      "- Validation Loss: 23.13156319376248\n",
      "- Train Time: 205.71650218963623\n",
      "- Elapsed Time: 210.72049045562744\n",
      "\n",
      "Epoch 35\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 22.028387919339266\n",
      "- Validation Loss: 23.03875249891139\n",
      "- Train Time: 211.54146265983582\n",
      "- Elapsed Time: 216.70503425598145\n",
      "\n",
      "Epoch 36\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 21.96189019463279\n",
      "- Validation Loss: 22.906725990238474\n",
      "- Train Time: 217.4846897125244\n",
      "- Elapsed Time: 222.80501341819763\n",
      "\n",
      "Epoch 37\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 21.922305446813915\n",
      "- Validation Loss: 22.932641435025342\n",
      "- Train Time: 225.58849167823792\n",
      "- Elapsed Time: 231.06547141075134\n",
      "\n",
      "Epoch 38\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 21.850273681672153\n",
      "- Validation Loss: 22.935186599617573\n",
      "- Train Time: 233.05105137825012\n",
      "- Elapsed Time: 238.7299826145172\n",
      "\n",
      "Epoch 39\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 21.797286862775312\n",
      "- Validation Loss: 22.86471533419481\n",
      "- Train Time: 240.85501861572266\n",
      "- Elapsed Time: 246.71849656105042\n",
      "\n",
      "Epoch 40\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 21.768382090576424\n",
      "- Validation Loss: 22.784615367206175\n",
      "- Train Time: 248.3766119480133\n",
      "- Elapsed Time: 254.39167881011963\n",
      "\n",
      "Epoch 41\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 21.732255659024577\n",
      "- Validation Loss: 22.77014242713131\n",
      "- Train Time: 255.48212456703186\n",
      "- Elapsed Time: 261.67953538894653\n",
      "\n",
      "Epoch 42\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 21.668731693788008\n",
      "- Validation Loss: 22.76762741715161\n",
      "- Train Time: 262.99078488349915\n",
      "- Elapsed Time: 269.3506963253021\n",
      "\n",
      "Epoch 43\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 21.669333226621642\n",
      "- Validation Loss: 22.7279520817657\n",
      "- Train Time: 270.25927472114563\n",
      "- Elapsed Time: 276.76559805870056\n",
      "\n",
      "Epoch 44\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 21.60285250647994\n",
      "- Validation Loss: 22.696628292994713\n",
      "- Train Time: 277.1792664527893\n",
      "- Elapsed Time: 283.86824440956116\n",
      "\n",
      "Epoch 45\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 21.570135247411805\n",
      "- Validation Loss: 22.65648532269606\n",
      "- Train Time: 283.0074064731598\n",
      "- Elapsed Time: 289.81934356689453\n",
      "\n",
      "Epoch 46\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 21.538053553951674\n",
      "- Validation Loss: 22.863312308468036\n",
      "- Train Time: 289.4593176841736\n",
      "- Elapsed Time: 296.45901346206665\n",
      "\n",
      "Epoch 47\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 21.504435992832025\n",
      "- Validation Loss: 22.64917014250115\n",
      "- Train Time: 297.60142183303833\n",
      "- Elapsed Time: 304.798597574234\n",
      "\n",
      "Epoch 48\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 21.442374482824782\n",
      "- Validation Loss: 22.61524026785324\n",
      "- Train Time: 305.9939901828766\n",
      "- Elapsed Time: 313.34736251831055\n",
      "\n",
      "Epoch 49\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 21.451194111768864\n",
      "- Validation Loss: 22.56864601106786\n",
      "- Train Time: 313.8952946662903\n",
      "- Elapsed Time: 321.3700284957886\n",
      "\n",
      "Epoch 50\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 21.39158150302477\n",
      "- Validation Loss: 22.525636672973633\n",
      "- Train Time: 318.94181060791016\n",
      "- Elapsed Time: 326.53639793395996\n",
      "\n",
      "Epoch 51\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 21.331822822901827\n",
      "- Validation Loss: 22.69650822966846\n",
      "- Train Time: 326.0976531505585\n",
      "- Elapsed Time: 333.82528138160706\n",
      "\n",
      "Epoch 52\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 21.35472145986951\n",
      "- Validation Loss: 22.617783802658764\n",
      "- Train Time: 334.3528163433075\n",
      "- Elapsed Time: 342.25732016563416\n",
      "\n",
      "Epoch 53\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 21.328326909404154\n",
      "- Validation Loss: 22.426912386025954\n",
      "- Train Time: 339.6122450828552\n",
      "- Elapsed Time: 347.63723707199097\n",
      "\n",
      "Epoch 54\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 21.25452068738701\n",
      "- Validation Loss: 22.431364265840443\n",
      "- Train Time: 346.708215713501\n",
      "- Elapsed Time: 354.89516162872314\n",
      "\n",
      "Epoch 55\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 21.238272700033896\n",
      "- Validation Loss: 22.523022139250344\n",
      "- Train Time: 353.98147678375244\n",
      "- Elapsed Time: 362.3205740451813\n",
      "\n",
      "Epoch 56\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 21.217396704618597\n",
      "- Validation Loss: 22.6584840034371\n",
      "- Train Time: 360.4019784927368\n",
      "- Elapsed Time: 368.88053846359253\n",
      "\n",
      "Epoch 57\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 21.17598094270249\n",
      "- Validation Loss: 22.377928876165132\n",
      "- Train Time: 367.8139114379883\n",
      "- Elapsed Time: 376.48747849464417\n",
      "\n",
      "Epoch 58\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 21.196242905057165\n",
      "- Validation Loss: 22.324220038172026\n",
      "- Train Time: 375.540935754776\n",
      "- Elapsed Time: 384.37963461875916\n",
      "\n",
      "Epoch 59\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 21.12678049536776\n",
      "- Validation Loss: 22.485910935188407\n",
      "- Train Time: 383.3272683620453\n",
      "- Elapsed Time: 392.36362290382385\n",
      "\n",
      "Epoch 60\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 21.14751426483974\n",
      "- Validation Loss: 22.65210372298511\n",
      "- Train Time: 388.43067026138306\n",
      "- Elapsed Time: 397.587518453598\n",
      "\n",
      "Epoch 61\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 21.099402523828932\n",
      "- Validation Loss: 22.529070071320035\n",
      "- Train Time: 394.0916142463684\n",
      "- Elapsed Time: 403.3779630661011\n",
      "\n",
      "Epoch 62\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 21.044728677528948\n",
      "- Validation Loss: 22.62054446206164\n",
      "- Train Time: 399.64456939697266\n",
      "- Elapsed Time: 409.08772587776184\n",
      "\n",
      "Epoch 63\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 21.000704583254727\n",
      "- Validation Loss: 22.34600067138672\n",
      "- Train Time: 406.8135974407196\n",
      "- Elapsed Time: 416.41004180908203\n",
      "\n",
      "Epoch 64\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 21.01406920882296\n",
      "- Validation Loss: 22.17388708911725\n",
      "- Train Time: 412.7997658252716\n",
      "- Elapsed Time: 422.5196707248688\n",
      "\n",
      "Epoch 65\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 20.951888010324524\n",
      "- Validation Loss: 22.572096789061135\n",
      "- Train Time: 418.10009503364563\n",
      "- Elapsed Time: 427.9415361881256\n",
      "\n",
      "Epoch 66\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 20.930052880610315\n",
      "- Validation Loss: 22.569011666881504\n",
      "- Train Time: 425.24082040786743\n",
      "- Elapsed Time: 435.2414450645447\n",
      "\n",
      "Epoch 67\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 20.909529733657838\n",
      "- Validation Loss: 22.145067229199764\n",
      "- Train Time: 430.34063696861267\n",
      "- Elapsed Time: 440.46199321746826\n",
      "\n",
      "Epoch 68\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 20.925401836584424\n",
      "- Validation Loss: 22.07477326179618\n",
      "- Train Time: 435.386572599411\n",
      "- Elapsed Time: 445.6632113456726\n",
      "\n",
      "Epoch 69\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 20.864218740226807\n",
      "- Validation Loss: 21.985303871667206\n",
      "- Train Time: 442.7243723869324\n",
      "- Elapsed Time: 453.15681076049805\n",
      "\n",
      "Epoch 70\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 20.846343817198573\n",
      "- Validation Loss: 22.10348200442186\n",
      "- Train Time: 450.64631819725037\n",
      "- Elapsed Time: 461.27283334732056\n",
      "\n",
      "Epoch 71\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 20.799092504800843\n",
      "- Validation Loss: 22.157171220921757\n",
      "- Train Time: 456.33442759513855\n",
      "- Elapsed Time: 467.0995771884918\n",
      "\n",
      "Epoch 72\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 20.777039807690077\n",
      "- Validation Loss: 22.09743543881089\n",
      "- Train Time: 463.89727330207825\n",
      "- Elapsed Time: 474.81551361083984\n",
      "\n",
      "Epoch 73\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 20.77564379439866\n",
      "- Validation Loss: 22.17083779377724\n",
      "- Train Time: 471.10937237739563\n",
      "- Elapsed Time: 482.2245557308197\n",
      "\n",
      "Epoch 74\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 20.761564503425408\n",
      "- Validation Loss: 22.176177017724335\n",
      "- Train Time: 478.70494556427\n",
      "- Elapsed Time: 489.97349762916565\n",
      "\n",
      "Epoch 75\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 20.692512836929197\n",
      "- Validation Loss: 22.30098802651932\n",
      "- Train Time: 486.42244243621826\n",
      "- Elapsed Time: 497.93447613716125\n",
      "\n",
      "Epoch 76\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 20.157446686689518\n",
      "- Validation Loss: 21.474456666120844\n",
      "- Train Time: 494.5811369419098\n",
      "- Elapsed Time: 506.25103855133057\n",
      "\n",
      "Epoch 77\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 20.081036690641042\n",
      "- Validation Loss: 21.47124220719978\n",
      "- Train Time: 502.64362025260925\n",
      "- Elapsed Time: 514.5432996749878\n",
      "\n",
      "Epoch 78\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 20.05736594003094\n",
      "- Validation Loss: 21.470756537878692\n",
      "- Train Time: 508.51721119880676\n",
      "- Elapsed Time: 520.5411922931671\n",
      "\n",
      "Epoch 79\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 20.04458891655788\n",
      "- Validation Loss: 21.467639958680564\n",
      "- Train Time: 515.9336888790131\n",
      "- Elapsed Time: 528.0824837684631\n",
      "\n",
      "Epoch 80\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 20.036072586784677\n",
      "- Validation Loss: 21.466089782430164\n",
      "- Train Time: 523.1202571392059\n",
      "- Elapsed Time: 535.4003565311432\n",
      "\n",
      "Epoch 81\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 20.028585404403938\n",
      "- Validation Loss: 21.464876972027678\n",
      "- Train Time: 529.4733505249023\n",
      "- Elapsed Time: 541.9573345184326\n",
      "\n",
      "Epoch 82\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 20.023227973417804\n",
      "- Validation Loss: 21.45976521363899\n",
      "- Train Time: 536.0140943527222\n",
      "- Elapsed Time: 548.6402502059937\n",
      "\n",
      "Epoch 83\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 20.014354950928492\n",
      "- Validation Loss: 21.465877134408522\n",
      "- Train Time: 542.2508225440979\n",
      "- Elapsed Time: 554.9981458187103\n",
      "\n",
      "Epoch 84\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 20.008624745597523\n",
      "- Validation Loss: 21.46179723739624\n",
      "- Train Time: 548.2989494800568\n",
      "- Elapsed Time: 561.1882755756378\n",
      "\n",
      "Epoch 85\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 20.00238420155423\n",
      "- Validation Loss: 21.461926154236295\n",
      "- Train Time: 554.3336102962494\n",
      "- Elapsed Time: 567.416435956955\n",
      "\n",
      "Epoch 86\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 19.998742136679404\n",
      "- Validation Loss: 21.459142407374596\n",
      "- Train Time: 560.634134054184\n",
      "- Elapsed Time: 573.8858001232147\n",
      "\n",
      "Epoch 87\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 19.994652954211904\n",
      "- Validation Loss: 21.457396884462728\n",
      "- Train Time: 567.6289031505585\n",
      "- Elapsed Time: 581.0147006511688\n",
      "\n",
      "Epoch 88\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 19.990710575324446\n",
      "- Validation Loss: 21.452006546419057\n",
      "- Train Time: 573.4503412246704\n",
      "- Elapsed Time: 586.9587738513947\n",
      "\n",
      "Epoch 89\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 19.982824861116647\n",
      "- Validation Loss: 21.45223714344537\n",
      "- Train Time: 580.1272535324097\n",
      "- Elapsed Time: 593.76455950737\n",
      "\n",
      "Epoch 90\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 19.980691958261914\n",
      "- Validation Loss: 21.44733085917003\n",
      "- Train Time: 587.3809704780579\n",
      "- Elapsed Time: 601.1408133506775\n",
      "\n",
      "Epoch 91\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 19.974058174101774\n",
      "- Validation Loss: 21.44925333136943\n",
      "- Train Time: 593.1133606433868\n",
      "- Elapsed Time: 607.0053389072418\n",
      "\n",
      "Epoch 92\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 19.96893774891688\n",
      "- Validation Loss: 21.448268876146916\n",
      "- Train Time: 598.3441982269287\n",
      "- Elapsed Time: 612.3573389053345\n",
      "\n",
      "Epoch 93\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 19.968311034746407\n",
      "- Validation Loss: 21.446569613556363\n",
      "- Train Time: 602.7466425895691\n",
      "- Elapsed Time: 616.8796293735504\n",
      "\n",
      "Epoch 94\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 19.962707354017525\n",
      "- Validation Loss: 21.44179860157753\n",
      "- Train Time: 609.8362762928009\n",
      "- Elapsed Time: 624.1651775836945\n",
      "\n",
      "Epoch 95\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 19.95763952318302\n",
      "- Validation Loss: 21.43661157408757\n",
      "- Train Time: 616.4969193935394\n",
      "- Elapsed Time: 630.9600651264191\n",
      "\n",
      "Epoch 96\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 19.955345582962035\n",
      "- Validation Loss: 21.435681663342375\n",
      "- Train Time: 623.1554028987885\n",
      "- Elapsed Time: 637.7400023937225\n",
      "\n",
      "Epoch 97\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 19.94751699345171\n",
      "- Validation Loss: 21.435973686958427\n",
      "- Train Time: 629.8594000339508\n",
      "- Elapsed Time: 644.6341078281403\n",
      "\n",
      "Epoch 98\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 19.946045834170885\n",
      "- Validation Loss: 21.43205346634139\n",
      "- Train Time: 636.1414866447449\n",
      "- Elapsed Time: 651.0701696872711\n",
      "\n",
      "Epoch 99\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 19.94059657814089\n",
      "- Validation Loss: 21.427643384506453\n",
      "- Train Time: 641.1574866771698\n",
      "- Elapsed Time: 656.2058465480804\n",
      "\n",
      "Epoch 100\n",
      "Training & Validating ------------------------------------------------------------\n",
      "- Training Loss: 19.936648345978792\n",
      "- Validation Loss: 21.4315324826027\n",
      "- Train Time: 648.5822114944458\n",
      "- Elapsed Time: 663.7606456279755\n",
      "\n"
     ]
    }
   ],
   "source": [
    "city = 'austin' \n",
    "split = 'train' #'test' #'train'\n",
    "batch_size = 32\n",
    "hidden_dim = 64\n",
    "num_iters = 100\n",
    "austin_train_mod, austin_losses = fit(city=city, split=split, hidden_dim=hidden_dim,\n",
    "                                      batch_size=batch_size, num_iters=num_iters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "dc73caa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(1, 'Austin', 'train', austin_losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7600bf49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 50, 2])\n",
      "torch.Size([32, 60, 2])\n",
      "torch.Size([32, 60, 2])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis_results('austin', 'train', 32, austin_train_mod, 23)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "202669c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6325\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(198, 198)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# intialize a dataset\n",
    "city = 'austin' \n",
    "split = 'test' \n",
    "test_dataset = ArgoverseDataset(city = city, split = split, transform=False, normalized=True)   \n",
    "temp_dataset = ArgoverseDataset(city = city, split = split, transform=False, normalized=False)   \n",
    "print(len(test_dataset)) # SHOULD BE 6325\n",
    "\n",
    "# test_dataset.start_pos\n",
    "\n",
    "batch_sz = 32  # batch size \n",
    "test_loader = DataLoader(test_dataset, batch_size=batch_sz)\n",
    "temp_loader = DataLoader(temp_dataset, batch_size=batch_sz)\n",
    "austin_train_mod.to('cpu')\n",
    "len(test_loader), len(temp_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0bda38a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def vis_test(idx, transform=True):\n",
    "    '''\n",
    "    This is the last batch, which is usually not complete and need to fill with 0s.\n",
    "    Check if I convert the prediction back correctly or not\n",
    "    '''\n",
    "    for X in test_loader:\n",
    "        if len(X) == batch_sz:\n",
    "            continue\n",
    "        print(len(X))\n",
    "        to_fill = np.zeros([batch_sz-len(X), 50, 2])\n",
    "        X = torch.from_numpy(np.append(X, to_fill, axis=0)).float()\n",
    "        X = X.cpu().float()\n",
    "        pred = austin_train_mod(X)\n",
    "        pred = pred.reshape(batch_sz, -1, 2).cpu().detach().numpy()\n",
    "        \n",
    "        if not transform:\n",
    "            plt.scatter(X[idx, :, 0], X[idx, :, 1], label='input')\n",
    "            plt.scatter(pred[idx, :, 0], pred[idx, :, 1], label='pred')\n",
    "            plt.legend()\n",
    "            plt.show()\n",
    "        else:\n",
    "            rotation =  test_dataset.rotate_matrix[-1].T\n",
    "            X[idx] = X[idx] @ np.linalg.inv(rotation)\n",
    "            X[idx] = X[idx] + test_dataset.start_pos[-1, : ]\n",
    "            pred[idx] = pred[idx] @ np.linalg.inv(rotation)\n",
    "            pred[idx] = pred[idx] + test_dataset.start_pos[-1, : ]\n",
    "\n",
    "            plt.scatter(X[idx, :, 0], X[idx, :, 1], label='input')\n",
    "            plt.scatter(pred[idx, :, 0], pred[idx, :, 1], label='pred')\n",
    "            plt.legend()\n",
    "            plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c73dcc71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis_test(20, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5e10634a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for X in temp_loader:\n",
    "    if len(X) == batch_sz:\n",
    "        continue\n",
    "    # the index here should refer to the above cell's number\n",
    "    plt.scatter(X[20, :, 0], X[20, :, 1], label='input')\n",
    "    plt.legend\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2cbf1b92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21\n"
     ]
    },
    {
     "data": {
      "image/png": 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AG4HHc91X0jhgckTskNQGzAPWVrDoVkVOpJu1jrKCh6SLgduBdmCNpN6IuLCISy8F3p9j/2LgH3PkQXojogsYDzySBI6xpALHnWVUwWrMiXSz1lDuaKvvA98f5pylOfb9ebHnJvu7kp+vAO8ssZjW4JxIN2s+Xp7E6s4r85o1Hy9PYnXnlXnNmo+Dh9WdV+Y1az7utrKG4JV5zZqLWx7WkDyh0KyxOXhYQ/LKvGaNzd1W1pA8odCssTl4WMPyhEKzxuVuK2sqnlBo1hjc8rCm4gmFZo3BLQ9rKp5QaNYYHDysqXhCoVljcLeVNR1PKDSrP7c8rOl5QqFZ7Tl4WNPzhEKz2nO3lTU9Tyg0qz0HD2sJnlBoVlvutrKW5QmFZtXjloe1LE8oNKsetzysZXlCoVn1lBU8JF0i6RlJQ5K6cxw/SdIeSTdk7Fss6WlJfZIeljQl2T9e0v2Stkp6UlJHnme+M7l+q6SvSlI5dbDW5QmFZtVTbstjE7AQ+EWe4yuAh9IbksYBXwHOjYhOoA+4Ljn8MeBPEfHW5Lqb89zz68A1wCnJ531l1sFa2IJZ01i/5DxeXP4B1i85j117D+Q8z3kQs9KUlfOIiM0AuX75l7QAeAF4JXN38pkkaSdwHLA1OXYRsDT5/gBwhyRFxKFfEyW9BTguIh5Ptu8BFpARoMwKyZcHmTp5Ig9uHDhquK9zIWa5VSXnIWkScCOwLHN/RBwArgWeBrYDpwN3JYenAb9LznsdeBl4U9atpwH9Gdv9yT6zouSbUHjuzHZuWv20cyFmRRo2eEhaK2lTjs9FBS5bBqyIiD1Z92ojFTxmAVNJdVvdlD6c4z6RtV3MOZnPu0ZSj6SewcHBAsW10SJXHuSLC8/gZ88N5p0TYmZHG7bbKiLOH8F9zwQWSboFmAwMSdoPPJnc83kASauAJck1/cCJQH+SGzke+GPWffuB6Rnb00m1YPKVfSWwEqC7uztvkLHRJdeEwuvv7815rnMhZrlVZZ5HRMxNf5e0FNgTEXdImgqcLqk9IgaBC4DNyak/BK4EHgcWAesy8x3JfX8vabeks0gFoo8At1ejDja6eE6IWWnKHap7saR+4GxgjaRHCp0fEdtJdWn9QlIf0AV8ITl8F/AmSVuBT3G4RYKk3ozbXAt8k1Si/XmcLLcK8JwQs9Io65f7ltXd3R09PT31LoY1sOzRVntfe50/5RjaO23yRNYvOa8OJTSrPUkbIuKoeXxensQs4ZdMmRXPy5OY5eGXTJnl5+BhlkehOSF+wZSNdu62Mssj10umzp3Zzvc2DPgFUzbqOXiYFZCdB/ELpsxSHDzMSpAvWT6wa5/ng9io4pyHWQnyJcsFng9io4qDh1kJciXRxdELrHldLGt1Dh5mJci1sGK+abaeD2KtzDPMzco0Z/m6nOtiTZ7YxqTx45wHsaaWb4a5Wx5mZfK6WDYaOXiYlcnvSrfRyEN1zSrA62LZaOPgYVYFfj+ItTp3W5lVgfMg1uocPMyqwHkQa3UeqmtWIzOWrMk7J2Ta5InuyrKG5KG6ZnXmpU2slTh4mNWIlzaxVuLgYVYjXtrEWolzHmZ15KVNrNFVJech6RJJz0gaknTUzSWdJGmPpBsy9i2W9LSkPkkPS5qS7P+UpGeT/T+VdHKeZz4qaYuk3uRzQjl1MKsnD+m1ZlVut9UmYCHwizzHVwAPpTckjQO+ApwbEZ1AH3Bdcngj0J3sfwC4pcBzL4+IruTzhzLrYFY3HtJrzaqsGeYRsRlA0lHHJC0AXgBeydydfCZJ2gkcB2xN7vWzjPOeAK4op2xmzaLYpU0Gdu1jxpI17sayhlCVhLmkScCNwLLM/RFxALgWeBrYDpwO3JXjFh8jo8WSw7eTLqu/U67Idbgc10jqkdQzODhYajXM6iLfkF7A3VjWMIYNHpLWStqU43NRgcuWASsiYk/WvdpIBY9ZwFRS3VY3ZZ1zBdAN3Jrn3pdHxBnA3OTz4XyFiIiVEdEdEd3t7e3D1NSsMeTKg2RzN5bV27DdVhFx/gjueyawSNItwGRgSNJ+4Mnkns8DSFoFLElfJOl84LPAuyPi1TzlGUh+7pZ0LzAbuGcEZTRrSOnuqFsf2cL2JGmey8CufV5k0eqmKqvqRsTc9HdJS4E9EXGHpKnA6ZLaI2IQuABI501mAd8A3pcvCZ4k3CdHxI6kFTMPWFuNOpjVU2YeJN9w3vTMdDjclZW+1qzayh2qe7GkfuBsYI2kRwqdHxHbSXVp/UJSH9AFfCE5fCtwDPDdJJ/xw4zn9CZfxwOPJNf2AgPAneXUwazReWa6NSJPEjRrAg9uHDjUjZXvXSFpAndjWcXkmyTol0GZNYHs4bz5urLgyBFZ6WvNKs1rW5k1IY/Isnpz8DBrQtkz0/NJTyycs3yd54VYRbnbyqxJFTMiC9yNZdXhlodZC3A3ltWag4dZC3A3ltWau63MWoS7sayW3PIwa0HFdmP9l1W/dkvERsQtD7MWVOz6WAeTScJuiVip3PIwa1ELZk1j/ZLzeHH5B5hWYJn3NCfUrRQOHmajQDHdWOCEuhXP3VZmo0B2N9YY6VCXVTYn1K0YXhjRbBR6cOMAN61+mn0HDhY8b6zEUIQXWhzFvDCimR3ihLqVyzkPs1HKCXUrh4OHmTmhbiVzt5WZOaFuJXPC3MyO4oS6pTlhbmZFc0LdhuOch5nl5IS6FeLgYWbDckLdspUVPCRdIukZSUOSjuoTk3SSpD2SbsjYt1jS05L6JD0saUqy/ypJg5J6k89f5XnmO5Prt0r6qqRCry8wswrIfl/I2AL/22Um1B1AWldZCXNJpwFDwDeAGyKiJ+v495LjT0bElySNA7YDp0fEDkm3AHsjYqmkq4DuiLhumGc+Bfwt8ATwT8BXI+Kh4crqhLlZ5TihPnrkS5iX1fKIiM0RkbOTU9IC4AXgmczdyWdS0mI4jlQwKYqktwDHRcTjkYp69wALRlZ6MxupYt9ceDDCLZEWVZWch6RJwI3Assz9EXEAuBZ4mqQFAtyVccoHk+6sBySdmOPW04D+jO3+ZF++clwjqUdSz+Dg4MgqY2Y5jSSh/sn7e50PaRHDBg9JayVtyvG5qMBly4AVEbEn615tpILHLGAq0AfclBz+EdAREZ3AWuDuXMXJsS9vv1tErIyI7ojobm9vL1BcMytHsQl1cCukVQw7zyMizh/Bfc8EFiU5jcnAkKT9wJPJPZ8HkLQKWJLs25lx/Z3AzTnu2w9Mz9ieTgndXmZWHaXMUIfDr8C9/v5e50OaVFUmCUbE3PR3SUuBPRFxh6SpwOmS2iNiELgA2Jyc95aI+H1y2fz0/qz7/l7SbklnkQpEHwFur0YdzKw0C2ZNOxQAikmoe4Jhcyt3qO7FkvqBs4E1kh4pdH5EbCfVpfULSX1AF/CF5PAnkmG/vwY+AVyV8ZzejNtcC3wT2Ao8Dww70srMaiszoV4M50Oaj9e2MrOqKnZYb9rEtrF8ceEZboU0iKoM1TUzG04pEwzBrZBm4YURzazqSs2HgHMhjc4tDzOrqVLyIW6FNC7nPMysbkrJh4jUpK5pHtpbU36fh5k1nMz5IQO79hU8N/1rrruzGoNbHmbWEEodlQVuhdSCWx5m1tCKaYXMH/MYnxm3iqnawfaYwi3/eik3rX7tiOutNtzyMLOGk6sVMn/MYyxv+yb/Rq8d2jcUqVzIv6idNy/8AnReWofStja3PMysaWS3QgR8ZtyqIwIHwJhkysibGYQffSK14QBSE255mFnDe3DjAPN/8JeMyb+I9mHHnwjv/ZyDyN3z4cWfH96e8W648ocl38YzzM2saS2YNY0xx08f/kSAl38Hq6+Gm2dA36rqFqxR3XHmkYEDUtt3z6/YIxw8zKw5vPdz0FbcQosA7PsjrL4Glh4PK942egJJ3yrY8VzuY9kBpQwOHmbWHDovhf/01VS3FJD73XDZkm6ul3+XyomMhgDyg+tq8hgHDzNrHp2XwvWbYOnLsHBlRiApwoF9qe6sVm6F/PhTcPDVmjzKwcPMmlM6kCy8s7TurFbNifStgp67Cp8z490Ve5yDh5k1t3R31sQ/K+26VsuJFNNdNYLRVvk4eJhZ8+u8FG58MdUKGWlOpFlbI32r4PPtw3dXdX+soo/1PA8za019q+Cnn08FhlJN/DP4jzc3/lyRvlXw/b+GGGY9sDFvgM8NjugRnudhZqPLSHMikHRpNXhLpG9VqtttuMABsOB/VPzxXp7EzFpbuvXw0I2poFCKdBD50Sdh3HjY9yc4fnr9Z7D/+FPDJ8fTuj9WlbK65WFmrW/EOZHEgVeSwBNJfuSa1D/g9XD3/OIDx4x3w7wvV6UYZQUPSZdIekbSkKSj+sQknSRpj6QbMvYtlvS0pD5JD0uakuxfIak3+fxG0q48z3xU0paMc08opw5mNopkzxMpdYTWIZH6B3zZG2s3WiudGC92lvgI17IqVlkJc0mnAUPAN4AbIqIn6/j3kuNPRsSXJI0DtgOnR8QOSbcAeyNiadZ1HwdmRcRHczzz0VzPGo4T5maWU9+qkXVp5aIxEEOVW5xxpGXr/ljFWhxVWZI9IjYnN8/1wAXAC8ArmbuTzyRJO4HjgK05br0Y+PtyymZmVpTOS1OfSgSRGEr9TA/9XX01h9++TnGjuMotRwUDRyEVGaqb3RqQNAlYC1wA3ADsiYgvJccWAd8iFVT+L3BuxOHhApJOBp4Apmfuz3rWm4CDwPeA/xZ5KiHpGuAagJNOOumdL730Utl1NbMWV8mWSK1VIXCMeKiupLWSNuX4XFTgsmXAiojYk3WvNuBaYBYwFegDbsq69jLggVyBI3F5RJwBzE0+H85XiIhYGRHdEdHd3t5esJ5mZkCO5LqgbVK9S1XY2PGp8tagxZE2bLdVRJw/gvueCSxKchqTgSFJ+4Enk3s+DyBpFbAk69rLgL8pUJ6B5OduSfcCs4F7RlBGM7P80t1ZaY3aIqlyYjyfqszziIi56e+SlpLqtrpD0lTgdEntETFIqltrc8a5pwJvBB7Pdd8k4T45Sba3AfNIdY+ZmVVXZm7k0Mz1jHxGrb1hEsy7rW7zTcoKHpIuBm4H2oE1knoj4sJ850fEdknLgF9IOgC8BFyVccpi4B+zcxjJfbuA8cAjSeAYSypw3FlOHczMSpKrRTLSZVBGos5BI81rW5mZVUpmINHYZOmQCrVO6rTeVlWG6pqZWYbsVkmmUnImTbAwo4OHmVktFAosTchrW5mZWckcPMzMrGQOHmZmVjIHDzMzK5mDh5mZlWzUzPOQNEhqUmI9TQF21LkM1dCK9XKdmoPrVH0nR8RRiwOOmuDRCCT15Jps0+xasV6uU3NwnerH3VZmZlYyBw8zMyuZg0dtrax3AaqkFevlOjUH16lOnPMwM7OSueVhZmYlc/AwM7OSOXhUkKQJkp6S9GtJzyQvvkLSJcn2kKTurGtukrRV0hZJeV+kVS8F6nSrpOck9Un6vqTJGdc0a53+a1KfXkk/Sd58mb6mKeuUcfwGSSFpSsa+pqyTpKWSBpI/p15J78+4pqHrBIX/rCR9PCn7M8lrvNP7G69eEeFPhT6k3vpyTPK9jdQ7288CTgNOBR4FujPOPx34Nak3JM4AngfG1rseRdbpPwDjkv03Aze3QJ2OyzjnE8D/bPY6JdsnAo+QmiQ7pdnrBCwFbshxfsPXaZh6nUvq7ajjk2MnNHK93PKooEjZk2y2JZ+IiM0RsSXHJReReu3uqxHxIrAVmF2j4halQJ1+EhGvJ/ufAKYn35u5Tv+acdokDr/+rWnrlGyvAD7Dka+za/Y65dLwdYKC9boWWB4Rrybn/SE5pyHr5eBRYZLGSuoF/gD8c0Q8WeD0aUDmi4/7k30NpYg6fRR4KPne1HWS9A+SfgdcDnwuOb1p6yRpPjAQEb/OOr1p65Qcui7pYvyWpDcm+5qiTpC3Xn8BzJX0pKSfS/r3yekNWS8HjwqLiIMR0UXqN/HZkt5W4HTlukVVClaGQnWS9FngdeB/p3flukXVC1mifHWKiM9GxImk6nNdcnqz1qkT+CyHg2CmZq3T24CvA/8O6AJ+D/z35PSmqBPkrdc44I2kurA+DaySJBq0Xg4eVRIRu0jlON5X4LR+Uv3RadOB7dUrVXmy6yTpSmAecHkknbM0eZ0y3At8MPnerHW6iFQf+a8lbSNV7v8j6c00b53eFxH/kvzjOwTcyeEunKaqExz1968fWJ10az0FDJFaJLEh6+XgUUGS2tOjjiRNBM4HnitwyQ+ByySNlzQDOAV4quoFLUG+Okl6H3AjMD8i9mZc0sx1OiXjtPkc/rNr1jptjIgTIqIjIjpI/SP0joj4fzRvnZ6T9JaM0y4GNiXfG75OUPDfiQeB85L9fwG8gdTqug1Zr3H1LkCLeQtwt6SxpALzqoj4saSLgduBdmCNpN6IuDAinpG0CniWVNfP30TEwbqVPrd8ddpKavTHP6da1jwREX/d5HX6nqRTSf3G9xLw1wDNXKd8JzdznST9L0ldpLputgH/GZqmTpC/Xm8AviVpE/AacGXSom/Ienl5EjMzK5m7rczMrGQOHmZmVjIHDzMzK5mDh5mZlczBw8zMSubgYWZmJXPwMDOzkv1/GbE5z5Q8d48AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis_test(20, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3c2a6d9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_pred(test_loader, batch_sz, model):\n",
