main_torch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import scipy.io #initializes libraries\n",
    "\n",
    "import time\n",
    "import math\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mat = scipy.io.loadmat('./datasets/Oxford_Battery_Degradation_Dataset_1.mat') #reads dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['__header__', '__version__', '__globals__', 'Cell1', 'Cell2', 'Cell3', 'Cell4', 'Cell5', 'Cell6', 'Cell7', 'Cell8'])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "input_data = [\n",
    "    [[], []],  # cell 1, [0, 1, 2, ... 82], [730, 729, ... value for q, ... , 530]\n",
    "    [[], []],\n",
    "    [[], []],\n",
    "    [[], []],\n",
    "    [[], []],\n",
    "    [[], []],\n",
    "    [[], []],\n",
    "    [[], []]\n",
    "]\n",
    "\n",
    "CELL_SIZE = [83, 78, 82, 52, 49, 51, 82, 82] # number of the cycles for each cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in range(0, 8): #breaks down dataset to return the values that we need\n",
    "    cell_num = \"Cell{}\".format(i + 1)\n",
    "    for j in range(0, CELL_SIZE[i]):\n",
    "        cyc_num = \"cyc{:04d}\".format(j * 100) # if j=47, cyc_num = \"cyc4700\"; if j=1, cyc_num = \"cyc0100\"\n",
    "        try:\n",
    "            curr = mat[cell_num][0][cyc_num][0][0][\"C1ch\"][0][0]['q'][0][-1][0]\n",
    "        except ValueError:\n",
    "            curr = float(\"NaN\")\n",
    "        input_data[i][0].append(j)\n",
    "        input_data[i][1].append(curr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_x = []\n",
    "train_y = []\n",
    "#test_x is a 2d dataset\n",
    "test_x = [ \n",
    "    [],\n",
    "    [],\n",
    "    [],\n",
    "    []\n",
    "]\n",
    "#test_y is a 2d dataset\n",
    "test_y = [\n",
    "    [],\n",
    "    [],\n",
    "    [],\n",
    "    []\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in range(0, 8):\n",
    "\n",
    "    df_input = pd.DataFrame(input_data[i]).transpose()\n",
    "    df_input = df_input.rename(columns={0: \"cycle\", 1: \"q_value\"})\n",
    "    df_input['interpolate_time'] = df_input['q_value'].interpolate(option='time') #time interpolate preprocessing of cells 1 to 8\n",
    "    df_input['soh'] = df_input['interpolate_time'] / 740\n",
    "\n",
    "    df_input = df_input.drop(\"cycle\", axis=1)\n",
    "    df_input = df_input.drop(\"q_value\", axis=1)\n",
    "    df_input = df_input.drop(\"interpolate_time\", axis=1)\n",
    "\n",
    "    # normalisation\n",
    "    sc = MinMaxScaler()\n",
    "    label_sc = MinMaxScaler()\n",
    "    data = sc.fit_transform(df_input.values)\n",
    "    label_sc.fit(df_input.iloc[:, 0].values.reshape(-1, 1)) #MinMaxScaler preprocessing of cells 1 to 8\n",
    "\n",
    "    # windowing of time series\n",
    "    lookback = 10 #data windowing value\n",
    "    inputs = np.zeros((len(data) - lookback, lookback, df_input.shape[1]))\n",
    "    labels = np.zeros(len(data) - lookback)\n",
    "\n",
    "    for j in range(lookback, len(data)):\n",
    "        inputs[j - lookback] = data[j - lookback:j]\n",
    "        labels[j - lookback] = data[j, 0]\n",
    "    inputs = inputs.reshape(-1, lookback, df_input.shape[1])\n",
    "    labels = labels.reshape(-1, 1)\n",
    "\n",
    "    if (i < 4):\n",
    "        if len(train_x) == 0:\n",
    "            train_x = inputs[:]\n",
    "            train_y = labels[:]\n",
    "        else:\n",
    "            train_x = np.concatenate((train_x, inputs[:]))\n",
    "            train_y = np.concatenate((train_y, labels[:]))\n",
    "    else:\n",
    "        test_x[i - 4] = inputs\n",
    "        test_y[i - 4] = labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(255, 10, 1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(train_x).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(255, 1)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(train_y).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39, 10, 1)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(test_x[0]).shape # test_x = [[Cell5], [Cell6], [Cell7], [Cell8]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39, 1)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(test_y[0]).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "train_data = TensorDataset(torch.from_numpy(train_x), torch.from_numpy(train_y))\n",
    "train_loader = DataLoader(train_data, shuffle=True, batch_size=batch_size, drop_last=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# torch.cuda.is_available() checks and returns a Boolean True if a GPU is available, else it'll return False\n",
    "is_cuda = torch.cuda.is_available()\n",
    "\n",
    "# If we have a GPU available, we'll set our device to GPU. We'll use this device variable later in our code.\n",
    "if is_cuda:\n",
    "    device = torch.device(\"cuda\")\n",
    "else:\n",
    "    device = torch.device(\"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class GRUNet(nn.Module):\n",
    "    def __init__(self, input_dim, hidden_dim, output_dim, n_layers, drop_prob=0.2):\n",
    "        super(GRUNet, self).__init__() \n",
    "        self.hidden_dim = hidden_dim\n",
    "        self.n_layers = n_layers\n",
    "\n",
    "        self.gru = nn.GRU(input_dim, hidden_dim, n_layers, batch_first=True, dropout=drop_prob)\n",
    "        self.fc = nn.Linear(hidden_dim, output_dim)\n",
    "        self.relu = nn.ReLU()\n",
    "\n",
    "    def forward(self, x, h):\n",
    "        out, h = self.gru(x, h)\n",
    "        out = self.fc(self.relu(out[:, -1]))\n",
    "        return out, h\n",
    "\n",
    "    def init_hidden(self, batch_size):\n",
    "        weight = next(self.parameters()).data\n",
    "        hidden = weight.new(self.n_layers, batch_size, self.hidden_dim).zero_().to(device)\n",
    "        return hidden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class LSTMNet(nn.Module):\n",
    "    def __init__(self, input_dim, hidden_dim, output_dim, n_layers, drop_prob=0.2):\n",
    "        super(LSTMNet, self).__init__()\n",
    "        self.hidden_dim = hidden_dim\n",
    "        self.n_layers = n_layers\n",
    "\n",
    "        self.lstm = nn.LSTM(input_dim, hidden_dim, n_layers, batch_first=True, dropout=drop_prob)\n",
    "        self.fc = nn.Linear(hidden_dim, output_dim)\n",
    "        self.relu = nn.ReLU()\n",
    "\n",
    "    def forward(self, x, h):\n",
    "        out, h = self.lstm(x, h)\n",
    "        out = self.fc(self.relu(out[:,-1]))\n",
    "        return out, h\n",
    "\n",
    "    def init_hidden(self, batch_size):\n",
    "        weight = next(self.parameters()).data\n",
    "        hidden = (weight.new(self.n_layers, batch_size, self.hidden_dim).zero_().to(device),\n",
    "                  weight.new(self.n_layers, batch_size, self.hidden_dim).zero_().to(device))\n",
    "        return hidden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def train(train_loader, learn_rate, hidden_dim=256, EPOCHS=100, model_type=\"GRU\"):\n",
    "    # Setting common hyperparameters\n",
    "    input_dim = next(iter(train_loader))[0].shape[2]\n",
    "    output_dim = 1\n",
    "    n_layers = 2\n",
    "\n",
    "    # Instantiating the models\n",
    "    if model_type == \"GRU\":\n",
    "        model = GRUNet(input_dim, hidden_dim, output_dim, n_layers)\n",
    "    else:\n",
    "        model = LSTMNet(input_dim, hidden_dim, output_dim, n_layers)\n",
    "\n",
    "    model.to(device)\n",
    "\n",
    "    print(\"----------------------------------------\")\n",
    "    print(\"The\", model_type, \"has the layers as: \")\n",
    "    print(model)\n",
    "    print(\"----------------------------------------\")\n",
    "\n",
    "    # Defining loss function and optimizer\n",
    "    criterion = nn.MSELoss()\n",
    "    optimizer = torch.optim.Adam(model.parameters(), lr=learn_rate)\n",
    "\n",
    "    model.train()\n",
    "    print(\"Starting Training of {} model\".format(model_type))\n",
    "    epoch_times = []\n",
    "    # Start training loop\n",
    "    for epoch in range(1, EPOCHS + 1):\n",
    "        start_time = time.process_time()\n",
    "        h = model.init_hidden(batch_size)\n",
    "        avg_loss = 0.\n",
    "        counter = 0\n",
    "        for x, label in train_loader:\n",
    "            counter += 1\n",
    "            if model_type == \"GRU\":\n",
    "                h = h.data\n",
    "            else:\n",
    "                h = tuple([e.data for e in h])\n",
    "            model.zero_grad()\n",
    "\n",
    "            out, h = model(x.to(device).float(), h)\n",
    "            loss = criterion(out, label.to(device).float())\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            avg_loss += loss.item()\n",
    "\n",
    "            print(\"Epoch {}......Step: {}/{}....... Average Loss for Epoch: {}\".format(epoch, counter,\n",
    "                                                                                       len(train_loader),\n",
    "                                                                                       avg_loss / counter))\n",
    "\n",
    "        current_time = time.process_time()\n",
    "        print(\"Epoch {}/{} Done, Total Loss: {}\".format(epoch, EPOCHS, avg_loss / len(train_loader)))\n",
    "        print(\"Total Time Elapsed: {} seconds\".format(str(current_time - start_time)))\n",
    "        epoch_times.append(current_time - start_time)\n",
    "    print(\"Total Training Time: {} seconds\".format(str(sum(epoch_times))))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def evaluate(model, test_x, test_y, label_scalers):\n",
    "    model.eval()\n",
    "    outputs = []\n",
    "    targets = []\n",
    "    start_time = time.process_time()\n",
    "\n",
    "    inp = torch.from_numpy(np.array(test_x))\n",
    "    labs = torch.from_numpy(np.array(test_y))\n",
    "    h = model.init_hidden(inp.shape[0])\n",
    "    out, h = model(inp.to(device).float(), h)\n",
    "    outputs.append(label_sc.inverse_transform(out.cpu().detach().numpy()).reshape(-1))\n",
    "    targets.append(label_sc.inverse_transform(labs.numpy()).reshape(-1))\n",
    "\n",
    "    print(\"Evaluation Time: {}\".format(str(time.process_time() - start_time)))\n",
    "    MAE = 0\n",
    "    RMSE = 0\n",
    "    for i in range(len(outputs)):\n",
    "        MAE += np.mean(abs(outputs[i] - targets[i])) / len(outputs)\n",
    "        RMSE += np.mean((outputs[i] - targets[i]) ** 2) / len(outputs)\n",
    "    RMSE = math.sqrt(RMSE)\n",
    "    print(\"MAE: {}\".format(MAE))\n",
    "    print(\"RMSE: {}\".format(RMSE))\n",
    "    return outputs, targets, MAE, RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------\n",
      "The GRU has the layers as: \n",
      "GRUNet(\n",
      "  (gru): GRU(1, 256, num_layers=2, batch_first=True, dropout=0.2)\n",
      "  (fc): Linear(in_features=256, out_features=1, bias=True)\n",
      "  (relu): ReLU()\n",
      ")\n",
      "----------------------------------------\n",
      "Starting Training of GRU model\n",
      "Epoch 1......Step: 1/7....... Average Loss for Epoch: 0.1797698438167572\n",
      "Epoch 1......Step: 2/7....... Average Loss for Epoch: 0.1523757055401802\n",
      "Epoch 1......Step: 3/7....... Average Loss for Epoch: 0.12683428823947906\n",
      "Epoch 1......Step: 4/7....... Average Loss for Epoch: 0.10486210137605667\n",
      "Epoch 1......Step: 5/7....... Average Loss for Epoch: 0.09503555521368981\n",
      "Epoch 1......Step: 6/7....... Average Loss for Epoch: 0.08607776773472627\n",
      "Epoch 1......Step: 7/7....... Average Loss for Epoch: 0.08170223182865552\n",
      "Epoch 1/100 Done, Total Loss: 0.08170223182865552\n",
      "Total Time Elapsed: 1.28125 seconds\n",
      "Epoch 2......Step: 1/7....... Average Loss for Epoch: 0.03651454299688339\n",
      "Epoch 2......Step: 2/7....... Average Loss for Epoch: 0.03705592639744282\n",
      "Epoch 2......Step: 3/7....... Average Loss for Epoch: 0.039326452960570656\n",
      "Epoch 2......Step: 4/7....... Average Loss for Epoch: 0.03636996494606137\n",
      "Epoch 2......Step: 5/7....... Average Loss for Epoch: 0.03519864827394485\n",
      "Epoch 2......Step: 6/7....... Average Loss for Epoch: 0.031914971924076475\n",
      "Epoch 2......Step: 7/7....... Average Loss for Epoch: 0.03028090071997472\n",
      "Epoch 2/100 Done, Total Loss: 0.03028090071997472\n",
      "Total Time Elapsed: 1.296875 seconds\n",
      "Epoch 3......Step: 1/7....... Average Loss for Epoch: 0.02278733067214489\n",
      "Epoch 3......Step: 2/7....... Average Loss for Epoch: 0.02569567132741213\n",
      "Epoch 3......Step: 3/7....... Average Loss for Epoch: 0.02188671411325534\n",
      "Epoch 3......Step: 4/7....... Average Loss for Epoch: 0.017925327410921454\n",
      "Epoch 3......Step: 5/7....... Average Loss for Epoch: 0.01638486385345459\n",
      "Epoch 3......Step: 6/7....... Average Loss for Epoch: 0.015195094825079044\n",
      "Epoch 3......Step: 7/7....... Average Loss for Epoch: 0.014355534155453955\n",
      "Epoch 3/100 Done, Total Loss: 0.014355534155453955\n",
      "Total Time Elapsed: 1.28125 seconds\n",
      "Epoch 4......Step: 1/7....... Average Loss for Epoch: 0.002967502921819687\n",
      "Epoch 4......Step: 2/7....... Average Loss for Epoch: 0.0028169690631330013\n",
      "Epoch 4......Step: 3/7....... Average Loss for Epoch: 0.004509533445040385\n",
      "Epoch 4......Step: 4/7....... Average Loss for Epoch: 0.0047265071189031005\n",
      "Epoch 4......Step: 5/7....... Average Loss for Epoch: 0.004132768441922963\n",
      "Epoch 4......Step: 6/7....... Average Loss for Epoch: 0.003914701422521223\n",
      "Epoch 4......Step: 7/7....... Average Loss for Epoch: 0.0037343665192435893\n",
      "Epoch 4/100 Done, Total Loss: 0.0037343665192435893\n",
      "Total Time Elapsed: 1.046875 seconds\n",
      "Epoch 5......Step: 1/7....... Average Loss for Epoch: 0.0014780028723180294\n",
      "Epoch 5......Step: 2/7....... Average Loss for Epoch: 0.001530048728454858\n",
      "Epoch 5......Step: 3/7....... Average Loss for Epoch: 0.0011904279429775972\n",
      "Epoch 5......Step: 4/7....... Average Loss for Epoch: 0.0011002771352650598\n",
      "Epoch 5......Step: 5/7....... Average Loss for Epoch: 0.0012051370344124735\n",
      "Epoch 5......Step: 6/7....... Average Loss for Epoch: 0.0012183791938393067\n",
      "Epoch 5......Step: 7/7....... Average Loss for Epoch: 0.0013269388832018844\n",
      "Epoch 5/100 Done, Total Loss: 0.0013269388832018844\n",
      "Total Time Elapsed: 1.65625 seconds\n",
      "Epoch 6......Step: 1/7....... Average Loss for Epoch: 0.0006520231254398823\n",
      "Epoch 6......Step: 2/7....... Average Loss for Epoch: 0.0006967713416088372\n",
      "Epoch 6......Step: 3/7....... Average Loss for Epoch: 0.0012692702972951035\n",
      "Epoch 6......Step: 4/7....... Average Loss for Epoch: 0.0013935119059169665\n",
      "Epoch 6......Step: 5/7....... Average Loss for Epoch: 0.0013842140440829098\n",
      "Epoch 6......Step: 6/7....... Average Loss for Epoch: 0.0013076213654130697\n",
      "Epoch 6......Step: 7/7....... Average Loss for Epoch: 0.001275589390258704\n",
      "Epoch 6/100 Done, Total Loss: 0.001275589390258704\n",
      "Total Time Elapsed: 1.65625 seconds\n",
      "Epoch 7......Step: 1/7....... Average Loss for Epoch: 0.002248495351523161\n",
      "Epoch 7......Step: 2/7....... Average Loss for Epoch: 0.001558881369419396\n",
      "Epoch 7......Step: 3/7....... Average Loss for Epoch: 0.0015342161447430651\n",
      "Epoch 7......Step: 4/7....... Average Loss for Epoch: 0.001250835251994431\n",
      "Epoch 7......Step: 5/7....... Average Loss for Epoch: 0.0012830074410885572\n",
      "Epoch 7......Step: 6/7....... Average Loss for Epoch: 0.0012133537675254047\n",
      "Epoch 7......Step: 7/7....... Average Loss for Epoch: 0.0011403528790521836\n",
      "Epoch 7/100 Done, Total Loss: 0.0011403528790521836\n",
      "Total Time Elapsed: 1.453125 seconds\n",
      "Epoch 8......Step: 1/7....... Average Loss for Epoch: 0.0013676440576091409\n",
      "Epoch 8......Step: 2/7....... Average Loss for Epoch: 0.0012932568206451833\n",
      "Epoch 8......Step: 3/7....... Average Loss for Epoch: 0.0009790595116404195\n",
      "Epoch 8......Step: 4/7....... Average Loss for Epoch: 0.0008938966784626245\n",
      "Epoch 8......Step: 5/7....... Average Loss for Epoch: 0.0008135503274388611\n",
      "Epoch 8......Step: 6/7....... Average Loss for Epoch: 0.0007794840591183553\n",
      "Epoch 8......Step: 7/7....... Average Loss for Epoch: 0.0007190903166442045\n",
      "Epoch 8/100 Done, Total Loss: 0.0007190903166442045\n",
      "Total Time Elapsed: 1.015625 seconds\n",
      "Epoch 9......Step: 1/7....... Average Loss for Epoch: 0.0011477811494842172\n",
      "Epoch 9......Step: 2/7....... Average Loss for Epoch: 0.000884787121322006\n",
      "Epoch 9......Step: 3/7....... Average Loss for Epoch: 0.0008053491086078187\n",
      "Epoch 9......Step: 4/7....... Average Loss for Epoch: 0.0007327199418796226\n",
      "Epoch 9......Step: 5/7....... Average Loss for Epoch: 0.0007814449490979314\n",
      "Epoch 9......Step: 6/7....... Average Loss for Epoch: 0.0007116458873497322\n",
      "Epoch 9......Step: 7/7....... Average Loss for Epoch: 0.0006511723747410413\n",
      "Epoch 9/100 Done, Total Loss: 0.0006511723747410413\n",
      "Total Time Elapsed: 1.234375 seconds\n",
      "Epoch 10......Step: 1/7....... Average Loss for Epoch: 0.0018379919929429889\n",
      "Epoch 10......Step: 2/7....... Average Loss for Epoch: 0.001059530593920499\n",
      "Epoch 10......Step: 3/7....... Average Loss for Epoch: 0.0008343221949568639\n",
      "Epoch 10......Step: 4/7....... Average Loss for Epoch: 0.0007281583748408593\n",
      "Epoch 10......Step: 5/7....... Average Loss for Epoch: 0.0006942488544154912\n",
      "Epoch 10......Step: 6/7....... Average Loss for Epoch: 0.0006556180693830053\n",
      "Epoch 10......Step: 7/7....... Average Loss for Epoch: 0.0006696606370886522\n",
      "Epoch 10/100 Done, Total Loss: 0.0006696606370886522\n",
      "Total Time Elapsed: 1.375 seconds\n",
      "Epoch 11......Step: 1/7....... Average Loss for Epoch: 0.0006481057498604059\n",
      "Epoch 11......Step: 2/7....... Average Loss for Epoch: 0.0010078371851705015\n",
      "Epoch 11......Step: 3/7....... Average Loss for Epoch: 0.0009024769921476642\n",
      "Epoch 11......Step: 4/7....... Average Loss for Epoch: 0.0008160230208886787\n",
      "Epoch 11......Step: 5/7....... Average Loss for Epoch: 0.0007228639384265989\n",
      "Epoch 11......Step: 6/7....... Average Loss for Epoch: 0.0006468038482125849\n",
      "Epoch 11......Step: 7/7....... Average Loss for Epoch: 0.0006603434282754149\n",
      "Epoch 11/100 Done, Total Loss: 0.0006603434282754149\n",
      "Total Time Elapsed: 1.171875 seconds\n",
      "Epoch 12......Step: 1/7....... Average Loss for Epoch: 0.0011652022367343307\n",
      "Epoch 12......Step: 2/7....... Average Loss for Epoch: 0.000687503139488399\n",
      "Epoch 12......Step: 3/7....... Average Loss for Epoch: 0.0007059761943916479\n",
      "Epoch 12......Step: 4/7....... Average Loss for Epoch: 0.00074092808063142\n",
      "Epoch 12......Step: 5/7....... Average Loss for Epoch: 0.0006606244831345975\n",