    "    '''\n",
    "    Remember to use test_dataset stats, NOT train_dataset\n",
    "    '''\n",
    "    count_row = 0\n",
    "    out = []\n",
    "\n",
    "    for X in test_loader:\n",
    "        X = X.cpu().float()\n",
    "        if len(X) != batch_sz:\n",
    "            print(len(X))\n",
    "            to_fill = np.zeros([batch_sz-len(X), 50, 2])\n",
    "            X = torch.from_numpy(np.append(X, to_fill, axis=0)).float()\n",
    "            \n",
    "#             a = test_dataset.rotate_matrix[-1].T\n",
    "#             temp = X[20]@np.linalg.inv(a) + test_dataset.start_pos[-1]\n",
    "#             plt.scatter(temp[:, 0], temp[:, 1], label='input')\n",
    "\n",
    "        pred = model(X)\n",
    "        pred = pred.reshape(batch_sz, -1, 2).cpu().detach().numpy()\n",
    "        for i in range(batch_sz):\n",
    "            if count_row >= len(test_dataset):\n",
    "                break\n",
    "                \n",
    "#             if count_row == (len(test_dataset) - 1):\n",
    "#                 plt.scatter(X[i, :, 0], X[i, :, 1], label='input')\n",
    "#                 plt.scatter(pred[i, :, 0], pred[i, :, 1], label='pred')\n",
    "#                 plt.legend()\n",
    "#                 plt.show()\n",
    "\n",
    "            rotation =  test_dataset.rotate_matrix[count_row].T\n",
    "            pred[i] = pred[i] @ np.linalg.inv(rotation)\n",
    "            pred[i] = pred[i] + test_dataset.start_pos[count_row, : ]\n",
    "\n",
    "    #         print(pred[i, 0, :])\n",
    "                \n",
    "            out.append(pred[i])\n",
    "            count_row += 1 \n",
    "            \n",
    "    #         print(count_row)\n",
    "    #         print(pred[0, :5, :])\n",
    "\n",
    "    out = np.array(out).reshape(len(test_dataset), -1)\n",
    "\n",
    "    return out\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d45f71e8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(6325, 120)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "austin_array = make_pred(test_loader, batch_sz, austin_train_mod)\n",
    "austin_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d6d960a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  363.7334, -1486.4718], dtype=float32)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "austin_array[6324, -2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0f5b2d89",
   "metadata": {},
   "outputs": [],
   "source": [
    "# idx=6324\n",
    "# a = test_dataset.start_pos[idx, 0]\n",
    "# b = test_dataset.start_pos[idx, 1]\n",
    "# plt.scatter(X[idx-6272, :, 0]+a,\n",
    "#             X[idx-6272, :, 1]+b, label='seed')\n",
    "# # plt.scatter(y[idx, :, 0], y[idx, :, 1], label='ground truth')\n",
    "# plt.scatter(pred[idx-6272, :, 0], pred[idx-6272, :, 1], label='prediction')\n",
    "# plt.legend()\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3078a34c",
   "metadata": {},
   "source": [
    "### miami\n",
    "- best KAGGLE val loss: 24.166294670104982\n",
    "- best val loss: 18.53021953259295"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "52e09d55",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f7605300735a4f138a82d677e4d109a3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1\n",
      "Training & Validating -----------------------------------------------------------------------------\n",
      "- Training Loss: 83.25551618293709\n",
      "- Validation Loss: 65.95937818114521\n",
      "- Train Time: 11.035842895507812\n",
      "- Elapsed Time: 11.289653301239014\n",
      "\n",
      "Epoch 2\n",
      "Training & Validating -----------------------------------------------------------------------------\n",
      "- Training Loss: 65.92275670057123\n",
      "- Validation Loss: 63.416712643807394\n",
      "- Train Time: 21.206794261932373\n",
      "- Elapsed Time: 21.660753965377808\n",
      "\n",
      "Epoch 3\n",
      "Training & Validating -----------------------------------------------------------------------------\n",
      "- Training Loss: 63.226445281898584\n",
      "- Validation Loss: 61.46928233988801\n",
      "- Train Time: 30.42730450630188\n",
      "- Elapsed Time: 31.072884559631348\n",
      "\n",
      "Epoch 4\n",
      "Training & Validating -----------------------------------------------------------------------------\n",
      "- Training Loss: 55.55032601199307\n",
      "- Validation Loss: 38.78792406383314\n",
      "- Train Time: 40.453447580337524\n",
      "- Elapsed Time: 41.36046314239502\n",
      "\n",
      "Epoch 5\n",
      "Training & Validating -----------------------------------------------------------------------------\n",
      "- Training Loss: 33.86375414423582\n",
      "- Validation Loss: 29.56579774443866\n",
      "- Train Time: 49.995991706848145\n",
      "- Elapsed Time: 51.128798961639404\n",
      "\n",
      "Epoch 6\n",
      "Training & Validating -----------------------------------------------------------------------------\n",
      "- Training Loss: 30.505147477157976\n",
      "- Validation Loss: 28.239338696351524\n",
      "- Train Time: 59.115411043167114\n",
      "- Elapsed Time: 60.478028774261475\n",
      "\n",
      "Epoch 7\n",
      "Training & Validating -----------------------------------------------------------------------------\n",
      "- Training Loss: 28.653615154446367\n",
      "- Validation Loss: 26.75091851106164\n",
      "- Train Time: 65.6947729587555\n",
      "- Elapsed Time: 67.28190469741821\n",
      "\n",
      "Epoch 8\n",
      "Training & Validating -----------------------------------------------------------------------------\n",
      "- Training Loss: 28.28103645154562\n",
      "- Validation Loss: 27.098086251152885\n",
      "- Train Time: 72.3094642162323\n",
      "- Elapsed Time: 74.05078625679016\n",
      "\n",
      "Epoch 9\n",
      "Training & Validating -----------------------------------------------------------------------------\n",
      "- Training Loss: 27.398847224254645\n",
      "- Validation Loss: 28.014585411339475\n",
      "- Train Time: 80.43749332427979\n",
      "- Elapsed Time: 82.33314728736877\n",
      "\n",
      "Epoch 10\n",
      "Training & Validating -----------------------------------------------------------------------------\n",
      "- Training Loss: 27.10555026480361\n",
      "- Validation Loss: 25.28546911094621\n",
      "- Train Time: 89.05407452583313\n",
      "- Elapsed Time: 91.10583424568176\n",
      "\n",
      "Epoch 11\n",
      "Training & Validating -----------------------------------------------------------------------------\n",
      "- Training Loss: 26.453084511069537\n",
      "- Validation Loss: 26.68167906197888\n",
      "- Train Time: 98.45201969146729\n",
      "- Elapsed Time: 100.72591948509216\n",
      "\n",
      "Epoch 12\n",
      "Training & Validating -----------------------------------------------------------------------------\n",
      "- Training Loss: 26.599695224182483\n",
      "- Validation Loss: 30.005223647892823\n",
      "- Train Time: 107.48744416236877\n",
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      "\n",
      "Epoch 13\n",
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      "- Training Loss: 26.412005333878874\n",
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      "\n",
      "Epoch 14\n",
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      "- Training Loss: 26.975898351835756\n",
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      "\n",
      "Epoch 15\n",
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      "- Training Loss: 25.993928171390863\n",
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      "\n",
      "Epoch 16\n",
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      "- Training Loss: 25.911033336316375\n",
      "- Validation Loss: 25.41388726373862\n",
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      "\n",
      "Epoch 17\n",
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      "- Training Loss: 21.45470822481625\n",
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      "\n",
      "Epoch 18\n",
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      "- Training Loss: 21.237382235030783\n",
      "- Validation Loss: 20.94719039626986\n",
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      "\n",
      "Epoch 19\n",
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      "- Training Loss: 21.098756145644973\n",
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      "\n",
      "Epoch 20\n",
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      "- Training Loss: 20.981485536966158\n",
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      "\n",
      "Epoch 21\n",
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      "- Training Loss: 20.8844764274247\n",
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      "\n",
      "Epoch 22\n",
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      "- Training Loss: 20.809848378532227\n",
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      "\n",
      "Epoch 23\n",
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      "- Training Loss: 20.703208332148073\n",
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      "\n",
      "Epoch 24\n",
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      "- Training Loss: 20.599257924746297\n",
      "- Validation Loss: 20.212376951474194\n",
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      "\n",
      "Epoch 25\n",
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      "- Training Loss: 20.534272487346943\n",
      "- Validation Loss: 20.15529057017544\n",
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      "\n",
      "Epoch 26\n",
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      "- Training Loss: 20.453374645674543\n",
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      "\n",
      "Epoch 27\n",
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      "- Training Loss: 20.39370959299799\n",
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      "- Elapsed Time: 250.3036413192749\n",
      "\n",
      "Epoch 28\n",
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      "- Training Loss: 20.343780410929348\n",
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      "\n",
      "Epoch 29\n",
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      "- Training Loss: 20.30687356904467\n",
      "- Validation Loss: 19.88892400195027\n",
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      "- Elapsed Time: 265.7430510520935\n",
      "\n",
      "Epoch 30\n",
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      "- Training Loss: 20.233395947590132\n",
      "- Validation Loss: 19.855837133195664\n",
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      "\n",
      "Epoch 31\n",
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      "- Training Loss: 20.190156742150197\n",
      "- Validation Loss: 19.826434910645958\n",
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      "\n",
      "Epoch 32\n",
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      "- Training Loss: 20.1495791187422\n",
      "- Validation Loss: 19.782297198535407\n",
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      "\n",
      "Epoch 33\n",
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      "- Training Loss: 20.098245915706958\n",
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      "\n",
      "Epoch 34\n",
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      "- Training Loss: 20.048530538234697\n",
      "- Validation Loss: 19.664228840878135\n",
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      "\n",
      "Epoch 35\n",
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      "- Training Loss: 20.021285397049375\n",
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      "- Elapsed Time: 312.5984547138214\n",
      "\n",
      "Epoch 36\n",
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      "- Training Loss: 19.987047124232642\n",
      "- Validation Loss: 19.56212704084073\n",
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      "- Elapsed Time: 318.30215525627136\n",
      "\n",
      "Epoch 37\n",
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      "- Training Loss: 19.954452684485073\n",
      "- Validation Loss: 19.547423039263453\n",
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      "- Elapsed Time: 324.3854863643646\n",
      "\n",
      "Epoch 38\n",
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      "- Training Loss: 19.924421427245285\n",
      "- Validation Loss: 19.51036333898355\n",
      "- Train Time: 322.5603070259094\n",
      "- Elapsed Time: 329.9367184638977\n",
      "\n",
      "Epoch 39\n",
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      "- Training Loss: 19.900672655839188\n",
      "- Validation Loss: 19.52186001392833\n",
      "- Train Time: 328.2618317604065\n",
      "- Elapsed Time: 335.8063645362854\n",
      "\n",
      "Epoch 40\n",
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      "- Training Loss: 19.871470211241117\n",
      "- Validation Loss: 19.446071259459558\n",
      "- Train Time: 336.27252650260925\n",
      "- Elapsed Time: 344.01549315452576\n",
      "\n",
      "Epoch 41\n",
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      "- Training Loss: 19.830190781554485\n",
      "- Validation Loss: 19.411730548791716\n",
      "- Train Time: 345.98033237457275\n",
      "- Elapsed Time: 353.915344953537\n",
      "\n",
      "Epoch 42\n",
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      "- Training Loss: 19.82454565254118\n",
      "- Validation Loss: 19.453871568044026\n",
      "- Train Time: 354.72409439086914\n",
      "- Elapsed Time: 362.82236433029175\n",
      "\n",
      "Epoch 43\n",
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      "- Training Loss: 19.79845838417295\n",
      "- Validation Loss: 19.380763112452993\n",
      "- Train Time: 362.99734902381897\n",
      "- Elapsed Time: 371.2560307979584\n",
      "\n",
      "Epoch 44\n",
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      "- Training Loss: 19.75129146458799\n",
      "- Validation Loss: 19.36904017409386\n",
      "- Train Time: 373.04623436927795\n",
      "- Elapsed Time: 381.49395298957825\n",
      "\n",
      "Epoch 45\n",
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      "- Training Loss: 19.745935160803963\n",
      "- Validation Loss: 19.34836092608714\n",
      "- Train Time: 381.7986378669739\n",
      "- Elapsed Time: 390.4800193309784\n",
      "\n",
      "Epoch 46\n",
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      "- Training Loss: 19.711598486613674\n",
      "- Validation Loss: 19.378321909765052\n",
      "- Train Time: 388.8521018028259\n",
      "- Elapsed Time: 397.70453095436096\n",
      "\n",
      "Epoch 47\n",
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      "- Training Loss: 19.70053817290372\n",
      "- Validation Loss: 19.31650801708824\n",
      "- Train Time: 398.10827374458313\n",
      "- Elapsed Time: 407.1171336174011\n",
      "\n",
      "Epoch 48\n",
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      "- Training Loss: 19.680439859956177\n",
      "- Validation Loss: 19.297307006099768\n",
      "- Train Time: 404.79035997390747\n",
      "- Elapsed Time: 413.9874005317688\n",
      "\n",
      "Epoch 49\n",
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      "- Training Loss: 19.64999940893152\n",
      "- Validation Loss: 19.295689822637545\n",
      "- Train Time: 412.30568623542786\n",
      "- Elapsed Time: 421.69722962379456\n",
      "\n",
      "Epoch 50\n",
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      "- Training Loss: 19.634765807474206\n",
      "- Validation Loss: 19.31271711706418\n",
      "- Train Time: 421.97333812713623\n",
      "- Elapsed Time: 431.52760553359985\n",
      "\n",
      "Epoch 51\n",
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      "- Training Loss: 19.61137531184349\n",
      "- Validation Loss: 19.26260436766329\n",
      "- Train Time: 430.8889343738556\n",
      "- Elapsed Time: 440.65827798843384\n",
      "\n",
      "Epoch 52\n",
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      "- Training Loss: 19.578287186588714\n",
      "- Validation Loss: 19.25341603072763\n",
      "- Train Time: 440.1028163433075\n",
      "- Elapsed Time: 450.07044768333435\n",
      "\n",
      "Epoch 53\n",
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      "- Training Loss: 19.571017330665317\n",
      "- Validation Loss: 19.187690361201415\n",
      "- Train Time: 448.1209182739258\n",
      "- Elapsed Time: 458.2488384246826\n",
      "\n",
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      "- Training Loss: 19.54132189408532\n",
      "- Validation Loss: 19.190624928613854\n",
      "- Train Time: 455.66212368011475\n",
      "- Elapsed Time: 465.9474868774414\n",
      "\n",
      "Epoch 55\n",
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      "- Training Loss: 19.526242060436307\n",
      "- Validation Loss: 19.184940070436713\n",
      "- Train Time: 464.84731340408325\n",
      "- Elapsed Time: 475.2975056171417\n",
      "\n",
      "Epoch 56\n",
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      "- Training Loss: 19.521599416356743\n",
      "- Validation Loss: 19.113349376366152\n",
      "- Train Time: 472.19288325309753\n",
      "- Elapsed Time: 482.82494139671326\n",
      "\n",
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      "- Training Loss: 19.493327661414416\n",
      "- Validation Loss: 19.17680849387632\n",
      "- Train Time: 482.16820645332336\n",
      "- Elapsed Time: 492.9993510246277\n",
      "\n",
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      "- Training Loss: 19.469059367756266\n",
      "- Validation Loss: 19.234593291031686\n",
      "- Train Time: 490.34674406051636\n",
      "- Elapsed Time: 501.3813636302948\n",
      "\n",
      "Epoch 59\n",
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      "- Training Loss: 19.439844670878276\n",
      "- Validation Loss: 19.207434085377475\n",
      "- Train Time: 498.8658275604248\n",
      "- Elapsed Time: 510.0546119213104\n",
      "\n",
      "Epoch 60\n",
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      "- Training Loss: 19.423512721724407\n",
      "- Validation Loss: 19.108684127093756\n",
      "- Train Time: 504.92483258247375\n",
      "- Elapsed Time: 516.2969129085541\n",
      "\n",
      "Epoch 61\n",
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      "- Training Loss: 19.39894887255944\n",
      "- Validation Loss: 19.1408697011178\n",
      "- Train Time: 512.1235492229462\n",
      "- Elapsed Time: 523.6906373500824\n",
      "\n",
      "Epoch 62\n",
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      "- Training Loss: 19.370262494608134\n",
      "- Validation Loss: 19.16322760554085\n",
      "- Train Time: 519.8413505554199\n",
      "- Elapsed Time: 531.6052720546722\n",
      "\n",
      "Epoch 63\n",
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      "- Training Loss: 19.35184525672898\n",
      "- Validation Loss: 19.108325378239503\n",
      "- Train Time: 526.2703311443329\n",
      "- Elapsed Time: 538.1881566047668\n",
      "\n",
      "Epoch 64\n",
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      "- Training Loss: 19.34389416911021\n",
      "- Validation Loss: 18.979406326137788\n",
      "- Train Time: 533.4919362068176\n",
      "- Elapsed Time: 545.5824420452118\n",
      "\n",
      "Epoch 65\n",
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      "- Training Loss: 19.320294896170797\n",
      "- Validation Loss: 19.12312662671184\n",
      "- Train Time: 540.5688557624817\n",
      "- Elapsed Time: 552.8142993450165\n",
      "\n",
      "Epoch 66\n",
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      "- Training Loss: 19.306978444553764\n",
      "- Validation Loss: 19.08566162879007\n",
      "- Train Time: 546.7946269512177\n",
      "- Elapsed Time: 559.1932604312897\n",
      "\n",
      "Epoch 67\n",
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      "- Training Loss: 19.29254778508764\n",
      "- Validation Loss: 18.99584134140907\n",
      "- Train Time: 553.8420429229736\n",
      "- Elapsed Time: 566.3968775272369\n",
      "\n",
      "Epoch 68\n",
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      "- Training Loss: 19.26106891373164\n",
      "- Validation Loss: 19.150461623543187\n",
      "- Train Time: 560.4250063896179\n",
      "- Elapsed Time: 573.1332733631134\n",
      "\n",
      "Epoch 69\n",
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      "- Training Loss: 19.267160611994125\n",
      "- Validation Loss: 19.057825643416734\n",
      "- Train Time: 567.4694991111755\n",
      "- Elapsed Time: 580.3388390541077\n",
      "\n",
      "Epoch 70\n",
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      "- Training Loss: 19.241252202254074\n",
      "- Validation Loss: 19.122657154038635\n",
      "- Train Time: 573.3448874950409\n",
      "- Elapsed Time: 586.3675582408905\n",
      "\n",
      "Epoch 71\n",
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      "- Training Loss: 18.599465403159204\n",
      "- Validation Loss: 18.718454263363665\n",
      "- Train Time: 579.8247599601746\n",
      "- Elapsed Time: 593.0030102729797\n",
      "\n",
      "Epoch 72\n",
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      "- Training Loss: 18.543493052952968\n",
      "- Validation Loss: 18.696549903579623\n",
      "- Train Time: 586.5691769123077\n",
      "- Elapsed Time: 599.9162757396698\n",
      "\n",
      "Epoch 73\n",
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      "- Training Loss: 18.527690407839913\n",
      "- Validation Loss: 18.68400714550799\n",
      "- Train Time: 593.1080203056335\n",
      "- Elapsed Time: 606.6260380744934\n",
      "\n",
      "Epoch 74\n",
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      "- Training Loss: 18.515750172374013\n",
      "- Validation Loss: 18.672386969739232\n",
      "- Train Time: 599.446043252945\n",
      "- Elapsed Time: 613.1898403167725\n",
      "\n",
      "Epoch 75\n",
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      "- Training Loss: 18.506476679692828\n",
      "- Validation Loss: 18.662703497367993\n",
      "- Train Time: 605.3557391166687\n",
      "- Elapsed Time: 619.2771861553192\n",
      "\n",
      "Epoch 76\n",
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      "- Training Loss: 18.496901182027962\n",
      "- Validation Loss: 18.657976415422226\n",
      "- Train Time: 612.2324867248535\n",
      "- Elapsed Time: 626.3212118148804\n",
      "\n",
      "Epoch 77\n",
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      "- Training Loss: 18.48882357012324\n",
      "- Validation Loss: 18.65423584263227\n",
      "- Train Time: 621.2032101154327\n",
      "- Elapsed Time: 635.4600310325623\n",
      "\n",
      "Epoch 78\n",
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      "- Training Loss: 18.482600633452304\n",
      "- Validation Loss: 18.651543215701455\n",
      "- Train Time: 628.9163386821747\n",
      "- Elapsed Time: 643.3269560337067\n",
      "\n",
      "Epoch 79\n",
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      "- Training Loss: 18.475481177886685\n",
      "- Validation Loss: 18.64392854177464\n",
      "- Train Time: 636.7906119823456\n",
      "- Elapsed Time: 651.4113872051239\n",
      "\n",
      "Epoch 80\n",
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      "- Training Loss: 18.468992106592417\n",
      "- Validation Loss: 18.63992378167939\n",
      "- Train Time: 647.3278465270996\n",
      "- Elapsed Time: 662.1918954849243\n",
      "\n",
      "Epoch 81\n",
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      "- Training Loss: 18.46339469520061\n",
      "- Validation Loss: 18.640223581191393\n",
      "- Train Time: 653.9994077682495\n",
      "- Elapsed Time: 669.0173008441925\n",
      "\n",
      "Epoch 82\n",
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      "- Training Loss: 18.456985162625255\n",
      "- Validation Loss: 18.63074180536103\n",
      "- Train Time: 662.9027643203735\n",
      "- Elapsed Time: 678.0926883220673\n",
      "\n",
      "Epoch 83\n",
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      "- Training Loss: 18.451820547378826\n",
      "- Validation Loss: 18.62895014969229\n",
      "- Train Time: 672.9555993080139\n",
      "- Elapsed Time: 688.3409988880157\n",
      "\n",
      "Epoch 84\n",
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      "- Training Loss: 18.446333532265562\n",
      "- Validation Loss: 18.622027377636112\n",
      "- Train Time: 680.7122926712036\n",
      "- Elapsed Time: 696.2930152416229\n",
      "\n",
      "Epoch 85\n",
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      "- Training Loss: 18.44128722811639\n",
      "- Validation Loss: 18.61293818379006\n",
      "- Train Time: 689.1587862968445\n",
      "- Elapsed Time: 704.9649188518524\n",
      "\n",
      "Epoch 86\n",
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      "- Training Loss: 18.435731189052134\n",
      "- Validation Loss: 18.609166005898636\n",
      "- Train Time: 698.027893781662\n",
      "- Elapsed Time: 714.073486328125\n",
      "\n",
      "Epoch 87\n",
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      "- Training Loss: 18.430984124724297\n",
      "- Validation Loss: 18.602164154164274\n",
      "- Train Time: 708.5261588096619\n",
      "- Elapsed Time: 724.7810945510864\n",
      "\n",
      "Epoch 88\n",
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      "- Training Loss: 18.42600415580567\n",
      "- Validation Loss: 18.596428082003232\n",
      "- Train Time: 717.158401966095\n",
      "- Elapsed Time: 733.6044068336487\n",
      "\n",
      "Epoch 89\n",
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      "- Training Loss: 18.420504466749115\n",
      "- Validation Loss: 18.593809356466373\n",
      "- Train Time: 727.3918976783752\n",
      "- Elapsed Time: 744.0774383544922\n",
      "\n",
      "Epoch 90\n",
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      "- Training Loss: 18.41616797524263\n",
      "- Validation Loss: 18.588783197235642\n",
      "- Train Time: 736.8975393772125\n",
      "- Elapsed Time: 753.8096661567688\n",
      "\n",
      "Epoch 91\n",
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      "- Training Loss: 18.41182918246515\n",
      "- Validation Loss: 18.58334091811152\n",
      "- Train Time: 746.939352273941\n",
      "- Elapsed Time: 764.0394134521484\n",
      "\n",
      "Epoch 92\n",
      "Training & Validating -----------------------------------------------------------------------------\n",
      "- Training Loss: 18.407015845169926\n",
      "- Validation Loss: 18.579276505966632\n",
      "- Train Time: 754.765593290329\n",
      "- Elapsed Time: 772.1409342288971\n",
      "\n",
      "Epoch 93\n",
      "Training & Validating -----------------------------------------------------------------------------\n",
      "- Training Loss: 18.40312420327816\n",
      "- Validation Loss: 18.577444494816294\n",
      "- Train Time: 764.6443774700165\n",
      "- Elapsed Time: 782.2473938465118\n",
      "\n",
      "Epoch 94\n",
      "Training & Validating -----------------------------------------------------------------------------\n",
      "- Training Loss: 18.39798737589436\n",
      "- Validation Loss: 18.570257094868442\n",
      "- Train Time: 773.2944977283478\n",
      "- Elapsed Time: 791.1159017086029\n",
      "\n",
      "Epoch 95\n",
      "Training & Validating -----------------------------------------------------------------------------\n",
      "- Training Loss: 18.39329498879125\n",
      "- Validation Loss: 18.564884484162803\n",
      "- Train Time: 782.4993145465851\n",
      "- Elapsed Time: 800.5565850734711\n",
      "\n",
      "Epoch 96\n",
      "Training & Validating -----------------------------------------------------------------------------\n",
      "- Training Loss: 18.390571846065015\n",
      "- Validation Loss: 18.55997061311153\n",
      "- Train Time: 792.9009718894958\n",
      "- Elapsed Time: 811.1482591629028\n",
      "\n",
      "Epoch 97\n",
      "Training & Validating -----------------------------------------------------------------------------\n",
      "- Training Loss: 18.38563804497007\n",
      "- Validation Loss: 18.550258181945622\n",
      "- Train Time: 800.3195040225983\n",
      "- Elapsed Time: 818.753776550293\n",
      "\n",
      "Epoch 98\n",
      "Training & Validating -----------------------------------------------------------------------------\n",
      "- Training Loss: 18.38083642393678\n",
      "- Validation Loss: 18.539229493392142\n",
      "- Train Time: 808.1377809047699\n",
      "- Elapsed Time: 826.8110718727112\n",
      "\n",
      "Epoch 99\n",
      "Training & Validating -----------------------------------------------------------------------------\n",
      "- Training Loss: 18.37655316588951\n",
      "- Validation Loss: 18.534902745520164\n",
      "- Train Time: 819.1689167022705\n",
      "- Elapsed Time: 838.0540146827698\n",
      "\n",
      "Epoch 100\n",
      "Training & Validating -----------------------------------------------------------------------------\n",
      "- Training Loss: 18.371773413711775\n",
      "- Validation Loss: 18.53021953259295\n",
      "- Train Time: 829.0752139091492\n",
      "- Elapsed Time: 848.1899347305298\n",
      "\n"
     ]
    }
   ],
   "source": [
    "city = 'miami' \n",
    "split = 'train' #'test' #'train'\n",
    "batch_size = 32\n",
    "hidden_dim = 64\n",
    "num_iters = 100\n",
    "learning_rate = 0.01\n",
    "miami_train_mod, miami_losses = fit(city=city, split=split, batch_size=batch_size,\n",
    "                                    hidden_dim=hidden_dim, learning_rate=learning_rate,\n",
    "                                    num_iters=num_iters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8c1a4da4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(1, 'Miami', 'train', miami_losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "2a0b06f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 50, 2])\n",
      "torch.Size([32, 60, 2])\n",
      "torch.Size([32, 60, 2])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis_results('miami', 'train', 32, miami_train_mod, 31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c6849127",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7971\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(250, 250)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# intialize a dataset\n",
    "city = 'miami' \n",
    "split = 'test' \n",
    "test_dataset = ArgoverseDataset(city = city, split = split, transform=False, normalized=True)\n",
    "temp_dataset = ArgoverseDataset(city = city, split = split, transform=False, normalized=False)\n",
    "print(len(test_dataset)) # should be 7971\n",
    "\n",
    "# test_dataset.start_pos\n",
    "\n",
    "batch_sz = 32  # batch size \n",
    "test_loader = DataLoader(test_dataset, batch_size=batch_sz)\n",
    "temp_loader = DataLoader(temp_dataset, batch_size=batch_sz)\n",
    "miami_train_mod.to('cpu')\n",
    "len(test_loader), len(temp_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "01cbd774",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis_test(2, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "7051e3a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for X in temp_loader:\n",
    "    if len(X) == batch_sz:\n",
    "        continue\n",
    "    # the index here should refer to the above cell's number\n",
    "    plt.scatter(X[2, :, 0], X[2, :, 1], label='input')\n",
    "    plt.legend\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "646b3640",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAD4CAYAAAAHHSreAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAdoElEQVR4nO3df5BV9Znn8ffTTcdGAdkF3Ao02jgjYNSm1V6UiCwTE82sBl1/U5iJ5YwUKaqympFVdxWzhKpxxiTuWlZkiavEXSSiQUBnJkTNIMZomUYQUcERZUxDshBYoy4gv57945zbOdy+P0/fvufcez+vqi5On1/3ud2Hfu7z/X7P95i7IyIiEtWUdAAiIpI+Sg4iItKHkoOIiPSh5CAiIn0oOYiISB+Dkg6gEkaOHOnt7e1JhyEiUlPWr1//e3cflWtbXSSH9vZ2uru7kw5DRKSmmNm/5NumZiUREelDyUFERPpQchARkT7qos8hl0OHDtHT08OBAweSDqVqWltbaWtro6WlJelQRKTG1W1y6OnpYejQobS3t2NmSYcz4NydPXv20NPTw7hx45IOR0RqXN02Kx04cIARI0Y0RGIAMDNGjBjRUJWSSEPbtBzuPxO+Mzz4d9Pyip6+bisHoGESQ0ajvV+RhrRpOfzj7bB/7x/X/eE38My3guWOayvyMnVbOYiI1J1Ny4MkEE0MGYf2wwsLKvZSSg4D6Itf/GLFz7l9+3Yef/zxip9XRFJu03J4ek6QBPL5Q0/FXk7JYQD96le/qvg5lRxEGsym5fC342DFzeBHCu97YlvFXlbJIbRyww4uuPcXjLvj77ng3l+wcsOOfp9zyJAhAKxdu5bp06dz9dVXM3HiRGbNmkXmCXzt7e3cfvvtTJ48mcmTJ/Pee+8BcOONN/LUU0/1Odcdd9zBSy+9RGdnJ/fff3+/YxSRFCvUjJStZTBcNL9iL13XHdKlWrlhB3eueJP9h4KsvOOj/dy54k0Arjh7TEVeY8OGDbz11luMHj2aCy64gJdffpmpU6cCMGzYMF577TUee+wxbrnlFp599tm857n33nv53ve+V3AfEalxm5YH/Qd/+E1p+w/+1/Dnf1uxzmhQ5QDAfWu29iaGjP2HjnDfmq0Ve43JkyfT1tZGU1MTnZ2dbN++vXfbzJkze/995ZVXKvaaIlKDMtVCKYnBmuHKH8HtH1Q0MYAqBwB2fpS7gyff+jiOO+643uXm5mYOHz7c+310CGpmedCgQRw9ehQIbnA7ePBgxWIRkRQqt1poGQxfe6DiSSFDlQMwevjgstZX2hNPPNH775QpU4CgL2L9+vUArFq1ikOHDgEwdOhQPvnkk6rEJSJVUk61AEEz0gAmBlDlAMC8SyYc0+cAMLilmXmXTKjK63/22Wecd955HD16lGXLlgFw8803c/nllzN58mQuuugiTjjhBAA6OjoYNGgQkyZN4sYbb+TWW2+tSowiMgDKrRZOHBt0Og9gUsiwzKiZvDuYPQJcBuxy9zPDdZOARcAQYDswy90/NrNZwLzI4R3AOe6+0czWAp8HMm01F7v7rqzXagfeATKN/a+6+5xib6Krq8uzH/bzzjvvcPrppxc7tNfKDTu4b81Wdn60n9HDBzPvkgkV64wuJPOgopEjR1bkfOW+bxFJSKZaKHTfQsYANSGZ2Xp378q1rZTKYQnwIPBYZN3DwG3u/qKZ3USQEO5296XA0vBFzwJWufvGyHGz3L3YI9u2uXtnCXFV1BVnj6lKMhCRBpfiaiGqaHJw93XhJ/qoCcC6cPk5YA1wd9Y+M4Fl/Q2w3kVHLYlInUtBtVCquB3Sm4EZ4fI1wNgc+1xH3+TwqJltNLO7Lf8scePMbIOZvWhmF+YLwMxmm1m3mXXv3r277DcgIlJ1LywoLTGcODbRxADxO6RvAh4ws/nAauCYcZZmdh6wz903R1bPcvcdZjYU+CnwdY5tqgL4LXCyu+8xs3OBlWZ2hrt/nB2Auy8GFkPQ5xDzfYiIDKzeZqQeoMifqoSrhahYlYO7b3H3i939XILqYFvWLteTVTW4+47w30+Ax4HJOc77mbvvCZfXh+cdHydGEZHEHTNEtUhiSEG1EBWrcjCzk9x9l5k1AXcRjFzKbGsiaGqaFlk3CBju7r83sxaC0U/P5zjvKGCvux8xs1OB04D348QoIpKIaKVgTcUny0tRtRBVtHIws2XAK8AEM+sxs78EZprZu8AWYCfwaOSQaUCPu0f/qB8HrDGzTcBGYAfwo/D8M8xsQeTYTWb2BvAUMMfdS5hxqv6tXbuWyy67LOkwRKSQ7EqhYGKw1FULUaWMVpqZZ9N/z7P/WuD8rHX/Dzg3z/6rCfotcPefEvRHNIwjR47Q3NycdBgiUgnldDjfurn4fgnS9BkZA/A81u3btzNx4kS+8Y1v0NHRwdVXX82+fftob29nwYIFTJ06lSeffJKf//znTJkyhXPOOYdrrrmGTz/9FICf/exnTJw4kalTp7JixYp+xyMiAyTz96OUexcqPLX2QFFygL6lYOZ5rBVIEFu3bmX27Nls2rSJYcOG8cMf/hCA1tZWfvnLX/LlL3+ZhQsX8vzzz/P666/T1dXFD37wAw4cOMDNN9/MM888w0svvcTvfve7fsciIgOglHmRrJm0NyNl09xKkLsUzDyPtZ+/xLFjx3LBBRcAcMMNN/DAAw8AcN111wHw6quv8vbbb/fuc/DgQaZMmcKWLVsYN24cp512Wu+xixcv7lcsIlJBpd7pnNIO52KUHCD/c1cr8DzW7Hv9Mt9nJtJzd77yla/0TriXsXHjxj7HikhKlHqnc0JTX1SCmpUg/3NXK/A81g8//LD3AT7Lli3rffpbxvnnn8/LL7/c+3jQffv28e677zJx4kQ++OADtm3b1nusiCQo2i/59JzSEsOtm2syMYCSQ+Ci+UHpF1WhTqPTTz+dH//4x3R0dLB3716++c1vHrN91KhRLFmyhJkzZ9LR0cH555/Pli1baG1tZfHixVx66aVMnTqVU045pd+xiEhMZQ1RpWY6nQtRsxL8MbNnblw5sa1ipWBTUxOLFi06Zl32ZHtf+tKX+PWvf93n2K9+9ats2bKl3zGISEzlzqAKNd2UFKXkkNFxbc3/MkWkgsqZQRVqtuM5HzUrDaD29nY2b073jS4ikkcpN7TV4BDVUtV15eDuDTXip9hT/USkBHU+RLVUdVs5tLa2smfPnob5g+nu7Nmzh9bW1qRDEaldpdzQBnVZKWSr28qhra2Nnp4eGulBQK2trbS19X/4rUhDKWcW1TqvFqLqNjm0tLQwbty4pMMQkTTL7nQulBjqZBRSqeo2OYiIFFVHs6hWWt32OYiI5FWHs6hWmioHEWkspdy/YM3gRyt6Q2ytUXIQkcagIaplUXIQkfrXALOoVpqSg4jUr3LmRmrATudClBxEpD6VMzdSg3Y6F6LRSiJSn8oZpqo+hj5UOYhI/Yje7UyRqXPU8VyQkoOI1IdympHU8VyUkoOI1LZyOp1VLZRMyUFEalfJ1YI19A1tcSg5iEjt0RDVAafkICK1RUNUq0JDWUWktmiIalWochCR9NMQ1apTchCRdNMQ1UQoOYhIOmmIaqKUHEQkfTRENXFKDiKSHhqimhpKDiKSDhqimioayioi6aAhqqmiykFEkqXHd6aSkoOIJEeP70wtJQcRqa7oDW3WBH4k/76qFhKj5CAi1ZNdKRRKDKoWElW0Q9rMHjGzXWa2ObJukpm9YmZvmtkzZjYsXD/LzDZGvo6aWWe4ba2ZbY1sOynP691pZu+F+15SofcpImlQTqfzrZuVGBJUymilJcBXs9Y9DNzh7mcBTwPzANx9qbt3unsn8HVgu7tvjBw3K7Pd3Xdlv5CZfQG4HjgjfM0fmllzeW9JRFJn03K4/8zS73bWMNXEFU0O7r4O2Ju1egKwLlx+Drgqx6EzgWVlxnM58BN3/8zdPwDeAyaXeQ4RSZNMU1KhxGDNBHc7a5hqWsTtc9gMzABWAdcAY3Pscx3BH/uoR83sCPBTYKG7Z0+vOAZ4NfJ9T7iuDzObDcwGOPnkk8uNX0QGmoao1rS4N8HdBMw1s/XAUOBgdKOZnQfsc/fove2zwmaoC8Ovr+c4r+VYl3N+Xndf7O5d7t41atSoOO9BRAZKKdUCqFJIsViVg7tvAS4GMLPxwKVZu1xPVpOSu+8I//3EzB4naC56LOu4Ho6tQtqAnXFiFJEqK2eIKmhupJSLVTlkRhqZWRNwF7Aosq2JoKnpJ5F1g8xsZLjcAlxG0DSVbTVwvZkdZ2bjgNOA1+LEKCJVdEyl4MUTgzqdU69o5WBmy4DpwEgz6wHuAYaY2dxwlxXAo5FDpgE97v5+ZN1xwJowMTQDzwM/Cs8/A+hy9/nu/paZLQfeBg4Dc92LXWUikrhSh6iC7l+oEda3T7j2dHV1eXd3d9JhiDQePZCnppnZenfvyrVNd0iLSDylzItkzeBH9UCeGqTkICLl0RDVhqDkICKl0yyqDUPJQUSK0+M7G46Sg4gUpsd3NiQ9JlRECtPjOxuSKgcR6St6t3PuGWz+SB3PdUnJQUSOVU4zkjqe65aSg4gEdEObRCg5iEgZ1YLphrYGoeQg0sg0RFXyUHIQaVQaoioFaCirSKPSEFUpQJWDSKPR3EhSAiUHkUaiuZGkREoOIo1A1YKUSclBpN6pWpAYlBxE6lF0+gtrKv5MZw1TlSxKDiL1JrtSKJYYNExVclByEKkX5dzQlqGmJMlDyUGkHpRzQxuo41mK0k1wIvWglBvarJlgbiTd1CbFqXIQqWUaoioDRMlBpFZpiKoMICUHkVqjakGqQMlBpJaoWpAqUXIQqQV67oJUmZKDSNrpuQuSACUHkbQq96Y2NSVJBSk5iKRRudWCOp6lwpQcRNJE1YKkhJKDSFqoWpAUUXIQSZqqBUkhJQeRJKlakJTSxHsiSSplwjzQZHlSdaocRJKgKTAk5ZQcRKpNU2BIDVByEKkWVQtSQ4r2OZjZI2a2y8w2R9ZNMrNXzOxNM3vGzIaF62eZ2cbI11Ez68w63+roubK2tZvZ/sjxi/r5/kTSIVMtFEsM6luQlCilclgCPAg8Fln3MHCbu79oZjcB84C73X0psBTAzM4CVrn7xsxBZnYl8GmR19vm7p2lvgGRVNOEeVKjilYO7r4O2Ju1egKwLlx+Drgqx6EzgWWZb8xsCPBtYGGsSEVqTanVAmjCPEmduENZNwMzwuVrgLE59rmOSHIAvgt8H9hX5NzjzGyDmb1oZhfm28nMZptZt5l17969u4zQRQbYpuVw/5mw4mYNU5WaFTc53ATMNbP1wFDgYHSjmZ0H7HP3zeH3ncCfuvvTRc77W+Bkdz+boMp4PNOfkc3dF7t7l7t3jRo1KubbEKmwcquFK38UNCUpMUjKxBqt5O5bgIsBzGw8cGnWLtdzbNUwBTjXzLaHr3mSma119+lZ5/0M+CxcXm9m24DxQHecOEWqRlNgSJ2JlRzM7CR332VmTcBdwKLItiaCpqZpmXXu/hDwULi9HXg2OzGE20YBe939iJmdCpwGvB8nRpGq0RQYUoeKJgczWwZMB0aaWQ9wDzDEzOaGu6wAHo0cMg3ocfeS/qib2Qygy93nh8cuMLPDwBFgjrtnd4aLpIOqBalj5u5Jx9BvXV1d3t2tliepIlULUgfMbL27d+XapjukRcqhakEahJKDSKlULUgDUXIQKUbVgjQgJQeRQlQtSINSchDJRdWCNDglB5FsqhZElBxEjrFpOTw9B/xI8X1VLUgdU3IQychUDMUSg6oFaQBxJ94TqT8vLCjt0Z1KDNIAVDmIlNL5rGpBGoySgzS2UjqfrVmJQRqOkoM0plKHqqpikAal5CCNp9ShqhqNJA1MyUEaRzk3tp04NnhCm0iDUnKQxlDujW0XzR/4mERSTMlB6p9ubBMpm5KD1Dfd2CYSi26Ck/qmG9tEYlHlIPXtDz35t6laEMlLlYPUtxPbcq/XjW0iBSk5SH27aH5QIUS1DIb/sEiJQaQAJQepbx3XBhXCiWMBU/+CSInU5yD1r+NaJQORMqlyEBGRPpQcRESkDyUHERHpQ8lBRET6UHIQEZE+lBxERKQPDWWVhrJyww7uW7OVnR/tZ/Twwcy7ZAJXnD0m6bBEUkfJQRrGyg07uHPFm+w/FMzQuuOj/dy54k0AJQiRLEoOUvcy1cKOj/rOzrr/0BHuW7NVyUEki5KD1KVoQjDAC+y7M0fSEGl0Sg5Sd7KbjwolBoDRwwcX2UOk8Sg5SF1ZuWEHf738DY54sZQQGNzSzLxLJgxwVCK1R8lB6kamYig1MYzRaCWRvJQcpOYV6nDOZXBLM39z5VlKCiIFKDlITcvuX8gn0ymtakGkNEWTg5k9AlwG7HL3M8N1k4BFwBBgOzDL3T82s1nAvMjhHcA57r4xcr7VwKmZc+V4vTuBvwSOAN9y9zUx3pfUuXKqhWYzvn/tJCUEkTKUUjksAR4EHousexi4zd1fNLObCBLC3e6+FFgKYGZnAauyEsOVwKf5XsjMvgBcD5wBjAaeN7Px7l74Y6E0hHKGp2aoCUkknqJzK7n7OmBv1uoJwLpw+TngqhyHzgSWZb4xsyHAt4GFBV7ucuAn7v6Zu38AvAdMLhaj1L9M81GmUiglMYwZPliJQSSmuH0Om4EZwCrgGmBsjn2uI/hjn/Fd4PvAvgLnHQO8Gvm+J1wnDSzO8FQlBZH+iTsr603AXDNbDwwFDkY3mtl5wD533xx+3wn8qbs/XeS8lmNdzr8IZjbbzLrNrHv37t3lxi81Is7wVCUGkf6LVTm4+xbgYgAzGw9cmrXL9USalIApwLlmtj18zZPMbK27T886rodjq5A2YGeeGBYDiwG6urpK+8shNUPDU0WSFSs5mNlJ7r7LzJqAuwhGLmW2NRE0NU3LrHP3h4CHwu3twLM5EgPAauBxM/sBQYf0acBrcWKU2lNuh7OGp4oMnFKGsi4DpgMjzawHuAcYYmZzw11WAI9GDpkG9Lj7+6UEYGYzgC53n+/ub5nZcuBt4DAwVyOV6t/KDTv4zuq3+Gj/od51xRKDhqeKDCzzEtty06yrq8u7u7uTDkNKEK0Oms044l7ysNQMNSGJVIaZrXf3rlzbdIe0VE323cyZTuZyEoOakESqQ8lBBly5ncu5qFoQqS4lB6mo6DOaTxzcwsHDR9h36Gisc6nDWSQ5Sg5SEbk6laPL5fpXx7dwz9fOUEIQSYiSg/RbqTOjFqIqQSRdlBwktv72JWRGKykhiKSPkoOULVcTUjnUuSySfkoOkld257IZ/N99h8q+LyFKfQkitUHJQfoo1rlcamI4vqWJ41qa+WjfIUar6Uikpig5CBDvQTr5qA9BpPYpOTSwfAkhbmJQX4JI/VByqHH5+gUyI4GG51mXfXNaf2fYUl+CSH1RcqghxSati/YLZOYtKrYuLt2XIFLflBxSLl/TT5xJ6/pDncsijUXJIYUq3RcQhyoDkcam5JAicR56U0lKCCKSoeSQEpWYn6g/1KEsIlFKDgmrxLMOMp/4841MyrdO/Qciko+SQ0Lizk+USQSatE5EBpKSQwLKbUJSX4CIVJuSQxXFaUJSX4CIJEHJoQriNCGpShCRJCk5DLBym5A0P5GIpIGSwwBRE5KI1DIlhwpTE5KI1AMlhwpSE5KI1AslhwpQE5KI1Bslh5jiPjlNTUgiUguUHGLIbj4qJTGoCUlEaomSQxnizoOkJiQRqTVKDiWKM2uqmpBEpFYpORQRp1pQE5KI1DolhzzKvV9Bk+OJSD1Rcsiim9hERJQcgPjDUtV8JCL1quGTw10r32Tpqx/2JgTdryAi0uDJ4a6Vb/K/X/2wrGNULYhII2jY5LByww6WlpkYdL+CiDSKhk0O963ZWnITkpKCiDSaosnBzB4BLgN2ufuZ4bpJwCJgCLAdmOXuH5vZLGBe5PAO4Bx332hmPwM+H77mS8Bcdz/mjjIzawfeAbaGq1519znx315+O0u4b0H9CiLSqEqpHJYADwKPRdY9DNzm7i+a2U0ECeFud18KLAUws7OAVe6+MTzm2jCBGPAUcA3wkxyvt83dO2O8l7KMHj44741tN5x/MguvOGugQxARSa2mYju4+zpgb9bqCcC6cPk54Koch84ElkXO83G4OAj4HKUPDBoQ8y6ZwOCW5mPWGUoMIiJQQnLIYzMwI1y+BhibY5/riCQHADNbA+wCPiGoHnIZZ2YbzOxFM7swXwBmNtvMus2se/fu3WW/gSvOHsPfXHkWY4YPxgiakO6/rlOJQUQEMPfiH+DDvoBnI30OE4EHgBHAauBb7j4isv95wMPu3ucvrZm1EjQ9LXL357K2HQcMcfc9ZnYusBI4I1J15NTV1eXd3d1F34eIiPyRma13965c22JVDu6+xd0vdvdzCaqDbVm7XE9W1RA59gBBQrk8x7bP3H1PuLw+PO/4ODGKiEh8sZKDmZ0U/tsE3EUwconIumM6m81siJl9PlweBPx7YEuO844ys+Zw+VTgNOD9ODGKiEh8pQxlXQZMB0aaWQ9wDzDEzOaGu6wAHo0cMg3ocffoH/UTgNVhs1Ez8AvChGJmM4Aud58fHrvAzA4DR4A57p7dGS4iIgOspD6HtFOfg4hI+Sre5yAiIvWtLioHM9sN/AswEvh9wuFkS2NMkM640hgTpDOuNMYE6YwrjTFBOuI6xd1H5dpQF8khw8y685VISUljTJDOuNIYE6QzrjTGBOmMK40xQXrjylCzkoiI9KHkICIifdRbclicdAA5pDEmSGdcaYwJ0hlXGmOCdMaVxpggvXEBddbnICIilVFvlYOIiFSAkoOIiPSR+seEmtl2gim+jwCH3b3LzJ4geKYEwHDgI3fvzPckOTM7HngS+JPwPM+4+x1Jx5V1vtXAqZmZb5OMycw+R/CAp+nAUeC/uPtPUxDXTOA/EzwLZCdwg7vHGideTkzh/h3A/wCGEfxM/q27HwhnD14CDAb+AfiPHrOtthIxEXzgS+xazxdXOOFm5nxVvdYLxZTktV4kropd67G5e6q/CB5DOrLA9u8D88PldmBzjn2OB/4sXP4cwWNK/zzpuCL7Xgk8XmifasYE/FdgYbjcVOicVfwdDiJ4FsjI8Pu/A75TpZgGAZuASeH3I4DmcPk1YArBs6L+sT/XVSViSsG1nvdn5cld64V+f0le6/l+hxW91uN+pb5yKCR85Oi1wJcK7efu+4B/CpcPmtnrQFvScYX7DgG+DcwGlqchJuAmYCKAux9lAO/iLCMuC79OMLM9BJ+03qtSTBcDm9z9DQAPp5UPZxoe5u6vhN8/BlxBkCQSiQlI+lrPF1eS13remEj2Ws93XbVQpWu9kFroc3Dg52a23sxmZ227EPg/7v7PkXUFnyRnZsOBrwEvpCSu7xJ8mtjXz3gqElP48wH4rpm9bmZPmtm/SToudz8EfBN4k6DM/gLwP6sU03jAzWxN+DP5T+H6MUBP5LiecF2SMfVK6FovFFdS13rOmFJwreeMawCu9XiqXarEKNNGh/+eBLwBTItsewj468j3xwEjwuVzgd8QfLLLbB9E8KnuljTEBXQStAlDkaanKsY0kuACvyrc9m3gf6UgrhaCP3J/QvCp6kHgrirFdBvwQfizOR54BbiIoI3/+ch+F2Z+n0nFlIJrPd/PKslrPV9MSV/r+eKq6LUe9yv1lYO77wz/3QU8DUyG3ocGXQk8Edm32JPkFgP/7O7/LSVxTQHODTuxfgmMN7O1Cce0h+CT3dPhrk8C58SNqYJxdYbrtnnwP2s58MVqxERQEbzo7r/3oInyHwh+Jj0c22TTRvBJL8mYMhK51gvEldi1XiCmRK/1AnF1hueoyLUeV6qTg5mdYGZDM8sEbXSbw81fBra4e09k/7xPkjOzhcCJwC1picvdH3L30e7eDkwF3nX36QnH5MAzBKM3IPgk83acmCoZF7AD+IKZZWaQ/ArBqKYBjwlYA3SY2fHhf/J/B7zt7r8FPjGz88P25L8AViUZU3h8Ytd6vriSvNYLxJTotZ4vLip4rfdLtUuVcr6AUwlKszeAtwiGmWW2LSF4Ulx0/6vC/d4AXge+Fq5vIygf3wE2hl9/lXRcWfu0049Su5IxAacA6whGUrwAnJySuOaEv8NNBP+pR1QjpnD9DeG+m4G/i6zvCtdtIyj/LcmYkr7WC/2skrrWi/z+ErvWi8RVkWu9P1+aPkNERPpIdbOSiIgkQ8lBRET6UHIQEZE+lBxERKQPJQcREelDyUFERPpQchARkT7+P4CB57+vD5h2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis_test(2, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "d9005ec2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(7971, 120)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "miami_array = make_pred(test_loader, batch_sz, miami_train_mod)\n",
    "miami_array.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a86e91a2",
   "metadata": {},
   "source": [
    "### pittsburgh\n",
    "- best KAGGLE val loss: 24.98648140710943\n",
    "- best val loss: 18.70769113302231"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "f7009d3e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f41de3d55c974596aba2cb18380136e7",
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 1\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 72.27043764419805\n",
      "- Validation Loss: 35.605763126822076\n",
      "- Train Time: 5.825195550918579\n",
      "- Elapsed Time: 5.952212810516357\n",
      "\n",
      "Epoch 2\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 37.98031718356937\n",
      "- Validation Loss: 32.999563567778644\n",
      "- Train Time: 13.184351205825806\n",
      "- Elapsed Time: 13.533328533172607\n",
      "\n",
      "Epoch 3\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 34.97380641083312\n",
      "- Validation Loss: 31.8451889963711\n",
      "- Train Time: 18.82474422454834\n",
      "- Elapsed Time: 19.355647325515747\n",
      "\n",
      "Epoch 4\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 32.22329083533069\n",
      "- Validation Loss: 26.28345025988186\n",
      "- Train Time: 24.341465950012207\n",
      "- Elapsed Time: 25.068140506744385\n",
      "\n",
      "Epoch 5\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 31.853032048231636\n",
      "- Validation Loss: 25.470047922695386\n",
      "- Train Time: 29.585352897644043\n",
      "- Elapsed Time: 30.471986055374146\n",
      "\n",
      "Epoch 6\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 29.78413745468738\n",
      "- Validation Loss: 25.100330857669604\n",
      "- Train Time: 34.31820893287659\n",
      "- Elapsed Time: 35.33614206314087\n",
      "\n",
      "Epoch 7\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 30.07010543190576\n",
      "- Validation Loss: 25.378878249841577\n",
      "- Train Time: 38.421186685562134\n",
      "- Elapsed Time: 39.574554443359375\n",
      "\n",
      "Epoch 8\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 28.287735383495008\n",
      "- Validation Loss: 23.602908779593076\n",
      "- Train Time: 42.57478356361389\n",
      "- Elapsed Time: 43.90345573425293\n",
      "\n",
      "Epoch 9\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 28.376417606484654\n",
      "- Validation Loss: 23.082116035854114\n",
      "- Train Time: 47.84962177276611\n",
      "- Elapsed Time: 49.30049991607666\n",
      "\n",
      "Epoch 10\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 27.606263020459345\n",
      "- Validation Loss: 23.567766413969153\n",
      "- Train Time: 54.379082918167114\n",
      "- Elapsed Time: 55.962584495544434\n",
      "\n",
      "Epoch 11\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 27.077503153701233\n",
      "- Validation Loss: 22.90997095669017\n",
      "- Train Time: 60.69462251663208\n",
      "- Elapsed Time: 62.43019771575928\n",
      "\n",
      "Epoch 12\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 27.10094766679153\n",
      "- Validation Loss: 22.738870052730334\n",
      "- Train Time: 67.41452765464783\n",
      "- Elapsed Time: 69.26933360099792\n",
      "\n",
      "Epoch 13\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 26.62999194821501\n",
      "- Validation Loss: 22.76672096112195\n",
      "- Train Time: 74.54746723175049\n",
      "- Elapsed Time: 76.56373023986816\n",
      "\n",
      "Epoch 14\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 26.077204893227496\n",
      "- Validation Loss: 22.515326976776123\n",
      "- Train Time: 79.88431334495544\n",
      "- Elapsed Time: 82.02087712287903\n",
      "\n",
      "Epoch 15\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 26.029506190150393\n",
      "- Validation Loss: 22.643115807982053\n",
      "- Train Time: 85.25410389900208\n",
      "- Elapsed Time: 87.56497073173523\n",
      "\n",
      "Epoch 16\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 25.68797683404162\n",
      "- Validation Loss: 22.914900443133185\n",
      "- Train Time: 91.21983599662781\n",
      "- Elapsed Time: 93.68017148971558\n",
      "\n",
      "Epoch 17\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 26.116879038561404\n",
      "- Validation Loss: 22.879984904738034\n",
      "- Train Time: 98.44567513465881\n",
      "- Elapsed Time: 101.05761051177979\n",
      "\n",
      "Epoch 18\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 25.175467022883346\n",