      "Epoch 12......Step: 6/7....... Average Loss for Epoch: 0.0007589414696364353\n",
      "Epoch 12......Step: 7/7....... Average Loss for Epoch: 0.0006876431871205568\n",
      "Epoch 12/100 Done, Total Loss: 0.0006876431871205568\n",
      "Total Time Elapsed: 1.359375 seconds\n",
      "Epoch 13......Step: 1/7....... Average Loss for Epoch: 0.0009272776078432798\n",
      "Epoch 13......Step: 2/7....... Average Loss for Epoch: 0.0006442091544158757\n",
      "Epoch 13......Step: 3/7....... Average Loss for Epoch: 0.0005575249864098927\n",
      "Epoch 13......Step: 4/7....... Average Loss for Epoch: 0.0005032190310885198\n",
      "Epoch 13......Step: 5/7....... Average Loss for Epoch: 0.0005112109647598118\n",
      "Epoch 13......Step: 6/7....... Average Loss for Epoch: 0.0005442958451264227\n",
      "Epoch 13......Step: 7/7....... Average Loss for Epoch: 0.0005986044291473393\n",
      "Epoch 13/100 Done, Total Loss: 0.0005986044291473393\n",
      "Total Time Elapsed: 1.5625 seconds\n",
      "Epoch 14......Step: 1/7....... Average Loss for Epoch: 0.0007479694904759526\n",
      "Epoch 14......Step: 2/7....... Average Loss for Epoch: 0.0006759406824130565\n",
      "Epoch 14......Step: 3/7....... Average Loss for Epoch: 0.0006232862360775471\n",
      "Epoch 14......Step: 4/7....... Average Loss for Epoch: 0.0005681306356564164\n",
      "Epoch 14......Step: 5/7....... Average Loss for Epoch: 0.0005057636532001198\n",
      "Epoch 14......Step: 6/7....... Average Loss for Epoch: 0.000509284931467846\n",
      "Epoch 14......Step: 7/7....... Average Loss for Epoch: 0.0005062292745736029\n",
      "Epoch 14/100 Done, Total Loss: 0.0005062292745736029\n",
      "Total Time Elapsed: 1.484375 seconds\n",
      "Epoch 15......Step: 1/7....... Average Loss for Epoch: 0.0003945123462472111\n",
      "Epoch 15......Step: 2/7....... Average Loss for Epoch: 0.0006402148428605869\n",
      "Epoch 15......Step: 3/7....... Average Loss for Epoch: 0.0007864878765152147\n",
      "Epoch 15......Step: 4/7....... Average Loss for Epoch: 0.0008883448390406556\n",
      "Epoch 15......Step: 5/7....... Average Loss for Epoch: 0.0007744445581920445\n",
      "Epoch 15......Step: 6/7....... Average Loss for Epoch: 0.0007085249623438964\n",
      "Epoch 15......Step: 7/7....... Average Loss for Epoch: 0.0006747070250899664\n",
      "Epoch 15/100 Done, Total Loss: 0.0006747070250899664\n",
      "Total Time Elapsed: 1.453125 seconds\n",
      "Epoch 16......Step: 1/7....... Average Loss for Epoch: 0.0005027906736359\n",
      "Epoch 16......Step: 2/7....... Average Loss for Epoch: 0.0004164401616435498\n",
      "Epoch 16......Step: 3/7....... Average Loss for Epoch: 0.0005877720735346278\n",
      "Epoch 16......Step: 4/7....... Average Loss for Epoch: 0.0005218987716943957\n",
      "Epoch 16......Step: 5/7....... Average Loss for Epoch: 0.0005639084323775023\n",
      "Epoch 16......Step: 6/7....... Average Loss for Epoch: 0.0005328474362613633\n",
      "Epoch 16......Step: 7/7....... Average Loss for Epoch: 0.0005553963003746633\n",
      "Epoch 16/100 Done, Total Loss: 0.0005553963003746633\n",
      "Total Time Elapsed: 1.25 seconds\n",
      "Epoch 17......Step: 1/7....... Average Loss for Epoch: 0.0003791108902078122\n",
      "Epoch 17......Step: 2/7....... Average Loss for Epoch: 0.00043803373409900814\n",
      "Epoch 17......Step: 3/7....... Average Loss for Epoch: 0.0004313009849283844\n",
      "Epoch 17......Step: 4/7....... Average Loss for Epoch: 0.0005082105126348324\n",
      "Epoch 17......Step: 5/7....... Average Loss for Epoch: 0.00048601125017739833\n",
      "Epoch 17......Step: 6/7....... Average Loss for Epoch: 0.000451379635099632\n",
      "Epoch 17......Step: 7/7....... Average Loss for Epoch: 0.0004383167875598052\n",
      "Epoch 17/100 Done, Total Loss: 0.0004383167875598052\n",
      "Total Time Elapsed: 1.28125 seconds\n",
      "Epoch 18......Step: 1/7....... Average Loss for Epoch: 0.0003044598561245948\n",
      "Epoch 18......Step: 2/7....... Average Loss for Epoch: 0.000611353971180506\n",
      "Epoch 18......Step: 3/7....... Average Loss for Epoch: 0.0005563343099008004\n",
      "Epoch 18......Step: 4/7....... Average Loss for Epoch: 0.0007348632498178631\n",
      "Epoch 18......Step: 5/7....... Average Loss for Epoch: 0.0006687501911073924\n",
      "Epoch 18......Step: 6/7....... Average Loss for Epoch: 0.0006472042296081781\n",
      "Epoch 18......Step: 7/7....... Average Loss for Epoch: 0.0006220143612673772\n",
      "Epoch 18/100 Done, Total Loss: 0.0006220143612673772\n",
      "Total Time Elapsed: 1.5625 seconds\n",
      "Epoch 19......Step: 1/7....... Average Loss for Epoch: 0.001342259463854134\n",
      "Epoch 19......Step: 2/7....... Average Loss for Epoch: 0.0009274556650780141\n",
      "Epoch 19......Step: 3/7....... Average Loss for Epoch: 0.0008137598051689565\n",
      "Epoch 19......Step: 4/7....... Average Loss for Epoch: 0.000680710443702992\n",
      "Epoch 19......Step: 5/7....... Average Loss for Epoch: 0.0006804115080740302\n",
      "Epoch 19......Step: 6/7....... Average Loss for Epoch: 0.0006411424595474576\n",
      "Epoch 19......Step: 7/7....... Average Loss for Epoch: 0.0005842521620382156\n",
      "Epoch 19/100 Done, Total Loss: 0.0005842521620382156\n",
      "Total Time Elapsed: 1.421875 seconds\n",
      "Epoch 20......Step: 1/7....... Average Loss for Epoch: 0.0016756020486354828\n",
      "Epoch 20......Step: 2/7....... Average Loss for Epoch: 0.0011052213376387954\n",
      "Epoch 20......Step: 3/7....... Average Loss for Epoch: 0.0008587819853952775\n",
      "Epoch 20......Step: 4/7....... Average Loss for Epoch: 0.0008072036798694171\n",
      "Epoch 20......Step: 5/7....... Average Loss for Epoch: 0.000670723442453891\n",
      "Epoch 20......Step: 6/7....... Average Loss for Epoch: 0.0005894450417448146\n",
      "Epoch 20......Step: 7/7....... Average Loss for Epoch: 0.0005392335899939228\n",
      "Epoch 20/100 Done, Total Loss: 0.0005392335899939228\n",
      "Total Time Elapsed: 1.1875 seconds\n",
      "Epoch 21......Step: 1/7....... Average Loss for Epoch: 0.0013788474025204778\n",
      "Epoch 21......Step: 2/7....... Average Loss for Epoch: 0.0008699934114702046\n",
      "Epoch 21......Step: 3/7....... Average Loss for Epoch: 0.0007083421320809672\n",
      "Epoch 21......Step: 4/7....... Average Loss for Epoch: 0.0006890993608976714\n",
      "Epoch 21......Step: 5/7....... Average Loss for Epoch: 0.0006508330756332725\n",
      "Epoch 21......Step: 6/7....... Average Loss for Epoch: 0.0006216169955829779\n",
      "Epoch 21......Step: 7/7....... Average Loss for Epoch: 0.0006134008207092327\n",
      "Epoch 21/100 Done, Total Loss: 0.0006134008207092327\n",
      "Total Time Elapsed: 1.28125 seconds\n",
      "Epoch 22......Step: 1/7....... Average Loss for Epoch: 0.00047852215357124805\n",
      "Epoch 22......Step: 2/7....... Average Loss for Epoch: 0.0005409186123870313\n",
      "Epoch 22......Step: 3/7....... Average Loss for Epoch: 0.0004824369680136442\n",
      "Epoch 22......Step: 4/7....... Average Loss for Epoch: 0.0004339292354416102\n",
      "Epoch 22......Step: 5/7....... Average Loss for Epoch: 0.0005277470219880343\n",
      "Epoch 22......Step: 6/7....... Average Loss for Epoch: 0.0006668996065855026\n",
      "Epoch 22......Step: 7/7....... Average Loss for Epoch: 0.0005965982771678162\n",
      "Epoch 22/100 Done, Total Loss: 0.0005965982771678162\n",
      "Total Time Elapsed: 1.375 seconds\n",
      "Epoch 23......Step: 1/7....... Average Loss for Epoch: 0.00018365301366429776\n",
      "Epoch 23......Step: 2/7....... Average Loss for Epoch: 0.00026346125378040597\n",
      "Epoch 23......Step: 3/7....... Average Loss for Epoch: 0.00034614644149163115\n",
      "Epoch 23......Step: 4/7....... Average Loss for Epoch: 0.0003876997907354962\n",
      "Epoch 23......Step: 5/7....... Average Loss for Epoch: 0.0005256059666862711\n",
      "Epoch 23......Step: 6/7....... Average Loss for Epoch: 0.00048771097741943475\n",
      "Epoch 23......Step: 7/7....... Average Loss for Epoch: 0.0005072752974228933\n",
      "Epoch 23/100 Done, Total Loss: 0.0005072752974228933\n",
      "Total Time Elapsed: 0.953125 seconds\n",
      "Epoch 24......Step: 1/7....... Average Loss for Epoch: 0.0008945136796683073\n",
      "Epoch 24......Step: 2/7....... Average Loss for Epoch: 0.0005866308201802894\n",
      "Epoch 24......Step: 3/7....... Average Loss for Epoch: 0.000561541363519306\n",
      "Epoch 24......Step: 4/7....... Average Loss for Epoch: 0.0005005006314604543\n",
      "Epoch 24......Step: 5/7....... Average Loss for Epoch: 0.0006176573398988694\n",
      "Epoch 24......Step: 6/7....... Average Loss for Epoch: 0.0005818624437476198\n",
      "Epoch 24......Step: 7/7....... Average Loss for Epoch: 0.0005549675469020647\n",
      "Epoch 24/100 Done, Total Loss: 0.0005549675469020647\n",
      "Total Time Elapsed: 2.140625 seconds\n",
      "Epoch 25......Step: 1/7....... Average Loss for Epoch: 0.0003581177443265915\n",
      "Epoch 25......Step: 2/7....... Average Loss for Epoch: 0.0004262511502020061\n",
      "Epoch 25......Step: 3/7....... Average Loss for Epoch: 0.0003710935707204044\n",
      "Epoch 25......Step: 4/7....... Average Loss for Epoch: 0.0006258392968447879\n",
      "Epoch 25......Step: 5/7....... Average Loss for Epoch: 0.0005731462733820081\n",
      "Epoch 25......Step: 6/7....... Average Loss for Epoch: 0.0005475971217189605\n",
      "Epoch 25......Step: 7/7....... Average Loss for Epoch: 0.0005115073955883938\n",
      "Epoch 25/100 Done, Total Loss: 0.0005115073955883938\n",
      "Total Time Elapsed: 1.25 seconds\n",
      "Epoch 26......Step: 1/7....... Average Loss for Epoch: 0.0006025158800184727\n",
      "Epoch 26......Step: 2/7....... Average Loss for Epoch: 0.0004515455511864275\n",
      "Epoch 26......Step: 3/7....... Average Loss for Epoch: 0.0006268766592256725\n",
      "Epoch 26......Step: 4/7....... Average Loss for Epoch: 0.0005177418133825995\n",
      "Epoch 26......Step: 5/7....... Average Loss for Epoch: 0.0006195317080710083\n",
      "Epoch 26......Step: 6/7....... Average Loss for Epoch: 0.0005810333047217379\n",
      "Epoch 26......Step: 7/7....... Average Loss for Epoch: 0.0005644927815800267\n",
      "Epoch 26/100 Done, Total Loss: 0.0005644927815800267\n",
      "Total Time Elapsed: 1.15625 seconds\n",
      "Epoch 27......Step: 1/7....... Average Loss for Epoch: 0.00044648119364865124\n",
      "Epoch 27......Step: 2/7....... Average Loss for Epoch: 0.00030231451091822237\n",
      "Epoch 27......Step: 3/7....... Average Loss for Epoch: 0.0005904023128096014\n",
      "Epoch 27......Step: 4/7....... Average Loss for Epoch: 0.0005401848757173866\n",
      "Epoch 27......Step: 5/7....... Average Loss for Epoch: 0.0004991490510292351\n",
      "Epoch 27......Step: 6/7....... Average Loss for Epoch: 0.0005313685978762805\n",
      "Epoch 27......Step: 7/7....... Average Loss for Epoch: 0.0005867838959342667\n",
      "Epoch 27/100 Done, Total Loss: 0.0005867838959342667\n",
      "Total Time Elapsed: 0.984375 seconds\n",
      "Epoch 28......Step: 1/7....... Average Loss for Epoch: 0.00035799338365904987\n",
      "Epoch 28......Step: 2/7....... Average Loss for Epoch: 0.00034390691143926233\n",
      "Epoch 28......Step: 3/7....... Average Loss for Epoch: 0.0003497641688833634\n",
      "Epoch 28......Step: 4/7....... Average Loss for Epoch: 0.00037345386226661503\n",
      "Epoch 28......Step: 5/7....... Average Loss for Epoch: 0.00036479029222391546\n",
      "Epoch 28......Step: 6/7....... Average Loss for Epoch: 0.00036532681648774695\n",
      "Epoch 28......Step: 7/7....... Average Loss for Epoch: 0.0005501781249352332\n",
      "Epoch 28/100 Done, Total Loss: 0.0005501781249352332\n",
      "Total Time Elapsed: 1.234375 seconds\n",
      "Epoch 29......Step: 1/7....... Average Loss for Epoch: 0.0011600141879171133\n",
      "Epoch 29......Step: 2/7....... Average Loss for Epoch: 0.0007671849161852151\n",
      "Epoch 29......Step: 3/7....... Average Loss for Epoch: 0.0006431779668976864\n",
      "Epoch 29......Step: 4/7....... Average Loss for Epoch: 0.0005420053530542646\n",
      "Epoch 29......Step: 5/7....... Average Loss for Epoch: 0.00046132393181324004\n",
      "Epoch 29......Step: 6/7....... Average Loss for Epoch: 0.00044511280915079016\n",
      "Epoch 29......Step: 7/7....... Average Loss for Epoch: 0.00046026272632713826\n",
      "Epoch 29/100 Done, Total Loss: 0.00046026272632713826\n",
      "Total Time Elapsed: 0.875 seconds\n",
      "Epoch 30......Step: 1/7....... Average Loss for Epoch: 0.00035002309596166015\n",
      "Epoch 30......Step: 2/7....... Average Loss for Epoch: 0.00045782141387462616\n",
      "Epoch 30......Step: 3/7....... Average Loss for Epoch: 0.00040649618798245984\n",
      "Epoch 30......Step: 4/7....... Average Loss for Epoch: 0.0006338426028378308\n",
      "Epoch 30......Step: 5/7....... Average Loss for Epoch: 0.0005708916229195893\n",
      "Epoch 30......Step: 6/7....... Average Loss for Epoch: 0.000674919040951257\n",
      "Epoch 30......Step: 7/7....... Average Loss for Epoch: 0.000623145340276616\n",
      "Epoch 30/100 Done, Total Loss: 0.000623145340276616\n",
      "Total Time Elapsed: 1.015625 seconds\n",
      "Epoch 31......Step: 1/7....... Average Loss for Epoch: 0.0004650136106647551\n",
      "Epoch 31......Step: 2/7....... Average Loss for Epoch: 0.0005032462941017002\n",
      "Epoch 31......Step: 3/7....... Average Loss for Epoch: 0.0005812560363362232\n",
      "Epoch 31......Step: 4/7....... Average Loss for Epoch: 0.0005934467917541042\n",
      "Epoch 31......Step: 5/7....... Average Loss for Epoch: 0.000628005643375218\n",
      "Epoch 31......Step: 6/7....... Average Loss for Epoch: 0.0005771447516356906\n",
      "Epoch 31......Step: 7/7....... Average Loss for Epoch: 0.0006794462180031198\n",
      "Epoch 31/100 Done, Total Loss: 0.0006794462180031198\n",
      "Total Time Elapsed: 1.0 seconds\n",
      "Epoch 32......Step: 1/7....... Average Loss for Epoch: 0.0012372713536024094\n",
      "Epoch 32......Step: 2/7....... Average Loss for Epoch: 0.0007954997417982668\n",
      "Epoch 32......Step: 3/7....... Average Loss for Epoch: 0.0008432799368165433\n",
      "Epoch 32......Step: 4/7....... Average Loss for Epoch: 0.0007430538244079798\n",
      "Epoch 32......Step: 5/7....... Average Loss for Epoch: 0.0006822796305641532\n",
      "Epoch 32......Step: 6/7....... Average Loss for Epoch: 0.0006235316541278735\n",
      "Epoch 32......Step: 7/7....... Average Loss for Epoch: 0.0005788642197980412\n",
      "Epoch 32/100 Done, Total Loss: 0.0005788642197980412\n",
      "Total Time Elapsed: 1.03125 seconds\n",
      "Epoch 33......Step: 1/7....... Average Loss for Epoch: 0.000275411584880203\n",
      "Epoch 33......Step: 2/7....... Average Loss for Epoch: 0.00025619433290557936\n",
      "Epoch 33......Step: 3/7....... Average Loss for Epoch: 0.0004693942270629729\n",
      "Epoch 33......Step: 4/7....... Average Loss for Epoch: 0.00042498964103288017\n",
      "Epoch 33......Step: 5/7....... Average Loss for Epoch: 0.0006229891412658616\n",
      "Epoch 33......Step: 6/7....... Average Loss for Epoch: 0.0005727198949898593\n",
      "Epoch 33......Step: 7/7....... Average Loss for Epoch: 0.0005302322284218722\n",
      "Epoch 33/100 Done, Total Loss: 0.0005302322284218722\n",
      "Total Time Elapsed: 1.375 seconds\n",
      "Epoch 34......Step: 1/7....... Average Loss for Epoch: 0.00027543914620764554\n",
      "Epoch 34......Step: 2/7....... Average Loss for Epoch: 0.00028622041281778365\n",
      "Epoch 34......Step: 3/7....... Average Loss for Epoch: 0.0003111606832438459\n",
      "Epoch 34......Step: 4/7....... Average Loss for Epoch: 0.00043067406659247354\n",
      "Epoch 34......Step: 5/7....... Average Loss for Epoch: 0.0003977631393354386\n",
      "Epoch 34......Step: 6/7....... Average Loss for Epoch: 0.0005778902899085855\n",
      "Epoch 34......Step: 7/7....... Average Loss for Epoch: 0.0005543116671365819\n",
      "Epoch 34/100 Done, Total Loss: 0.0005543116671365819\n",
      "Total Time Elapsed: 0.828125 seconds\n",
      "Epoch 35......Step: 1/7....... Average Loss for Epoch: 0.0003837315598502755\n",
      "Epoch 35......Step: 2/7....... Average Loss for Epoch: 0.0003490159579087049\n",
      "Epoch 35......Step: 3/7....... Average Loss for Epoch: 0.0005847184802405536\n",
      "Epoch 35......Step: 4/7....... Average Loss for Epoch: 0.0005210089293541387\n",
      "Epoch 35......Step: 5/7....... Average Loss for Epoch: 0.0005145029979757964\n",
      "Epoch 35......Step: 6/7....... Average Loss for Epoch: 0.0004833336327768241\n",
      "Epoch 35......Step: 7/7....... Average Loss for Epoch: 0.000476096137260486\n",
      "Epoch 35/100 Done, Total Loss: 0.000476096137260486\n",
      "Total Time Elapsed: 1.046875 seconds\n",
      "Epoch 36......Step: 1/7....... Average Loss for Epoch: 0.00018320059461984783\n",
      "Epoch 36......Step: 2/7....... Average Loss for Epoch: 0.0002170003208448179\n",
      "Epoch 36......Step: 3/7....... Average Loss for Epoch: 0.00031285403626194846\n",
      "Epoch 36......Step: 4/7....... Average Loss for Epoch: 0.0003159234947816003\n",
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      "Epoch 36......Step: 7/7....... Average Loss for Epoch: 0.0005083143831663099\n",
      "Epoch 36/100 Done, Total Loss: 0.0005083143831663099\n",
      "Total Time Elapsed: 1.5 seconds\n",
      "Epoch 37......Step: 1/7....... Average Loss for Epoch: 0.0012521762400865555\n",
      "Epoch 37......Step: 2/7....... Average Loss for Epoch: 0.0007839389436412603\n",
      "Epoch 37......Step: 3/7....... Average Loss for Epoch: 0.0006596252205781639\n",
      "Epoch 37......Step: 4/7....... Average Loss for Epoch: 0.0006322708213701844\n",
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      "Epoch 37......Step: 6/7....... Average Loss for Epoch: 0.0006300572131294757\n",
      "Epoch 37......Step: 7/7....... Average Loss for Epoch: 0.000573163856253294\n",
      "Epoch 37/100 Done, Total Loss: 0.000573163856253294\n",
      "Total Time Elapsed: 1.078125 seconds\n",
      "Epoch 38......Step: 1/7....... Average Loss for Epoch: 0.00019926793174818158\n",
      "Epoch 38......Step: 2/7....... Average Loss for Epoch: 0.0003027284110430628\n",
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      "Epoch 38......Step: 4/7....... Average Loss for Epoch: 0.00053685340390075\n",
      "Epoch 38......Step: 5/7....... Average Loss for Epoch: 0.0005271527916193008\n",
      "Epoch 38......Step: 6/7....... Average Loss for Epoch: 0.0005807880855475863\n",
      "Epoch 38......Step: 7/7....... Average Loss for Epoch: 0.0005617502777438078\n",
      "Epoch 38/100 Done, Total Loss: 0.0005617502777438078\n",
      "Total Time Elapsed: 1.15625 seconds\n",
      "Epoch 39......Step: 1/7....... Average Loss for Epoch: 0.0016736387042328715\n",
      "Epoch 39......Step: 2/7....... Average Loss for Epoch: 0.0011248000664636493\n",
      "Epoch 39......Step: 3/7....... Average Loss for Epoch: 0.0008588649313120792\n",
      "Epoch 39......Step: 4/7....... Average Loss for Epoch: 0.0007333331523113884\n",
      "Epoch 39......Step: 5/7....... Average Loss for Epoch: 0.000675654225051403\n",