      "- Validation Loss: 21.95911151521346\n",
      "- Train Time: 103.21812725067139\n",
      "- Elapsed Time: 105.9506025314331\n",
      "\n",
      "Epoch 19\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 25.129905137361266\n",
      "- Validation Loss: 22.670594558996314\n",
      "- Train Time: 110.29890489578247\n",
      "- Elapsed Time: 113.18803787231445\n",
      "\n",
      "Epoch 20\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 24.98863716413772\n",
      "- Validation Loss: 22.169729625477512\n",
      "- Train Time: 115.18129587173462\n",
      "- Elapsed Time: 118.19059014320374\n",
      "\n",
      "Epoch 21\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 24.91025753309524\n",
      "- Validation Loss: 22.500791668891907\n",
      "- Train Time: 119.84866571426392\n",
      "- Elapsed Time: 122.98836183547974\n",
      "\n",
      "Epoch 22\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 24.611529065503014\n",
      "- Validation Loss: 22.472052097320557\n",
      "- Train Time: 124.26134872436523\n",
      "- Elapsed Time: 127.52147030830383\n",
      "\n",
      "Epoch 23\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 24.499870772844826\n",
      "- Validation Loss: 21.859276133425094\n",
      "- Train Time: 128.92971348762512\n",
      "- Elapsed Time: 132.35050749778748\n",
      "\n",
      "Epoch 24\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 24.771849391507168\n",
      "- Validation Loss: 24.028689110980313\n",
      "- Train Time: 134.33865571022034\n",
      "- Elapsed Time: 137.89145255088806\n",
      "\n",
      "Epoch 25\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 24.781111372452155\n",
      "- Validation Loss: 22.133799777311438\n",
      "- Train Time: 138.93680047988892\n",
      "- Elapsed Time: 142.61046767234802\n",
      "\n",
      "Epoch 26\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 24.403930182550468\n",
      "- Validation Loss: 21.39604974494261\n",
      "- Train Time: 145.18670225143433\n",
      "- Elapsed Time: 148.980366230011\n",
      "\n",
      "Epoch 27\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 24.330569597241148\n",
      "- Validation Loss: 22.87424678662244\n",
      "- Train Time: 150.63448548316956\n",
      "- Elapsed Time: 154.5610373020172\n",
      "\n",
      "Epoch 28\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 24.258751750381943\n",
      "- Validation Loss: 21.993575685164508\n",
      "- Train Time: 156.030170917511\n",
      "- Elapsed Time: 160.07738375663757\n",
      "\n",
      "Epoch 29\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 24.32942755315818\n",
      "- Validation Loss: 23.201219607802\n",
      "- Train Time: 161.66071557998657\n",
      "- Elapsed Time: 165.84900403022766\n",
      "\n",
      "Epoch 30\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 24.13887448209563\n",
      "- Validation Loss: 22.62393324515399\n",
      "- Train Time: 168.77387595176697\n",
      "- Elapsed Time: 173.12383604049683\n",
      "\n",
      "Epoch 31\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 24.071414142652273\n",
      "- Validation Loss: 23.616461459328146\n",
      "- Train Time: 175.77402091026306\n",
      "- Elapsed Time: 180.29277276992798\n",
      "\n",
      "Epoch 32\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 24.126003817016002\n",
      "- Validation Loss: 21.745569684926203\n",
      "- Train Time: 182.7761332988739\n",
      "- Elapsed Time: 187.46642804145813\n",
      "\n",
      "Epoch 33\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 20.755414415808286\n",
      "- Validation Loss: 19.773002491277808\n",
      "- Train Time: 189.69267988204956\n",
      "- Elapsed Time: 194.52208757400513\n",
      "\n",
      "Epoch 34\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 20.482062324199802\n",
      "- Validation Loss: 19.55772664266474\n",
      "- Train Time: 195.52479529380798\n",
      "- Elapsed Time: 200.50534224510193\n",
      "\n",
      "Epoch 35\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 20.380543611408058\n",
      "- Validation Loss: 19.520356816404007\n",
      "- Train Time: 202.9857747554779\n",
      "- Elapsed Time: 208.12279319763184\n",
      "\n",
      "Epoch 36\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 20.296787767628437\n",
      "- Validation Loss: 19.371869907659644\n",
      "- Train Time: 210.37286472320557\n",
      "- Elapsed Time: 215.76244640350342\n",
      "\n",
      "Epoch 37\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 20.22906640970629\n",
      "- Validation Loss: 19.392116083818323\n",
      "- Train Time: 215.64407873153687\n",
      "- Elapsed Time: 221.17186737060547\n",
      "\n",
      "Epoch 38\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 20.182461795853634\n",
      "- Validation Loss: 19.341996301622952\n",
      "- Train Time: 220.55842423439026\n",
      "- Elapsed Time: 226.2093026638031\n",
      "\n",
      "Epoch 39\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 20.137894559529872\n",
      "- Validation Loss: 19.32069486730239\n",
      "- Train Time: 227.206387758255\n",
      "- Elapsed Time: 233.06684064865112\n",
      "\n",
      "Epoch 40\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 20.09207111869762\n",
      "- Validation Loss: 19.34015970370349\n",
      "- Train Time: 234.422767162323\n",
      "- Elapsed Time: 240.4465274810791\n",
      "\n",
      "Epoch 41\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 20.04772586331648\n",
      "- Validation Loss: 19.24276726035511\n",
      "- Train Time: 240.31066989898682\n",
      "- Elapsed Time: 246.46593403816223\n",
      "\n",
      "Epoch 42\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 20.008500600951947\n",
      "- Validation Loss: 19.2714506037095\n",
      "- Train Time: 245.75594449043274\n",
      "- Elapsed Time: 252.0462520122528\n",
      "\n",
      "Epoch 43\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 19.96106461917653\n",
      "- Validation Loss: 19.264965751591852\n",
      "- Train Time: 250.26013493537903\n",
      "- Elapsed Time: 256.67940974235535\n",
      "\n",
      "Epoch 44\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 19.934269651867986\n",
      "- Validation Loss: 19.205962174078998\n",
      "- Train Time: 256.4900598526001\n",
      "- Elapsed Time: 263.108350276947\n",
      "\n",
      "Epoch 45\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 19.934981899323805\n",
      "- Validation Loss: 19.262015858117273\n",
      "- Train Time: 264.328355550766\n",
      "- Elapsed Time: 271.15400409698486\n",
      "\n",
      "Epoch 46\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 19.89877239863078\n",
      "- Validation Loss: 19.255082523121555\n",
      "- Train Time: 269.9014563560486\n",
      "- Elapsed Time: 276.89839816093445\n",
      "\n",
      "Epoch 47\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 19.85396869392956\n",
      "- Validation Loss: 19.228001534938812\n",
      "- Train Time: 276.52977681159973\n",
      "- Elapsed Time: 283.69149446487427\n",
      "\n",
      "Epoch 48\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 19.825434626707064\n",
      "- Validation Loss: 19.19724467572044\n",
      "- Train Time: 282.1243007183075\n",
      "- Elapsed Time: 289.4679560661316\n",
      "\n",
      "Epoch 49\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 19.79533600105959\n",
      "- Validation Loss: 19.252984236268436\n",
      "- Train Time: 287.28831005096436\n",
      "- Elapsed Time: 294.75519466400146\n",
      "\n",
      "Epoch 50\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 19.795502619416105\n",
      "- Validation Loss: 19.19206345431945\n",
      "- Train Time: 292.46050214767456\n",
      "- Elapsed Time: 300.12619519233704\n",
      "\n",
      "Epoch 51\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 19.757550916251013\n",
      "- Validation Loss: 19.148991970454944\n",
      "- Train Time: 299.1044387817383\n",
      "- Elapsed Time: 306.9234116077423\n",
      "\n",
      "Epoch 52\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 19.72725021449569\n",
      "- Validation Loss: 19.152855792466333\n",
      "- Train Time: 306.6451985836029\n",
      "- Elapsed Time: 314.5872800350189\n",
      "\n",
      "Epoch 53\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 19.702056717249302\n",
      "- Validation Loss: 19.149365663528442\n",
      "- Train Time: 312.9570324420929\n",
      "- Elapsed Time: 321.08253169059753\n",
      "\n",
      "Epoch 54\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 19.67193939405329\n",
      "- Validation Loss: 19.13839448550168\n",
      "- Train Time: 319.45529317855835\n",
      "- Elapsed Time: 327.745069026947\n",
      "\n",
      "Epoch 55\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 19.643404225508373\n",
      "- Validation Loss: 19.131161209414987\n",
      "- Train Time: 325.61695551872253\n",
      "- Elapsed Time: 334.04756927490234\n",
      "\n",
      "Epoch 56\n",
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      "- Training Loss: 19.643323724955515\n",
      "- Validation Loss: 19.053072301780475\n",
      "- Train Time: 332.8621473312378\n",
      "- Elapsed Time: 341.4447042942047\n",
      "\n",
      "Epoch 57\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 19.601954900361353\n",
      "- Validation Loss: 19.04353495906381\n",
      "- Train Time: 339.6375632286072\n",
      "- Elapsed Time: 348.36260533332825\n",
      "\n",
      "Epoch 58\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 19.582567543375728\n",
      "- Validation Loss: 19.16306682544596\n",
      "- Train Time: 345.5770220756531\n",
      "- Elapsed Time: 354.4362771511078\n",
      "\n",
      "Epoch 59\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 19.558429504142087\n",
      "- Validation Loss: 19.105525574263403\n",
      "- Train Time: 351.13567996025085\n",
      "- Elapsed Time: 360.1849262714386\n",
      "\n",
      "Epoch 60\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 19.5500106215477\n",
      "- Validation Loss: 19.09482568502426\n",
      "- Train Time: 358.1137673854828\n",
      "- Elapsed Time: 367.3235330581665\n",
      "\n",
      "Epoch 61\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 19.530406154838264\n",
      "- Validation Loss: 19.115105653510373\n",
      "- Train Time: 365.6057937145233\n",
      "- Elapsed Time: 374.967600107193\n",
      "\n",
      "Epoch 62\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 19.50527379871194\n",
      "- Validation Loss: 19.07885574242648\n",
      "- Train Time: 373.09405541419983\n",
      "- Elapsed Time: 382.6152184009552\n",
      "\n",
      "Epoch 63\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 19.490054981770857\n",
      "- Validation Loss: 19.07224099075093\n",
      "- Train Time: 378.10447454452515\n",
      "- Elapsed Time: 387.7484059333801\n",
      "\n",
      "Epoch 64\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 19.073432249960558\n",
      "- Validation Loss: 18.75911497368532\n",
      "- Train Time: 382.90420937538147\n",
      "- Elapsed Time: 392.67989802360535\n",
      "\n",
      "Epoch 65\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 19.026656081084333\n",
      "- Validation Loss: 18.75704724297804\n",
      "- Train Time: 387.55278396606445\n",
      "- Elapsed Time: 397.4682791233063\n",
      "\n",
      "Epoch 66\n",
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      "- Training Loss: 19.01455885951036\n",
      "- Validation Loss: 18.76004210991018\n",
      "- Train Time: 392.92258048057556\n",
      "- Elapsed Time: 402.97802329063416\n",
      "\n",
      "Epoch 67\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 19.006499289687163\n",
      "- Validation Loss: 18.758984951411975\n",
      "- Train Time: 399.9373052120209\n",
      "- Elapsed Time: 410.15387201309204\n",
      "\n",
      "Epoch 68\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 18.99988202721465\n",
      "- Validation Loss: 18.76249479195651\n",
      "- Train Time: 407.86087226867676\n",
      "- Elapsed Time: 418.2334372997284\n",
      "\n",
      "Epoch 69\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 18.993092081126044\n",
      "- Validation Loss: 18.749176723115585\n",
      "- Train Time: 415.0476031303406\n",
      "- Elapsed Time: 425.5512180328369\n",
      "\n",
      "Epoch 70\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 18.989166969567343\n",
      "- Validation Loss: 18.752282763228695\n",
      "- Train Time: 422.9643523693085\n",
      "- Elapsed Time: 433.63283944129944\n",
      "\n",
      "Epoch 71\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 18.985181785876456\n",
      "- Validation Loss: 18.74440129364238\n",
      "- Train Time: 430.0426199436188\n",
      "- Elapsed Time: 440.89431500434875\n",
      "\n",
      "Epoch 72\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 18.981589330956826\n",
      "- Validation Loss: 18.750536241952112\n",
      "- Train Time: 437.85814237594604\n",
      "- Elapsed Time: 448.87986040115356\n",
      "\n",
      "Epoch 73\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 18.97733237462885\n",
      "- Validation Loss: 18.749034422285416\n",
      "- Train Time: 443.67665338516235\n",
      "- Elapsed Time: 454.84709334373474\n",
      "\n",
      "Epoch 74\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 18.97337539445341\n",
      "- Validation Loss: 18.747304814703323\n",
      "- Train Time: 450.7299385070801\n",
      "- Elapsed Time: 462.0306007862091\n",
      "\n",
      "Epoch 75\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 18.97028034964418\n",
      "- Validation Loss: 18.747712286079633\n",
      "- Train Time: 455.846559047699\n",
      "- Elapsed Time: 467.280344247818\n",
      "\n",
      "Epoch 76\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 18.9643456815115\n",
      "- Validation Loss: 18.747008965295905\n",
      "- Train Time: 461.6569640636444\n",
      "- Elapsed Time: 473.21532011032104\n",
      "\n",
      "Epoch 77\n",
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      "- Training Loss: 18.960561109134574\n",
      "- Validation Loss: 18.74389172301573\n",
      "- Train Time: 468.770956993103\n",
      "- Elapsed Time: 480.4953393936157\n",
      "\n",
      "Epoch 78\n",
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      "- Training Loss: 18.908884601265775\n",
      "- Validation Loss: 18.704889185288373\n",
      "- Train Time: 476.5563633441925\n",
      "- Elapsed Time: 488.4524006843567\n",
      "\n",
      "Epoch 79\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 18.903535585777433\n",
      "- Validation Loss: 18.706077533609726\n",
      "- Train Time: 484.13583278656006\n",
      "- Elapsed Time: 496.2227153778076\n",
      "\n",
      "Epoch 80\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 18.902260430108488\n",
      "- Validation Loss: 18.707179164185245\n",
      "- Train Time: 491.8234100341797\n",
      "- Elapsed Time: 504.1040675640106\n",
      "\n",
      "Epoch 81\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 18.901027955260933\n",
      "- Validation Loss: 18.70819052878548\n",
      "- Train Time: 497.4147033691406\n",
      "- Elapsed Time: 509.82096338272095\n",
      "\n",
      "Epoch 82\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 18.900455428104774\n",
      "- Validation Loss: 18.709416838253247\n",
      "- Train Time: 502.997220993042\n",
      "- Elapsed Time: 515.5575633049011\n",
      "\n",
      "Epoch 83\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 18.89968162541296\n",
      "- Validation Loss: 18.709875471451703\n",
      "- Train Time: 509.84839725494385\n",
      "- Elapsed Time: 522.5681819915771\n",
      "\n",
      "Epoch 84\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 18.89894786808226\n",
      "- Validation Loss: 18.71002240741954\n",
      "- Train Time: 516.08922290802\n",
      "- Elapsed Time: 528.9297904968262\n",
      "\n",
      "Epoch 85\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 18.891083508535147\n",
      "- Validation Loss: 18.70813099426382\n",
      "- Train Time: 524.3051002025604\n",
      "- Elapsed Time: 537.3423993587494\n",
      "\n",
      "Epoch 86\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 18.890499021882324\n",
      "- Validation Loss: 18.707751743933734\n",
      "- Train Time: 531.8039510250092\n",
      "- Elapsed Time: 545.0289270877838\n",
      "\n",
      "Epoch 87\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 18.89024211885103\n",
      "- Validation Loss: 18.707651303094977\n",
      "- Train Time: 539.1073806285858\n",
      "- Elapsed Time: 552.4778611660004\n",
      "\n",
      "Epoch 88\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 18.890084357822644\n",
      "- Validation Loss: 18.70764646109413\n",
      "- Train Time: 545.4884948730469\n",
      "- Elapsed Time: 558.9962360858917\n",
      "\n",
      "Epoch 89\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 18.889962674745547\n",
      "- Validation Loss: 18.70766492100323\n",
      "- Train Time: 549.8711702823639\n",
      "- Elapsed Time: 563.5383379459381\n",
      "\n",
      "Epoch 90\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 18.889849492147857\n",
      "- Validation Loss: 18.70768492712694\n",
      "- Train Time: 554.5004558563232\n",
      "- Elapsed Time: 568.2968807220459\n",
      "\n",
      "Epoch 91\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 18.888823654526977\n",
      "- Validation Loss: 18.707681561217587\n",
      "- Train Time: 561.3021755218506\n",
      "- Elapsed Time: 575.2390632629395\n",
      "\n",
      "Epoch 92\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 18.88881344421237\n",
      "- Validation Loss: 18.707681196577408\n",
      "- Train Time: 566.3577604293823\n",
      "- Elapsed Time: 580.4628753662109\n",
      "\n",
      "Epoch 93\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 18.88880434924481\n",
      "- Validation Loss: 18.70768161380992\n",
      "- Train Time: 571.806075334549\n",
      "- Elapsed Time: 586.0721461772919\n",
      "\n",
      "Epoch 94\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 18.888795064944848\n",
      "- Validation Loss: 18.707685446037967\n",
      "- Train Time: 577.5967803001404\n",
      "- Elapsed Time: 591.9937491416931\n",
      "\n",
      "Epoch 95\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 18.888786524729014\n",
      "- Validation Loss: 18.70768783723607\n",
      "- Train Time: 582.8746554851532\n",
      "- Elapsed Time: 597.4228644371033\n",
      "\n",
      "Epoch 96\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 18.88877848238727\n",
      "- Validation Loss: 18.707690999788397\n",
      "- Train Time: 588.387186050415\n",
      "- Elapsed Time: 603.1147940158844\n",
      "\n",
      "Epoch 97\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 18.888685831836625\n",
      "- Validation Loss: 18.707689835744745\n",
      "- Train Time: 594.3641803264618\n",
      "- Elapsed Time: 609.2344460487366\n",
      "\n",
      "Epoch 98\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 18.88868523617975\n",
      "- Validation Loss: 18.707689239698297\n",
      "- Train Time: 599.3567581176758\n",
      "- Elapsed Time: 614.3617188930511\n",
      "\n",
      "Epoch 99\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 18.888685152032018\n",
      "- Validation Loss: 18.707690098706415\n",
      "- Train Time: 606.6355631351471\n",
      "- Elapsed Time: 621.8037285804749\n",
      "\n",
      "Epoch 100\n",
      "Training & Validating -------------------------------------------------------------\n",
      "- Training Loss: 18.888684549362832\n",
      "- Validation Loss: 18.70769113302231\n",
      "- Train Time: 614.8135340213776\n",
      "- Elapsed Time: 630.141786813736\n",
      "\n"
     ]
    }
   ],
   "source": [
    "city = 'pittsburgh' \n",
    "split = 'train' #'test' #'train'\n",
    "batch_size = 32\n",
    "hidden_dim = 32\n",
    "num_iters = 100\n",
    "pittsburgh_train_mod, pittsburgh_losses = fit(city=city, split=split,\n",
    "                                              hidden_dim=hidden_dim, batch_size=batch_size, num_iters=num_iters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "9c047c81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(1, 'Pittsburgh', 'train', pittsburgh_losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "17a61a32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 50, 2])\n",
      "torch.Size([32, 60, 2])\n",
      "torch.Size([32, 60, 2])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis_results('pittsburgh', 'train', 32, pittsburgh_train_mod, 29)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f50a64d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6361\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "199"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# intialize a dataset\n",
    "city = 'pittsburgh' \n",
    "split = 'test' \n",
    "test_dataset = ArgoverseDataset(city = city, split = split, transform=False, normalized=True)\n",
    "temp_dataset = ArgoverseDataset(city = city, split = split, transform=False, normalized=False)\n",
    "print(len(test_dataset)) # should be 6361\n",
    "\n",
    "# test_dataset.start_pos\n",
    "\n",
    "batch_sz = 32  # batch size \n",
    "test_loader = DataLoader(test_dataset, batch_size=batch_sz)\n",
    "temp_loader = DataLoader(temp_dataset, batch_size=batch_sz)\n",
    "pittsburgh_train_mod.to('cpu')\n",
    "len(test_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "44895c74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis_test(24, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "beaeb321",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for X in temp_loader:\n",
    "    if len(X) == batch_sz:\n",
    "        continue\n",
    "    # the index here should refer to the above cell's number\n",
    "    plt.scatter(X[24, :, 0], X[24, :, 1], label='input')\n",
    "    plt.legend\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "d927a3e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis_test(24, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "2577cfba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(6361, 120)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pittsburgh_array = make_pred(test_loader, batch_sz, pittsburgh_train_mod)\n",
    "pittsburgh_array.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01fce4da",
   "metadata": {},
   "source": [
    "### dearborn\n",
    "- best KAGGLE val loss: 31.318197677009984\n",
    "- best val loss: 24.707670839209307"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "8b259738",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "df91fd6fec3c453982d3574c8cd13e15",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1\n",
      "Training & Validating ----------------------------------\n",
      "- Training Loss: 137.69318099077358\n",
      "- Validation Loss: 83.16420148548328\n",
      "- Train Time: 4.47907829284668\n",
      "- Elapsed Time: 4.62790322303772\n",
      "\n",
      "Epoch 2\n",
      "Training & Validating ----------------------------------\n",
      "- Training Loss: 88.74421048996061\n",
      "- Validation Loss: 81.21725667150397\n",
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      "\n",
      "Epoch 3\n",
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      "- Training Loss: 84.68792486468027\n",
      "- Validation Loss: 79.5470225936488\n",
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      "\n",
      "Epoch 4\n",
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      "- Training Loss: 83.30840670508007\n",
      "- Validation Loss: 80.39799918626484\n",
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      "\n",
      "Epoch 5\n",
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      "- Training Loss: 82.30743777890538\n",
      "- Validation Loss: 88.69840150130422\n",
      "- Train Time: 17.979968547821045\n",
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      "\n",
      "Epoch 6\n",
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      "- Training Loss: 80.60711428730987\n",
      "- Validation Loss: 82.03785183555202\n",
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      "\n",
      "Epoch 7\n",
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      "- Training Loss: 69.68214208857958\n",
      "- Validation Loss: 44.59793688121595\n",
      "- Train Time: 23.530503749847412\n",
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      "\n",
      "Epoch 8\n",
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      "- Training Loss: 47.58718500303667\n",
      "- Validation Loss: 40.44728110965929\n",
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      "\n",
      "Epoch 9\n",
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      "- Training Loss: 41.27339830786683\n",
      "- Validation Loss: 34.43414010499653\n",
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      "\n",
      "Epoch 10\n",
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      "- Training Loss: 39.05452052105305\n",
      "- Validation Loss: 33.652895375301966\n",
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      "\n",
      "Epoch 11\n",
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      "- Training Loss: 37.10081485953442\n",
      "- Validation Loss: 34.83040962721172\n",
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      "\n",
      "Epoch 12\n",
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      "- Training Loss: 36.921634585358376\n",
      "- Validation Loss: 32.28546338332327\n",
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      "\n",
      "Epoch 13\n",
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      "- Training Loss: 36.30465511804403\n",
      "- Validation Loss: 34.08626092107672\n",
      "- Train Time: 42.67162799835205\n",
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      "\n",
      "Epoch 14\n",
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      "- Training Loss: 35.797492152036625\n",
      "- Validation Loss: 31.1912937540757\n",
      "- Train Time: 46.90356254577637\n",
      "- Elapsed Time: 48.184903621673584\n",
      "\n",
      "Epoch 15\n",
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      "- Training Loss: 36.30326402603194\n",
      "- Validation Loss: 31.356931347596017\n",
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      "\n",
      "Epoch 16\n",
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      "- Training Loss: 33.749620499999025\n",
      "- Validation Loss: 29.869594247717608\n",
      "- Train Time: 54.995999336242676\n",
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      "\n",
      "Epoch 17\n",
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      "- Training Loss: 34.15576049477555\n",
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      "- Train Time: 59.25636076927185\n",
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      "\n",
      "Epoch 18\n",
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      "- Training Loss: 33.417039806066555\n",
      "- Validation Loss: 30.040093346645957\n",
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      "- Elapsed Time: 63.778235912323\n",
      "\n",
      "Epoch 19\n",
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      "- Training Loss: 34.422883514748065\n",
      "- Validation Loss: 33.905061194771214\n",
      "- Train Time: 64.98524570465088\n",
      "- Elapsed Time: 66.83661341667175\n",
      "\n",
      "Epoch 20\n",
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      "- Training Loss: 33.74914271471112\n",
      "- Validation Loss: 33.311532497406006\n",
      "- Train Time: 68.33235239982605\n",
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      "\n",
      "Epoch 21\n",
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      "- Training Loss: 34.0862660255543\n",
      "- Validation Loss: 34.03759148246363\n",
      "- Train Time: 71.17399334907532\n",
      "- Elapsed Time: 73.19472312927246\n",
      "\n",
      "Epoch 22\n",
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      "- Training Loss: 32.9890447938165\n",
      "- Validation Loss: 33.12707065281115\n",
      "- Train Time: 74.94566798210144\n",
      "- Elapsed Time: 77.11211490631104\n",
      "\n",
      "Epoch 23\n",
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      "- Training Loss: 27.09410674488822\n",
      "- Validation Loss: 26.525553301761025\n",
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      "\n",
      "Epoch 24\n",
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      "- Training Loss: 26.5226516474125\n",
      "- Validation Loss: 26.7555417638076\n",
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      "- Elapsed Time: 83.5863881111145\n",
      "\n",
      "Epoch 25\n",
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      "- Training Loss: 26.35091655961303\n",
      "- Validation Loss: 26.79310589087637\n",
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      "- Elapsed Time: 86.75831246376038\n",
      "\n",
      "Epoch 26\n",
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      "- Training Loss: 26.179276772016703\n",
      "- Validation Loss: 26.668030738830566\n",
      "- Train Time: 87.37356853485107\n",
      "- Elapsed Time: 89.82683944702148\n",
      "\n",
      "Epoch 27\n",
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      "- Training Loss: 26.067484263070796\n",
      "- Validation Loss: 26.450681498176174\n",
      "- Train Time: 90.27565240859985\n",
      "- Elapsed Time: 92.80753350257874\n",
      "\n",
      "Epoch 28\n",
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      "- Training Loss: 25.95485450916512\n",
      "- Validation Loss: 26.460410871003802\n",
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      "\n",
      "Epoch 29\n",
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      "- Training Loss: 25.859142638916193\n",
      "- Validation Loss: 26.502934054324502\n",
      "- Train Time: 96.64722037315369\n",
      "- Elapsed Time: 99.41355204582214\n",
      "\n",
      "Epoch 30\n",
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      "- Training Loss: 25.75989475984906\n",
      "- Validation Loss: 26.31987879150792\n",
      "- Train Time: 99.57944178581238\n",
      "- Elapsed Time: 102.45738554000854\n",
      "\n",
      "Epoch 31\n",
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      "- Training Loss: 25.677544421927873\n",
      "- Validation Loss: 26.296436836844997\n",
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      "- Elapsed Time: 106.0768346786499\n",
      "\n",
      "Epoch 32\n",
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      "- Training Loss: 25.591554660436717\n",
      "- Validation Loss: 26.35257271716469\n",
      "- Train Time: 105.94934129714966\n",
      "- Elapsed Time: 109.0687644481659\n",
      "\n",
      "Epoch 33\n",
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      "- Training Loss: 25.521690094193747\n",
      "- Validation Loss: 26.317934061351576\n",
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      "- Elapsed Time: 112.21571898460388\n",
      "\n",
      "Epoch 34\n",
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      "- Training Loss: 25.44447413086891\n",
      "- Validation Loss: 26.24372279016595\n",
      "- Train Time: 111.8107841014862\n",
      "- Elapsed Time: 115.06986045837402\n",
      "\n",
      "Epoch 35\n",
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      "- Training Loss: 25.411995744289353\n",
      "- Validation Loss: 26.280081535640516\n",
      "- Train Time: 115.03593182563782\n",
      "- Elapsed Time: 118.36991810798645\n",
      "\n",
      "Epoch 36\n",
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      "- Training Loss: 25.353155118088388\n",
      "- Validation Loss: 26.216923136460153\n",
      "- Train Time: 118.3520872592926\n",
      "- Elapsed Time: 121.81288504600525\n",
      "\n",
      "Epoch 37\n",
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      "- Training Loss: 25.30407404483751\n",
      "- Validation Loss: 26.217523537184064\n",
      "- Train Time: 121.32139325141907\n",
      "- Elapsed Time: 124.8710675239563\n",
      "\n",
      "Epoch 38\n",
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      "- Training Loss: 25.254199992085614\n",
      "- Validation Loss: 26.194572022086696\n",
      "- Train Time: 124.44803071022034\n",
      "- Elapsed Time: 128.07443380355835\n",
      "\n",
      "Epoch 39\n",
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      "- Training Loss: 25.199336683334305\n",
      "- Validation Loss: 26.19171522793017\n",
      "- Train Time: 127.11405229568481\n",
      "- Elapsed Time: 130.80864357948303\n",
      "\n",
      "Epoch 40\n",
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      "- Training Loss: 25.15461524766545\n",
      "- Validation Loss: 26.169250538474635\n",
      "- Train Time: 129.81963658332825\n",
      "- Elapsed Time: 133.61041617393494\n",
      "\n",
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      "- Training Loss: 25.096469659444896\n",
      "- Validation Loss: 26.135731546502363\n",
      "- Train Time: 133.603342294693\n",
      "- Elapsed Time: 137.51387405395508\n",
      "\n",
      "Epoch 42\n",
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      "- Training Loss: 25.04077263696249\n",
      "- Validation Loss: 26.083498503032484\n",
      "- Train Time: 136.6347587108612\n",
      "- Elapsed Time: 140.6136713027954\n",
      "\n",
      "Epoch 43\n",
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      "- Training Loss: 24.996915429137474\n",
      "- Validation Loss: 26.043480383722407\n",
      "- Train Time: 139.86756896972656\n",
      "- Elapsed Time: 143.97260761260986\n",
      "\n",
      "Epoch 44\n",
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      "- Training Loss: 24.99290327623833\n",
      "- Validation Loss: 26.146634440673026\n",
      "- Train Time: 142.8942427635193\n",
      "- Elapsed Time: 147.06784415245056\n",
      "\n",
      "Epoch 45\n",
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      "- Training Loss: 24.952681395896647\n",
      "- Validation Loss: 26.075932126296195\n",
      "- Train Time: 145.93452095985413\n",
      "- Elapsed Time: 150.17672204971313\n",
      "\n",
      "Epoch 46\n",
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      "- Training Loss: 24.885631614646247\n",
      "- Validation Loss: 26.022839558751958\n",
      "- Train Time: 149.24810814857483\n",
      "- Elapsed Time: 153.59979605674744\n",
      "\n",
      "Epoch 47\n",
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      "- Training Loss: 24.839071265486783\n",
      "- Validation Loss: 26.00431295445091\n",
      "- Train Time: 153.08863592147827\n",
      "- Elapsed Time: 157.54747414588928\n",
      "\n",
      "Epoch 48\n",
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      "- Training Loss: 24.804558839908864\n",
      "- Validation Loss: 25.94462675797312\n",
      "- Train Time: 156.7883608341217\n",
      "- Elapsed Time: 161.34533786773682\n",
      "\n",
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      "- Training Loss: 24.768521720586822\n",
      "- Validation Loss: 26.012809627934505\n",
      "- Train Time: 160.07600736618042\n",
      "- Elapsed Time: 164.7112488746643\n",
      "\n",
      "Epoch 50\n",
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      "- Training Loss: 24.75887439625208\n",
      "- Validation Loss: 26.08509504167657\n",
      "- Train Time: 164.0620129108429\n",
      "- Elapsed Time: 168.816965341568\n",
      "\n",
      "Epoch 51\n",
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      "- Training Loss: 24.685103235549704\n",
      "- Validation Loss: 25.99780895835475\n",
      "- Train Time: 167.5156900882721\n",
      "- Elapsed Time: 172.38820672035217\n",
      "\n",
      "Epoch 52\n",
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      "- Training Loss: 24.651525108620177\n",
      "- Validation Loss: 25.89854851521944\n",
      "- Train Time: 171.5644178390503\n",
      "- Elapsed Time: 176.5728976726532\n",
      "\n",
      "Epoch 53\n",
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      "- Training Loss: 24.62631186219149\n",
      "- Validation Loss: 25.869003070028203\n",
      "- Train Time: 175.63726711273193\n",
      "- Elapsed Time: 180.78687167167664\n",
      "\n",
      "Epoch 54\n",
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      "- Training Loss: 24.605148792266846\n",
      "- Validation Loss: 25.83739145178544\n",
      "- Train Time: 179.93557596206665\n",
      "- Elapsed Time: 185.2461974620819\n",
      "\n",
      "Epoch 55\n",
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      "- Training Loss: 24.56492959829264\n",
      "- Validation Loss: 25.792056397387856\n",
      "- Train Time: 184.50862908363342\n",
      "- Elapsed Time: 189.93896412849426\n",
      "\n",
      "Epoch 56\n",
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      "- Training Loss: 24.5324298961218\n",
      "- Validation Loss: 25.924980038090755\n",
      "- Train Time: 187.93174934387207\n",
      "- Elapsed Time: 193.49372506141663\n",
      "\n",
      "Epoch 57\n",
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      "- Training Loss: 24.49571344741555\n",
      "- Validation Loss: 25.753218412399292\n",
      "- Train Time: 191.19392085075378\n",
      "- Elapsed Time: 196.832111120224\n",
      "\n",
      "Epoch 58\n",
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      "- Training Loss: 24.461046336695205\n",
      "- Validation Loss: 25.88405610385694\n",
      "- Train Time: 195.27062940597534\n",
      "- Elapsed Time: 201.02970504760742\n",
      "\n",
      "Epoch 59\n",
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      "- Training Loss: 24.439021522915642\n",
      "- Validation Loss: 25.85920057798687\n",
      "- Train Time: 200.10488724708557\n",
      "- Elapsed Time: 205.99506759643555\n",
      "\n",
      "Epoch 60\n",
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      "- Training Loss: 24.388661977856657\n",
      "- Validation Loss: 25.812445239016885\n",
      "- Train Time: 204.41317772865295\n",
      "- Elapsed Time: 210.44211411476135\n",
      "\n",
      "Epoch 61\n",
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      "- Training Loss: 24.369865942833034\n",
      "- Validation Loss: 25.81128060190301\n",
      "- Train Time: 207.43882656097412\n",
      "- Elapsed Time: 213.54279732704163\n",
      "\n",
      "Epoch 62\n",
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      "- Training Loss: 24.31737692134325\n",
      "- Validation Loss: 25.76867144986203\n",
      "- Train Time: 210.6235866546631\n",
      "- Elapsed Time: 216.80627012252808\n",
      "\n",
      "Epoch 63\n",
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      "- Training Loss: 24.309931705857434\n",
      "- Validation Loss: 25.79867452069333\n",
      "- Train Time: 213.74020648002625\n",
      "- Elapsed Time: 219.999174118042\n",
      "\n",
      "Epoch 64\n",
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      "- Training Loss: 23.603697173817213\n",
      "- Validation Loss: 25.206543985166046\n",
      "- Train Time: 218.29019808769226\n",
      "- Elapsed Time: 224.687513589859\n",
      "\n",
      "Epoch 65\n",
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      "- Training Loss: 23.550568184880323\n",
      "- Validation Loss: 25.196546190663387\n",
      "- Train Time: 222.67614769935608\n",
      "- Elapsed Time: 229.17481875419617\n",
      "\n",
      "Epoch 66\n",
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      "- Training Loss: 23.532547475293626\n",
      "- Validation Loss: 25.193480090091104\n",
      "- Train Time: 225.9550051689148\n",
      "- Elapsed Time: 232.52337789535522\n",
      "\n",
      "Epoch 67\n",
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      "- Training Loss: 23.519405671330386\n",
      "- Validation Loss: 25.18487508673417\n",
      "- Train Time: 229.772643327713\n",
      "- Elapsed Time: 236.4467318058014\n",
      "\n",
      "Epoch 68\n",
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      "- Training Loss: 23.507291822932487\n",
      "- Validation Loss: 25.1713682224876\n",
      "- Train Time: 233.73491621017456\n",
      "- Elapsed Time: 240.51214957237244\n",
      "\n",
      "Epoch 69\n",
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      "- Training Loss: 23.496557327203973\n",
      "- Validation Loss: 25.15260346312272\n",
      "- Train Time: 237.39252400398254\n",
      "- Elapsed Time: 244.2424852848053\n",
      "\n",
      "Epoch 70\n",
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      "- Training Loss: 23.485793109550034\n",
      "- Validation Loss: 25.15019735537077\n",
      "- Train Time: 240.6462504863739\n",
      "- Elapsed Time: 247.56711053848267\n",
      "\n",
      "Epoch 71\n",
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      "- Training Loss: 23.47657597342203\n",
      "- Validation Loss: 25.14722136447304\n",
      "- Train Time: 244.3094618320465\n",
      "- Elapsed Time: 251.36519813537598\n",
      "\n",
      "Epoch 72\n",
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      "- Training Loss: 23.466709326866063\n",
      "- Validation Loss: 25.151785624654668\n",
      "- Train Time: 248.01477789878845\n",
      "- Elapsed Time: 255.19142842292786\n",
      "\n",
      "Epoch 73\n",
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      "- Training Loss: 23.46290883421898\n",
      "- Validation Loss: 25.164071559906006\n",
      "- Train Time: 250.71404194831848\n",
      "- Elapsed Time: 257.96923828125\n",
      "\n",
      "Epoch 74\n",
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      "- Training Loss: 23.455352953007054\n",
      "- Validation Loss: 25.17500554887872\n",
      "- Train Time: 254.7206175327301\n",
      "- Elapsed Time: 262.11130690574646\n",
      "\n",
      "Epoch 75\n",
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      "- Training Loss: 23.448423525621724\n",
      "- Validation Loss: 25.177075637014287\n",
      "- Train Time: 258.063729763031\n",
      "- Elapsed Time: 265.52586936950684\n",
      "\n",
      "Epoch 76\n",
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      "- Training Loss: 23.442077648501062\n",
      "- Validation Loss: 25.1682952830666\n",
      "- Train Time: 260.9724576473236\n",
      "- Elapsed Time: 268.50505566596985\n",
      "\n",
      "Epoch 77\n",
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      "- Training Loss: 23.43676530205926\n",
      "- Validation Loss: 25.177566591062043\n",
      "- Train Time: 263.4402391910553\n",
      "- Elapsed Time: 271.0436613559723\n",
      "\n",
      "Epoch 78\n",
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      "- Training Loss: 23.360447906477507\n",
      "- Validation Loss: 24.693983216034738\n",
      "- Train Time: 266.37625336647034\n",
      "- Elapsed Time: 274.0689945220947\n",
      "\n",
      "Epoch 79\n",
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      "- Training Loss: 23.338577302389368\n",
      "- Validation Loss: 24.69554232296191\n",
      "- Train Time: 269.43129563331604\n",
      "- Elapsed Time: 277.195702791214\n",
      "\n",
      "Epoch 80\n",
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      "- Training Loss: 23.33353272634883\n",
      "- Validation Loss: 24.697342759684513\n",
      "- Train Time: 272.46835064888\n",
      "- Elapsed Time: 280.3040888309479\n",
      "\n",
      "Epoch 81\n",
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      "- Training Loss: 23.33070475486822\n",
      "- Validation Loss: 24.699726782347028\n",
      "- Train Time: 275.6440749168396\n",
      "- Elapsed Time: 283.5861825942993\n",
      "\n",
      "Epoch 82\n",
      "Training & Validating ----------------------------------\n",
      "- Training Loss: 23.328669848137125\n",
      "- Validation Loss: 24.70094064662331\n",
      "- Train Time: 279.2065863609314\n",
      "- Elapsed Time: 287.21746802330017\n",
      "\n",
      "Epoch 83\n",
      "Training & Validating ----------------------------------\n",
      "- Training Loss: 23.327117513085522\n",
      "- Validation Loss: 24.70182886876558\n",
      "- Train Time: 283.32716941833496\n",
      "- Elapsed Time: 291.43071389198303\n",
      "\n",
      "Epoch 84\n",
      "Training & Validating ----------------------------------\n",
      "- Training Loss: 23.325737801402113\n",
      "- Validation Loss: 24.702411300257634\n",
      "- Train Time: 285.8981394767761\n",
      "- Elapsed Time: 294.06968569755554\n",
      "\n",
      "Epoch 85\n",
      "Training & Validating ----------------------------------\n",
      "- Training Loss: 23.316581601320312\n",
      "- Validation Loss: 24.707829977336683\n",
      "- Train Time: 288.44612193107605\n",
      "- Elapsed Time: 296.68818283081055\n",
      "\n",
      "Epoch 86\n",
      "Training & Validating ----------------------------------\n",
      "- Training Loss: 23.315717134364817\n",
      "- Validation Loss: 24.70858255185579\n",
      "- Train Time: 290.8984432220459\n",
      "- Elapsed Time: 299.2103052139282\n",
      "\n",
      "Epoch 87\n",
      "Training & Validating ----------------------------------\n",
      "- Training Loss: 23.315217952395596\n",
      "- Validation Loss: 24.708658105448674\n",
      "- Train Time: 293.4510374069214\n",
      "- Elapsed Time: 301.83208417892456\n",
      "\n",
      "Epoch 88\n",
      "Training & Validating ----------------------------------\n",
      "- Training Loss: 23.31486297624056\n",
      "- Validation Loss: 24.708566916616338\n",
      "- Train Time: 295.8821532726288\n",
      "- Elapsed Time: 304.3440215587616\n",
      "\n",
      "Epoch 89\n",
      "Training & Validating ----------------------------------\n",
      "- Training Loss: 23.314555129339528\n",
      "- Validation Loss: 24.70856204785799\n",
      "- Train Time: 300.2318902015686\n",
      "- Elapsed Time: 308.76918029785156\n",
      "\n",
      "Epoch 90\n",
      "Training & Validating ----------------------------------\n",
      "- Training Loss: 23.314325008974517\n",
      "- Validation Loss: 24.708604323236568\n",
      "- Train Time: 303.21798753738403\n",
      "- Elapsed Time: 311.8240876197815\n",
      "\n",
      "Epoch 91\n",
      "Training & Validating ----------------------------------\n",
      "- Training Loss: 23.312434463306914\n",
      "- Validation Loss: 24.708364361210872\n",
      "- Train Time: 306.7275438308716\n",
      "- Elapsed Time: 315.402241230011\n",
      "\n",
      "Epoch 92\n",
      "Training & Validating ----------------------------------\n",
      "- Training Loss: 23.312405320101007\n",
      "- Validation Loss: 24.708171166871722\n",
      "- Train Time: 309.69476199150085\n",
      "- Elapsed Time: 318.44675517082214\n",
      "\n",
      "Epoch 93\n",
      "Training & Validating ----------------------------------\n",
      "- Training Loss: 23.31237576312797\n",
      "- Validation Loss: 24.70800837717558\n",
      "- Train Time: 313.96315026283264\n",
      "- Elapsed Time: 322.7962954044342\n",
      "\n",
      "Epoch 94\n",
      "Training & Validating ----------------------------------\n",
      "- Training Loss: 23.31235040551008\n",
      "- Validation Loss: 24.707880371495296\n",
      "- Train Time: 317.86849761009216\n",
      "- Elapsed Time: 326.7834315299988\n",
      "\n",
      "Epoch 95\n",
      "Training & Validating ----------------------------------\n",
      "- Training Loss: 23.31232776267584\n",
      "- Validation Loss: 24.707773170973127\n",
      "- Train Time: 320.42826437950134\n",
      "- Elapsed Time: 329.4119002819061\n",
      "\n",
      "Epoch 96\n",
      "Training & Validating ----------------------------------\n",
      "- Training Loss: 23.312306013911268\n",
      "- Validation Loss: 24.707676762028743\n",
      "- Train Time: 323.3615474700928\n",
      "- Elapsed Time: 332.41354632377625\n",
      "\n",
      "Epoch 97\n",
      "Training & Validating ----------------------------------\n",
      "- Training Loss: 23.31205963326055\n",
      "- Validation Loss: 24.7076735496521\n",
      "- Train Time: 326.57531094551086\n",
      "- Elapsed Time: 335.69628071784973\n",
      "\n",
      "Epoch 98\n",
      "Training & Validating ----------------------------------\n",
      "- Training Loss: 23.31206018287082\n",
      "- Validation Loss: 24.70766894440902\n",
      "- Train Time: 329.1764602661133\n",
      "- Elapsed Time: 338.3739926815033\n",
      "\n",
      "Epoch 99\n",
      "Training & Validating ----------------------------------\n",
      "- Training Loss: 23.3120586532493\n",
      "- Validation Loss: 24.707670061211836\n",
      "- Train Time: 331.9624927043915\n",
      "- Elapsed Time: 341.2408254146576\n",