      "Epoch 39......Step: 6/7....... Average Loss for Epoch: 0.0006182656507007778\n",
      "Epoch 39......Step: 7/7....... Average Loss for Epoch: 0.0005712804559152573\n",
      "Epoch 39/100 Done, Total Loss: 0.0005712804559152573\n",
      "Total Time Elapsed: 1.234375 seconds\n",
      "Epoch 40......Step: 1/7....... Average Loss for Epoch: 0.0002865841088350862\n",
      "Epoch 40......Step: 2/7....... Average Loss for Epoch: 0.00024336753995157778\n",
      "Epoch 40......Step: 3/7....... Average Loss for Epoch: 0.00041507436738659936\n",
      "Epoch 40......Step: 4/7....... Average Loss for Epoch: 0.000572910183109343\n",
      "Epoch 40......Step: 5/7....... Average Loss for Epoch: 0.0005030075291870162\n",
      "Epoch 40......Step: 6/7....... Average Loss for Epoch: 0.000516901294759009\n",
      "Epoch 40......Step: 7/7....... Average Loss for Epoch: 0.0004755845626017877\n",
      "Epoch 40/100 Done, Total Loss: 0.0004755845626017877\n",
      "Total Time Elapsed: 0.953125 seconds\n",
      "Epoch 41......Step: 1/7....... Average Loss for Epoch: 0.0002482923155184835\n",
      "Epoch 41......Step: 2/7....... Average Loss for Epoch: 0.0008184268226614222\n",
      "Epoch 41......Step: 3/7....... Average Loss for Epoch: 0.0006984237406868488\n",
      "Epoch 41......Step: 4/7....... Average Loss for Epoch: 0.0005973666775389574\n",
      "Epoch 41......Step: 5/7....... Average Loss for Epoch: 0.0005321824282873422\n",
      "Epoch 41......Step: 6/7....... Average Loss for Epoch: 0.0005038439752145981\n",
      "Epoch 41......Step: 7/7....... Average Loss for Epoch: 0.0005074032856750169\n",
      "Epoch 41/100 Done, Total Loss: 0.0005074032856750169\n",
      "Total Time Elapsed: 0.90625 seconds\n",
      "Epoch 42......Step: 1/7....... Average Loss for Epoch: 0.0004393224371597171\n",
      "Epoch 42......Step: 2/7....... Average Loss for Epoch: 0.0005391279410105199\n",
      "Epoch 42......Step: 3/7....... Average Loss for Epoch: 0.0006948286706271271\n",
      "Epoch 42......Step: 4/7....... Average Loss for Epoch: 0.0006114902353147045\n",
      "Epoch 42......Step: 5/7....... Average Loss for Epoch: 0.0005328898812877014\n",
      "Epoch 42......Step: 6/7....... Average Loss for Epoch: 0.0005218687171388107\n",
      "Epoch 42......Step: 7/7....... Average Loss for Epoch: 0.0005337917807212632\n",
      "Epoch 42/100 Done, Total Loss: 0.0005337917807212632\n",
      "Total Time Elapsed: 1.015625 seconds\n",
      "Epoch 43......Step: 1/7....... Average Loss for Epoch: 0.0006070113158784807\n",
      "Epoch 43......Step: 2/7....... Average Loss for Epoch: 0.0005190045048948377\n",
      "Epoch 43......Step: 3/7....... Average Loss for Epoch: 0.0007057654632565876\n",
      "Epoch 43......Step: 4/7....... Average Loss for Epoch: 0.0006445494727813639\n",
      "Epoch 43......Step: 5/7....... Average Loss for Epoch: 0.0005703011353034526\n",
      "Epoch 43......Step: 6/7....... Average Loss for Epoch: 0.0005359908633787805\n",
      "Epoch 43......Step: 7/7....... Average Loss for Epoch: 0.0005019768099633179\n",
      "Epoch 43/100 Done, Total Loss: 0.0005019768099633179\n",
      "Total Time Elapsed: 0.9375 seconds\n",
      "Epoch 44......Step: 1/7....... Average Loss for Epoch: 0.0001476605684729293\n",
      "Epoch 44......Step: 2/7....... Average Loss for Epoch: 0.00026257648278260604\n",
      "Epoch 44......Step: 3/7....... Average Loss for Epoch: 0.0006323836520702267\n",
      "Epoch 44......Step: 4/7....... Average Loss for Epoch: 0.0005749306692450773\n",
      "Epoch 44......Step: 5/7....... Average Loss for Epoch: 0.000490305051789619\n",
      "Epoch 44......Step: 6/7....... Average Loss for Epoch: 0.0005349960338207893\n",
      "Epoch 44......Step: 7/7....... Average Loss for Epoch: 0.0004999064279088218\n",
      "Epoch 44/100 Done, Total Loss: 0.0004999064279088218\n",
      "Total Time Elapsed: 1.5 seconds\n",
      "Epoch 45......Step: 1/7....... Average Loss for Epoch: 0.00020048453006893396\n",
      "Epoch 45......Step: 2/7....... Average Loss for Epoch: 0.000379151722881943\n",
      "Epoch 45......Step: 3/7....... Average Loss for Epoch: 0.00036019446755138534\n",
      "Epoch 45......Step: 4/7....... Average Loss for Epoch: 0.00032004483364289626\n",
      "Epoch 45......Step: 5/7....... Average Loss for Epoch: 0.0005116620275657624\n",
      "Epoch 45......Step: 6/7....... Average Loss for Epoch: 0.0005506869395806765\n",
      "Epoch 45......Step: 7/7....... Average Loss for Epoch: 0.0005140149566744055\n",
      "Epoch 45/100 Done, Total Loss: 0.0005140149566744055\n",
      "Total Time Elapsed: 1.359375 seconds\n",
      "Epoch 46......Step: 1/7....... Average Loss for Epoch: 0.0006936393328942358\n",
      "Epoch 46......Step: 2/7....... Average Loss for Epoch: 0.0005102780560264364\n",
      "Epoch 46......Step: 3/7....... Average Loss for Epoch: 0.0007589377249435832\n",
      "Epoch 46......Step: 4/7....... Average Loss for Epoch: 0.0007100278671714477\n",
      "Epoch 46......Step: 5/7....... Average Loss for Epoch: 0.000610876566497609\n",
      "Epoch 46......Step: 6/7....... Average Loss for Epoch: 0.0005568248064567646\n",
      "Epoch 46......Step: 7/7....... Average Loss for Epoch: 0.0006018002667198223\n",
      "Epoch 46/100 Done, Total Loss: 0.0006018002667198223\n",
      "Total Time Elapsed: 1.234375 seconds\n",
      "Epoch 47......Step: 1/7....... Average Loss for Epoch: 0.00020967682939954102\n",
      "Epoch 47......Step: 2/7....... Average Loss for Epoch: 0.00023345713270828128\n",
      "Epoch 47......Step: 3/7....... Average Loss for Epoch: 0.0005550479205946127\n",
      "Epoch 47......Step: 4/7....... Average Loss for Epoch: 0.00046095511424937285\n",
      "Epoch 47......Step: 5/7....... Average Loss for Epoch: 0.0004345234454376623\n",
      "Epoch 47......Step: 6/7....... Average Loss for Epoch: 0.0004584077954253492\n",
      "Epoch 47......Step: 7/7....... Average Loss for Epoch: 0.0004648663764653195\n",
      "Epoch 47/100 Done, Total Loss: 0.0004648663764653195\n",
      "Total Time Elapsed: 1.15625 seconds\n",
      "Epoch 48......Step: 1/7....... Average Loss for Epoch: 0.00020963205315638334\n",
      "Epoch 48......Step: 2/7....... Average Loss for Epoch: 0.0008626829148852266\n",
      "Epoch 48......Step: 3/7....... Average Loss for Epoch: 0.000677734967515183\n",
      "Epoch 48......Step: 4/7....... Average Loss for Epoch: 0.0005855025410710368\n",
      "Epoch 48......Step: 5/7....... Average Loss for Epoch: 0.0005342297110473738\n",
      "Epoch 48......Step: 6/7....... Average Loss for Epoch: 0.0005113640678852486\n",
      "Epoch 48......Step: 7/7....... Average Loss for Epoch: 0.0005443422768231747\n",
      "Epoch 48/100 Done, Total Loss: 0.0005443422768231747\n",
      "Total Time Elapsed: 1.421875 seconds\n",
      "Epoch 49......Step: 1/7....... Average Loss for Epoch: 0.0005126249743625522\n",
      "Epoch 49......Step: 2/7....... Average Loss for Epoch: 0.0005049552710261196\n",
      "Epoch 49......Step: 3/7....... Average Loss for Epoch: 0.00048620371186795336\n",
      "Epoch 49......Step: 4/7....... Average Loss for Epoch: 0.0004794123160536401\n",
      "Epoch 49......Step: 5/7....... Average Loss for Epoch: 0.00043640940566547215\n",
      "Epoch 49......Step: 6/7....... Average Loss for Epoch: 0.0004573002564332758\n",
      "Epoch 49......Step: 7/7....... Average Loss for Epoch: 0.0005750090806811516\n",
      "Epoch 49/100 Done, Total Loss: 0.0005750090806811516\n",
      "Total Time Elapsed: 1.46875 seconds\n",
      "Epoch 50......Step: 1/7....... Average Loss for Epoch: 0.001052956678904593\n",
      "Epoch 50......Step: 2/7....... Average Loss for Epoch: 0.0007663511496502906\n",
      "Epoch 50......Step: 3/7....... Average Loss for Epoch: 0.0006654589184715102\n",
      "Epoch 50......Step: 4/7....... Average Loss for Epoch: 0.0005527556932065636\n",
      "Epoch 50......Step: 5/7....... Average Loss for Epoch: 0.0005424241535365582\n",
      "Epoch 50......Step: 6/7....... Average Loss for Epoch: 0.00048155285670266795\n",
      "Epoch 50......Step: 7/7....... Average Loss for Epoch: 0.000509286652751533\n",
      "Epoch 50/100 Done, Total Loss: 0.000509286652751533\n",
      "Total Time Elapsed: 1.359375 seconds\n",
      "Epoch 51......Step: 1/7....... Average Loss for Epoch: 0.0002317959733773023\n",
      "Epoch 51......Step: 2/7....... Average Loss for Epoch: 0.0002798224886646494\n",
      "Epoch 51......Step: 3/7....... Average Loss for Epoch: 0.0005486684288674345\n",
      "Epoch 51......Step: 4/7....... Average Loss for Epoch: 0.00047837205056566745\n",
      "Epoch 51......Step: 5/7....... Average Loss for Epoch: 0.00045111154322512447\n",
      "Epoch 51......Step: 6/7....... Average Loss for Epoch: 0.0004154093088194107\n",
      "Epoch 51......Step: 7/7....... Average Loss for Epoch: 0.00039471727879052717\n",
      "Epoch 51/100 Done, Total Loss: 0.00039471727879052717\n",
      "Total Time Elapsed: 1.171875 seconds\n",
      "Epoch 52......Step: 1/7....... Average Loss for Epoch: 0.00020626290643122047\n",
      "Epoch 52......Step: 2/7....... Average Loss for Epoch: 0.0002671945476322435\n",
      "Epoch 52......Step: 3/7....... Average Loss for Epoch: 0.00029259632477381575\n",
      "Epoch 52......Step: 4/7....... Average Loss for Epoch: 0.00031376028709928505\n",
      "Epoch 52......Step: 5/7....... Average Loss for Epoch: 0.0005605310056125745\n",
      "Epoch 52......Step: 6/7....... Average Loss for Epoch: 0.0005029116121780438\n",
      "Epoch 52......Step: 7/7....... Average Loss for Epoch: 0.00046801525292851565\n",
      "Epoch 52/100 Done, Total Loss: 0.00046801525292851565\n",
      "Total Time Elapsed: 1.328125 seconds\n",
      "Epoch 53......Step: 1/7....... Average Loss for Epoch: 0.00033282797085121274\n",
      "Epoch 53......Step: 2/7....... Average Loss for Epoch: 0.00033467146568000317\n",
      "Epoch 53......Step: 3/7....... Average Loss for Epoch: 0.0003495196093960355\n",
      "Epoch 53......Step: 4/7....... Average Loss for Epoch: 0.0003768445167224854\n",
      "Epoch 53......Step: 5/7....... Average Loss for Epoch: 0.0005251889117062092\n",
      "Epoch 53......Step: 6/7....... Average Loss for Epoch: 0.0004775149257814822\n",
      "Epoch 53......Step: 7/7....... Average Loss for Epoch: 0.00043719452099009814\n",
      "Epoch 53/100 Done, Total Loss: 0.00043719452099009814\n",
      "Total Time Elapsed: 1.5 seconds\n",
      "Epoch 54......Step: 1/7....... Average Loss for Epoch: 0.0003336950612720102\n",
      "Epoch 54......Step: 2/7....... Average Loss for Epoch: 0.0002713015637709759\n",
      "Epoch 54......Step: 3/7....... Average Loss for Epoch: 0.00024301031468591341\n",
      "Epoch 54......Step: 4/7....... Average Loss for Epoch: 0.00022053074280847795\n",
      "Epoch 54......Step: 5/7....... Average Loss for Epoch: 0.0003062889940338209\n",
      "Epoch 54......Step: 6/7....... Average Loss for Epoch: 0.0004090809986034098\n",
      "Epoch 54......Step: 7/7....... Average Loss for Epoch: 0.00042035065000943305\n",
      "Epoch 54/100 Done, Total Loss: 0.00042035065000943305\n",
      "Total Time Elapsed: 1.140625 seconds\n",
      "Epoch 55......Step: 1/7....... Average Loss for Epoch: 0.0009899042779579759\n",
      "Epoch 55......Step: 2/7....... Average Loss for Epoch: 0.0007179634558269754\n",
      "Epoch 55......Step: 3/7....... Average Loss for Epoch: 0.0005752128587725261\n",
      "Epoch 55......Step: 4/7....... Average Loss for Epoch: 0.0005012402543798089\n",
      "Epoch 55......Step: 5/7....... Average Loss for Epoch: 0.0004891988704912364\n",
      "Epoch 55......Step: 6/7....... Average Loss for Epoch: 0.0004445483209565282\n",
      "Epoch 55......Step: 7/7....... Average Loss for Epoch: 0.00044286291813477874\n",
      "Epoch 55/100 Done, Total Loss: 0.00044286291813477874\n",
      "Total Time Elapsed: 1.21875 seconds\n",
      "Epoch 56......Step: 1/7....... Average Loss for Epoch: 0.0001892779255285859\n",
      "Epoch 56......Step: 2/7....... Average Loss for Epoch: 0.00022648928279522806\n",
      "Epoch 56......Step: 3/7....... Average Loss for Epoch: 0.00038515988853760064\n",
      "Epoch 56......Step: 4/7....... Average Loss for Epoch: 0.0003955530919483863\n",
      "Epoch 56......Step: 5/7....... Average Loss for Epoch: 0.0003950119426008314\n",
      "Epoch 56......Step: 6/7....... Average Loss for Epoch: 0.00049193622059344\n",
      "Epoch 56......Step: 7/7....... Average Loss for Epoch: 0.0005003825353924185\n",
      "Epoch 56/100 Done, Total Loss: 0.0005003825353924185\n",
      "Total Time Elapsed: 1.21875 seconds\n",
      "Epoch 57......Step: 1/7....... Average Loss for Epoch: 0.0002068855392280966\n",
      "Epoch 57......Step: 2/7....... Average Loss for Epoch: 0.0003269139415351674\n",
      "Epoch 57......Step: 3/7....... Average Loss for Epoch: 0.00041158315919650096\n",
      "Epoch 57......Step: 4/7....... Average Loss for Epoch: 0.0004068330963491462\n",
      "Epoch 57......Step: 5/7....... Average Loss for Epoch: 0.0005328888248186559\n",
      "Epoch 57......Step: 6/7....... Average Loss for Epoch: 0.0005199619578585649\n",
      "Epoch 57......Step: 7/7....... Average Loss for Epoch: 0.00048540265249487547\n",
      "Epoch 57/100 Done, Total Loss: 0.00048540265249487547\n",
      "Total Time Elapsed: 1.40625 seconds\n",
      "Epoch 58......Step: 1/7....... Average Loss for Epoch: 0.0008120181737467647\n",
      "Epoch 58......Step: 2/7....... Average Loss for Epoch: 0.0005619864969048649\n",
      "Epoch 58......Step: 3/7....... Average Loss for Epoch: 0.00047279791518424946\n",
      "Epoch 58......Step: 4/7....... Average Loss for Epoch: 0.0004814834683202207\n",
      "Epoch 58......Step: 5/7....... Average Loss for Epoch: 0.00042271012207493184\n",
      "Epoch 58......Step: 6/7....... Average Loss for Epoch: 0.0003871743950488356\n",
      "Epoch 58......Step: 7/7....... Average Loss for Epoch: 0.0003639442896071289\n",
      "Epoch 58/100 Done, Total Loss: 0.0003639442896071289\n",
      "Total Time Elapsed: 1.65625 seconds\n",
      "Epoch 59......Step: 1/7....... Average Loss for Epoch: 0.00014865693810861558\n",
      "Epoch 59......Step: 2/7....... Average Loss for Epoch: 0.00026042019453598186\n",
      "Epoch 59......Step: 3/7....... Average Loss for Epoch: 0.00026285257869555306\n",
      "Epoch 59......Step: 4/7....... Average Loss for Epoch: 0.0003516564720484894\n",
      "Epoch 59......Step: 5/7....... Average Loss for Epoch: 0.0003664262214442715\n",
      "Epoch 59......Step: 6/7....... Average Loss for Epoch: 0.00038962246374770376\n",
      "Epoch 59......Step: 7/7....... Average Loss for Epoch: 0.0004057083507567378\n",
      "Epoch 59/100 Done, Total Loss: 0.0004057083507567378\n",
      "Total Time Elapsed: 1.21875 seconds\n",
      "Epoch 60......Step: 1/7....... Average Loss for Epoch: 0.00126198073849082\n",
      "Epoch 60......Step: 2/7....... Average Loss for Epoch: 0.000724569967132993\n",
      "Epoch 60......Step: 3/7....... Average Loss for Epoch: 0.0005986827794307222\n",
      "Epoch 60......Step: 4/7....... Average Loss for Epoch: 0.0005413641702034511\n",
      "Epoch 60......Step: 5/7....... Average Loss for Epoch: 0.0005237736913841218\n",
      "Epoch 60......Step: 6/7....... Average Loss for Epoch: 0.00047055753141952056\n",
      "Epoch 60......Step: 7/7....... Average Loss for Epoch: 0.0005287439229765109\n",
      "Epoch 60/100 Done, Total Loss: 0.0005287439229765109\n",
      "Total Time Elapsed: 1.28125 seconds\n",
      "Epoch 61......Step: 1/7....... Average Loss for Epoch: 9.274578769691288e-05\n",
      "Epoch 61......Step: 2/7....... Average Loss for Epoch: 0.00021740702504757792\n",
      "Epoch 61......Step: 3/7....... Average Loss for Epoch: 0.0005268370053575685\n",
      "Epoch 61......Step: 4/7....... Average Loss for Epoch: 0.000559340558538679\n",
      "Epoch 61......Step: 5/7....... Average Loss for Epoch: 0.0005209793511312455\n",
      "Epoch 61......Step: 6/7....... Average Loss for Epoch: 0.0004995504374771068\n",
      "Epoch 61......Step: 7/7....... Average Loss for Epoch: 0.0004553785318941144\n",
      "Epoch 61/100 Done, Total Loss: 0.0004553785318941144\n",
      "Total Time Elapsed: 1.34375 seconds\n",
      "Epoch 62......Step: 1/7....... Average Loss for Epoch: 0.00037403625901788473\n",
      "Epoch 62......Step: 2/7....... Average Loss for Epoch: 0.000271508950390853\n",
      "Epoch 62......Step: 3/7....... Average Loss for Epoch: 0.0002810779163458695\n",
      "Epoch 62......Step: 4/7....... Average Loss for Epoch: 0.0002772469670162536\n",
      "Epoch 62......Step: 5/7....... Average Loss for Epoch: 0.00040653618634678423\n",
      "Epoch 62......Step: 6/7....... Average Loss for Epoch: 0.00046061716178276885\n",
      "Epoch 62......Step: 7/7....... Average Loss for Epoch: 0.00045086176500522664\n",
      "Epoch 62/100 Done, Total Loss: 0.00045086176500522664\n",
      "Total Time Elapsed: 1.0 seconds\n",
      "Epoch 63......Step: 1/7....... Average Loss for Epoch: 0.000166884099598974\n",
      "Epoch 63......Step: 2/7....... Average Loss for Epoch: 0.0002441495889797807\n",
      "Epoch 63......Step: 3/7....... Average Loss for Epoch: 0.00023572324425913393\n",
      "Epoch 63......Step: 4/7....... Average Loss for Epoch: 0.00035590596235124394\n",
      "Epoch 63......Step: 5/7....... Average Loss for Epoch: 0.00047660039854235947\n",
      "Epoch 63......Step: 6/7....... Average Loss for Epoch: 0.00043356077124675113\n",
      "Epoch 63......Step: 7/7....... Average Loss for Epoch: 0.00047153686838490624\n",
      "Epoch 63/100 Done, Total Loss: 0.00047153686838490624\n",
      "Total Time Elapsed: 1.140625 seconds\n",
      "Epoch 64......Step: 1/7....... Average Loss for Epoch: 0.0010646418668329716\n",
      "Epoch 64......Step: 2/7....... Average Loss for Epoch: 0.0006003999005770311\n",
      "Epoch 64......Step: 3/7....... Average Loss for Epoch: 0.00046921041697108495\n",
      "Epoch 64......Step: 4/7....... Average Loss for Epoch: 0.0004748017636302393\n",
      "Epoch 64......Step: 5/7....... Average Loss for Epoch: 0.0004441957542439923\n",
      "Epoch 64......Step: 6/7....... Average Loss for Epoch: 0.00047418981315180037\n",
      "Epoch 64......Step: 7/7....... Average Loss for Epoch: 0.0004651821405527049\n",
      "Epoch 64/100 Done, Total Loss: 0.0004651821405527049\n",
      "Total Time Elapsed: 1.03125 seconds\n",
      "Epoch 65......Step: 1/7....... Average Loss for Epoch: 0.0002750246203504503\n",
      "Epoch 65......Step: 2/7....... Average Loss for Epoch: 0.00027289811987429857\n",
      "Epoch 65......Step: 3/7....... Average Loss for Epoch: 0.0002933211896258096\n",
      "Epoch 65......Step: 4/7....... Average Loss for Epoch: 0.0003803576109930873\n",
      "Epoch 65......Step: 5/7....... Average Loss for Epoch: 0.0003887913888320327\n",
      "Epoch 65......Step: 6/7....... Average Loss for Epoch: 0.00036643643397837877\n",
      "Epoch 65......Step: 7/7....... Average Loss for Epoch: 0.0004728843591042927\n",
      "Epoch 65/100 Done, Total Loss: 0.0004728843591042927\n",
      "Total Time Elapsed: 0.984375 seconds\n",
      "Epoch 66......Step: 1/7....... Average Loss for Epoch: 0.0002138930285582319\n",
      "Epoch 66......Step: 2/7....... Average Loss for Epoch: 0.00023249147488968447\n",
      "Epoch 66......Step: 3/7....... Average Loss for Epoch: 0.000354461725995255\n",
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      "Epoch 66......Step: 5/7....... Average Loss for Epoch: 0.0004169713851297274\n",
      "Epoch 66......Step: 6/7....... Average Loss for Epoch: 0.0003872390734613873\n",
      "Epoch 66......Step: 7/7....... Average Loss for Epoch: 0.0004854248815848093\n",
      "Epoch 66/100 Done, Total Loss: 0.0004854248815848093\n",
      "Total Time Elapsed: 1.109375 seconds\n",
      "Epoch 67......Step: 1/7....... Average Loss for Epoch: 0.00029088303563185036\n",
      "Epoch 67......Step: 2/7....... Average Loss for Epoch: 0.00032985504367388785\n",
      "Epoch 67......Step: 3/7....... Average Loss for Epoch: 0.000685986461273084\n",
      "Epoch 67......Step: 4/7....... Average Loss for Epoch: 0.0006388252368196845\n",
      "Epoch 67......Step: 5/7....... Average Loss for Epoch: 0.0005921893229242414\n",
      "Epoch 67......Step: 6/7....... Average Loss for Epoch: 0.0005251381420142328\n",
      "Epoch 67......Step: 7/7....... Average Loss for Epoch: 0.000492441064644871\n",
      "Epoch 67/100 Done, Total Loss: 0.000492441064644871\n",
      "Total Time Elapsed: 1.328125 seconds\n",
      "Epoch 68......Step: 1/7....... Average Loss for Epoch: 0.0002545577590353787\n",
      "Epoch 68......Step: 2/7....... Average Loss for Epoch: 0.000393981346860528\n",
      "Epoch 68......Step: 3/7....... Average Loss for Epoch: 0.000432351875739793\n",
      "Epoch 68......Step: 4/7....... Average Loss for Epoch: 0.0003876353512168862\n",
      "Epoch 68......Step: 5/7....... Average Loss for Epoch: 0.00038022855878807606\n",
      "Epoch 68......Step: 6/7....... Average Loss for Epoch: 0.0003683705241807426\n",
      "Epoch 68......Step: 7/7....... Average Loss for Epoch: 0.00046277730143629014\n",
      "Epoch 68/100 Done, Total Loss: 0.00046277730143629014\n",
      "Total Time Elapsed: 1.4375 seconds\n",
      "Epoch 69......Step: 1/7....... Average Loss for Epoch: 0.00023129898181650788\n",
      "Epoch 69......Step: 2/7....... Average Loss for Epoch: 0.0002555021710577421\n",
      "Epoch 69......Step: 3/7....... Average Loss for Epoch: 0.0003558701282599941\n",
      "Epoch 69......Step: 4/7....... Average Loss for Epoch: 0.00033934838938876055\n",
      "Epoch 69......Step: 5/7....... Average Loss for Epoch: 0.000349622595240362\n",
      "Epoch 69......Step: 6/7....... Average Loss for Epoch: 0.00033955159354566905\n",
      "Epoch 69......Step: 7/7....... Average Loss for Epoch: 0.00031708280896834495\n",
      "Epoch 69/100 Done, Total Loss: 0.00031708280896834495\n",
      "Total Time Elapsed: 1.78125 seconds\n",
      "Epoch 70......Step: 1/7....... Average Loss for Epoch: 0.00026066016289405525\n",
      "Epoch 70......Step: 2/7....... Average Loss for Epoch: 0.00026496557984501123\n",
      "Epoch 70......Step: 3/7....... Average Loss for Epoch: 0.000484092781941096\n",
      "Epoch 70......Step: 4/7....... Average Loss for Epoch: 0.0005564999883063138\n",
      "Epoch 70......Step: 5/7....... Average Loss for Epoch: 0.0004902226908598095\n",
      "Epoch 70......Step: 6/7....... Average Loss for Epoch: 0.0004608298671276619\n",
      "Epoch 70......Step: 7/7....... Average Loss for Epoch: 0.000421966243136142\n",
      "Epoch 70/100 Done, Total Loss: 0.000421966243136142\n",
      "Total Time Elapsed: 1.96875 seconds\n",
      "Epoch 71......Step: 1/7....... Average Loss for Epoch: 0.0011894464259967208\n",
      "Epoch 71......Step: 2/7....... Average Loss for Epoch: 0.0006596659950446337\n",
      "Epoch 71......Step: 3/7....... Average Loss for Epoch: 0.0006418968744886419\n",
      "Epoch 71......Step: 4/7....... Average Loss for Epoch: 0.0006475420668721199\n",
      "Epoch 71......Step: 5/7....... Average Loss for Epoch: 0.0005505916284164414\n",
      "Epoch 71......Step: 6/7....... Average Loss for Epoch: 0.0005204680855968036\n",
      "Epoch 71......Step: 7/7....... Average Loss for Epoch: 0.0004723315089774717\n",
      "Epoch 71/100 Done, Total Loss: 0.0004723315089774717\n",
      "Total Time Elapsed: 1.71875 seconds\n",
      "Epoch 72......Step: 1/7....... Average Loss for Epoch: 0.00023924396373331547\n",
      "Epoch 72......Step: 2/7....... Average Loss for Epoch: 0.00022133834863780066\n",
      "Epoch 72......Step: 3/7....... Average Loss for Epoch: 0.00019813770874558637\n",
      "Epoch 72......Step: 4/7....... Average Loss for Epoch: 0.00018592059132060967\n",
      "Epoch 72......Step: 5/7....... Average Loss for Epoch: 0.00020130329940002412\n",
      "Epoch 72......Step: 6/7....... Average Loss for Epoch: 0.000321189492145398\n",
      "Epoch 72......Step: 7/7....... Average Loss for Epoch: 0.00032910799615949927\n",
      "Epoch 72/100 Done, Total Loss: 0.00032910799615949927\n",
      "Total Time Elapsed: 1.734375 seconds\n",
      "Epoch 73......Step: 1/7....... Average Loss for Epoch: 0.00021241359354462475\n",
      "Epoch 73......Step: 2/7....... Average Loss for Epoch: 0.00022065037774154916\n",
      "Epoch 73......Step: 3/7....... Average Loss for Epoch: 0.0002489763865014538\n",
      "Epoch 73......Step: 4/7....... Average Loss for Epoch: 0.0004086126027686987\n",
      "Epoch 73......Step: 5/7....... Average Loss for Epoch: 0.00036468695325311276\n",
      "Epoch 73......Step: 6/7....... Average Loss for Epoch: 0.0003421315341256559\n",
      "Epoch 73......Step: 7/7....... Average Loss for Epoch: 0.0003426698427314737\n",
      "Epoch 73/100 Done, Total Loss: 0.0003426698427314737\n",
      "Total Time Elapsed: 1.796875 seconds\n",
      "Epoch 74......Step: 1/7....... Average Loss for Epoch: 0.0012321147369220853\n",
      "Epoch 74......Step: 2/7....... Average Loss for Epoch: 0.0007934029126772657\n",
      "Epoch 74......Step: 3/7....... Average Loss for Epoch: 0.0006370299767392377\n",
      "Epoch 74......Step: 4/7....... Average Loss for Epoch: 0.0006239494105102494\n",
      "Epoch 74......Step: 5/7....... Average Loss for Epoch: 0.0005937726236879826\n",
      "Epoch 74......Step: 6/7....... Average Loss for Epoch: 0.0005161556376454731\n",
      "Epoch 74......Step: 7/7....... Average Loss for Epoch: 0.000485914884068604\n",
      "Epoch 74/100 Done, Total Loss: 0.000485914884068604\n",
      "Total Time Elapsed: 1.4375 seconds\n",
      "Epoch 75......Step: 1/7....... Average Loss for Epoch: 0.0002568942145444453\n",
      "Epoch 75......Step: 2/7....... Average Loss for Epoch: 0.00039059892878867686\n",
      "Epoch 75......Step: 3/7....... Average Loss for Epoch: 0.00044918188359588385\n",
      "Epoch 75......Step: 4/7....... Average Loss for Epoch: 0.00041645701276138425\n",
      "Epoch 75......Step: 5/7....... Average Loss for Epoch: 0.000571825122460723\n",
      "Epoch 75......Step: 6/7....... Average Loss for Epoch: 0.0005111071926269991\n",
      "Epoch 75......Step: 7/7....... Average Loss for Epoch: 0.0004531024897005409\n",
      "Epoch 75/100 Done, Total Loss: 0.0004531024897005409\n",
      "Total Time Elapsed: 1.765625 seconds\n",
      "Epoch 76......Step: 1/7....... Average Loss for Epoch: 0.0004130638553760946\n",
      "Epoch 76......Step: 2/7....... Average Loss for Epoch: 0.00030442048591794446\n",
      "Epoch 76......Step: 3/7....... Average Loss for Epoch: 0.00025582386782237637\n",
      "Epoch 76......Step: 4/7....... Average Loss for Epoch: 0.0003260683843109291\n",
      "Epoch 76......Step: 5/7....... Average Loss for Epoch: 0.0005108460405608639\n",
      "Epoch 76......Step: 6/7....... Average Loss for Epoch: 0.0004929920905851759\n",
      "Epoch 76......Step: 7/7....... Average Loss for Epoch: 0.00044611981138587\n",
      "Epoch 76/100 Done, Total Loss: 0.00044611981138587\n",
      "Total Time Elapsed: 1.078125 seconds\n",
      "Epoch 77......Step: 1/7....... Average Loss for Epoch: 0.0002633102412801236\n",
      "Epoch 77......Step: 2/7....... Average Loss for Epoch: 0.0004877622559433803\n",
      "Epoch 77......Step: 3/7....... Average Loss for Epoch: 0.0003993580903625116\n",
      "Epoch 77......Step: 4/7....... Average Loss for Epoch: 0.00034625714033609256\n",
      "Epoch 77......Step: 5/7....... Average Loss for Epoch: 0.00040992700378410517\n",
      "Epoch 77......Step: 6/7....... Average Loss for Epoch: 0.00040824141857835156\n",
      "Epoch 77......Step: 7/7....... Average Loss for Epoch: 0.0003786981459208099\n",
      "Epoch 77/100 Done, Total Loss: 0.0003786981459208099\n",
      "Total Time Elapsed: 1.484375 seconds\n",
      "Epoch 78......Step: 1/7....... Average Loss for Epoch: 0.0007449960103258491\n",
      "Epoch 78......Step: 2/7....... Average Loss for Epoch: 0.0007396373548544943\n",
      "Epoch 78......Step: 3/7....... Average Loss for Epoch: 0.0006890562362968922\n",
      "Epoch 78......Step: 4/7....... Average Loss for Epoch: 0.0006179538904689252\n",
      "Epoch 78......Step: 5/7....... Average Loss for Epoch: 0.000532979698618874\n",
      "Epoch 78......Step: 6/7....... Average Loss for Epoch: 0.0005122392321936786\n",
      "Epoch 78......Step: 7/7....... Average Loss for Epoch: 0.0005969910749367305\n",
      "Epoch 78/100 Done, Total Loss: 0.0005969910749367305\n",
      "Total Time Elapsed: 1.890625 seconds\n",
      "Epoch 79......Step: 1/7....... Average Loss for Epoch: 0.0002653046103660017\n",
      "Epoch 79......Step: 2/7....... Average Loss for Epoch: 0.0002816681517288089\n",
      "Epoch 79......Step: 3/7....... Average Loss for Epoch: 0.0002822481619659811\n",
      "Epoch 79......Step: 4/7....... Average Loss for Epoch: 0.0004700458885054104\n",
      "Epoch 79......Step: 5/7....... Average Loss for Epoch: 0.0005760865693446249\n",
      "Epoch 79......Step: 6/7....... Average Loss for Epoch: 0.0005246446865688389\n",
      "Epoch 79......Step: 7/7....... Average Loss for Epoch: 0.0004749891273344734\n",
      "Epoch 79/100 Done, Total Loss: 0.0004749891273344734\n",
      "Total Time Elapsed: 1.828125 seconds\n",
      "Epoch 80......Step: 1/7....... Average Loss for Epoch: 0.0005129528581164777\n",
      "Epoch 80......Step: 2/7....... Average Loss for Epoch: 0.0004355571436462924\n",
      "Epoch 80......Step: 3/7....... Average Loss for Epoch: 0.0004607798085392763\n",
      "Epoch 80......Step: 4/7....... Average Loss for Epoch: 0.0006085202403482981\n",
      "Epoch 80......Step: 5/7....... Average Loss for Epoch: 0.000549983762903139\n",
      "Epoch 80......Step: 6/7....... Average Loss for Epoch: 0.0007241487570960695\n",
      "Epoch 80......Step: 7/7....... Average Loss for Epoch: 0.0006401006394298747\n",
      "Epoch 80/100 Done, Total Loss: 0.0006401006394298747\n",
      "Total Time Elapsed: 2.734375 seconds\n",
      "Epoch 81......Step: 1/7....... Average Loss for Epoch: 0.00047368285595439374\n",
      "Epoch 81......Step: 2/7....... Average Loss for Epoch: 0.0008600457076681778\n",
      "Epoch 81......Step: 3/7....... Average Loss for Epoch: 0.00062066491227597\n",
      "Epoch 81......Step: 4/7....... Average Loss for Epoch: 0.0005263443417788949\n",
      "Epoch 81......Step: 5/7....... Average Loss for Epoch: 0.00048385098052676766\n",
      "Epoch 81......Step: 6/7....... Average Loss for Epoch: 0.0005344301146881966\n",
      "Epoch 81......Step: 7/7....... Average Loss for Epoch: 0.0005405885063477658\n",
      "Epoch 81/100 Done, Total Loss: 0.0005405885063477658\n",
      "Total Time Elapsed: 2.859375 seconds\n",
      "Epoch 82......Step: 1/7....... Average Loss for Epoch: 0.0013751413207501173\n",
      "Epoch 82......Step: 2/7....... Average Loss for Epoch: 0.000873262615641579\n",
      "Epoch 82......Step: 3/7....... Average Loss for Epoch: 0.0006802494754083455\n",
      "Epoch 82......Step: 4/7....... Average Loss for Epoch: 0.0005514953882084228\n",
      "Epoch 82......Step: 5/7....... Average Loss for Epoch: 0.0005896486982237548\n",
      "Epoch 82......Step: 6/7....... Average Loss for Epoch: 0.0005584179743891582\n",
      "Epoch 82......Step: 7/7....... Average Loss for Epoch: 0.0005150407003904027\n",
      "Epoch 82/100 Done, Total Loss: 0.0005150407003904027\n",
      "Total Time Elapsed: 1.640625 seconds\n",
      "Epoch 83......Step: 1/7....... Average Loss for Epoch: 0.0004521523369476199\n",
      "Epoch 83......Step: 2/7....... Average Loss for Epoch: 0.0008250902174040675\n",
      "Epoch 83......Step: 3/7....... Average Loss for Epoch: 0.0006641500416056564\n",
      "Epoch 83......Step: 4/7....... Average Loss for Epoch: 0.0005728597534471191\n",
      "Epoch 83......Step: 5/7....... Average Loss for Epoch: 0.0005204047891311347\n",
      "Epoch 83......Step: 6/7....... Average Loss for Epoch: 0.0005238881276454777\n",
      "Epoch 83......Step: 7/7....... Average Loss for Epoch: 0.00047982380367882015\n",
      "Epoch 83/100 Done, Total Loss: 0.00047982380367882015\n",
      "Total Time Elapsed: 2.96875 seconds\n",
      "Epoch 84......Step: 1/7....... Average Loss for Epoch: 0.0002661106700543314\n",
      "Epoch 84......Step: 2/7....... Average Loss for Epoch: 0.00028776466206181794\n",
      "Epoch 84......Step: 3/7....... Average Loss for Epoch: 0.0004915315560841312\n",
      "Epoch 84......Step: 4/7....... Average Loss for Epoch: 0.0004710329958470538\n",
      "Epoch 84......Step: 5/7....... Average Loss for Epoch: 0.0005437682033516466\n",
      "Epoch 84......Step: 6/7....... Average Loss for Epoch: 0.0005346580680149297\n",
      "Epoch 84......Step: 7/7....... Average Loss for Epoch: 0.0006301091385207006\n",
      "Epoch 84/100 Done, Total Loss: 0.0006301091385207006\n",
      "Total Time Elapsed: 2.3125 seconds\n",
      "Epoch 85......Step: 1/7....... Average Loss for Epoch: 0.0004397022712510079\n",
      "Epoch 85......Step: 2/7....... Average Loss for Epoch: 0.0003556539595592767\n",
      "Epoch 85......Step: 3/7....... Average Loss for Epoch: 0.000344730302458629\n",
      "Epoch 85......Step: 4/7....... Average Loss for Epoch: 0.00033940025605261326\n",
      "Epoch 85......Step: 5/7....... Average Loss for Epoch: 0.0003076656605117023\n",
      "Epoch 85......Step: 6/7....... Average Loss for Epoch: 0.0003781324388304104\n",
      "Epoch 85......Step: 7/7....... Average Loss for Epoch: 0.00034270071773789823\n",
      "Epoch 85/100 Done, Total Loss: 0.00034270071773789823\n",
      "Total Time Elapsed: 3.09375 seconds\n",
      "Epoch 86......Step: 1/7....... Average Loss for Epoch: 0.00016134159523062408\n",
      "Epoch 86......Step: 2/7....... Average Loss for Epoch: 0.0006060548330424353\n",
      "Epoch 86......Step: 3/7....... Average Loss for Epoch: 0.0005123092172046503\n",
      "Epoch 86......Step: 4/7....... Average Loss for Epoch: 0.0004685407766373828\n",
      "Epoch 86......Step: 5/7....... Average Loss for Epoch: 0.000403748091775924\n",
      "Epoch 86......Step: 6/7....... Average Loss for Epoch: 0.00036500389978755265\n",
      "Epoch 86......Step: 7/7....... Average Loss for Epoch: 0.00039414051570929587\n",
      "Epoch 86/100 Done, Total Loss: 0.00039414051570929587\n",
      "Total Time Elapsed: 2.65625 seconds\n",
      "Epoch 87......Step: 1/7....... Average Loss for Epoch: 0.0009470224031247199\n",
      "Epoch 87......Step: 2/7....... Average Loss for Epoch: 0.0005722147834603675\n",
      "Epoch 87......Step: 3/7....... Average Loss for Epoch: 0.000523286362295039\n",
      "Epoch 87......Step: 4/7....... Average Loss for Epoch: 0.0005395895095716696\n",
      "Epoch 87......Step: 5/7....... Average Loss for Epoch: 0.0005064420605776831\n",
      "Epoch 87......Step: 6/7....... Average Loss for Epoch: 0.00044908180522422\n",
      "Epoch 87......Step: 7/7....... Average Loss for Epoch: 0.0004143301983796326\n",
      "Epoch 87/100 Done, Total Loss: 0.0004143301983796326\n",
      "Total Time Elapsed: 1.609375 seconds\n",
      "Epoch 88......Step: 1/7....... Average Loss for Epoch: 0.0013169500743970275\n",
      "Epoch 88......Step: 2/7....... Average Loss for Epoch: 0.0008394553733523935\n",
      "Epoch 88......Step: 3/7....... Average Loss for Epoch: 0.000637856443063356\n",
      "Epoch 88......Step: 4/7....... Average Loss for Epoch: 0.000614323256741045\n",
      "Epoch 88......Step: 5/7....... Average Loss for Epoch: 0.0005597399809630588\n",
      "Epoch 88......Step: 6/7....... Average Loss for Epoch: 0.0004937337968537273\n",
      "Epoch 88......Step: 7/7....... Average Loss for Epoch: 0.0005092116293131507\n",
      "Epoch 88/100 Done, Total Loss: 0.0005092116293131507\n",
      "Total Time Elapsed: 2.578125 seconds\n",
      "Epoch 89......Step: 1/7....... Average Loss for Epoch: 0.00023629359202459455\n",
      "Epoch 89......Step: 2/7....... Average Loss for Epoch: 0.00031469593523070216\n",
      "Epoch 89......Step: 3/7....... Average Loss for Epoch: 0.0002643879997776821\n",
      "Epoch 89......Step: 4/7....... Average Loss for Epoch: 0.00034216331187053584\n",
      "Epoch 89......Step: 5/7....... Average Loss for Epoch: 0.0003926364210201427\n",
      "Epoch 89......Step: 6/7....... Average Loss for Epoch: 0.00036992291037070874\n",
      "Epoch 89......Step: 7/7....... Average Loss for Epoch: 0.000539410171248684\n",
      "Epoch 89/100 Done, Total Loss: 0.000539410171248684\n",
      "Total Time Elapsed: 2.578125 seconds\n",
      "Epoch 90......Step: 1/7....... Average Loss for Epoch: 0.00018836912931874394\n",
      "Epoch 90......Step: 2/7....... Average Loss for Epoch: 0.00031883438350632787\n",
      "Epoch 90......Step: 3/7....... Average Loss for Epoch: 0.0003308434970676899\n",
      "Epoch 90......Step: 4/7....... Average Loss for Epoch: 0.00029965604335302487\n",
      "Epoch 90......Step: 5/7....... Average Loss for Epoch: 0.0002933974552433938\n",
      "Epoch 90......Step: 6/7....... Average Loss for Epoch: 0.0004160286965391909\n",
      "Epoch 90......Step: 7/7....... Average Loss for Epoch: 0.0005476232410208988\n",
      "Epoch 90/100 Done, Total Loss: 0.0005476232410208988\n",
      "Total Time Elapsed: 2.90625 seconds\n",
      "Epoch 91......Step: 1/7....... Average Loss for Epoch: 0.0013472936116158962\n",
      "Epoch 91......Step: 2/7....... Average Loss for Epoch: 0.0008710690017323941\n",
      "Epoch 91......Step: 3/7....... Average Loss for Epoch: 0.000849832392608126\n",
      "Epoch 91......Step: 4/7....... Average Loss for Epoch: 0.0007803629850968719\n",
      "Epoch 91......Step: 5/7....... Average Loss for Epoch: 0.0006639827450271695\n",
      "Epoch 91......Step: 6/7....... Average Loss for Epoch: 0.0006133195856818929\n",
      "Epoch 91......Step: 7/7....... Average Loss for Epoch: 0.0006945774449767279\n",
      "Epoch 91/100 Done, Total Loss: 0.0006945774449767279\n",
      "Total Time Elapsed: 2.09375 seconds\n",
      "Epoch 92......Step: 1/7....... Average Loss for Epoch: 0.0002361563965678215\n",
      "Epoch 92......Step: 2/7....... Average Loss for Epoch: 0.00022726928000338376\n",
      "Epoch 92......Step: 3/7....... Average Loss for Epoch: 0.0003162429784424603\n",
      "Epoch 92......Step: 4/7....... Average Loss for Epoch: 0.00041087582940235734\n",
      "Epoch 92......Step: 5/7....... Average Loss for Epoch: 0.00046382255386561154\n",
      "Epoch 92......Step: 6/7....... Average Loss for Epoch: 0.0004445627855602652\n",
      "Epoch 92......Step: 7/7....... Average Loss for Epoch: 0.00041252567981635887\n",
      "Epoch 92/100 Done, Total Loss: 0.00041252567981635887\n",
      "Total Time Elapsed: 2.4375 seconds\n",
      "Epoch 93......Step: 1/7....... Average Loss for Epoch: 0.0003635641769506037\n",