      "\n",
      "Epoch 100\n",
      "Training & Validating ----------------------------------\n",
      "- Training Loss: 23.31205835799838\n",
      "- Validation Loss: 24.707670839209307\n",
      "- Train Time: 335.4308533668518\n",
      "- Elapsed Time: 344.7791795730591\n",
      "\n"
     ]
    }
   ],
   "source": [
    "city = 'dearborn' \n",
    "split = 'train' #'test' #'train'\n",
    "hidden_dim = 64\n",
    "batch_size = 32\n",
    "num_iters = 100\n",
    "dearborn_train_mod, dearborn_losses = fit(city=city, split=split,\n",
    "                                          hidden_dim=hidden_dim, batch_size=batch_size, num_iters=num_iters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "fc42a157",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(1, 'Dearborn', 'train', dearborn_losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "6f92be35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 50, 2])\n",
      "torch.Size([32, 60, 2])\n",
      "torch.Size([32, 60, 2])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis_results('dearborn', 'train', 32, dearborn_train_mod, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "d8b01eac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3671\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "115"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# intialize a dataset\n",
    "city = 'dearborn' \n",
    "split = 'test' \n",
    "test_dataset = ArgoverseDataset(city = city, split = split, transform=False, normalized=True)\n",
    "temp_dataset = ArgoverseDataset(city = city, split = split, transform=False, normalized=False)\n",
    "print(len(test_dataset)) # should be 3671\n",
    "\n",
    "# test_dataset.start_pos\n",
    "\n",
    "batch_sz = 32  # batch size \n",
    "test_loader = DataLoader(test_dataset, batch_size=batch_sz)\n",
    "temp_loader = DataLoader(temp_dataset, batch_size=batch_sz)\n",
    "dearborn_train_mod.to('cpu')\n",
    "len(test_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "a9e4b831",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis_test(22, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "950e01ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for X in temp_loader:\n",
    "    if len(X) == batch_sz:\n",
    "        continue\n",
    "    # the index here should refer to the above cell's number\n",
    "    plt.scatter(X[22, :, 0], X[22, :, 1], label='input')\n",
    "    plt.legend\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "e7c5e762",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis_test(22, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "dec23f5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(3671, 120)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dearborn_array = make_pred(test_loader, batch_sz, dearborn_train_mod)\n",
    "dearborn_array.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da8e17bb",
   "metadata": {},
   "source": [
    "### washington-dc\n",
    "- best KAGGLE val loss: 28.915434670448303\n",
    "- best val loss: 21.85133969783783 (could be better ~21.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "84879cf1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "05b8c6c8af014a5697174d1ea46fa151",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1\n",
      "Training & Validating ------------------------------------\n",
      "- Training Loss: 279.09086294068817\n",
      "- Validation Loss: 61.879762613773345\n",
      "- Train Time: 5.101280689239502\n",
      "- Elapsed Time: 5.188570499420166\n",
      "\n",
      "Epoch 2\n",
      "Training & Validating ------------------------------------\n",
      "- Training Loss: 68.38796791582476\n",
      "- Validation Loss: 111.836683177948\n",
      "- Train Time: 9.68432092666626\n",
      "- Elapsed Time: 9.849931001663208\n",
      "\n",
      "Epoch 3\n",
      "Training & Validating ------------------------------------\n",
      "- Training Loss: 67.57212781642681\n",
      "- Validation Loss: 72.90643620491028\n",
      "- Train Time: 14.447901964187622\n",
      "- Elapsed Time: 14.689737558364868\n",
      "\n",
      "Epoch 4\n",
      "Training & Validating ------------------------------------\n",
      "- Training Loss: 65.06190559482047\n",
      "- Validation Loss: 58.37341730594635\n",
      "- Train Time: 19.22600817680359\n",
      "- Elapsed Time: 19.556092739105225\n",
      "\n",
      "Epoch 5\n",
      "Training & Validating ------------------------------------\n",
      "- Training Loss: 62.805628573696914\n",
      "- Validation Loss: 79.37635073661804\n",
      "- Train Time: 23.61782479286194\n",
      "- Elapsed Time: 24.02547311782837\n",
      "\n",
      "Epoch 6\n",
      "Training & Validating ------------------------------------\n",
      "- Training Loss: 60.79986400762316\n",
      "- Validation Loss: 78.67500767707824\n",
      "- Train Time: 29.190207958221436\n",
      "- Elapsed Time: 29.681694984436035\n",
      "\n",
      "Epoch 7\n",
      "Training & Validating ------------------------------------\n",
      "- Training Loss: 48.26909786034684\n",
      "- Validation Loss: 53.765152549743654\n",
      "- Train Time: 33.96574354171753\n",
      "- Elapsed Time: 34.54466938972473\n",
      "\n",
      "Epoch 8\n",
      "Training & Validating ------------------------------------\n",
      "- Training Loss: 36.921512315286456\n",
      "- Validation Loss: 57.04708766937256\n",
      "- Train Time: 38.69559860229492\n",
      "- Elapsed Time: 39.37615132331848\n",
      "\n",
      "Epoch 9\n",
      "Training & Validating ------------------------------------\n",
      "- Training Loss: 33.794801713353365\n",
      "- Validation Loss: 47.393914222717285\n",
      "- Train Time: 43.36319041252136\n",
      "- Elapsed Time: 44.126832008361816\n",
      "\n",
      "Epoch 10\n",
      "Training & Validating ------------------------------------\n",
      "- Training Loss: 32.74670776741281\n",
      "- Validation Loss: 59.20752432346344\n",
      "- Train Time: 48.6772780418396\n",
      "- Elapsed Time: 49.52587342262268\n",
      "\n",
      "Epoch 11\n",
      "Training & Validating ------------------------------------\n",
      "- Training Loss: 31.2338201986492\n",
      "- Validation Loss: 54.38402817249298\n",
      "- Train Time: 53.78575420379639\n",
      "- Elapsed Time: 54.71575117111206\n",
      "\n",
      "Epoch 12\n",
      "Training & Validating ------------------------------------\n",
      "- Training Loss: 31.381725502277607\n",
      "- Validation Loss: 49.10843391418457\n",
      "- Train Time: 58.56289029121399\n",
      "- Elapsed Time: 59.57711458206177\n",
      "\n",
      "Epoch 13\n",
      "Training & Validating ------------------------------------\n",
      "- Training Loss: 30.575971046205385\n",
      "- Validation Loss: 43.50974504947662\n",
      "- Train Time: 63.31857180595398\n",
      "- Elapsed Time: 64.4167001247406\n",
      "\n",
      "Epoch 14\n",
      "Training & Validating ------------------------------------\n",
      "- Training Loss: 29.546397605653627\n",
      "- Validation Loss: 46.27409999370575\n",
      "- Train Time: 68.19311833381653\n",
      "- Elapsed Time: 69.37192296981812\n",
      "\n",
      "Epoch 15\n",
      "Training & Validating ------------------------------------\n",
      "- Training Loss: 29.084013488411244\n",
      "- Validation Loss: 49.4629045009613\n",
      "- Train Time: 72.78985977172852\n",
      "- Elapsed Time: 74.04664397239685\n",
      "\n",
      "Epoch 16\n",
      "Training & Validating ------------------------------------\n",
      "- Training Loss: 28.90471934481879\n",
      "- Validation Loss: 45.28768975734711\n",
      "- Train Time: 77.6266348361969\n",
      "- Elapsed Time: 78.96223044395447\n",
      "\n",
      "Epoch 17\n",
      "Training & Validating ------------------------------------\n",
      "- Training Loss: 28.873335346991187\n",
      "- Validation Loss: 42.85305976867676\n",
      "- Train Time: 82.1703188419342\n",
      "- Elapsed Time: 83.5898027420044\n",
      "\n",
      "Epoch 18\n",
      "Training & Validating ------------------------------------\n",
      "- Training Loss: 28.673269097976263\n",
      "- Validation Loss: 41.1861759185791\n",
      "- Train Time: 86.79778337478638\n",
      "- Elapsed Time: 88.29308533668518\n",
      "\n",
      "Epoch 19\n",
      "Training & Validating ------------------------------------\n",
      "- Training Loss: 29.138928118331656\n",
      "- Validation Loss: 35.922553586959836\n",
      "- Train Time: 91.37476396560669\n",
      "- Elapsed Time: 92.94627976417542\n",
      "\n",
      "Epoch 20\n",
      "Training & Validating ------------------------------------\n",
      "- Training Loss: 28.569937453085547\n",
      "- Validation Loss: 38.739323353767396\n",
      "- Train Time: 95.48480558395386\n",
      "- Elapsed Time: 97.13779211044312\n",
      "\n",
      "Epoch 21\n",
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      "- Training Loss: 28.345641238913352\n",
      "- Validation Loss: 35.4272141456604\n",
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      "\n",
      "Epoch 22\n",
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      "- Training Loss: 28.65295447565574\n",
      "- Validation Loss: 43.79041972160339\n",
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      "\n",
      "Epoch 23\n",
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      "- Training Loss: 30.11650572033877\n",
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      "\n",
      "Epoch 24\n",
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      "- Training Loss: 27.99403949073665\n",
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      "\n",
      "Epoch 25\n",
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      "- Training Loss: 27.8020306213126\n",
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      "\n",
      "Epoch 26\n",
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      "- Training Loss: 27.571084532289873\n",
      "- Validation Loss: 34.31933310031891\n",
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      "\n",
      "Epoch 27\n",
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      "- Training Loss: 28.134586342131893\n",
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      "\n",
      "Epoch 28\n",
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      "- Training Loss: 27.758940530745363\n",
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      "\n",
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      "- Training Loss: 27.649283742377772\n",
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      "\n",
      "Epoch 30\n",
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      "- Training Loss: 27.775044626952536\n",
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      "\n",
      "Epoch 31\n",
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      "- Training Loss: 27.635348027582328\n",
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      "\n",
      "Epoch 32\n",
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      "- Training Loss: 27.128293162551373\n",
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      "\n",
      "Epoch 33\n",
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      "- Training Loss: 27.45344595619328\n",
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      "\n",
      "Epoch 34\n",
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      "- Training Loss: 27.01173491109142\n",
      "- Validation Loss: 32.39124273061752\n",
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      "\n",
      "Epoch 35\n",
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      "- Training Loss: 26.91592563318284\n",
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      "\n",
      "Epoch 36\n",
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      "- Training Loss: 26.855250566703837\n",
      "- Validation Loss: 28.82068923711777\n",
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      "\n",
      "Epoch 37\n",
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      "- Training Loss: 26.648193418650337\n",
      "- Validation Loss: 30.204774367809296\n",
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      "\n",
      "Epoch 38\n",
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      "- Training Loss: 23.228503567079155\n",
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      "\n",
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      "- Training Loss: 22.923254144784494\n",
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      "\n",
      "Epoch 40\n",
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      "- Training Loss: 22.86679431088063\n",
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      "\n",
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      "- Training Loss: 22.83065883230768\n",
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      "\n",
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      "- Training Loss: 22.774522811668355\n",
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      "\n",
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      "- Training Loss: 22.7037202284481\n",
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      "\n",
      "Epoch 44\n",
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      "- Training Loss: 22.674520516263847\n",
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      "\n",
      "Epoch 45\n",
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      "- Training Loss: 22.6435197874986\n",
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      "\n",
      "Epoch 46\n",
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      "- Training Loss: 22.570914238197368\n",
      "- Validation Loss: 22.23338634967804\n",
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      "\n",
      "Epoch 47\n",
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      "- Training Loss: 22.53969152856268\n",
      "- Validation Loss: 22.196581280231477\n",
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      "\n",
      "Epoch 48\n",
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      "- Training Loss: 22.52118041897347\n",
      "- Validation Loss: 22.222431433200835\n",
      "- Train Time: 222.00483632087708\n",
      "- Elapsed Time: 226.15244722366333\n",
      "\n",
      "Epoch 49\n",
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      "- Training Loss: 22.454793777255063\n",
      "- Validation Loss: 22.25979859828949\n",
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      "- Elapsed Time: 230.65881609916687\n",
      "\n",
      "Epoch 50\n",
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      "- Training Loss: 22.417038696246912\n",
      "- Validation Loss: 22.191733765602113\n",
      "- Train Time: 230.86199259757996\n",
      "- Elapsed Time: 235.1713252067566\n",
      "\n",
      "Epoch 51\n",
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      "- Training Loss: 22.394129152456042\n",
      "- Validation Loss: 22.186715686321257\n",
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      "\n",
      "Epoch 52\n",
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      "- Training Loss: 22.34593699650211\n",
      "- Validation Loss: 22.14156495332718\n",
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      "- Elapsed Time: 244.65739750862122\n",
      "\n",
      "Epoch 53\n",
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      "- Training Loss: 22.327678766039853\n",
      "- Validation Loss: 22.149734795093536\n",
      "- Train Time: 244.77367544174194\n",
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      "\n",
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      "- Training Loss: 22.30308357655014\n",
      "- Validation Loss: 22.134064400196074\n",
      "- Train Time: 249.36927580833435\n",
      "- Elapsed Time: 253.99186754226685\n",
      "\n",
      "Epoch 55\n",
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      "- Training Loss: 22.27891048146875\n",
      "- Validation Loss: 22.15333721637726\n",
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      "\n",
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      "- Training Loss: 22.231861372678978\n",
      "- Validation Loss: 22.1222895860672\n",
      "- Train Time: 258.5220046043396\n",
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      "\n",
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      "- Training Loss: 22.19972188433231\n",
      "- Validation Loss: 22.118280029296876\n",
      "- Train Time: 263.23972725868225\n",
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      "\n",
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      "- Training Loss: 22.16556912100776\n",
      "- Validation Loss: 22.114572501182558\n",
      "- Train Time: 268.0870203971863\n",
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      "\n",
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      "- Training Loss: 22.168433907282285\n",
      "- Validation Loss: 22.131291699409484\n",
      "- Train Time: 272.6310679912567\n",
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      "\n",
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      "- Training Loss: 22.1342013251057\n",
      "- Validation Loss: 22.12782974243164\n",
      "- Train Time: 277.55378770828247\n",
      "- Elapsed Time: 282.6539189815521\n",
      "\n",
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      "- Training Loss: 22.119440218361703\n",
      "- Validation Loss: 22.1133922457695\n",
      "- Train Time: 282.692262172699\n",
      "- Elapsed Time: 287.87502241134644\n",
      "\n",
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      "- Training Loss: 22.097540295585084\n",
      "- Validation Loss: 22.10344992876053\n",
      "- Train Time: 288.00906801223755\n",
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      "\n",
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      "- Training Loss: 22.08234930301898\n",
      "- Validation Loss: 22.11952167749405\n",
      "- Train Time: 293.35887718200684\n",
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      "\n",
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      "- Training Loss: 22.058463324499392\n",
      "- Validation Loss: 22.114605164527894\n",
      "- Train Time: 298.4253742694855\n",
      "- Elapsed Time: 303.89005064964294\n",
      "\n",
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      "- Training Loss: 22.027584438165906\n",
      "- Validation Loss: 22.14598684310913\n",
      "- Train Time: 302.9951491355896\n",
      "- Elapsed Time: 308.5367708206177\n",
      "\n",
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      "- Training Loss: 21.99970935326255\n",
      "- Validation Loss: 22.11740735769272\n",
      "- Train Time: 308.2874035835266\n",
      "- Elapsed Time: 313.9342632293701\n",
      "\n",
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      "- Training Loss: 21.984794641726584\n",
      "- Validation Loss: 22.11054276227951\n",
      "- Train Time: 312.925537109375\n",
      "- Elapsed Time: 318.65405440330505\n",
      "\n",
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      "- Training Loss: 21.962637452130817\n",
      "- Validation Loss: 22.092857360839844\n",
      "- Train Time: 317.8220489025116\n",
      "- Elapsed Time: 323.62668442726135\n",
      "\n",
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      "- Training Loss: 21.942849413466057\n",
      "- Validation Loss: 22.13857183456421\n",
      "- Train Time: 322.37990164756775\n",
      "- Elapsed Time: 328.2679443359375\n",
      "\n",
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      "- Training Loss: 21.940760549260766\n",
      "- Validation Loss: 22.094822216033936\n",
      "- Train Time: 327.78853583335876\n",
      "- Elapsed Time: 333.78272128105164\n",
      "\n",
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      "- Training Loss: 21.915082116153357\n",
      "- Validation Loss: 22.10839114189148\n",
      "- Train Time: 332.57956743240356\n",
      "- Elapsed Time: 338.6575472354889\n",
      "\n",
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      "- Training Loss: 21.884040389930345\n",
      "- Validation Loss: 22.119904232025146\n",
      "- Train Time: 337.44253063201904\n",
      "- Elapsed Time: 343.59667205810547\n",
      "\n",
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      "- Training Loss: 21.87047852205308\n",
      "- Validation Loss: 22.120409870147704\n",
      "- Train Time: 343.06798243522644\n",
      "- Elapsed Time: 349.36782217025757\n",
      "\n",
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      "- Training Loss: 21.866811524438596\n",
      "- Validation Loss: 22.131731343269347\n",
      "- Train Time: 348.58059191703796\n",
      "- Elapsed Time: 354.96571683883667\n",
      "\n",
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      "- Training Loss: 21.36099821427909\n",
      "- Validation Loss: 21.8857892036438\n",
      "- Train Time: 353.1395366191864\n",
      "- Elapsed Time: 359.6082637310028\n",
      "\n",
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      "- Training Loss: 21.30559435075159\n",
      "- Validation Loss: 21.875127732753754\n",
      "- Train Time: 357.4313178062439\n",
      "- Elapsed Time: 363.9762029647827\n",
      "\n",
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      "- Training Loss: 21.292017006742363\n",
      "- Validation Loss: 21.868329179286956\n",
      "- Train Time: 361.76782059669495\n",
      "- Elapsed Time: 368.3934338092804\n",
      "\n",
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      "- Training Loss: 21.286176449686124\n",
      "- Validation Loss: 21.864549660682677\n",
      "- Train Time: 365.91659688949585\n",
      "- Elapsed Time: 372.61838459968567\n",
      "\n",
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      "- Training Loss: 21.27840448347903\n",
      "- Validation Loss: 21.861797392368317\n",
      "- Train Time: 370.33633637428284\n",
      "- Elapsed Time: 377.1266553401947\n",
      "\n",
      "Epoch 80\n",
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      "- Training Loss: 21.274018109832678\n",
      "- Validation Loss: 21.856687247753143\n",
      "- Train Time: 375.06624150276184\n",
      "- Elapsed Time: 381.9851324558258\n",
      "\n",
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      "- Training Loss: 21.267239432308557\n",
      "- Validation Loss: 21.856686520576478\n",
      "- Train Time: 379.26613306999207\n",
      "- Elapsed Time: 386.2657058238983\n",
      "\n",
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      "- Training Loss: 21.26356049268944\n",
      "- Validation Loss: 21.855490696430206\n",
      "- Train Time: 383.30136275291443\n",
      "- Elapsed Time: 390.3773031234741\n",
      "\n",
      "Epoch 83\n",
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      "- Training Loss: 21.258354916756982\n",
      "- Validation Loss: 21.857649171352385\n",
      "- Train Time: 387.708877325058\n",
      "- Elapsed Time: 394.86055541038513\n",
      "\n",
      "Epoch 84\n",
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      "- Training Loss: 21.255977709649017\n",
      "- Validation Loss: 21.85452823638916\n",
      "- Train Time: 391.8893144130707\n",
      "- Elapsed Time: 399.12356066703796\n",
      "\n",
      "Epoch 85\n",
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      "- Training Loss: 21.250937082490868\n",
      "- Validation Loss: 21.85559092760086\n",
      "- Train Time: 395.92999267578125\n",
      "- Elapsed Time: 403.2446484565735\n",
      "\n",
      "Epoch 86\n",
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      "- Training Loss: 21.247707798994707\n",
      "- Validation Loss: 21.855772721767426\n",
      "- Train Time: 399.9594156742096\n",
      "- Elapsed Time: 407.3551547527313\n",
      "\n",
      "Epoch 87\n",
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      "- Training Loss: 21.202092673897084\n",
      "- Validation Loss: 21.85600126981735\n",
      "- Train Time: 404.0154435634613\n",
      "- Elapsed Time: 411.4912815093994\n",
      "\n",
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      "- Training Loss: 21.195320618086757\n",
      "- Validation Loss: 21.85597780942917\n",
      "- Train Time: 408.430246591568\n",
      "- Elapsed Time: 415.98827743530273\n",
      "\n",
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      "- Training Loss: 21.19213064599432\n",