      "Epoch 93......Step: 2/7....... Average Loss for Epoch: 0.0010230177140329033\n",
      "Epoch 93......Step: 3/7....... Average Loss for Epoch: 0.0007408739572080473\n",
      "Epoch 93......Step: 4/7....... Average Loss for Epoch: 0.0006541938782902434\n",
      "Epoch 93......Step: 5/7....... Average Loss for Epoch: 0.0006053977645933628\n",
      "Epoch 93......Step: 6/7....... Average Loss for Epoch: 0.0005524091538973153\n",
      "Epoch 93......Step: 7/7....... Average Loss for Epoch: 0.0005230339967446136\n",
      "Epoch 93/100 Done, Total Loss: 0.0005230339967446136\n",
      "Total Time Elapsed: 3.484375 seconds\n",
      "Epoch 94......Step: 1/7....... Average Loss for Epoch: 0.001174768665805459\n",
      "Epoch 94......Step: 2/7....... Average Loss for Epoch: 0.0008263039489975199\n",
      "Epoch 94......Step: 3/7....... Average Loss for Epoch: 0.0007578350972228994\n",
      "Epoch 94......Step: 4/7....... Average Loss for Epoch: 0.0006781907068216242\n",
      "Epoch 94......Step: 5/7....... Average Loss for Epoch: 0.0005843525374075398\n",
      "Epoch 94......Step: 6/7....... Average Loss for Epoch: 0.0005722513087675907\n",
      "Epoch 94......Step: 7/7....... Average Loss for Epoch: 0.0005257238039381004\n",
      "Epoch 94/100 Done, Total Loss: 0.0005257238039381004\n",
      "Total Time Elapsed: 3.90625 seconds\n",
      "Epoch 95......Step: 1/7....... Average Loss for Epoch: 0.000224237228394486\n",
      "Epoch 95......Step: 2/7....... Average Loss for Epoch: 0.00039698446198599413\n",
      "Epoch 95......Step: 3/7....... Average Loss for Epoch: 0.0006778345026153451\n",
      "Epoch 95......Step: 4/7....... Average Loss for Epoch: 0.0005633517321257386\n",
      "Epoch 95......Step: 5/7....... Average Loss for Epoch: 0.00048676871228963136\n",
      "Epoch 95......Step: 6/7....... Average Loss for Epoch: 0.0004985559789929539\n",
      "Epoch 95......Step: 7/7....... Average Loss for Epoch: 0.000525116704271308\n",
      "Epoch 95/100 Done, Total Loss: 0.000525116704271308\n",
      "Total Time Elapsed: 2.515625 seconds\n",
      "Epoch 96......Step: 1/7....... Average Loss for Epoch: 0.0008925882866606116\n",
      "Epoch 96......Step: 2/7....... Average Loss for Epoch: 0.0005789702845504507\n",
      "Epoch 96......Step: 3/7....... Average Loss for Epoch: 0.0004146135058059978\n",
      "Epoch 96......Step: 4/7....... Average Loss for Epoch: 0.0005833065861224895\n",
      "Epoch 96......Step: 5/7....... Average Loss for Epoch: 0.0005195149147766642\n",
      "Epoch 96......Step: 6/7....... Average Loss for Epoch: 0.0005549962067258699\n",
      "Epoch 96......Step: 7/7....... Average Loss for Epoch: 0.0005276205598160491\n",
      "Epoch 96/100 Done, Total Loss: 0.0005276205598160491\n",
      "Total Time Elapsed: 2.484375 seconds\n",
      "Epoch 97......Step: 1/7....... Average Loss for Epoch: 0.0003541511541698128\n",
      "Epoch 97......Step: 2/7....... Average Loss for Epoch: 0.0003025460900971666\n",
      "Epoch 97......Step: 3/7....... Average Loss for Epoch: 0.0006192831885224829\n",
      "Epoch 97......Step: 4/7....... Average Loss for Epoch: 0.000497919631015975\n",
      "Epoch 97......Step: 5/7....... Average Loss for Epoch: 0.0004185844853054732\n",
      "Epoch 97......Step: 6/7....... Average Loss for Epoch: 0.0004037706773184861\n",
      "Epoch 97......Step: 7/7....... Average Loss for Epoch: 0.00044755273847840726\n",
      "Epoch 97/100 Done, Total Loss: 0.00044755273847840726\n",
      "Total Time Elapsed: 2.671875 seconds\n",
      "Epoch 98......Step: 1/7....... Average Loss for Epoch: 0.00016320763097610325\n",
      "Epoch 98......Step: 2/7....... Average Loss for Epoch: 0.00036203988565830514\n",
      "Epoch 98......Step: 3/7....... Average Loss for Epoch: 0.000610249965878514\n",
      "Epoch 98......Step: 4/7....... Average Loss for Epoch: 0.0005123241608089302\n",
      "Epoch 98......Step: 5/7....... Average Loss for Epoch: 0.00043941202748101207\n",
      "Epoch 98......Step: 6/7....... Average Loss for Epoch: 0.0004333329149327862\n",
      "Epoch 98......Step: 7/7....... Average Loss for Epoch: 0.00043732406213946106\n",
      "Epoch 98/100 Done, Total Loss: 0.00043732406213946106\n",
      "Total Time Elapsed: 2.859375 seconds\n",
      "Epoch 99......Step: 1/7....... Average Loss for Epoch: 0.00021907918562646955\n",
      "Epoch 99......Step: 2/7....... Average Loss for Epoch: 0.0006585408045793884\n",
      "Epoch 99......Step: 3/7....... Average Loss for Epoch: 0.0004897119118444001\n",
      "Epoch 99......Step: 4/7....... Average Loss for Epoch: 0.00042978494820999913\n",
      "Epoch 99......Step: 5/7....... Average Loss for Epoch: 0.000413675545132719\n",
      "Epoch 99......Step: 6/7....... Average Loss for Epoch: 0.00038552807382075116\n",
      "Epoch 99......Step: 7/7....... Average Loss for Epoch: 0.00036124169543784646\n",
      "Epoch 99/100 Done, Total Loss: 0.00036124169543784646\n",
      "Total Time Elapsed: 2.109375 seconds\n",
      "Epoch 100......Step: 1/7....... Average Loss for Epoch: 0.00029634227394126356\n",
      "Epoch 100......Step: 2/7....... Average Loss for Epoch: 0.00023882284585852176\n",
      "Epoch 100......Step: 3/7....... Average Loss for Epoch: 0.0005737764004152268\n",
      "Epoch 100......Step: 4/7....... Average Loss for Epoch: 0.00048475589574081823\n",
      "Epoch 100......Step: 5/7....... Average Loss for Epoch: 0.00044883385417051615\n",
      "Epoch 100......Step: 6/7....... Average Loss for Epoch: 0.0005347237214058017\n",
      "Epoch 100......Step: 7/7....... Average Loss for Epoch: 0.00047813339291938713\n",
      "Epoch 100/100 Done, Total Loss: 0.00047813339291938713\n",
      "Total Time Elapsed: 2.421875 seconds\n",
      "Total Training Time: 158.78125 seconds\n",
      "----------------------------------------\n",
      "The LSTM has the layers as: \n",
      "LSTMNet(\n",
      "  (lstm): LSTM(1, 256, num_layers=2, batch_first=True, dropout=0.2)\n",
      "  (fc): Linear(in_features=256, out_features=1, bias=True)\n",
      "  (relu): ReLU()\n",
      ")\n",
      "----------------------------------------\n",
      "Starting Training of LSTM model\n",
      "Epoch 1......Step: 1/7....... Average Loss for Epoch: 0.19924069941043854\n",
      "Epoch 1......Step: 2/7....... Average Loss for Epoch: 0.1865975335240364\n",
      "Epoch 1......Step: 3/7....... Average Loss for Epoch: 0.1748817414045334\n",
      "Epoch 1......Step: 4/7....... Average Loss for Epoch: 0.16186484321951866\n",
      "Epoch 1......Step: 5/7....... Average Loss for Epoch: 0.14454194158315659\n",
      "Epoch 1......Step: 6/7....... Average Loss for Epoch: 0.1295968722552061\n",
      "Epoch 1......Step: 7/7....... Average Loss for Epoch: 0.12575039746505873\n",
      "Epoch 1/100 Done, Total Loss: 0.12575039746505873\n",
      "Total Time Elapsed: 4.046875 seconds\n",
      "Epoch 2......Step: 1/7....... Average Loss for Epoch: 0.061777275055646896\n",
      "Epoch 2......Step: 2/7....... Average Loss for Epoch: 0.0517441239207983\n",
      "Epoch 2......Step: 3/7....... Average Loss for Epoch: 0.05185983826716741\n",
      "Epoch 2......Step: 4/7....... Average Loss for Epoch: 0.05268531944602728\n",
      "Epoch 2......Step: 5/7....... Average Loss for Epoch: 0.051143770664930345\n",
      "Epoch 2......Step: 6/7....... Average Loss for Epoch: 0.05250433956583341\n",
      "Epoch 2......Step: 7/7....... Average Loss for Epoch: 0.05149956792593002\n",
      "Epoch 2/100 Done, Total Loss: 0.05149956792593002\n",
      "Total Time Elapsed: 3.125 seconds\n",
      "Epoch 3......Step: 1/7....... Average Loss for Epoch: 0.03907855227589607\n",
      "Epoch 3......Step: 2/7....... Average Loss for Epoch: 0.03949025645852089\n",
      "Epoch 3......Step: 3/7....... Average Loss for Epoch: 0.040860679000616074\n",
      "Epoch 3......Step: 4/7....... Average Loss for Epoch: 0.04275378677994013\n",
      "Epoch 3......Step: 5/7....... Average Loss for Epoch: 0.04437453001737594\n",
      "Epoch 3......Step: 6/7....... Average Loss for Epoch: 0.042450023194154106\n",
      "Epoch 3......Step: 7/7....... Average Loss for Epoch: 0.04158868906753404\n",
      "Epoch 3/100 Done, Total Loss: 0.04158868906753404\n",
      "Total Time Elapsed: 3.421875 seconds\n",
      "Epoch 4......Step: 1/7....... Average Loss for Epoch: 0.034010566771030426\n",
      "Epoch 4......Step: 2/7....... Average Loss for Epoch: 0.03263741545379162\n",
      "Epoch 4......Step: 3/7....... Average Loss for Epoch: 0.03152550136049589\n",
      "Epoch 4......Step: 4/7....... Average Loss for Epoch: 0.029452672228217125\n",
      "Epoch 4......Step: 5/7....... Average Loss for Epoch: 0.0283568624407053\n",
      "Epoch 4......Step: 6/7....... Average Loss for Epoch: 0.02700833013902108\n",
      "Epoch 4......Step: 7/7....... Average Loss for Epoch: 0.026412918365427425\n",
      "Epoch 4/100 Done, Total Loss: 0.026412918365427425\n",
      "Total Time Elapsed: 3.484375 seconds\n",
      "Epoch 5......Step: 1/7....... Average Loss for Epoch: 0.01335478387773037\n",
      "Epoch 5......Step: 2/7....... Average Loss for Epoch: 0.012549379374831915\n",
      "Epoch 5......Step: 3/7....... Average Loss for Epoch: 0.01316117929915587\n",
      "Epoch 5......Step: 4/7....... Average Loss for Epoch: 0.014608249068260193\n",
      "Epoch 5......Step: 5/7....... Average Loss for Epoch: 0.013744371943175793\n",
      "Epoch 5......Step: 6/7....... Average Loss for Epoch: 0.017281668726354837\n",
      "Epoch 5......Step: 7/7....... Average Loss for Epoch: 0.018065496613936766\n",
      "Epoch 5/100 Done, Total Loss: 0.018065496613936766\n",
      "Total Time Elapsed: 3.484375 seconds\n",
      "Epoch 6......Step: 1/7....... Average Loss for Epoch: 0.008283592760562897\n",
      "Epoch 6......Step: 2/7....... Average Loss for Epoch: 0.0062091180589050055\n",
      "Epoch 6......Step: 3/7....... Average Loss for Epoch: 0.006287442209819953\n",
      "Epoch 6......Step: 4/7....... Average Loss for Epoch: 0.01216371008194983\n",
      "Epoch 6......Step: 5/7....... Average Loss for Epoch: 0.010511834174394608\n",
      "Epoch 6......Step: 6/7....... Average Loss for Epoch: 0.010315327439457178\n",
      "Epoch 6......Step: 7/7....... Average Loss for Epoch: 0.011172802985778876\n",
      "Epoch 6/100 Done, Total Loss: 0.011172802985778876\n",
      "Total Time Elapsed: 3.34375 seconds\n",
      "Epoch 7......Step: 1/7....... Average Loss for Epoch: 0.007765338756144047\n",
      "Epoch 7......Step: 2/7....... Average Loss for Epoch: 0.00940613029524684\n",
      "Epoch 7......Step: 3/7....... Average Loss for Epoch: 0.007598145554463069\n",
      "Epoch 7......Step: 4/7....... Average Loss for Epoch: 0.006280914647504687\n",
      "Epoch 7......Step: 5/7....... Average Loss for Epoch: 0.006791851669549942\n",
      "Epoch 7......Step: 6/7....... Average Loss for Epoch: 0.007062900190552075\n",
      "Epoch 7......Step: 7/7....... Average Loss for Epoch: 0.006831524893641472\n",
      "Epoch 7/100 Done, Total Loss: 0.006831524893641472\n",
      "Total Time Elapsed: 3.3125 seconds\n",
      "Epoch 8......Step: 1/7....... Average Loss for Epoch: 0.005634118337184191\n",
      "Epoch 8......Step: 2/7....... Average Loss for Epoch: 0.004090977949090302\n",
      "Epoch 8......Step: 3/7....... Average Loss for Epoch: 0.0054080291495968895\n",
      "Epoch 8......Step: 4/7....... Average Loss for Epoch: 0.0063959083636291325\n",
      "Epoch 8......Step: 5/7....... Average Loss for Epoch: 0.006123537989333272\n",
      "Epoch 8......Step: 6/7....... Average Loss for Epoch: 0.00541021649648125\n",
      "Epoch 8......Step: 7/7....... Average Loss for Epoch: 0.0049302882814247695\n",
      "Epoch 8/100 Done, Total Loss: 0.0049302882814247695\n",
      "Total Time Elapsed: 3.0625 seconds\n",
      "Epoch 9......Step: 1/7....... Average Loss for Epoch: 0.00863324198871851\n",
      "Epoch 9......Step: 2/7....... Average Loss for Epoch: 0.005646797828376293\n",
      "Epoch 9......Step: 3/7....... Average Loss for Epoch: 0.004091400575513641\n",
      "Epoch 9......Step: 4/7....... Average Loss for Epoch: 0.0033783960971049964\n",
      "Epoch 9......Step: 5/7....... Average Loss for Epoch: 0.0030152911320328714\n",
      "Epoch 9......Step: 6/7....... Average Loss for Epoch: 0.002744434867054224\n",
      "Epoch 9......Step: 7/7....... Average Loss for Epoch: 0.0026678655496133225\n",
      "Epoch 9/100 Done, Total Loss: 0.0026678655496133225\n",
      "Total Time Elapsed: 2.890625 seconds\n",
      "Epoch 10......Step: 1/7....... Average Loss for Epoch: 0.0013242032146081328\n",
      "Epoch 10......Step: 2/7....... Average Loss for Epoch: 0.0009228540875483304\n",
      "Epoch 10......Step: 3/7....... Average Loss for Epoch: 0.0007603359602702161\n",
      "Epoch 10......Step: 4/7....... Average Loss for Epoch: 0.0007536999473813921\n",
      "Epoch 10......Step: 5/7....... Average Loss for Epoch: 0.0009351223940029741\n",
      "Epoch 10......Step: 6/7....... Average Loss for Epoch: 0.0009682379313744605\n",
      "Epoch 10......Step: 7/7....... Average Loss for Epoch: 0.0009226335761403399\n",
      "Epoch 10/100 Done, Total Loss: 0.0009226335761403399\n",
      "Total Time Elapsed: 3.21875 seconds\n",
      "Epoch 11......Step: 1/7....... Average Loss for Epoch: 0.0007317058043554425\n",
      "Epoch 11......Step: 2/7....... Average Loss for Epoch: 0.0010276436805725098\n",
      "Epoch 11......Step: 3/7....... Average Loss for Epoch: 0.0012442615892117221\n",
      "Epoch 11......Step: 4/7....... Average Loss for Epoch: 0.001256932329852134\n",
      "Epoch 11......Step: 5/7....... Average Loss for Epoch: 0.0011138667934574187\n",
      "Epoch 11......Step: 6/7....... Average Loss for Epoch: 0.0011247724469285458\n",
      "Epoch 11......Step: 7/7....... Average Loss for Epoch: 0.001260021111063127\n",
      "Epoch 11/100 Done, Total Loss: 0.001260021111063127\n",
      "Total Time Elapsed: 2.6875 seconds\n",
      "Epoch 12......Step: 1/7....... Average Loss for Epoch: 0.0026480834931135178\n",
      "Epoch 12......Step: 2/7....... Average Loss for Epoch: 0.00180797214852646\n",
      "Epoch 12......Step: 3/7....... Average Loss for Epoch: 0.0013647763310776402\n",
      "Epoch 12......Step: 4/7....... Average Loss for Epoch: 0.0014496106959995814\n",
      "Epoch 12......Step: 5/7....... Average Loss for Epoch: 0.0015503237547818572\n",
      "Epoch 12......Step: 6/7....... Average Loss for Epoch: 0.0014854741990954305\n",
      "Epoch 12......Step: 7/7....... Average Loss for Epoch: 0.0013525575840113951\n",
      "Epoch 12/100 Done, Total Loss: 0.0013525575840113951\n",
      "Total Time Elapsed: 2.625 seconds\n",
      "Epoch 13......Step: 1/7....... Average Loss for Epoch: 0.0009878790006041527\n",
      "Epoch 13......Step: 2/7....... Average Loss for Epoch: 0.0009694687032606453\n",
      "Epoch 13......Step: 3/7....... Average Loss for Epoch: 0.0009019150553892056\n",
      "Epoch 13......Step: 4/7....... Average Loss for Epoch: 0.0011169073986820877\n",
      "Epoch 13......Step: 5/7....... Average Loss for Epoch: 0.0010252900538034737\n",
      "Epoch 13......Step: 6/7....... Average Loss for Epoch: 0.000934921563991035\n",
      "Epoch 13......Step: 7/7....... Average Loss for Epoch: 0.0009288807050324976\n",
      "Epoch 13/100 Done, Total Loss: 0.0009288807050324976\n",
      "Total Time Elapsed: 3.109375 seconds\n",
      "Epoch 14......Step: 1/7....... Average Loss for Epoch: 0.0004992760368622839\n",
      "Epoch 14......Step: 2/7....... Average Loss for Epoch: 0.0005659939779434353\n",
      "Epoch 14......Step: 3/7....... Average Loss for Epoch: 0.0007741793718499442\n",
      "Epoch 14......Step: 4/7....... Average Loss for Epoch: 0.0006805735538364388\n",
      "Epoch 14......Step: 5/7....... Average Loss for Epoch: 0.0006809047481510788\n",
      "Epoch 14......Step: 6/7....... Average Loss for Epoch: 0.0006783862918382511\n",
      "Epoch 14......Step: 7/7....... Average Loss for Epoch: 0.000719111125882981\n",
      "Epoch 14/100 Done, Total Loss: 0.000719111125882981\n",
      "Total Time Elapsed: 3.171875 seconds\n",
      "Epoch 15......Step: 1/7....... Average Loss for Epoch: 0.0005903408164158463\n",
      "Epoch 15......Step: 2/7....... Average Loss for Epoch: 0.0005747819959651679\n",
      "Epoch 15......Step: 3/7....... Average Loss for Epoch: 0.000552787600706021\n",
      "Epoch 15......Step: 4/7....... Average Loss for Epoch: 0.0006775255897082388\n",
      "Epoch 15......Step: 5/7....... Average Loss for Epoch: 0.0007241140119731426\n",
      "Epoch 15......Step: 6/7....... Average Loss for Epoch: 0.0007570771655688683\n",
      "Epoch 15......Step: 7/7....... Average Loss for Epoch: 0.0006969283567741513\n",
      "Epoch 15/100 Done, Total Loss: 0.0006969283567741513\n",
      "Total Time Elapsed: 2.640625 seconds\n",
      "Epoch 16......Step: 1/7....... Average Loss for Epoch: 0.0009323317208327353\n",
      "Epoch 16......Step: 2/7....... Average Loss for Epoch: 0.0009439350978937\n",
      "Epoch 16......Step: 3/7....... Average Loss for Epoch: 0.0008122178648288051\n",
      "Epoch 16......Step: 4/7....... Average Loss for Epoch: 0.0006981310943956487\n",
      "Epoch 16......Step: 5/7....... Average Loss for Epoch: 0.0007586849795188754\n",
      "Epoch 16......Step: 6/7....... Average Loss for Epoch: 0.0007027144844566161\n",
      "Epoch 16......Step: 7/7....... Average Loss for Epoch: 0.0006658266564565045\n",
      "Epoch 16/100 Done, Total Loss: 0.0006658266564565045\n",
      "Total Time Elapsed: 3.1875 seconds\n",
      "Epoch 17......Step: 1/7....... Average Loss for Epoch: 0.0007885693921707571\n",
      "Epoch 17......Step: 2/7....... Average Loss for Epoch: 0.0006267728022066876\n",
      "Epoch 17......Step: 3/7....... Average Loss for Epoch: 0.0005985022386691222\n",
      "Epoch 17......Step: 4/7....... Average Loss for Epoch: 0.0005509013644768856\n",
      "Epoch 17......Step: 5/7....... Average Loss for Epoch: 0.0005136305640917272\n",
      "Epoch 17......Step: 6/7....... Average Loss for Epoch: 0.0005294894460045422\n",
      "Epoch 17......Step: 7/7....... Average Loss for Epoch: 0.0007020081268689994\n",
      "Epoch 17/100 Done, Total Loss: 0.0007020081268689994\n",
      "Total Time Elapsed: 2.59375 seconds\n",
      "Epoch 18......Step: 1/7....... Average Loss for Epoch: 0.00027264771051704884\n",
      "Epoch 18......Step: 2/7....... Average Loss for Epoch: 0.0005808707501273602\n",
      "Epoch 18......Step: 3/7....... Average Loss for Epoch: 0.0007407382169427971\n",
      "Epoch 18......Step: 4/7....... Average Loss for Epoch: 0.0007080078212311491\n",
      "Epoch 18......Step: 5/7....... Average Loss for Epoch: 0.0006847278214991093\n",
      "Epoch 18......Step: 6/7....... Average Loss for Epoch: 0.0006141722357521454\n",
      "Epoch 18......Step: 7/7....... Average Loss for Epoch: 0.0005869951647972422\n",
      "Epoch 18/100 Done, Total Loss: 0.0005869951647972422\n",
      "Total Time Elapsed: 2.859375 seconds\n",
      "Epoch 19......Step: 1/7....... Average Loss for Epoch: 0.0014084640424698591\n",
      "Epoch 19......Step: 2/7....... Average Loss for Epoch: 0.0008952516363933682\n",
      "Epoch 19......Step: 3/7....... Average Loss for Epoch: 0.000732196873286739\n",
      "Epoch 19......Step: 4/7....... Average Loss for Epoch: 0.0006815081505919807\n",
      "Epoch 19......Step: 5/7....... Average Loss for Epoch: 0.0006601330765988678\n",
      "Epoch 19......Step: 6/7....... Average Loss for Epoch: 0.0006323253473965451\n",
      "Epoch 19......Step: 7/7....... Average Loss for Epoch: 0.0007187510117156697\n",
      "Epoch 19/100 Done, Total Loss: 0.0007187510117156697\n",
      "Total Time Elapsed: 2.828125 seconds\n",
      "Epoch 20......Step: 1/7....... Average Loss for Epoch: 0.0005332208820618689\n",
      "Epoch 20......Step: 2/7....... Average Loss for Epoch: 0.0005617901915684342\n",
      "Epoch 20......Step: 3/7....... Average Loss for Epoch: 0.0004804796966103216\n",
      "Epoch 20......Step: 4/7....... Average Loss for Epoch: 0.000496827793540433\n",
      "Epoch 20......Step: 5/7....... Average Loss for Epoch: 0.0005074408371001482\n",
      "Epoch 20......Step: 6/7....... Average Loss for Epoch: 0.0005300641641952097\n",
      "Epoch 20......Step: 7/7....... Average Loss for Epoch: 0.0005587341563243951\n",
      "Epoch 20/100 Done, Total Loss: 0.0005587341563243951\n",
      "Total Time Elapsed: 2.5625 seconds\n",
      "Epoch 21......Step: 1/7....... Average Loss for Epoch: 0.0005021356628276408\n",
      "Epoch 21......Step: 2/7....... Average Loss for Epoch: 0.0006838597182650119\n",
      "Epoch 21......Step: 3/7....... Average Loss for Epoch: 0.0005987714976072311\n",
      "Epoch 21......Step: 4/7....... Average Loss for Epoch: 0.0006764909339835867\n",
      "Epoch 21......Step: 5/7....... Average Loss for Epoch: 0.0006298567983321846\n",
      "Epoch 21......Step: 6/7....... Average Loss for Epoch: 0.000638850925800701\n",
      "Epoch 21......Step: 7/7....... Average Loss for Epoch: 0.0005837683981683638\n",
      "Epoch 21/100 Done, Total Loss: 0.0005837683981683638\n",
      "Total Time Elapsed: 2.953125 seconds\n",
      "Epoch 22......Step: 1/7....... Average Loss for Epoch: 0.001836431329138577\n",
      "Epoch 22......Step: 2/7....... Average Loss for Epoch: 0.0011562994332052767\n",
      "Epoch 22......Step: 3/7....... Average Loss for Epoch: 0.0010093973639110725\n",
      "Epoch 22......Step: 4/7....... Average Loss for Epoch: 0.0008658486185595393\n",
      "Epoch 22......Step: 5/7....... Average Loss for Epoch: 0.0007933883694931865\n",
      "Epoch 22......Step: 6/7....... Average Loss for Epoch: 0.0007598598604090512\n",
      "Epoch 22......Step: 7/7....... Average Loss for Epoch: 0.0007610232236662082\n",
      "Epoch 22/100 Done, Total Loss: 0.0007610232236662082\n",
      "Total Time Elapsed: 3.21875 seconds\n",
      "Epoch 23......Step: 1/7....... Average Loss for Epoch: 0.0013652655761688948\n",
      "Epoch 23......Step: 2/7....... Average Loss for Epoch: 0.0010309789795428514\n",
      "Epoch 23......Step: 3/7....... Average Loss for Epoch: 0.0008134890522342175\n",
      "Epoch 23......Step: 4/7....... Average Loss for Epoch: 0.0007781711246934719\n",
      "Epoch 23......Step: 5/7....... Average Loss for Epoch: 0.0007412215520162136\n",
      "Epoch 23......Step: 6/7....... Average Loss for Epoch: 0.000725235494125324\n",
      "Epoch 23......Step: 7/7....... Average Loss for Epoch: 0.0006412427568907983\n",
      "Epoch 23/100 Done, Total Loss: 0.0006412427568907983\n",
      "Total Time Elapsed: 2.484375 seconds\n",
      "Epoch 24......Step: 1/7....... Average Loss for Epoch: 0.0006060480955056846\n",
      "Epoch 24......Step: 2/7....... Average Loss for Epoch: 0.000552252575289458\n",
      "Epoch 24......Step: 3/7....... Average Loss for Epoch: 0.0005312395320894817\n",
      "Epoch 24......Step: 4/7....... Average Loss for Epoch: 0.0006359177496051416\n",
      "Epoch 24......Step: 5/7....... Average Loss for Epoch: 0.0005843084072694182\n",
      "Epoch 24......Step: 6/7....... Average Loss for Epoch: 0.0005833247269038111\n",
      "Epoch 24......Step: 7/7....... Average Loss for Epoch: 0.0005575903175797846\n",
      "Epoch 24/100 Done, Total Loss: 0.0005575903175797846\n",
      "Total Time Elapsed: 2.875 seconds\n",
      "Epoch 25......Step: 1/7....... Average Loss for Epoch: 0.0005183776956982911\n",
      "Epoch 25......Step: 2/7....... Average Loss for Epoch: 0.0008684505301062018\n",
      "Epoch 25......Step: 3/7....... Average Loss for Epoch: 0.0007127914480709782\n",
      "Epoch 25......Step: 4/7....... Average Loss for Epoch: 0.0007057685215841047\n",
      "Epoch 25......Step: 5/7....... Average Loss for Epoch: 0.0006548926408868283\n",
      "Epoch 25......Step: 6/7....... Average Loss for Epoch: 0.0005851965009545287\n",
      "Epoch 25......Step: 7/7....... Average Loss for Epoch: 0.0007273432732160602\n",
      "Epoch 25/100 Done, Total Loss: 0.0007273432732160602\n",
      "Total Time Elapsed: 3.03125 seconds\n",
      "Epoch 26......Step: 1/7....... Average Loss for Epoch: 0.0008023526752367616\n",
      "Epoch 26......Step: 2/7....... Average Loss for Epoch: 0.0009526177309453487\n",
      "Epoch 26......Step: 3/7....... Average Loss for Epoch: 0.0007827064352265248\n",
      "Epoch 26......Step: 4/7....... Average Loss for Epoch: 0.0007849105822970159\n",
      "Epoch 26......Step: 5/7....... Average Loss for Epoch: 0.0007741486129816622\n",
      "Epoch 26......Step: 6/7....... Average Loss for Epoch: 0.0007046193689651167\n",
      "Epoch 26......Step: 7/7....... Average Loss for Epoch: 0.000651587956651513\n",
      "Epoch 26/100 Done, Total Loss: 0.000651587956651513\n",
      "Total Time Elapsed: 3.265625 seconds\n",
      "Epoch 27......Step: 1/7....... Average Loss for Epoch: 0.0010533053427934647\n",
      "Epoch 27......Step: 2/7....... Average Loss for Epoch: 0.0008132067159749568\n",
      "Epoch 27......Step: 3/7....... Average Loss for Epoch: 0.0009343365284924706\n",
      "Epoch 27......Step: 4/7....... Average Loss for Epoch: 0.0007632138513145037\n",
      "Epoch 27......Step: 5/7....... Average Loss for Epoch: 0.0006998328259214759\n",
      "Epoch 27......Step: 6/7....... Average Loss for Epoch: 0.00067770408350043\n",
      "Epoch 27......Step: 7/7....... Average Loss for Epoch: 0.0006306105850463999\n",
      "Epoch 27/100 Done, Total Loss: 0.0006306105850463999\n",
      "Total Time Elapsed: 3.234375 seconds\n",
      "Epoch 28......Step: 1/7....... Average Loss for Epoch: 0.001131342607550323\n",
      "Epoch 28......Step: 2/7....... Average Loss for Epoch: 0.0007325055194087327\n",
      "Epoch 28......Step: 3/7....... Average Loss for Epoch: 0.0006007691845297813\n",
      "Epoch 28......Step: 4/7....... Average Loss for Epoch: 0.0005500280385604128\n",
      "Epoch 28......Step: 5/7....... Average Loss for Epoch: 0.0005197484861128032\n",
      "Epoch 28......Step: 6/7....... Average Loss for Epoch: 0.0005419214527743558\n",
      "Epoch 28......Step: 7/7....... Average Loss for Epoch: 0.0006148960175258773\n",
      "Epoch 28/100 Done, Total Loss: 0.0006148960175258773\n",
      "Total Time Elapsed: 3.234375 seconds\n",
      "Epoch 29......Step: 1/7....... Average Loss for Epoch: 0.0003491576644591987\n",
      "Epoch 29......Step: 2/7....... Average Loss for Epoch: 0.000896347250090912\n",
      "Epoch 29......Step: 3/7....... Average Loss for Epoch: 0.0008628921350464225\n",
      "Epoch 29......Step: 4/7....... Average Loss for Epoch: 0.0007526699555455707\n",
      "Epoch 29......Step: 5/7....... Average Loss for Epoch: 0.0006545580341480673\n",
      "Epoch 29......Step: 6/7....... Average Loss for Epoch: 0.0008933000402369847\n",
      "Epoch 29......Step: 7/7....... Average Loss for Epoch: 0.0009009367869501668\n",
      "Epoch 29/100 Done, Total Loss: 0.0009009367869501668\n",
      "Total Time Elapsed: 3.09375 seconds\n",
      "Epoch 30......Step: 1/7....... Average Loss for Epoch: 0.0002631968818604946\n",
      "Epoch 30......Step: 2/7....... Average Loss for Epoch: 0.00054550202912651\n",
      "Epoch 30......Step: 3/7....... Average Loss for Epoch: 0.0008078609243966639\n",
      "Epoch 30......Step: 4/7....... Average Loss for Epoch: 0.0007156258361646906\n",
      "Epoch 30......Step: 5/7....... Average Loss for Epoch: 0.0006589932658243924\n",
      "Epoch 30......Step: 6/7....... Average Loss for Epoch: 0.0008930984816591566\n",
      "Epoch 30......Step: 7/7....... Average Loss for Epoch: 0.0009071186692121305\n",
      "Epoch 30/100 Done, Total Loss: 0.0009071186692121305\n",
      "Total Time Elapsed: 3.0625 seconds\n",
      "Epoch 31......Step: 1/7....... Average Loss for Epoch: 0.0007231415365822613\n",
      "Epoch 31......Step: 2/7....... Average Loss for Epoch: 0.0007843484345357865\n",
      "Epoch 31......Step: 3/7....... Average Loss for Epoch: 0.0009796685189940035\n",
      "Epoch 31......Step: 4/7....... Average Loss for Epoch: 0.0009140708862105384\n",
      "Epoch 31......Step: 5/7....... Average Loss for Epoch: 0.0007719702291069553\n",
      "Epoch 31......Step: 6/7....... Average Loss for Epoch: 0.0009197707186103798\n",
      "Epoch 31......Step: 7/7....... Average Loss for Epoch: 0.0009547626645404048\n",
      "Epoch 31/100 Done, Total Loss: 0.0009547626645404048\n",
      "Total Time Elapsed: 2.59375 seconds\n",
      "Epoch 32......Step: 1/7....... Average Loss for Epoch: 0.0015840526903048158\n",
      "Epoch 32......Step: 2/7....... Average Loss for Epoch: 0.0013737838016822934\n",
      "Epoch 32......Step: 3/7....... Average Loss for Epoch: 0.0012857438220332067\n",
      "Epoch 32......Step: 4/7....... Average Loss for Epoch: 0.0013423987547867\n",
      "Epoch 32......Step: 5/7....... Average Loss for Epoch: 0.0011933307047002017\n",
      "Epoch 32......Step: 6/7....... Average Loss for Epoch: 0.001080018487603714\n",
      "Epoch 32......Step: 7/7....... Average Loss for Epoch: 0.0010110755018623812\n",
      "Epoch 32/100 Done, Total Loss: 0.0010110755018623812\n",
      "Total Time Elapsed: 2.546875 seconds\n",
      "Epoch 33......Step: 1/7....... Average Loss for Epoch: 0.0014230066444724798\n",
      "Epoch 33......Step: 2/7....... Average Loss for Epoch: 0.0013631608453579247\n",
      "Epoch 33......Step: 3/7....... Average Loss for Epoch: 0.0009858572642163683\n",
      "Epoch 33......Step: 4/7....... Average Loss for Epoch: 0.0009437106855330057\n",
      "Epoch 33......Step: 5/7....... Average Loss for Epoch: 0.0009492826473433524\n",
      "Epoch 33......Step: 6/7....... Average Loss for Epoch: 0.0008830519655020908\n",
      "Epoch 33......Step: 7/7....... Average Loss for Epoch: 0.0008103033511101135\n",
      "Epoch 33/100 Done, Total Loss: 0.0008103033511101135\n",
      "Total Time Elapsed: 2.8125 seconds\n",
      "Epoch 34......Step: 1/7....... Average Loss for Epoch: 0.0006960222963243723\n",
      "Epoch 34......Step: 2/7....... Average Loss for Epoch: 0.0006686996784992516\n",
      "Epoch 34......Step: 3/7....... Average Loss for Epoch: 0.0008470877461756269\n",
      "Epoch 34......Step: 4/7....... Average Loss for Epoch: 0.0007604676211485639\n",
      "Epoch 34......Step: 5/7....... Average Loss for Epoch: 0.000704701483482495\n",
      "Epoch 34......Step: 6/7....... Average Loss for Epoch: 0.0007224818546092138\n",
      "Epoch 34......Step: 7/7....... Average Loss for Epoch: 0.0006807558695852224\n",
      "Epoch 34/100 Done, Total Loss: 0.0006807558695852224\n",
      "Total Time Elapsed: 2.75 seconds\n",
      "Epoch 35......Step: 1/7....... Average Loss for Epoch: 0.0003299141535535455\n",
      "Epoch 35......Step: 2/7....... Average Loss for Epoch: 0.000802824564743787\n",
      "Epoch 35......Step: 3/7....... Average Loss for Epoch: 0.0006852578371763229\n",
      "Epoch 35......Step: 4/7....... Average Loss for Epoch: 0.0006051180826034397\n",
      "Epoch 35......Step: 5/7....... Average Loss for Epoch: 0.000545175204752013\n",
      "Epoch 35......Step: 6/7....... Average Loss for Epoch: 0.0005062918304853762\n",
      "Epoch 35......Step: 7/7....... Average Loss for Epoch: 0.0004861160247985806\n",
      "Epoch 35/100 Done, Total Loss: 0.0004861160247985806\n",
      "Total Time Elapsed: 2.828125 seconds\n",
      "Epoch 36......Step: 1/7....... Average Loss for Epoch: 0.000567623064853251\n",
      "Epoch 36......Step: 2/7....... Average Loss for Epoch: 0.0003919562586816028\n",
      "Epoch 36......Step: 3/7....... Average Loss for Epoch: 0.00045248205424286425\n",
      "Epoch 36......Step: 4/7....... Average Loss for Epoch: 0.0004517557244980708\n",
      "Epoch 36......Step: 5/7....... Average Loss for Epoch: 0.00040287385054398327\n",
      "Epoch 36......Step: 6/7....... Average Loss for Epoch: 0.00039272704695273813\n",
      "Epoch 36......Step: 7/7....... Average Loss for Epoch: 0.0004819857734089185\n",
      "Epoch 36/100 Done, Total Loss: 0.0004819857734089185\n",
      "Total Time Elapsed: 2.765625 seconds\n",
      "Epoch 37......Step: 1/7....... Average Loss for Epoch: 0.00033795711351558566\n",
      "Epoch 37......Step: 2/7....... Average Loss for Epoch: 0.0005676976288668811\n",
      "Epoch 37......Step: 3/7....... Average Loss for Epoch: 0.0004885048838332295\n",
      "Epoch 37......Step: 4/7....... Average Loss for Epoch: 0.0006181694334372878\n",
      "Epoch 37......Step: 5/7....... Average Loss for Epoch: 0.0005872315028682351\n",
      "Epoch 37......Step: 6/7....... Average Loss for Epoch: 0.0005808072746731341\n",
      "Epoch 37......Step: 7/7....... Average Loss for Epoch: 0.0005693135899491608\n",
      "Epoch 37/100 Done, Total Loss: 0.0005693135899491608\n",
      "Total Time Elapsed: 2.59375 seconds\n",
      "Epoch 38......Step: 1/7....... Average Loss for Epoch: 0.0004180216637905687\n",
      "Epoch 38......Step: 2/7....... Average Loss for Epoch: 0.0007102339150151238\n",
      "Epoch 38......Step: 3/7....... Average Loss for Epoch: 0.0006151514341278622\n",
      "Epoch 38......Step: 4/7....... Average Loss for Epoch: 0.0005559154451475479\n",
      "Epoch 38......Step: 5/7....... Average Loss for Epoch: 0.0006901896384079009\n",
      "Epoch 38......Step: 6/7....... Average Loss for Epoch: 0.0006565145255687336\n",
      "Epoch 38......Step: 7/7....... Average Loss for Epoch: 0.0006286821444518864\n",
      "Epoch 38/100 Done, Total Loss: 0.0006286821444518864\n",
      "Total Time Elapsed: 2.734375 seconds\n",
      "Epoch 39......Step: 1/7....... Average Loss for Epoch: 0.0007968390709720552\n",
      "Epoch 39......Step: 2/7....... Average Loss for Epoch: 0.0005703431961592287\n",
      "Epoch 39......Step: 3/7....... Average Loss for Epoch: 0.0005499745020642877\n",
      "Epoch 39......Step: 4/7....... Average Loss for Epoch: 0.0005556051910389215\n",
      "Epoch 39......Step: 5/7....... Average Loss for Epoch: 0.0005470019532367587\n",
      "Epoch 39......Step: 6/7....... Average Loss for Epoch: 0.0004994629416614771\n",
      "Epoch 39......Step: 7/7....... Average Loss for Epoch: 0.0004716217060506876\n",
      "Epoch 39/100 Done, Total Loss: 0.0004716217060506876\n",
      "Total Time Elapsed: 2.546875 seconds\n",
      "Epoch 40......Step: 1/7....... Average Loss for Epoch: 0.0008405161206610501\n",
      "Epoch 40......Step: 2/7....... Average Loss for Epoch: 0.0005340959905879572\n",
      "Epoch 40......Step: 3/7....... Average Loss for Epoch: 0.0006127296849930038\n",
      "Epoch 40......Step: 4/7....... Average Loss for Epoch: 0.0006101008548284881\n",
      "Epoch 40......Step: 5/7....... Average Loss for Epoch: 0.0007201648957561701\n",
      "Epoch 40......Step: 6/7....... Average Loss for Epoch: 0.0006931447181462621\n",
      "Epoch 40......Step: 7/7....... Average Loss for Epoch: 0.0006284826418517955\n",
      "Epoch 40/100 Done, Total Loss: 0.0006284826418517955\n",
      "Total Time Elapsed: 2.71875 seconds\n",
      "Epoch 41......Step: 1/7....... Average Loss for Epoch: 0.0012332131154835224\n",
      "Epoch 41......Step: 2/7....... Average Loss for Epoch: 0.0009739453962538391\n",
      "Epoch 41......Step: 3/7....... Average Loss for Epoch: 0.0007917499460745603\n",
      "Epoch 41......Step: 4/7....... Average Loss for Epoch: 0.0006937232537893578\n",
      "Epoch 41......Step: 5/7....... Average Loss for Epoch: 0.0006295369355939328\n",
      "Epoch 41......Step: 6/7....... Average Loss for Epoch: 0.0006636145796316365\n",
      "Epoch 41......Step: 7/7....... Average Loss for Epoch: 0.000664305199669408\n",
      "Epoch 41/100 Done, Total Loss: 0.000664305199669408\n",
      "Total Time Elapsed: 3.125 seconds\n",
      "Epoch 42......Step: 1/7....... Average Loss for Epoch: 0.0002785739488899708\n",
      "Epoch 42......Step: 2/7....... Average Loss for Epoch: 0.0005157690902706236\n",
      "Epoch 42......Step: 3/7....... Average Loss for Epoch: 0.0008554997039027512\n",
      "Epoch 42......Step: 4/7....... Average Loss for Epoch: 0.0007315857947105542\n",
      "Epoch 42......Step: 5/7....... Average Loss for Epoch: 0.0006857748841866851\n",
      "Epoch 42......Step: 6/7....... Average Loss for Epoch: 0.0006994578579906374\n",
      "Epoch 42......Step: 7/7....... Average Loss for Epoch: 0.0006754403501482946\n",
      "Epoch 42/100 Done, Total Loss: 0.0006754403501482946\n",
      "Total Time Elapsed: 2.5 seconds\n",
      "Epoch 43......Step: 1/7....... Average Loss for Epoch: 0.00039036880480125546\n",
      "Epoch 43......Step: 2/7....... Average Loss for Epoch: 0.00025887839728966355\n",
      "Epoch 43......Step: 3/7....... Average Loss for Epoch: 0.0006852963318427404\n",
      "Epoch 43......Step: 4/7....... Average Loss for Epoch: 0.0007257356483023614\n",
      "Epoch 43......Step: 5/7....... Average Loss for Epoch: 0.0006842541159130632\n",
      "Epoch 43......Step: 6/7....... Average Loss for Epoch: 0.0006866987775235126\n",
      "Epoch 43......Step: 7/7....... Average Loss for Epoch: 0.0006647708526413355\n",
      "Epoch 43/100 Done, Total Loss: 0.0006647708526413355\n",
      "Total Time Elapsed: 3.234375 seconds\n",
      "Epoch 44......Step: 1/7....... Average Loss for Epoch: 0.0015787896700203419\n",
      "Epoch 44......Step: 2/7....... Average Loss for Epoch: 0.0009389941697008908\n",
      "Epoch 44......Step: 3/7....... Average Loss for Epoch: 0.0008043312700465322\n",
      "Epoch 44......Step: 4/7....... Average Loss for Epoch: 0.0007723713060840964\n",
      "Epoch 44......Step: 5/7....... Average Loss for Epoch: 0.0007074127206578851\n",
      "Epoch 44......Step: 6/7....... Average Loss for Epoch: 0.0006652957915018002\n",
      "Epoch 44......Step: 7/7....... Average Loss for Epoch: 0.000686066150332668\n",
      "Epoch 44/100 Done, Total Loss: 0.000686066150332668\n",
      "Total Time Elapsed: 2.84375 seconds\n",
      "Epoch 45......Step: 1/7....... Average Loss for Epoch: 0.0004631896154023707\n",
      "Epoch 45......Step: 2/7....... Average Loss for Epoch: 0.00040183377859648317\n",
      "Epoch 45......Step: 3/7....... Average Loss for Epoch: 0.0003355857722150783\n",
      "Epoch 45......Step: 4/7....... Average Loss for Epoch: 0.0003160897031193599\n",
      "Epoch 45......Step: 5/7....... Average Loss for Epoch: 0.0004271142301149666\n",
      "Epoch 45......Step: 6/7....... Average Loss for Epoch: 0.0005022661741046855\n",
      "Epoch 45......Step: 7/7....... Average Loss for Epoch: 0.0005962283965865416\n",
      "Epoch 45/100 Done, Total Loss: 0.0005962283965865416\n",
      "Total Time Elapsed: 3.0 seconds\n",
      "Epoch 46......Step: 1/7....... Average Loss for Epoch: 0.0008002286194823682\n",
      "Epoch 46......Step: 2/7....... Average Loss for Epoch: 0.000666630599880591\n",