      "- Validation Loss: 21.85566439628601\n",
      "- Train Time: 412.6119816303253\n",
      "- Elapsed Time: 420.25016689300537\n",
      "\n",
      "Epoch 90\n",
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      "- Training Loss: 21.18995963275762\n",
      "- Validation Loss: 21.85528874397278\n",
      "- Train Time: 416.7005898952484\n",
      "- Elapsed Time: 424.42118072509766\n",
      "\n",
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      "- Training Loss: 21.18850878325615\n",
      "- Validation Loss: 21.85454330444336\n",
      "- Train Time: 420.7732572555542\n",
      "- Elapsed Time: 428.57617688179016\n",
      "\n",
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      "- Training Loss: 21.187352040854606\n",
      "- Validation Loss: 21.853916943073273\n",
      "- Train Time: 424.83461117744446\n",
      "- Elapsed Time: 432.7213032245636\n",
      "\n",
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      "- Training Loss: 21.186445659036796\n",
      "- Validation Loss: 21.85323497056961\n",
      "- Train Time: 428.9501256942749\n",
      "- Elapsed Time: 436.91760897636414\n",
      "\n",
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      "- Training Loss: 21.185793520995926\n",
      "- Validation Loss: 21.852914559841157\n",
      "- Train Time: 433.301424741745\n",
      "- Elapsed Time: 441.34624218940735\n",
      "\n",
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      "- Training Loss: 21.185170895486905\n",
      "- Validation Loss: 21.852711343765257\n",
      "- Train Time: 437.6569154262543\n",
      "- Elapsed Time: 445.7780303955078\n",
      "\n",
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      "- Training Loss: 21.184560164562246\n",
      "- Validation Loss: 21.852697920799255\n",
      "- Train Time: 442.2054386138916\n",
      "- Elapsed Time: 450.40350341796875\n",
      "\n",
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      "- Training Loss: 21.18399786027097\n",
      "- Validation Loss: 21.852547216415406\n",
      "- Train Time: 446.5464084148407\n",
      "- Elapsed Time: 454.81995725631714\n",
      "\n",
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      "- Training Loss: 21.18341542607513\n",
      "- Validation Loss: 21.85251554250717\n",
      "- Train Time: 451.13329005241394\n",
      "- Elapsed Time: 459.48976278305054\n",
      "\n",
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      "- Training Loss: 21.174669863769363\n",
      "- Validation Loss: 21.851565051078797\n",
      "- Train Time: 455.3356969356537\n",
      "- Elapsed Time: 463.77231097221375\n",
      "\n",
      "Epoch 100\n",
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      "- Training Loss: 21.174505916089643\n",
      "- Validation Loss: 21.85133969783783\n",
      "- Train Time: 459.78744292259216\n",
      "- Elapsed Time: 468.3018252849579\n",
      "\n"
     ]
    }
   ],
   "source": [
    "city = 'washington-dc' \n",
    "split = 'train' #'test' #'train'\n",
    "hidden_dim = 128\n",
    "batch_size = 32\n",
    "num_iters = 100\n",
    "washington_train_mod, washington_losses = fit(city=city, split=split,\n",
    "                                              hidden_dim=hidden_dim, batch_size=batch_size, num_iters=num_iters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "2387e228",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(2, 'Washington-DC', 'train', washington_losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "bd64fd83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 50, 2])\n",
      "torch.Size([32, 60, 2])\n",
      "torch.Size([32, 60, 2])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis_results('washington-dc', 'train', 32, washington_train_mod, 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "b9948760",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3829\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "120"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# intialize a dataset\n",
    "city = 'washington-dc' \n",
    "split = 'test' \n",
    "test_dataset = ArgoverseDataset(city = city, split = split, transform=False, normalized=True)\n",
    "temp_dataset = ArgoverseDataset(city = city, split = split, transform=False, normalized=False)\n",
    "print(len(test_dataset)) # should be 3829\n",
    "\n",
    "# test_dataset.start_pos\n",
    "\n",
    "batch_sz = 32  # batch size \n",
    "test_loader = DataLoader(test_dataset, batch_size=batch_sz)\n",
    "temp_loader = DataLoader(temp_dataset, batch_size=batch_sz)\n",
    "washington_train_mod.to('cpu')\n",
    "len(test_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "1dd7060a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis_test(20, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "1c9a8315",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for X in temp_loader:\n",
    "    if len(X) == batch_sz:\n",
    "        continue\n",
    "    # the index here should refer to the above cell's number\n",
    "#     print(X[20])\n",
    "    plt.scatter(X[20, :, 0], X[20, :, 1], label='input')\n",
    "    plt.legend\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "f5c411ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis_test(20, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "e5d5d994",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(3829, 120)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "washington_array = make_pred(test_loader, batch_sz, washington_train_mod)\n",
    "washington_array.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c6949cb",
   "metadata": {},
   "source": [
    "### palo-alto\n",
    "- best KAGGLE val loss: 37.11707135793325\n",
    "- best val loss: 23.354386381200843"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "51e3ad52",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "55cedf5ca7b2440dbda53bfc8d6b67ba",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/150 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 189.87137542634053\n",
      "- Validation Loss: 61.5089162878088\n",
      "- Train Time: 2.12998366355896\n",
      "- Elapsed Time: 2.168370008468628\n",
      "\n",
      "Epoch 2\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 68.65271600559133\n",
      "- Validation Loss: 51.734536042084564\n",
      "- Train Time: 3.723102569580078\n",
      "- Elapsed Time: 3.7966954708099365\n",
      "\n",
      "Epoch 3\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 53.984677323247986\n",
      "- Validation Loss: 40.02789497375488\n",
      "- Train Time: 5.3143699169158936\n",
      "- Elapsed Time: 5.4228503704071045\n",
      "\n",
      "Epoch 4\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 49.8145067345141\n",
      "- Validation Loss: 40.839714875092376\n",
      "- Train Time: 7.502635478973389\n",
      "- Elapsed Time: 7.676073789596558\n",
      "\n",
      "Epoch 5\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 44.95579079421408\n",
      "- Validation Loss: 39.495107805406725\n",
      "- Train Time: 9.282348871231079\n",
      "- Elapsed Time: 9.494992733001709\n",
      "\n",
      "Epoch 6\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 47.05782582427344\n",
      "- Validation Loss: 48.16632946117504\n",
      "- Train Time: 10.909367561340332\n",
      "- Elapsed Time: 11.15708613395691\n",
      "\n",
      "Epoch 7\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 41.722198155230515\n",
      "- Validation Loss: 38.38952951173525\n",
      "- Train Time: 12.729278326034546\n",
      "- Elapsed Time: 13.042238712310791\n",
      "\n",
      "Epoch 8\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 42.36636578364613\n",
      "- Validation Loss: 38.077005953402136\n",
      "- Train Time: 14.238595485687256\n",
      "- Elapsed Time: 14.627326011657715\n",
      "\n",
      "Epoch 9\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 39.71092324299345\n",
      "- Validation Loss: 39.731113279188\n",
      "- Train Time: 15.770527839660645\n",
      "- Elapsed Time: 16.197935581207275\n",
      "\n",
      "Epoch 10\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 41.22365878526818\n",
      "- Validation Loss: 42.69519120293695\n",
      "- Train Time: 17.182596683502197\n",
      "- Elapsed Time: 17.64421558380127\n",
      "\n",
      "Epoch 11\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 38.803117188926265\n",
      "- Validation Loss: 34.04721399255701\n",
      "- Train Time: 18.5080726146698\n",
      "- Elapsed Time: 19.03565239906311\n",
      "\n",
      "Epoch 12\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 41.09292248236319\n",
      "- Validation Loss: 49.64674305271458\n",
      "- Train Time: 19.98041844367981\n",
      "- Elapsed Time: 20.541985988616943\n",
      "\n",
      "Epoch 13\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 39.57541034766404\n",
      "- Validation Loss: 37.2093049642202\n",
      "- Train Time: 21.43782377243042\n",
      "- Elapsed Time: 22.043876886367798\n",
      "\n",
      "Epoch 14\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 37.14120257006908\n",
      "- Validation Loss: 37.81502909273715\n",
      "- Train Time: 22.878384590148926\n",
      "- Elapsed Time: 23.519447565078735\n",
      "\n",
      "Epoch 15\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 36.47759003172288\n",
      "- Validation Loss: 43.13864316167058\n",
      "- Train Time: 24.19803762435913\n",
      "- Elapsed Time: 24.87301254272461\n",
      "\n",
      "Epoch 16\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 36.04392027359688\n",
      "- Validation Loss: 35.85441805865314\n",
      "- Train Time: 25.624841690063477\n",
      "- Elapsed Time: 26.344184637069702\n",
      "\n",
      "Epoch 17\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 36.17755340327141\n",
      "- Validation Loss: 42.43406053491541\n",
      "- Train Time: 27.45977258682251\n",
      "- Elapsed Time: 28.251357555389404\n",
      "\n",
      "Epoch 18\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 28.36490480836138\n",
      "- Validation Loss: 28.22770592973039\n",
      "- Train Time: 29.262274026870728\n",
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      "\n",
      "Epoch 19\n",
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      "- Training Loss: 27.121742200426958\n",
      "- Validation Loss: 27.78345803956728\n",
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      "\n",
      "Epoch 20\n",
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      "- Training Loss: 26.797678579562497\n",
      "- Validation Loss: 27.417660635870856\n",
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      "\n",
      "Epoch 21\n",
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      "- Training Loss: 26.520084791438162\n",
      "- Validation Loss: 26.901173179214066\n",
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      "\n",
      "Epoch 22\n",
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      "- Training Loss: 26.306718905530982\n",
      "- Validation Loss: 26.623196060593063\n",
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      "\n",
      "Epoch 23\n",
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      "- Training Loss: 26.131891641135738\n",
      "- Validation Loss: 26.336067148157067\n",
      "- Train Time: 36.948654651641846\n",
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      "\n",
      "Epoch 24\n",
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      "- Training Loss: 26.038986845610758\n",
      "- Validation Loss: 26.246125762527054\n",
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      "\n",
      "Epoch 25\n",
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      "- Training Loss: 25.85703729238991\n",
      "- Validation Loss: 26.23051359846785\n",
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      "\n",
      "Epoch 26\n",
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      "- Training Loss: 25.764321596049413\n",
      "- Validation Loss: 25.914229496105296\n",
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      "\n",
      "Epoch 27\n",
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      "- Training Loss: 25.612178740232565\n",
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      "\n",
      "Epoch 28\n",
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      "- Training Loss: 25.472440510192328\n",
      "- Validation Loss: 25.875105780524176\n",
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      "\n",
      "Epoch 29\n",
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      "- Training Loss: 25.340481132711076\n",
      "- Validation Loss: 25.73206447910618\n",
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      "\n",
      "Epoch 30\n",
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      "- Training Loss: 25.19873826793818\n",
      "- Validation Loss: 25.55534994280016\n",
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      "- Elapsed Time: 51.02120232582092\n",
      "\n",
      "Epoch 31\n",
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      "- Training Loss: 25.033502247637742\n",
      "- Validation Loss: 25.45700789786674\n",
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      "\n",
      "Epoch 32\n",
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      "- Training Loss: 24.970978974588547\n",
      "- Validation Loss: 25.470404960013724\n",
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      "\n",
      "Epoch 33\n",
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      "- Training Loss: 24.85070322812134\n",
      "- Validation Loss: 25.61500240016628\n",
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      "- Elapsed Time: 56.16543102264404\n",
      "\n",
      "Epoch 34\n",
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      "- Training Loss: 24.65346360984825\n",
      "- Validation Loss: 25.43999347171268\n",
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      "- Elapsed Time: 57.90155243873596\n",
      "\n",
      "Epoch 35\n",
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      "- Training Loss: 24.545511930561915\n",
      "- Validation Loss: 25.20489094708417\n",
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      "- Elapsed Time: 59.517194509506226\n",
      "\n",
      "Epoch 36\n",
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      "- Training Loss: 24.372344056647325\n",
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      "\n",
      "Epoch 37\n",
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      "- Training Loss: 24.329899997314996\n",
      "- Validation Loss: 25.124138703217376\n",
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      "- Elapsed Time: 62.98856782913208\n",
      "\n",
      "Epoch 38\n",
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      "- Training Loss: 24.10257688969462\n",
      "- Validation Loss: 25.134580612182617\n",
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      "- Elapsed Time: 64.40673518180847\n",
      "\n",
      "Epoch 39\n",
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      "- Training Loss: 23.88202856697739\n",
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      "\n",
      "Epoch 40\n",
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      "- Training Loss: 23.989230453083707\n",
      "- Validation Loss: 24.918358905895335\n",
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      "- Elapsed Time: 67.4645094871521\n",
      "\n",
      "Epoch 41\n",
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      "- Training Loss: 23.65417992962574\n",
      "- Validation Loss: 24.994614446485365\n",
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      "- Elapsed Time: 69.28277063369751\n",
      "\n",
      "Epoch 42\n",
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      "- Training Loss: 23.54494459763479\n",
      "- Validation Loss: 24.908334732055664\n",
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      "- Elapsed Time: 70.61810564994812\n",
      "\n",
      "Epoch 43\n",
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      "- Training Loss: 23.441520846562145\n",
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      "- Elapsed Time: 71.96231198310852\n",
      "\n",
      "Epoch 44\n",
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      "- Training Loss: 23.233904518783977\n",
      "- Validation Loss: 25.02523406776222\n",
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      "- Elapsed Time: 73.3247926235199\n",
      "\n",
      "Epoch 45\n",
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      "- Training Loss: 23.110240067501802\n",
      "- Validation Loss: 24.969369398581016\n",
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      "- Elapsed Time: 75.16186714172363\n",
      "\n",
      "Epoch 46\n",
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      "- Training Loss: 22.952663795176882\n",
      "- Validation Loss: 24.920133668023187\n",
      "- Train Time: 74.94962191581726\n",
      "- Elapsed Time: 77.02547264099121\n",
      "\n",
      "Epoch 47\n",
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      "- Training Loss: 22.907126672897565\n",
      "- Validation Loss: 24.94167554700697\n",
      "- Train Time: 76.47903180122375\n",
      "- Elapsed Time: 78.60645008087158\n",
      "\n",
      "Epoch 48\n",
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      "- Training Loss: 22.76667075058119\n",
      "- Validation Loss: 24.80192228265711\n",
      "- Train Time: 78.16967463493347\n",
      "- Elapsed Time: 80.34062457084656\n",
      "\n",
      "Epoch 49\n",
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      "- Training Loss: 22.68221645609915\n",
      "- Validation Loss: 24.913356549031025\n",
      "- Train Time: 79.74316549301147\n",
      "- Elapsed Time: 81.95509910583496\n",
      "\n",
      "Epoch 50\n",
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      "- Training Loss: 22.566796789537197\n",
      "- Validation Loss: 24.775326187546188\n",
      "- Train Time: 81.31428599357605\n",
      "- Elapsed Time: 83.59872221946716\n",
      "\n",
      "Epoch 51\n",
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      "- Training Loss: 22.45981413108305\n",
      "- Validation Loss: 24.902150875813252\n",
      "- Train Time: 83.39640045166016\n",
      "- Elapsed Time: 85.71838927268982\n",
      "\n",
      "Epoch 52\n",
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      "- Training Loss: 22.354883093508487\n",
      "- Validation Loss: 24.782483487515837\n",
      "- Train Time: 84.70899891853333\n",
      "- Elapsed Time: 87.06495761871338\n",
      "\n",
      "Epoch 53\n",
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      "- Training Loss: 22.208706592593774\n",
      "- Validation Loss: 24.865856583054\n",
      "- Train Time: 86.74604201316833\n",
      "- Elapsed Time: 89.1657464504242\n",
      "\n",
      "Epoch 54\n",
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      "- Training Loss: 22.088430382023933\n",
      "- Validation Loss: 24.86645608334928\n",
      "- Train Time: 88.30045652389526\n",
      "- Elapsed Time: 90.75407242774963\n",
      "\n",
      "Epoch 55\n",
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      "- Training Loss: 22.01062989588661\n",
      "- Validation Loss: 24.86047989613301\n",
      "- Train Time: 90.39696192741394\n",
      "- Elapsed Time: 92.91559386253357\n",
      "\n",
      "Epoch 56\n",
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      "- Training Loss: 21.918942411858534\n",
      "- Validation Loss: 24.72216441180255\n",
      "- Train Time: 92.55931067466736\n",
      "- Elapsed Time: 95.13923358917236\n",
      "\n",
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      "- Training Loss: 21.773220101874376\n",
      "- Validation Loss: 24.70228112710489\n",
      "- Train Time: 95.05715417861938\n",
      "- Elapsed Time: 97.7122585773468\n",
      "\n",
      "Epoch 58\n",
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      "- Training Loss: 21.6939259523454\n",
      "- Validation Loss: 24.91098932317785\n",
      "- Train Time: 96.62935185432434\n",
      "- Elapsed Time: 99.32151889801025\n",
      "\n",
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      "- Training Loss: 21.55013259440572\n",
      "- Validation Loss: 24.947882497632826\n",
      "- Train Time: 98.11930680274963\n",
      "- Elapsed Time: 100.84553670883179\n",
      "\n",
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      "- Training Loss: 21.46966131646131\n",
      "- Validation Loss: 24.814254167917614\n",
      "- Train Time: 99.69141268730164\n",
      "- Elapsed Time: 102.45188999176025\n",
      "\n",
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      "- Training Loss: 21.355119916027306\n",
      "- Validation Loss: 24.92183223930565\n",
      "- Train Time: 101.51118040084839\n",
      "- Elapsed Time: 104.30522656440735\n",
      "\n",
      "Epoch 62\n",
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      "- Training Loss: 21.280819205219032\n",
      "- Validation Loss: 24.872419073775006\n",
      "- Train Time: 102.72254014015198\n",
      "- Elapsed Time: 105.5703649520874\n",
      "\n",
      "Epoch 63\n",
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      "- Training Loss: 21.19471542913058\n",
      "- Validation Loss: 24.77290019473514\n",
      "- Train Time: 104.30656170845032\n",
      "- Elapsed Time: 107.2188925743103\n",
      "\n",
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      "- Training Loss: 19.97026665911123\n",
      "- Validation Loss: 23.668266734561406\n",
      "- Train Time: 105.65638065338135\n",
      "- Elapsed Time: 108.60224390029907\n",
      "\n",
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      "- Training Loss: 19.65743187414786\n",
      "- Validation Loss: 23.5452536247872\n",
      "- Train Time: 106.83855366706848\n",
      "- Elapsed Time: 109.81926131248474\n",
      "\n",
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      "- Training Loss: 19.557629409812677\n",
      "- Validation Loss: 23.54103423453666\n",
      "- Train Time: 108.02826595306396\n",
      "- Elapsed Time: 111.04256844520569\n",
      "\n",
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      "- Training Loss: 19.50444774033408\n",
      "- Validation Loss: 23.558043608794343\n",
      "- Train Time: 109.38703036308289\n",
      "- Elapsed Time: 112.43604516983032\n",
      "\n",
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      "- Training Loss: 19.45967217085977\n",
      "- Validation Loss: 23.557172749493574\n",
      "- Train Time: 110.84382748603821\n",
      "- Elapsed Time: 113.92947220802307\n",
      "\n",
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      "- Training Loss: 19.424132198537492\n",
      "- Validation Loss: 23.569616549723857\n",
      "- Train Time: 112.67500901222229\n",
      "- Elapsed Time: 115.82443857192993\n",
      "\n",
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      "- Training Loss: 19.391836111198902\n",
      "- Validation Loss: 23.56173396754909\n",
      "- Train Time: 114.155681848526\n",
      "- Elapsed Time: 117.33922600746155\n",
      "\n",
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      "- Training Loss: 19.360681054146426\n",
      "- Validation Loss: 23.565686200116133\n",
      "- Train Time: 115.62086343765259\n",
      "- Elapsed Time: 118.83803701400757\n",
      "\n",
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      "- Training Loss: 19.32894860530819\n",
      "- Validation Loss: 23.565111392253154\n",
      "- Train Time: 117.10464358329773\n",
      "- Elapsed Time: 120.36037230491638\n",
      "\n",
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      "- Training Loss: 19.21367499524125\n",
      "- Validation Loss: 23.41443139153558\n",
      "- Train Time: 118.47521495819092\n",
      "- Elapsed Time: 121.76920676231384\n",
      "\n",
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      "- Training Loss: 19.129708315569143\n",
      "- Validation Loss: 23.394261153968603\n",
      "- Train Time: 119.95094418525696\n",
      "- Elapsed Time: 123.28282570838928\n",
      "\n",
      "Epoch 75\n",
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      "- Training Loss: 19.112468896350805\n",
      "- Validation Loss: 23.38330588469634\n",
      "- Train Time: 121.26747226715088\n",
      "- Elapsed Time: 124.63283371925354\n",
      "\n",
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      "- Training Loss: 19.102505507030543\n",