      "Epoch 46......Step: 3/7....... Average Loss for Epoch: 0.000741168080518643\n",
      "Epoch 46......Step: 4/7....... Average Loss for Epoch: 0.0006101654689700808\n",
      "Epoch 46......Step: 5/7....... Average Loss for Epoch: 0.0006261659989831969\n",
      "Epoch 46......Step: 6/7....... Average Loss for Epoch: 0.0005812180097564124\n",
      "Epoch 46......Step: 7/7....... Average Loss for Epoch: 0.0005639008366935221\n",
      "Epoch 46/100 Done, Total Loss: 0.0005639008366935221\n",
      "Total Time Elapsed: 2.640625 seconds\n",
      "Epoch 47......Step: 1/7....... Average Loss for Epoch: 0.0005695772124454379\n",
      "Epoch 47......Step: 2/7....... Average Loss for Epoch: 0.0007623393030371517\n",
      "Epoch 47......Step: 3/7....... Average Loss for Epoch: 0.0007664287307610115\n",
      "Epoch 47......Step: 4/7....... Average Loss for Epoch: 0.000761541465180926\n",
      "Epoch 47......Step: 5/7....... Average Loss for Epoch: 0.0008691090741194784\n",
      "Epoch 47......Step: 6/7....... Average Loss for Epoch: 0.0007664820538290466\n",
      "Epoch 47......Step: 7/7....... Average Loss for Epoch: 0.0007145245825605733\n",
      "Epoch 47/100 Done, Total Loss: 0.0007145245825605733\n",
      "Total Time Elapsed: 3.1875 seconds\n",
      "Epoch 48......Step: 1/7....... Average Loss for Epoch: 0.00037847767816856503\n",
      "Epoch 48......Step: 2/7....... Average Loss for Epoch: 0.0004887162067461759\n",
      "Epoch 48......Step: 3/7....... Average Loss for Epoch: 0.0005375917923326293\n",
      "Epoch 48......Step: 4/7....... Average Loss for Epoch: 0.0004714409587904811\n",
      "Epoch 48......Step: 5/7....... Average Loss for Epoch: 0.0006528941914439202\n",
      "Epoch 48......Step: 6/7....... Average Loss for Epoch: 0.0005877671840911111\n",
      "Epoch 48......Step: 7/7....... Average Loss for Epoch: 0.0005920259864069521\n",
      "Epoch 48/100 Done, Total Loss: 0.0005920259864069521\n",
      "Total Time Elapsed: 3.15625 seconds\n",
      "Epoch 49......Step: 1/7....... Average Loss for Epoch: 0.0009508670773357153\n",
      "Epoch 49......Step: 2/7....... Average Loss for Epoch: 0.0007028577529126778\n",
      "Epoch 49......Step: 3/7....... Average Loss for Epoch: 0.0006108190088222424\n",
      "Epoch 49......Step: 4/7....... Average Loss for Epoch: 0.0007561843376606703\n",
      "Epoch 49......Step: 5/7....... Average Loss for Epoch: 0.0007143675233237445\n",
      "Epoch 49......Step: 6/7....... Average Loss for Epoch: 0.0006632768199779093\n",
      "Epoch 49......Step: 7/7....... Average Loss for Epoch: 0.0006381377336635653\n",
      "Epoch 49/100 Done, Total Loss: 0.0006381377336635653\n",
      "Total Time Elapsed: 2.921875 seconds\n",
      "Epoch 50......Step: 1/7....... Average Loss for Epoch: 0.00023276083811651915\n",
      "Epoch 50......Step: 2/7....... Average Loss for Epoch: 0.0003328071252326481\n",
      "Epoch 50......Step: 3/7....... Average Loss for Epoch: 0.0005224438840135311\n",
      "Epoch 50......Step: 4/7....... Average Loss for Epoch: 0.00044581292604561895\n",
      "Epoch 50......Step: 5/7....... Average Loss for Epoch: 0.00047081747325137255\n",
      "Epoch 50......Step: 6/7....... Average Loss for Epoch: 0.0006148280517663807\n",
      "Epoch 50......Step: 7/7....... Average Loss for Epoch: 0.0005932277001972709\n",
      "Epoch 50/100 Done, Total Loss: 0.0005932277001972709\n",
      "Total Time Elapsed: 2.953125 seconds\n",
      "Epoch 51......Step: 1/7....... Average Loss for Epoch: 0.0009342054254375398\n",
      "Epoch 51......Step: 2/7....... Average Loss for Epoch: 0.0005540746278711595\n",
      "Epoch 51......Step: 3/7....... Average Loss for Epoch: 0.0007216159186403578\n",
      "Epoch 51......Step: 4/7....... Average Loss for Epoch: 0.0006530912614834961\n",
      "Epoch 51......Step: 5/7....... Average Loss for Epoch: 0.0005771127442130819\n",
      "Epoch 51......Step: 6/7....... Average Loss for Epoch: 0.000546975728260198\n",
      "Epoch 51......Step: 7/7....... Average Loss for Epoch: 0.0005367428633949853\n",
      "Epoch 51/100 Done, Total Loss: 0.0005367428633949853\n",
      "Total Time Elapsed: 2.4375 seconds\n",
      "Epoch 52......Step: 1/7....... Average Loss for Epoch: 0.0003480884770397097\n",
      "Epoch 52......Step: 2/7....... Average Loss for Epoch: 0.0004394536808831617\n",
      "Epoch 52......Step: 3/7....... Average Loss for Epoch: 0.00043918042986964184\n",
      "Epoch 52......Step: 4/7....... Average Loss for Epoch: 0.0003938164154533297\n",
      "Epoch 52......Step: 5/7....... Average Loss for Epoch: 0.0005129168275743723\n",
      "Epoch 52......Step: 6/7....... Average Loss for Epoch: 0.0005074216169305146\n",
      "Epoch 52......Step: 7/7....... Average Loss for Epoch: 0.0005364526961264866\n",
      "Epoch 52/100 Done, Total Loss: 0.0005364526961264866\n",
      "Total Time Elapsed: 2.875 seconds\n",
      "Epoch 53......Step: 1/7....... Average Loss for Epoch: 0.0012650199932977557\n",
      "Epoch 53......Step: 2/7....... Average Loss for Epoch: 0.0009604732622392476\n",
      "Epoch 53......Step: 3/7....... Average Loss for Epoch: 0.0008171656906294326\n",
      "Epoch 53......Step: 4/7....... Average Loss for Epoch: 0.0006984566280152649\n",
      "Epoch 53......Step: 5/7....... Average Loss for Epoch: 0.0007841482292860746\n",
      "Epoch 53......Step: 6/7....... Average Loss for Epoch: 0.000753362556376184\n",
      "Epoch 53......Step: 7/7....... Average Loss for Epoch: 0.0006790768072408225\n",
      "Epoch 53/100 Done, Total Loss: 0.0006790768072408225\n",
      "Total Time Elapsed: 3.09375 seconds\n",
      "Epoch 54......Step: 1/7....... Average Loss for Epoch: 0.0014434588374570012\n",
      "Epoch 54......Step: 2/7....... Average Loss for Epoch: 0.0011872343020513654\n",
      "Epoch 54......Step: 3/7....... Average Loss for Epoch: 0.0011296211741864681\n",
      "Epoch 54......Step: 4/7....... Average Loss for Epoch: 0.000920463447982911\n",
      "Epoch 54......Step: 5/7....... Average Loss for Epoch: 0.0009833826508838684\n",
      "Epoch 54......Step: 6/7....... Average Loss for Epoch: 0.0009834990535940353\n",
      "Epoch 54......Step: 7/7....... Average Loss for Epoch: 0.0009137024628996317\n",
      "Epoch 54/100 Done, Total Loss: 0.0009137024628996317\n",
      "Total Time Elapsed: 3.5625 seconds\n",
      "Epoch 55......Step: 1/7....... Average Loss for Epoch: 0.0007533560856245458\n",
      "Epoch 55......Step: 2/7....... Average Loss for Epoch: 0.0007541950617451221\n",
      "Epoch 55......Step: 3/7....... Average Loss for Epoch: 0.0006041816571572175\n",
      "Epoch 55......Step: 4/7....... Average Loss for Epoch: 0.0007745707334834151\n",
      "Epoch 55......Step: 5/7....... Average Loss for Epoch: 0.0007591310131829232\n",
      "Epoch 55......Step: 6/7....... Average Loss for Epoch: 0.0007223097539584463\n",
      "Epoch 55......Step: 7/7....... Average Loss for Epoch: 0.0006782286509405822\n",
      "Epoch 55/100 Done, Total Loss: 0.0006782286509405822\n",
      "Total Time Elapsed: 2.953125 seconds\n",
      "Epoch 56......Step: 1/7....... Average Loss for Epoch: 0.0008174593676812947\n",
      "Epoch 56......Step: 2/7....... Average Loss for Epoch: 0.0006171706336317584\n",
      "Epoch 56......Step: 3/7....... Average Loss for Epoch: 0.0005348933142765114\n",
      "Epoch 56......Step: 4/7....... Average Loss for Epoch: 0.0005728812320739962\n",
      "Epoch 56......Step: 5/7....... Average Loss for Epoch: 0.0008279024448711425\n",
      "Epoch 56......Step: 6/7....... Average Loss for Epoch: 0.0007353059627348557\n",
      "Epoch 56......Step: 7/7....... Average Loss for Epoch: 0.0006743634044791438\n",
      "Epoch 56/100 Done, Total Loss: 0.0006743634044791438\n",
      "Total Time Elapsed: 2.640625 seconds\n",
      "Epoch 57......Step: 1/7....... Average Loss for Epoch: 0.0010084141977131367\n",
      "Epoch 57......Step: 2/7....... Average Loss for Epoch: 0.0007607015140820295\n",
      "Epoch 57......Step: 3/7....... Average Loss for Epoch: 0.0006297073656848321\n",
      "Epoch 57......Step: 4/7....... Average Loss for Epoch: 0.0006036652557668276\n",
      "Epoch 57......Step: 5/7....... Average Loss for Epoch: 0.00063037762301974\n",
      "Epoch 57......Step: 6/7....... Average Loss for Epoch: 0.000668855951516889\n",
      "Epoch 57......Step: 7/7....... Average Loss for Epoch: 0.0006390706278450255\n",
      "Epoch 57/100 Done, Total Loss: 0.0006390706278450255\n",
      "Total Time Elapsed: 2.75 seconds\n",
      "Epoch 58......Step: 1/7....... Average Loss for Epoch: 0.00042525408207438886\n",
      "Epoch 58......Step: 2/7....... Average Loss for Epoch: 0.0005971173086436465\n",
      "Epoch 58......Step: 3/7....... Average Loss for Epoch: 0.0005385089122379819\n",
      "Epoch 58......Step: 4/7....... Average Loss for Epoch: 0.0005310323758749291\n",
      "Epoch 58......Step: 5/7....... Average Loss for Epoch: 0.0004886566719505935\n",
      "Epoch 58......Step: 6/7....... Average Loss for Epoch: 0.00048774952786819387\n",
      "Epoch 58......Step: 7/7....... Average Loss for Epoch: 0.00045907557692511806\n",
      "Epoch 58/100 Done, Total Loss: 0.00045907557692511806\n",
      "Total Time Elapsed: 2.828125 seconds\n",
      "Epoch 59......Step: 1/7....... Average Loss for Epoch: 0.0004237813118379563\n",
      "Epoch 59......Step: 2/7....... Average Loss for Epoch: 0.0003813157381955534\n",
      "Epoch 59......Step: 3/7....... Average Loss for Epoch: 0.0003786978389446934\n",
      "Epoch 59......Step: 4/7....... Average Loss for Epoch: 0.00038703467726008967\n",
      "Epoch 59......Step: 5/7....... Average Loss for Epoch: 0.0003926582692656666\n",
      "Epoch 59......Step: 6/7....... Average Loss for Epoch: 0.0003792162121195967\n",
      "Epoch 59......Step: 7/7....... Average Loss for Epoch: 0.0004497839545365423\n",
      "Epoch 59/100 Done, Total Loss: 0.0004497839545365423\n",
      "Total Time Elapsed: 3.09375 seconds\n",
      "Epoch 60......Step: 1/7....... Average Loss for Epoch: 0.0013580037048086524\n",
      "Epoch 60......Step: 2/7....... Average Loss for Epoch: 0.0008883826958481222\n",
      "Epoch 60......Step: 3/7....... Average Loss for Epoch: 0.0007114051065097252\n",
      "Epoch 60......Step: 4/7....... Average Loss for Epoch: 0.0006277248394326307\n",
      "Epoch 60......Step: 5/7....... Average Loss for Epoch: 0.000656021962640807\n",
      "Epoch 60......Step: 6/7....... Average Loss for Epoch: 0.0006267586577450857\n",
      "Epoch 60......Step: 7/7....... Average Loss for Epoch: 0.0005963981051796249\n",
      "Epoch 60/100 Done, Total Loss: 0.0005963981051796249\n",
      "Total Time Elapsed: 2.859375 seconds\n",
      "Epoch 61......Step: 1/7....... Average Loss for Epoch: 0.0008171916124410927\n",
      "Epoch 61......Step: 2/7....... Average Loss for Epoch: 0.0005744691152358428\n",
      "Epoch 61......Step: 3/7....... Average Loss for Epoch: 0.0005344643044130256\n",
      "Epoch 61......Step: 4/7....... Average Loss for Epoch: 0.0005035720460000448\n",
      "Epoch 61......Step: 5/7....... Average Loss for Epoch: 0.0005283241916913539\n",
      "Epoch 61......Step: 6/7....... Average Loss for Epoch: 0.000648912241255554\n",
      "Epoch 61......Step: 7/7....... Average Loss for Epoch: 0.0006297691351002348\n",
      "Epoch 61/100 Done, Total Loss: 0.0006297691351002348\n",
      "Total Time Elapsed: 2.953125 seconds\n",
      "Epoch 62......Step: 1/7....... Average Loss for Epoch: 0.000464730488602072\n",
      "Epoch 62......Step: 2/7....... Average Loss for Epoch: 0.00043399100832175463\n",
      "Epoch 62......Step: 3/7....... Average Loss for Epoch: 0.0004320817824918777\n",
      "Epoch 62......Step: 4/7....... Average Loss for Epoch: 0.00044188321771798655\n",
      "Epoch 62......Step: 5/7....... Average Loss for Epoch: 0.0004925372370053083\n",
      "Epoch 62......Step: 6/7....... Average Loss for Epoch: 0.0005690985951029385\n",
      "Epoch 62......Step: 7/7....... Average Loss for Epoch: 0.0005613568646367639\n",
      "Epoch 62/100 Done, Total Loss: 0.0005613568646367639\n",
      "Total Time Elapsed: 3.53125 seconds\n",
      "Epoch 63......Step: 1/7....... Average Loss for Epoch: 0.000424580997787416\n",
      "Epoch 63......Step: 2/7....... Average Loss for Epoch: 0.0004665972082875669\n",
      "Epoch 63......Step: 3/7....... Average Loss for Epoch: 0.0006252267436745266\n",
      "Epoch 63......Step: 4/7....... Average Loss for Epoch: 0.000575983249291312\n",
      "Epoch 63......Step: 5/7....... Average Loss for Epoch: 0.0005710118974093347\n",
      "Epoch 63......Step: 6/7....... Average Loss for Epoch: 0.0006398317103351777\n",
      "Epoch 63......Step: 7/7....... Average Loss for Epoch: 0.0006237667195299375\n",
      "Epoch 63/100 Done, Total Loss: 0.0006237667195299375\n",
      "Total Time Elapsed: 3.03125 seconds\n",
      "Epoch 64......Step: 1/7....... Average Loss for Epoch: 0.0006048116483725607\n",
      "Epoch 64......Step: 2/7....... Average Loss for Epoch: 0.0007027337560430169\n",
      "Epoch 64......Step: 3/7....... Average Loss for Epoch: 0.0006246238287227849\n",
      "Epoch 64......Step: 4/7....... Average Loss for Epoch: 0.000820590285002254\n",
      "Epoch 64......Step: 5/7....... Average Loss for Epoch: 0.0007232774165458977\n",
      "Epoch 64......Step: 6/7....... Average Loss for Epoch: 0.0007164494406121472\n",
      "Epoch 64......Step: 7/7....... Average Loss for Epoch: 0.0006544471834786236\n",
      "Epoch 64/100 Done, Total Loss: 0.0006544471834786236\n",
      "Total Time Elapsed: 2.84375 seconds\n",
      "Epoch 65......Step: 1/7....... Average Loss for Epoch: 0.000556416402105242\n",
      "Epoch 65......Step: 2/7....... Average Loss for Epoch: 0.0007428657845593989\n",
      "Epoch 65......Step: 3/7....... Average Loss for Epoch: 0.0007205239380709827\n",
      "Epoch 65......Step: 4/7....... Average Loss for Epoch: 0.0006353770513669588\n",
      "Epoch 65......Step: 5/7....... Average Loss for Epoch: 0.000580511981388554\n",
      "Epoch 65......Step: 6/7....... Average Loss for Epoch: 0.0007216614661350226\n",
      "Epoch 65......Step: 7/7....... Average Loss for Epoch: 0.0006743303258969847\n",
      "Epoch 65/100 Done, Total Loss: 0.0006743303258969847\n",
      "Total Time Elapsed: 2.875 seconds\n",
      "Epoch 66......Step: 1/7....... Average Loss for Epoch: 0.0007964347023516893\n",
      "Epoch 66......Step: 2/7....... Average Loss for Epoch: 0.0009828914189711213\n",
      "Epoch 66......Step: 3/7....... Average Loss for Epoch: 0.0010368591950585444\n",
      "Epoch 66......Step: 4/7....... Average Loss for Epoch: 0.0009019715653266758\n",
      "Epoch 66......Step: 5/7....... Average Loss for Epoch: 0.0007758574152830988\n",
      "Epoch 66......Step: 6/7....... Average Loss for Epoch: 0.0007164597870238746\n",
      "Epoch 66......Step: 7/7....... Average Loss for Epoch: 0.0006501739221026323\n",
      "Epoch 66/100 Done, Total Loss: 0.0006501739221026323\n",
      "Total Time Elapsed: 2.765625 seconds\n",
      "Epoch 67......Step: 1/7....... Average Loss for Epoch: 0.0014330865815281868\n",
      "Epoch 67......Step: 2/7....... Average Loss for Epoch: 0.0008576901163905859\n",
      "Epoch 67......Step: 3/7....... Average Loss for Epoch: 0.0007631615347539386\n",
      "Epoch 67......Step: 4/7....... Average Loss for Epoch: 0.0006618021652684547\n",
      "Epoch 67......Step: 5/7....... Average Loss for Epoch: 0.0005920927447732538\n",
      "Epoch 67......Step: 6/7....... Average Loss for Epoch: 0.0005625115930646037\n",
      "Epoch 67......Step: 7/7....... Average Loss for Epoch: 0.0005849471739825926\n",
      "Epoch 67/100 Done, Total Loss: 0.0005849471739825926\n",
      "Total Time Elapsed: 3.21875 seconds\n",
      "Epoch 68......Step: 1/7....... Average Loss for Epoch: 0.0012732387986034155\n",
      "Epoch 68......Step: 2/7....... Average Loss for Epoch: 0.0009761656110640615\n",
      "Epoch 68......Step: 3/7....... Average Loss for Epoch: 0.0008546922472305596\n",
      "Epoch 68......Step: 4/7....... Average Loss for Epoch: 0.0007887466199463233\n",
      "Epoch 68......Step: 5/7....... Average Loss for Epoch: 0.000664904309087433\n",
      "Epoch 68......Step: 6/7....... Average Loss for Epoch: 0.0006507033273616495\n",
      "Epoch 68......Step: 7/7....... Average Loss for Epoch: 0.0006170905980980024\n",
      "Epoch 68/100 Done, Total Loss: 0.0006170905980980024\n",
      "Total Time Elapsed: 2.921875 seconds\n",
      "Epoch 69......Step: 1/7....... Average Loss for Epoch: 0.0003162796201650053\n",
      "Epoch 69......Step: 2/7....... Average Loss for Epoch: 0.00039625032513868064\n",
      "Epoch 69......Step: 3/7....... Average Loss for Epoch: 0.0004143019226224472\n",
      "Epoch 69......Step: 4/7....... Average Loss for Epoch: 0.0007471970529877581\n",
      "Epoch 69......Step: 5/7....... Average Loss for Epoch: 0.0006845553813036531\n",
      "Epoch 69......Step: 6/7....... Average Loss for Epoch: 0.0006190497855034968\n",
      "Epoch 69......Step: 7/7....... Average Loss for Epoch: 0.0005869250411966018\n",
      "Epoch 69/100 Done, Total Loss: 0.0005869250411966018\n",
      "Total Time Elapsed: 2.8125 seconds\n",
      "Epoch 70......Step: 1/7....... Average Loss for Epoch: 0.0004670359194278717\n",
      "Epoch 70......Step: 2/7....... Average Loss for Epoch: 0.0006671773444395512\n",
      "Epoch 70......Step: 3/7....... Average Loss for Epoch: 0.0005739394691772759\n",
      "Epoch 70......Step: 4/7....... Average Loss for Epoch: 0.000572448450839147\n",
      "Epoch 70......Step: 5/7....... Average Loss for Epoch: 0.0005032328015659004\n",
      "Epoch 70......Step: 6/7....... Average Loss for Epoch: 0.0005422420363174751\n",
      "Epoch 70......Step: 7/7....... Average Loss for Epoch: 0.0005540342161631477\n",
      "Epoch 70/100 Done, Total Loss: 0.0005540342161631477\n",
      "Total Time Elapsed: 2.9375 seconds\n",
      "Epoch 71......Step: 1/7....... Average Loss for Epoch: 0.0007354642730206251\n",
      "Epoch 71......Step: 2/7....... Average Loss for Epoch: 0.0005757441686000675\n",
      "Epoch 71......Step: 3/7....... Average Loss for Epoch: 0.0008320905229387184\n",
      "Epoch 71......Step: 4/7....... Average Loss for Epoch: 0.0007491275755455717\n",
      "Epoch 71......Step: 5/7....... Average Loss for Epoch: 0.0006487739447038621\n",
      "Epoch 71......Step: 6/7....... Average Loss for Epoch: 0.0005802992382086813\n",
      "Epoch 71......Step: 7/7....... Average Loss for Epoch: 0.0005821467244199344\n",
      "Epoch 71/100 Done, Total Loss: 0.0005821467244199344\n",
      "Total Time Elapsed: 3.390625 seconds\n",
      "Epoch 72......Step: 1/7....... Average Loss for Epoch: 0.0006112589035183191\n",
      "Epoch 72......Step: 2/7....... Average Loss for Epoch: 0.0009521641186438501\n",
      "Epoch 72......Step: 3/7....... Average Loss for Epoch: 0.00086012709653005\n",
      "Epoch 72......Step: 4/7....... Average Loss for Epoch: 0.0007573032198706642\n",
      "Epoch 72......Step: 5/7....... Average Loss for Epoch: 0.0006667766079772264\n",
      "Epoch 72......Step: 6/7....... Average Loss for Epoch: 0.0007079496232715125\n",
      "Epoch 72......Step: 7/7....... Average Loss for Epoch: 0.0006488885389574404\n",
      "Epoch 72/100 Done, Total Loss: 0.0006488885389574404\n",
      "Total Time Elapsed: 3.8125 seconds\n",
      "Epoch 73......Step: 1/7....... Average Loss for Epoch: 0.00034293701173737645\n",