      "- Validation Loss: 23.37547730110787\n",
      "- Train Time: 122.48600101470947\n",
      "- Elapsed Time: 125.88506889343262\n",
      "\n",
      "Epoch 77\n",
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      "- Training Loss: 19.095443738320462\n",
      "- Validation Loss: 23.3708479340012\n",
      "- Train Time: 123.82716369628906\n",
      "- Elapsed Time: 127.2597165107727\n",
      "\n",
      "Epoch 78\n",
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      "- Training Loss: 19.089361719986098\n",
      "- Validation Loss: 23.36802508379962\n",
      "- Train Time: 125.5127489566803\n",
      "- Elapsed Time: 128.9854257106781\n",
      "\n",
      "Epoch 79\n",
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      "- Training Loss: 19.083657237825477\n",
      "- Validation Loss: 23.36553720525793\n",
      "- Train Time: 126.85451316833496\n",
      "- Elapsed Time: 130.3619680404663\n",
      "\n",
      "Epoch 80\n",
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      "- Training Loss: 19.079083478061666\n",
      "- Validation Loss: 23.36372305895831\n",
      "- Train Time: 128.3668041229248\n",
      "- Elapsed Time: 131.94313311576843\n",
      "\n",
      "Epoch 81\n",
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      "- Training Loss: 19.07443686125894\n",
      "- Validation Loss: 23.361553939613135\n",
      "- Train Time: 130.66285920143127\n",
      "- Elapsed Time: 134.30578446388245\n",
      "\n",
      "Epoch 82\n",
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      "- Training Loss: 19.070114771053415\n",
      "- Validation Loss: 23.360856339738177\n",
      "- Train Time: 132.77848172187805\n",
      "- Elapsed Time: 136.47809958457947\n",
      "\n",
      "Epoch 83\n",
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      "- Training Loss: 19.065916500034955\n",
      "- Validation Loss: 23.361700238408268\n",
      "- Train Time: 134.29198384284973\n",
      "- Elapsed Time: 138.0366027355194\n",
      "\n",
      "Epoch 84\n",
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      "- Training Loss: 19.062242440017112\n",
      "- Validation Loss: 23.361907804334486\n",
      "- Train Time: 135.92929649353027\n",
      "- Elapsed Time: 139.7316734790802\n",
      "\n",
      "Epoch 85\n",
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      "- Training Loss: 19.058297605825814\n",
      "- Validation Loss: 23.361971339663945\n",
      "- Train Time: 137.97974228858948\n",
      "- Elapsed Time: 141.8727424144745\n",
      "\n",
      "Epoch 86\n",
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      "- Training Loss: 19.054628512272142\n",
      "- Validation Loss: 23.362441861951673\n",
      "- Train Time: 139.4851450920105\n",
      "- Elapsed Time: 143.4150333404541\n",
      "\n",
      "Epoch 87\n",
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      "- Training Loss: 19.051048759890595\n",
      "- Validation Loss: 23.362971589371966\n",
      "- Train Time: 141.15661668777466\n",
      "- Elapsed Time: 145.13540077209473\n",
      "\n",
      "Epoch 88\n",
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      "- Training Loss: 19.023099667240675\n",
      "- Validation Loss: 23.356204651497507\n",
      "- Train Time: 142.71569919586182\n",
      "- Elapsed Time: 146.73644828796387\n",
      "\n",
      "Epoch 89\n",
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      "- Training Loss: 19.019985203219452\n",
      "- Validation Loss: 23.355473724571436\n",
      "- Train Time: 144.11193752288818\n",
      "- Elapsed Time: 148.16701245307922\n",
      "\n",
      "Epoch 90\n",
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      "- Training Loss: 19.018999873707486\n",
      "- Validation Loss: 23.35537438779264\n",
      "- Train Time: 145.34847974777222\n",
      "- Elapsed Time: 149.43718123435974\n",
      "\n",
      "Epoch 91\n",
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      "- Training Loss: 19.018356828378288\n",
      "- Validation Loss: 23.3552806957348\n",
      "- Train Time: 146.62474083900452\n",
      "- Elapsed Time: 150.74686527252197\n",
      "\n",
      "Epoch 92\n",
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      "- Training Loss: 19.01786939185168\n",
      "- Validation Loss: 23.35526824641872\n",
      "- Train Time: 147.93563580513\n",
      "- Elapsed Time: 152.09151077270508\n",
      "\n",
      "Epoch 93\n",
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      "- Training Loss: 19.017411831932183\n",
      "- Validation Loss: 23.35517318828686\n",
      "- Train Time: 149.14885711669922\n",
      "- Elapsed Time: 153.33869314193726\n",
      "\n",
      "Epoch 94\n",
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      "- Training Loss: 19.016980964988914\n",
      "- Validation Loss: 23.355075604206807\n",
      "- Train Time: 150.67792677879333\n",
      "- Elapsed Time: 154.90114283561707\n",
      "\n",
      "Epoch 95\n",
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      "- Training Loss: 19.01319750267957\n",
      "- Validation Loss: 23.354918505694414\n",
      "- Train Time: 151.92656445503235\n",
      "- Elapsed Time: 156.18379950523376\n",
      "\n",
      "Epoch 96\n",
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      "- Training Loss: 19.01308183641745\n",
      "- Validation Loss: 23.354790996860814\n",
      "- Train Time: 153.23728036880493\n",
      "- Elapsed Time: 157.53382754325867\n",
      "\n",
      "Epoch 97\n",
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      "- Training Loss: 19.012987777811837\n",
      "- Validation Loss: 23.354696686203415\n",
      "- Train Time: 154.94187116622925\n",
      "- Elapsed Time: 159.29179167747498\n",
      "\n",
      "Epoch 98\n",
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      "- Training Loss: 19.01290061565818\n",
      "- Validation Loss: 23.3546196963336\n",
      "- Train Time: 156.34247660636902\n",
      "- Elapsed Time: 160.7299883365631\n",
      "\n",
      "Epoch 99\n",
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      "- Training Loss: 19.012828463262903\n",
      "- Validation Loss: 23.354550902907913\n",
      "- Train Time: 157.72431707382202\n",
      "- Elapsed Time: 162.15202975273132\n",
      "\n",
      "Epoch 100\n",
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      "- Training Loss: 19.012762835188507\n",
      "- Validation Loss: 23.354506956564414\n",
      "- Train Time: 159.3451726436615\n",
      "- Elapsed Time: 163.80932235717773\n",
      "\n",
      "Epoch 101\n",
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      "- Training Loss: 19.012319126185748\n",
      "- Validation Loss: 23.35449793532088\n",
      "- Train Time: 160.65137267112732\n",
      "- Elapsed Time: 165.15254735946655\n",
      "\n",
      "Epoch 102\n",
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      "- Training Loss: 19.012316797182894\n",
      "- Validation Loss: 23.354495564022578\n",
      "- Train Time: 161.93371510505676\n",
      "- Elapsed Time: 166.47454118728638\n",
      "\n",
      "Epoch 103\n",
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      "- Training Loss: 19.012315392140465\n",
      "- Validation Loss: 23.354492135950036\n",
      "- Train Time: 163.4457230567932\n",
      "- Elapsed Time: 168.0329315662384\n",
      "\n",
      "Epoch 104\n",
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      "- Training Loss: 19.012313159354008\n",
      "- Validation Loss: 23.35449100185085\n",
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      "- Elapsed Time: 169.68422389030457\n",
      "\n",
      "Epoch 105\n",
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      "- Training Loss: 19.01231006344985\n",
      "- Validation Loss: 23.354488218152845\n",
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      "- Elapsed Time: 171.3323061466217\n",
      "\n",
      "Epoch 106\n",
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      "- Training Loss: 19.012309389935048\n",
      "- Validation Loss: 23.35448816660288\n",
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      "- Elapsed Time: 173.2853672504425\n",
      "\n",
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      "- Training Loss: 19.012307084986293\n",
      "- Validation Loss: 23.354482676531816\n",
      "- Train Time: 170.59581565856934\n",
      "- Elapsed Time: 175.3918058872223\n",
      "\n",
      "Epoch 108\n",
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      "- Training Loss: 19.012305760595847\n",
      "- Validation Loss: 23.35448051143337\n",
      "- Train Time: 171.88804292678833\n",
      "- Elapsed Time: 176.71779656410217\n",
      "\n",
      "Epoch 109\n",
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      "- Training Loss: 19.012303965028032\n",
      "- Validation Loss: 23.35447765041042\n",
      "- Train Time: 173.24807572364807\n",
      "- Elapsed Time: 178.11135244369507\n",
      "\n",
      "Epoch 110\n",
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      "- Training Loss: 19.012301053066988\n",
      "- Validation Loss: 23.354475691511823\n",
      "- Train Time: 174.98146152496338\n",
      "- Elapsed Time: 179.87842106819153\n",
      "\n",
      "Epoch 111\n",
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      "- Training Loss: 19.01230005835921\n",
      "- Validation Loss: 23.354476774061048\n",
      "- Train Time: 176.20494723320007\n",
      "- Elapsed Time: 181.13543176651\n",
      "\n",
      "Epoch 112\n",
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      "- Training Loss: 19.012296869068543\n",
      "- Validation Loss: 23.354471851039577\n",
      "- Train Time: 177.7112216949463\n",
      "- Elapsed Time: 182.67794799804688\n",
      "\n",
      "Epoch 113\n",
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      "- Training Loss: 19.01229638798654\n",
      "- Validation Loss: 23.35446940241633\n",
      "- Train Time: 179.32849526405334\n",
      "- Elapsed Time: 184.32956266403198\n",
      "\n",
      "Epoch 114\n",
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      "- Training Loss: 19.012294738158275\n",
      "- Validation Loss: 23.354465046444453\n",
      "- Train Time: 180.7848515510559\n",
      "- Elapsed Time: 185.82271313667297\n",
      "\n",
      "Epoch 115\n",
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      "- Training Loss: 19.012292567629487\n",
      "- Validation Loss: 23.354464015445195\n",
      "- Train Time: 182.3085322380066\n",
      "- Elapsed Time: 187.41118168830872\n",
      "\n",
      "Epoch 116\n",
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      "- Training Loss: 19.012289604730352\n",
      "- Validation Loss: 23.354463371070654\n",
      "- Train Time: 183.7144672870636\n",
      "- Elapsed Time: 188.85365343093872\n",
      "\n",
      "Epoch 117\n",
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      "- Training Loss: 19.012288856223357\n",
      "- Validation Loss: 23.354458267624313\n",
      "- Train Time: 185.54370856285095\n",
      "- Elapsed Time: 190.75152897834778\n",
      "\n",
      "Epoch 118\n",
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      "- Training Loss: 19.01228720639509\n",
      "- Validation Loss: 23.35446040694778\n",
      "- Train Time: 187.5527892112732\n",
      "- Elapsed Time: 192.82467675209045\n",
      "\n",
      "Epoch 119\n",
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      "- Training Loss: 19.012285529682828\n",
      "- Validation Loss: 23.35445499420166\n",
      "- Train Time: 188.9993917942047\n",
      "- Elapsed Time: 194.34707045555115\n",
      "\n",
      "Epoch 120\n",
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      "- Training Loss: 19.012284277454683\n",
      "- Validation Loss: 23.354456592250514\n",
      "- Train Time: 190.96454977989197\n",
      "- Elapsed Time: 196.36449313163757\n",
      "\n",
      "Epoch 121\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 19.01228217201346\n",
      "- Validation Loss: 23.354448318481445\n",
      "- Train Time: 193.11474537849426\n",
      "- Elapsed Time: 198.54888486862183\n",
      "\n",
      "Epoch 122\n",
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      "- Training Loss: 19.012279867064706\n",
      "- Validation Loss: 23.35444973610543\n",
      "- Train Time: 194.4750690460205\n",
      "- Elapsed Time: 199.94754552841187\n",
      "\n",
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      "- Training Loss: 19.012279115727818\n",
      "- Validation Loss: 23.354448344256426\n",
      "- Train Time: 195.7736418247223\n",
      "- Elapsed Time: 201.27987670898438\n",
      "\n",
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      "- Training Loss: 19.01227757768037\n",
      "- Validation Loss: 23.354443756309717\n",
      "- Train Time: 197.26799774169922\n",
      "- Elapsed Time: 202.83217430114746\n",
      "\n",
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      "- Training Loss: 19.012275750983715\n",
      "- Validation Loss: 23.354443111935176\n",
      "- Train Time: 198.69399285316467\n",
      "- Elapsed Time: 204.32194662094116\n",
      "\n",
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      "- Training Loss: 19.012272045237374\n",
      "- Validation Loss: 23.35444146233636\n",
      "- Train Time: 200.7464201450348\n",
      "- Elapsed Time: 206.40923738479614\n",
      "\n",
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      "- Training Loss: 19.012270784519547\n",
      "- Validation Loss: 23.35443927146293\n",
      "- Train Time: 202.2803738117218\n",
      "- Elapsed Time: 208.0058150291443\n",
      "\n",
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      "- Training Loss: 19.012269126201595\n",
      "- Validation Loss: 23.354439993162412\n",
      "- Train Time: 204.09283804893494\n",
      "- Elapsed Time: 209.86156797409058\n",
      "\n",
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      "- Training Loss: 19.01226818243191\n",
      "- Validation Loss: 23.354434812391126\n",
      "- Train Time: 205.5247585773468\n",
      "- Elapsed Time: 211.32773542404175\n",
      "\n",
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      "- Training Loss: 19.012265540725753\n",
      "- Validation Loss: 23.35442960584486\n",
      "- Train Time: 207.2885558605194\n",
      "- Elapsed Time: 213.16538262367249\n",
      "\n",
      "Epoch 131\n",
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      "- Training Loss: 19.012263521596303\n",
      "- Validation Loss: 23.35443030176936\n",
      "- Train Time: 208.61757683753967\n",
      "- Elapsed Time: 214.528156042099\n",
      "\n",
      "Epoch 132\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 19.012262331625827\n",
      "- Validation Loss: 23.354425327197927\n",
      "- Train Time: 210.73009514808655\n",
      "- Elapsed Time: 216.69898200035095\n",
      "\n",
      "Epoch 133\n",
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      "- Training Loss: 19.012261192593446\n",
      "- Validation Loss: 23.354425301422943\n",
      "- Train Time: 212.9085395336151\n",
      "- Elapsed Time: 218.94226551055908\n",
      "\n",
      "Epoch 134\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 19.01225832166587\n",
      "- Validation Loss: 23.354423265199404\n",
      "- Train Time: 214.5110650062561\n",
      "- Elapsed Time: 220.57832551002502\n",
      "\n",
      "Epoch 135\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 19.01225719253812\n",
      "- Validation Loss: 23.354421435175716\n",
      "- Train Time: 215.74146151542664\n",
      "- Elapsed Time: 221.84287786483765\n",
      "\n",
      "Epoch 136\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 19.012255718163285\n",
      "- Validation Loss: 23.354418058653135\n",
      "- Train Time: 217.08057165145874\n",
      "- Elapsed Time: 223.25803565979004\n",
      "\n",
      "Epoch 137\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 19.012252813276977\n",
      "- Validation Loss: 23.354418728802656\n",
      "- Train Time: 219.20995235443115\n",
      "- Elapsed Time: 225.42648029327393\n",
      "\n",
      "Epoch 138\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 19.012251747821843\n",
      "- Validation Loss: 23.35441419240591\n",
      "- Train Time: 220.4403097629547\n",
      "- Elapsed Time: 226.6938693523407\n",
      "\n",
      "Epoch 139\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 19.01224970605324\n",
      "- Validation Loss: 23.354414604805612\n",
      "- Train Time: 221.9370665550232\n",
      "- Elapsed Time: 228.26058650016785\n",
      "\n",
      "Epoch 140\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 19.012247545429084\n",
      "- Validation Loss: 23.35441014573381\n",
      "- Train Time: 223.40362215042114\n",
      "- Elapsed Time: 229.76098704338074\n",
      "\n",
      "Epoch 141\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 19.01224703604814\n",
      "- Validation Loss: 23.354408238385176\n",
      "- Train Time: 224.87640810012817\n",
      "- Elapsed Time: 231.27395296096802\n",
      "\n",
      "Epoch 142\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 19.012244419811033\n",
      "- Validation Loss: 23.35440615061167\n",
      "- Train Time: 226.40543460845947\n",
      "- Elapsed Time: 232.83724427223206\n",
      "\n",
      "Epoch 143\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 19.012243077026277\n",
      "- Validation Loss: 23.354406923861116\n",
      "- Train Time: 227.947655916214\n",
      "- Elapsed Time: 234.41623735427856\n",
      "\n",
      "Epoch 144\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 19.012241575767447\n",
      "- Validation Loss: 23.35440426903802\n",
      "- Train Time: 229.49492621421814\n",
      "- Elapsed Time: 235.9990484714508\n",
      "\n",
      "Epoch 145\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 19.012239449102022\n",
      "- Validation Loss: 23.35440156266496\n",
      "- Train Time: 231.35750913619995\n",
      "- Elapsed Time: 237.92536091804504\n",
      "\n",
      "Epoch 146\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 19.01223846429887\n",
      "- Validation Loss: 23.354399835741198\n",
      "- Train Time: 232.99469447135925\n",
      "- Elapsed Time: 239.6128318309784\n",
      "\n",
      "Epoch 147\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 19.012236405550905\n",
      "- Validation Loss: 23.35440073786555\n",
      "- Train Time: 234.73375868797302\n",
      "- Elapsed Time: 241.42005348205566\n",
      "\n",
      "Epoch 148\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 19.012234585928987\n",
      "- Validation Loss: 23.3543955055443\n",
      "- Train Time: 236.65817141532898\n",
      "- Elapsed Time: 243.38264346122742\n",
      "\n",
      "Epoch 149\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 19.012232422474938\n",
      "- Validation Loss: 23.354393417770797\n",
      "- Train Time: 238.33759760856628\n",
      "- Elapsed Time: 245.12660026550293\n",
      "\n",
      "Epoch 150\n",
      "Training & Validating ----------------\n",
      "- Training Loss: 19.01223206873817\n",
      "- Validation Loss: 23.354386381200843\n",
      "- Train Time: 240.12905502319336\n",
      "- Elapsed Time: 246.95221948623657\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# intialize a dataset\n",
    "city = 'palo-alto' \n",
    "split = 'train' \n",
    "hidden_dim = 64 # 256\n",
    "batch_size = 32\n",
    "palo_train_mod, palo_losses = fit(city=city, split=split,\n",
    "                                        hidden_dim=hidden_dim, batch_size=batch_size, num_iters=150)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "65118060",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(1, 'Palo-Alto', 'train', palo_losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "b1ee0aeb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 50, 2])\n",
      "torch.Size([32, 60, 2])\n",
      "torch.Size([32, 60, 2])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis_results('palo-alto', 'train', 32, palo_train_mod, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "60e0a216",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1686\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "53"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# intialize a dataset\n",
    "city = 'palo-alto' \n",
    "split = 'test' \n",
    "test_dataset = ArgoverseDataset(city = city, split = split, transform=False, normalized=True)\n",
    "temp_dataset = ArgoverseDataset(city = city, split = split, transform=False, normalized=False)\n",
    "print(len(test_dataset))\n",
    "\n",
    "# test_dataset.start_pos\n",
    "\n",
    "batch_sz = 32  # batch size \n",
    "test_loader = DataLoader(test_dataset, batch_size=batch_sz)\n",
    "temp_loader = DataLoader(temp_dataset, batch_size=batch_sz)\n",
    "palo_train_mod.to('cpu')\n",
    "len(test_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "17b03a87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis_test(21, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "b8390543",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for X in temp_loader:\n",
    "    if len(X) == batch_sz:\n",
    "        continue\n",
    "    # the index here should refer to the above cell's number\n",
    "    plt.scatter(X[21, :, 0], X[21, :, 1], label='input')\n",
    "    plt.legend\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "a6bda34a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis_test(21, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "666b1e19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1686, 120)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "palo_array = make_pred(test_loader, batch_sz, palo_train_mod)\n",
    "palo_array.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a4ecb47",
   "metadata": {},
   "source": [
    "### Kaggle loss: 28.73365"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f22d7953",
   "metadata": {},
   "source": [
    "## Write file\n",
    "- austin_array 6325\n",
    "- miami_array 7971\n",
    "- pittsburgh_array 6361\n",
    "- dearborn_array 3671\n",
    "- washington_array 3829\n",
    "- palo_array 1686"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "d7774a81",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "fdb07c10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6325\n",
      "7971\n",
      "6361\n",
      "3671\n",
      "3829\n",
      "1686\n"
     ]
    }
   ],
   "source": [
    "cols = [['ID'] + ['v{}'.format(i) for i in range(120)]]\n",
    "\n",
    "with open('output.csv', 'w+') as file:\n",
    "    mywriter = csv.writer(file, delimiter=',')\n",
    "    mywriter.writerows(cols)\n",
    "\n",
    "with open('output.csv', 'a') as file:\n",
    "    mywriter = csv.writer(file, delimiter=',')\n",
    "    \n",
    "    count = 0\n",
    "    for i in range(len(austin_array)):\n",
    "        temp = [np.append(['{}_austin'.format(i)], austin_array[i])]\n",
    "        mywriter.writerows(temp)\n",
    "        count += 1\n",
    "    print(count)\n",
    "    \n",
    "    count = 0\n",
    "    for i in range(len(miami_array)):\n",
    "        temp = [np.append(['{}_miami'.format(i)], miami_array[i])]\n",
    "        mywriter.writerows(temp)\n",
    "        count += 1\n",
    "    print(count)\n",
    "    \n",
    "    count = 0\n",
    "    for i in range(len(pittsburgh_array)):\n",
    "        temp = [np.append(['{}_pittsburgh'.format(i)], pittsburgh_array[i])]\n",
    "        mywriter.writerows(temp)\n",
    "        count += 1\n",
    "    print(count)\n",
    "    \n",
    "    count = 0\n",
    "    for i in range(len(dearborn_array)):\n",
    "        temp = [np.append(['{}_dearborn'.format(i)], dearborn_array[i])]\n",
    "        mywriter.writerows(temp)\n",
    "        count += 1\n",
    "    print(count)\n",
    "    \n",
    "    count = 0\n",
    "    for i in range(len(washington_array)):\n",
    "        temp = [np.append(['{}_washington-dc'.format(i)], washington_array[i])]\n",
    "        mywriter.writerows(temp)\n",
    "        count += 1\n",
    "    print(count)\n",
    "    \n",
    "    count = 0\n",
    "    for i in range(len(palo_array)):\n",
    "        temp = [np.append(['{}_palo-alto'.format(i)], palo_array[i])]\n",
    "        mywriter.writerows(temp)\n",
    "        count += 1\n",
    "    print(count)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46f23558",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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