      "Epoch 73......Step: 2/7....... Average Loss for Epoch: 0.0003277744835941121\n",
      "Epoch 73......Step: 3/7....... Average Loss for Epoch: 0.00030081185589854914\n",
      "Epoch 73......Step: 4/7....... Average Loss for Epoch: 0.000531547746504657\n",
      "Epoch 73......Step: 5/7....... Average Loss for Epoch: 0.0004882110806647688\n",
      "Epoch 73......Step: 6/7....... Average Loss for Epoch: 0.0004994104820070788\n",
      "Epoch 73......Step: 7/7....... Average Loss for Epoch: 0.000490158235022266\n",
      "Epoch 73/100 Done, Total Loss: 0.000490158235022266\n",
      "Total Time Elapsed: 3.421875 seconds\n",
      "Epoch 74......Step: 1/7....... Average Loss for Epoch: 0.0003077391884289682\n",
      "Epoch 74......Step: 2/7....... Average Loss for Epoch: 0.00047094482579268515\n",
      "Epoch 74......Step: 3/7....... Average Loss for Epoch: 0.0007192401487069825\n",
      "Epoch 74......Step: 4/7....... Average Loss for Epoch: 0.0007584882550872862\n",
      "Epoch 74......Step: 5/7....... Average Loss for Epoch: 0.0007211307995021344\n",
      "Epoch 74......Step: 6/7....... Average Loss for Epoch: 0.0007194412173703313\n",
      "Epoch 74......Step: 7/7....... Average Loss for Epoch: 0.0006785522106968399\n",
      "Epoch 74/100 Done, Total Loss: 0.0006785522106968399\n",
      "Total Time Elapsed: 3.3125 seconds\n",
      "Epoch 75......Step: 1/7....... Average Loss for Epoch: 0.001253009308129549\n",
      "Epoch 75......Step: 2/7....... Average Loss for Epoch: 0.0008440609381068498\n",
      "Epoch 75......Step: 3/7....... Average Loss for Epoch: 0.0007974450321247181\n",
      "Epoch 75......Step: 4/7....... Average Loss for Epoch: 0.0006456708797486499\n",
      "Epoch 75......Step: 5/7....... Average Loss for Epoch: 0.0006671923911198974\n",
      "Epoch 75......Step: 6/7....... Average Loss for Epoch: 0.0006304289175507923\n",
      "Epoch 75......Step: 7/7....... Average Loss for Epoch: 0.0006173314343738769\n",
      "Epoch 75/100 Done, Total Loss: 0.0006173314343738769\n",
      "Total Time Elapsed: 3.0 seconds\n",
      "Epoch 76......Step: 1/7....... Average Loss for Epoch: 0.00037423885078169405\n",
      "Epoch 76......Step: 2/7....... Average Loss for Epoch: 0.0003377827088115737\n",
      "Epoch 76......Step: 3/7....... Average Loss for Epoch: 0.000570380623685196\n",
      "Epoch 76......Step: 4/7....... Average Loss for Epoch: 0.0005159556967555545\n",
      "Epoch 76......Step: 5/7....... Average Loss for Epoch: 0.0005531733680982142\n",
      "Epoch 76......Step: 6/7....... Average Loss for Epoch: 0.0005675689947869008\n",
      "Epoch 76......Step: 7/7....... Average Loss for Epoch: 0.0006424010041103299\n",
      "Epoch 76/100 Done, Total Loss: 0.0006424010041103299\n",
      "Total Time Elapsed: 2.78125 seconds\n",
      "Epoch 77......Step: 1/7....... Average Loss for Epoch: 0.000347134075127542\n",
      "Epoch 77......Step: 2/7....... Average Loss for Epoch: 0.0005990924255456775\n",
      "Epoch 77......Step: 3/7....... Average Loss for Epoch: 0.0005117676531275114\n",
      "Epoch 77......Step: 4/7....... Average Loss for Epoch: 0.0007015438168309629\n",
      "Epoch 77......Step: 5/7....... Average Loss for Epoch: 0.0006734810769557953\n",
      "Epoch 77......Step: 6/7....... Average Loss for Epoch: 0.0006344553257804364\n",
      "Epoch 77......Step: 7/7....... Average Loss for Epoch: 0.0006251094081172985\n",
      "Epoch 77/100 Done, Total Loss: 0.0006251094081172985\n",
      "Total Time Elapsed: 2.71875 seconds\n",
      "Epoch 78......Step: 1/7....... Average Loss for Epoch: 0.0003835922689177096\n",
      "Epoch 78......Step: 2/7....... Average Loss for Epoch: 0.0006651575095020235\n",
      "Epoch 78......Step: 3/7....... Average Loss for Epoch: 0.0005656342934040973\n",
      "Epoch 78......Step: 4/7....... Average Loss for Epoch: 0.0005758566039730795\n",
      "Epoch 78......Step: 5/7....... Average Loss for Epoch: 0.0005799018254037946\n",
      "Epoch 78......Step: 6/7....... Average Loss for Epoch: 0.0005793635258063053\n",
      "Epoch 78......Step: 7/7....... Average Loss for Epoch: 0.0006901436011373464\n",
      "Epoch 78/100 Done, Total Loss: 0.0006901436011373464\n",
      "Total Time Elapsed: 2.609375 seconds\n",
      "Epoch 79......Step: 1/7....... Average Loss for Epoch: 0.00037200539372861385\n",
      "Epoch 79......Step: 2/7....... Average Loss for Epoch: 0.000710751221049577\n",
      "Epoch 79......Step: 3/7....... Average Loss for Epoch: 0.000658079341519624\n",
      "Epoch 79......Step: 4/7....... Average Loss for Epoch: 0.0006033157114870846\n",
      "Epoch 79......Step: 5/7....... Average Loss for Epoch: 0.0006102751241996885\n",
      "Epoch 79......Step: 6/7....... Average Loss for Epoch: 0.0006897979571173588\n",
      "Epoch 79......Step: 7/7....... Average Loss for Epoch: 0.0006172407426804836\n",
      "Epoch 79/100 Done, Total Loss: 0.0006172407426804836\n",
      "Total Time Elapsed: 2.90625 seconds\n",
      "Epoch 80......Step: 1/7....... Average Loss for Epoch: 0.00026909669395536184\n",
      "Epoch 80......Step: 2/7....... Average Loss for Epoch: 0.0004837427113670856\n",
      "Epoch 80......Step: 3/7....... Average Loss for Epoch: 0.00044225606446464855\n",
      "Epoch 80......Step: 4/7....... Average Loss for Epoch: 0.0006114062562119216\n",
      "Epoch 80......Step: 5/7....... Average Loss for Epoch: 0.0005347139551304281\n",
      "Epoch 80......Step: 6/7....... Average Loss for Epoch: 0.000509037941810675\n",
      "Epoch 80......Step: 7/7....... Average Loss for Epoch: 0.0005432309740821698\n",
      "Epoch 80/100 Done, Total Loss: 0.0005432309740821698\n",
      "Total Time Elapsed: 2.46875 seconds\n",
      "Epoch 81......Step: 1/7....... Average Loss for Epoch: 0.0005470075411722064\n",
      "Epoch 81......Step: 2/7....... Average Loss for Epoch: 0.00041467032860964537\n",
      "Epoch 81......Step: 3/7....... Average Loss for Epoch: 0.0004344814709232499\n",
      "Epoch 81......Step: 4/7....... Average Loss for Epoch: 0.0006016252518747933\n",
      "Epoch 81......Step: 5/7....... Average Loss for Epoch: 0.0005668211146257818\n",
      "Epoch 81......Step: 6/7....... Average Loss for Epoch: 0.0006009294108177224\n",
      "Epoch 81......Step: 7/7....... Average Loss for Epoch: 0.0005401800922949665\n",
      "Epoch 81/100 Done, Total Loss: 0.0005401800922949665\n",
      "Total Time Elapsed: 2.546875 seconds\n",
      "Epoch 82......Step: 1/7....... Average Loss for Epoch: 0.0015115668065845966\n",
      "Epoch 82......Step: 2/7....... Average Loss for Epoch: 0.0009102850308408961\n",
      "Epoch 82......Step: 3/7....... Average Loss for Epoch: 0.0007194304974594464\n",
      "Epoch 82......Step: 4/7....... Average Loss for Epoch: 0.000638285324384924\n",
      "Epoch 82......Step: 5/7....... Average Loss for Epoch: 0.000572868890594691\n",
      "Epoch 82......Step: 6/7....... Average Loss for Epoch: 0.0005225861726406341\n",
      "Epoch 82......Step: 7/7....... Average Loss for Epoch: 0.0004923987144138664\n",
      "Epoch 82/100 Done, Total Loss: 0.0004923987144138664\n",
      "Total Time Elapsed: 2.625 seconds\n",
      "Epoch 83......Step: 1/7....... Average Loss for Epoch: 0.00026317028095945716\n",
      "Epoch 83......Step: 2/7....... Average Loss for Epoch: 0.0006970079557504505\n",
      "Epoch 83......Step: 3/7....... Average Loss for Epoch: 0.000654033695658048\n",
      "Epoch 83......Step: 4/7....... Average Loss for Epoch: 0.0005642784017254598\n",
      "Epoch 83......Step: 5/7....... Average Loss for Epoch: 0.0004961201775586233\n",
      "Epoch 83......Step: 6/7....... Average Loss for Epoch: 0.0004927479628046664\n",
      "Epoch 83......Step: 7/7....... Average Loss for Epoch: 0.0005510109996456387\n",
      "Epoch 83/100 Done, Total Loss: 0.0005510109996456387\n",
      "Total Time Elapsed: 2.640625 seconds\n",
      "Epoch 84......Step: 1/7....... Average Loss for Epoch: 0.0005693446728400886\n",
      "Epoch 84......Step: 2/7....... Average Loss for Epoch: 0.0006267867574933916\n",
      "Epoch 84......Step: 3/7....... Average Loss for Epoch: 0.0005990527764273187\n",
      "Epoch 84......Step: 4/7....... Average Loss for Epoch: 0.0007704964227741584\n",
      "Epoch 84......Step: 5/7....... Average Loss for Epoch: 0.0006599185318918899\n",
      "Epoch 84......Step: 6/7....... Average Loss for Epoch: 0.0006214704505206706\n",
      "Epoch 84......Step: 7/7....... Average Loss for Epoch: 0.0005711582468523245\n",
      "Epoch 84/100 Done, Total Loss: 0.0005711582468523245\n",
      "Total Time Elapsed: 2.875 seconds\n",
      "Epoch 85......Step: 1/7....... Average Loss for Epoch: 0.0011104193981736898\n",
      "Epoch 85......Step: 2/7....... Average Loss for Epoch: 0.001018168346490711\n",
      "Epoch 85......Step: 3/7....... Average Loss for Epoch: 0.0007653802555675308\n",
      "Epoch 85......Step: 4/7....... Average Loss for Epoch: 0.0006733089903718792\n",
      "Epoch 85......Step: 5/7....... Average Loss for Epoch: 0.0006677922268863767\n",
      "Epoch 85......Step: 6/7....... Average Loss for Epoch: 0.0006011195849472036\n",
      "Epoch 85......Step: 7/7....... Average Loss for Epoch: 0.000591265844247703\n",
      "Epoch 85/100 Done, Total Loss: 0.000591265844247703\n",
      "Total Time Elapsed: 3.15625 seconds\n",
      "Epoch 86......Step: 1/7....... Average Loss for Epoch: 0.00034348416374996305\n",
      "Epoch 86......Step: 2/7....... Average Loss for Epoch: 0.00032962542900349945\n",
      "Epoch 86......Step: 3/7....... Average Loss for Epoch: 0.00039281832869164646\n",
      "Epoch 86......Step: 4/7....... Average Loss for Epoch: 0.00041281836456619203\n",
      "Epoch 86......Step: 5/7....... Average Loss for Epoch: 0.0005749273812398314\n",
      "Epoch 86......Step: 6/7....... Average Loss for Epoch: 0.0005373071714226777\n",
      "Epoch 86......Step: 7/7....... Average Loss for Epoch: 0.000552404418288331\n",
      "Epoch 86/100 Done, Total Loss: 0.000552404418288331\n",
      "Total Time Elapsed: 2.71875 seconds\n",
      "Epoch 87......Step: 1/7....... Average Loss for Epoch: 0.0010764086619019508\n",
      "Epoch 87......Step: 2/7....... Average Loss for Epoch: 0.0008183711033780128\n",
      "Epoch 87......Step: 3/7....... Average Loss for Epoch: 0.0006426442802573243\n",
      "Epoch 87......Step: 4/7....... Average Loss for Epoch: 0.0005390354090195615\n",
      "Epoch 87......Step: 5/7....... Average Loss for Epoch: 0.0005756981467129662\n",
      "Epoch 87......Step: 6/7....... Average Loss for Epoch: 0.0005803025584706726\n",
      "Epoch 87......Step: 7/7....... Average Loss for Epoch: 0.0005350159252494839\n",
      "Epoch 87/100 Done, Total Loss: 0.0005350159252494839\n",
      "Total Time Elapsed: 2.859375 seconds\n",
      "Epoch 88......Step: 1/7....... Average Loss for Epoch: 0.0005320216296240687\n",
      "Epoch 88......Step: 2/7....... Average Loss for Epoch: 0.0004096927441423759\n",
      "Epoch 88......Step: 3/7....... Average Loss for Epoch: 0.00038998713716864586\n",
      "Epoch 88......Step: 4/7....... Average Loss for Epoch: 0.00038071165181463584\n",
      "Epoch 88......Step: 5/7....... Average Loss for Epoch: 0.00048294646549038587\n",
      "Epoch 88......Step: 6/7....... Average Loss for Epoch: 0.0005976409253586704\n",
      "Epoch 88......Step: 7/7....... Average Loss for Epoch: 0.0005811144988651254\n",
      "Epoch 88/100 Done, Total Loss: 0.0005811144988651254\n",
      "Total Time Elapsed: 2.875 seconds\n",
      "Epoch 89......Step: 1/7....... Average Loss for Epoch: 0.0005535007221624255\n",
      "Epoch 89......Step: 2/7....... Average Loss for Epoch: 0.0005541393766179681\n",
      "Epoch 89......Step: 3/7....... Average Loss for Epoch: 0.0005678742503126463\n",
      "Epoch 89......Step: 4/7....... Average Loss for Epoch: 0.0005352736625354737\n",
      "Epoch 89......Step: 5/7....... Average Loss for Epoch: 0.0005242318671662361\n",
      "Epoch 89......Step: 6/7....... Average Loss for Epoch: 0.0006033971294527873\n",
      "Epoch 89......Step: 7/7....... Average Loss for Epoch: 0.0006552216322493873\n",
      "Epoch 89/100 Done, Total Loss: 0.0006552216322493873\n",
      "Total Time Elapsed: 2.59375 seconds\n",
      "Epoch 90......Step: 1/7....... Average Loss for Epoch: 0.00016801916353870183\n",
      "Epoch 90......Step: 2/7....... Average Loss for Epoch: 0.0004033384393551387\n",
      "Epoch 90......Step: 3/7....... Average Loss for Epoch: 0.0004516040450350071\n",
      "Epoch 90......Step: 4/7....... Average Loss for Epoch: 0.0006265830488700885\n",
      "Epoch 90......Step: 5/7....... Average Loss for Epoch: 0.00067654772719834\n",
      "Epoch 90......Step: 6/7....... Average Loss for Epoch: 0.0006495556372101419\n",
      "Epoch 90......Step: 7/7....... Average Loss for Epoch: 0.0006238656954207857\n",
      "Epoch 90/100 Done, Total Loss: 0.0006238656954207857\n",
      "Total Time Elapsed: 3.03125 seconds\n",
      "Epoch 91......Step: 1/7....... Average Loss for Epoch: 0.0006525365170091391\n",
      "Epoch 91......Step: 2/7....... Average Loss for Epoch: 0.0009552636183798313\n",
      "Epoch 91......Step: 3/7....... Average Loss for Epoch: 0.0007106054205602655\n",
      "Epoch 91......Step: 4/7....... Average Loss for Epoch: 0.0007106777266017161\n",
      "Epoch 91......Step: 5/7....... Average Loss for Epoch: 0.000703128898749128\n",
      "Epoch 91......Step: 6/7....... Average Loss for Epoch: 0.0006552316917805001\n",
      "Epoch 91......Step: 7/7....... Average Loss for Epoch: 0.0006275741491533284\n",
      "Epoch 91/100 Done, Total Loss: 0.0006275741491533284\n",
      "Total Time Elapsed: 2.90625 seconds\n",
      "Epoch 92......Step: 1/7....... Average Loss for Epoch: 0.0004095371987204999\n",
      "Epoch 92......Step: 2/7....... Average Loss for Epoch: 0.00029566901503130794\n",
      "Epoch 92......Step: 3/7....... Average Loss for Epoch: 0.0007798781928916773\n",
      "Epoch 92......Step: 4/7....... Average Loss for Epoch: 0.0006877748382976279\n",
      "Epoch 92......Step: 5/7....... Average Loss for Epoch: 0.0006322554778307677\n",
      "Epoch 92......Step: 6/7....... Average Loss for Epoch: 0.0006420878150189916\n",
      "Epoch 92......Step: 7/7....... Average Loss for Epoch: 0.0005973289787237133\n",
      "Epoch 92/100 Done, Total Loss: 0.0005973289787237133\n",
      "Total Time Elapsed: 3.453125 seconds\n",
      "Epoch 93......Step: 1/7....... Average Loss for Epoch: 0.0012382136192172766\n",
      "Epoch 93......Step: 2/7....... Average Loss for Epoch: 0.0010033444268628955\n",
      "Epoch 93......Step: 3/7....... Average Loss for Epoch: 0.0008181988766106466\n",
      "Epoch 93......Step: 4/7....... Average Loss for Epoch: 0.000857186532812193\n",
      "Epoch 93......Step: 5/7....... Average Loss for Epoch: 0.0008155253948643803\n",
      "Epoch 93......Step: 6/7....... Average Loss for Epoch: 0.0007484499753142396\n",
      "Epoch 93......Step: 7/7....... Average Loss for Epoch: 0.0007112429177920733\n",
      "Epoch 93/100 Done, Total Loss: 0.0007112429177920733\n",
      "Total Time Elapsed: 2.71875 seconds\n",
      "Epoch 94......Step: 1/7....... Average Loss for Epoch: 0.0006124397041276097\n",
      "Epoch 94......Step: 2/7....... Average Loss for Epoch: 0.000950793968513608\n",
      "Epoch 94......Step: 3/7....... Average Loss for Epoch: 0.0007005679023374493\n",
      "Epoch 94......Step: 4/7....... Average Loss for Epoch: 0.0006397195720637683\n",
      "Epoch 94......Step: 5/7....... Average Loss for Epoch: 0.0006081964675104246\n",
      "Epoch 94......Step: 6/7....... Average Loss for Epoch: 0.000631924648284136\n",
      "Epoch 94......Step: 7/7....... Average Loss for Epoch: 0.0005679079830380422\n",
      "Epoch 94/100 Done, Total Loss: 0.0005679079830380422\n",
      "Total Time Elapsed: 2.78125 seconds\n",
      "Epoch 95......Step: 1/7....... Average Loss for Epoch: 0.0009326155995950103\n",
      "Epoch 95......Step: 2/7....... Average Loss for Epoch: 0.0009733500774018466\n",
      "Epoch 95......Step: 3/7....... Average Loss for Epoch: 0.0007996887531286726\n",
      "Epoch 95......Step: 4/7....... Average Loss for Epoch: 0.0006870800352771766\n",
      "Epoch 95......Step: 5/7....... Average Loss for Epoch: 0.0007298811164218932\n",
      "Epoch 95......Step: 6/7....... Average Loss for Epoch: 0.000659787270706147\n",
      "Epoch 95......Step: 7/7....... Average Loss for Epoch: 0.0006340853204684598\n",
      "Epoch 95/100 Done, Total Loss: 0.0006340853204684598\n",
      "Total Time Elapsed: 3.078125 seconds\n",
      "Epoch 96......Step: 1/7....... Average Loss for Epoch: 0.00027998635778203607\n",
      "Epoch 96......Step: 2/7....... Average Loss for Epoch: 0.000313595897750929\n",
      "Epoch 96......Step: 3/7....... Average Loss for Epoch: 0.00032317083484182757\n",
      "Epoch 96......Step: 4/7....... Average Loss for Epoch: 0.0003297566290711984\n",
      "Epoch 96......Step: 5/7....... Average Loss for Epoch: 0.00041521923849359156\n",
      "Epoch 96......Step: 6/7....... Average Loss for Epoch: 0.0005436984453505526\n",
      "Epoch 96......Step: 7/7....... Average Loss for Epoch: 0.000578184487364654\n",
      "Epoch 96/100 Done, Total Loss: 0.000578184487364654\n",
      "Total Time Elapsed: 3.1875 seconds\n",
      "Epoch 97......Step: 1/7....... Average Loss for Epoch: 0.0009075123234651983\n",
      "Epoch 97......Step: 2/7....... Average Loss for Epoch: 0.0006390674680005759\n",
      "Epoch 97......Step: 3/7....... Average Loss for Epoch: 0.000600382840881745\n",
      "Epoch 97......Step: 4/7....... Average Loss for Epoch: 0.0005896878865314648\n",
      "Epoch 97......Step: 5/7....... Average Loss for Epoch: 0.0005389158555772156\n",
      "Epoch 97......Step: 6/7....... Average Loss for Epoch: 0.0005481650732690468\n",
      "Epoch 97......Step: 7/7....... Average Loss for Epoch: 0.0005244774802122265\n",
      "Epoch 97/100 Done, Total Loss: 0.0005244774802122265\n",
      "Total Time Elapsed: 3.0 seconds\n",
      "Epoch 98......Step: 1/7....... Average Loss for Epoch: 0.00041771651012822986\n",
      "Epoch 98......Step: 2/7....... Average Loss for Epoch: 0.00047867209650576115\n",
      "Epoch 98......Step: 3/7....... Average Loss for Epoch: 0.0005117467371746898\n",
      "Epoch 98......Step: 4/7....... Average Loss for Epoch: 0.0004393016643007286\n",
      "Epoch 98......Step: 5/7....... Average Loss for Epoch: 0.0005284724000375717\n",
      "Epoch 98......Step: 6/7....... Average Loss for Epoch: 0.0005326742684701458\n",
      "Epoch 98......Step: 7/7....... Average Loss for Epoch: 0.0006169326827927892\n",
      "Epoch 98/100 Done, Total Loss: 0.0006169326827927892\n",
      "Total Time Elapsed: 3.125 seconds\n",
      "Epoch 99......Step: 1/7....... Average Loss for Epoch: 0.0003862034063786268\n",
      "Epoch 99......Step: 2/7....... Average Loss for Epoch: 0.0004621578846126795\n",
      "Epoch 99......Step: 3/7....... Average Loss for Epoch: 0.0004409106234864642\n",
      "Epoch 99......Step: 4/7....... Average Loss for Epoch: 0.0003974330975324847\n",
      "Epoch 99......Step: 5/7....... Average Loss for Epoch: 0.0003887006314471364\n",
      "Epoch 99......Step: 6/7....... Average Loss for Epoch: 0.00039176388236228377\n",
      "Epoch 99......Step: 7/7....... Average Loss for Epoch: 0.0003871492476069501\n",
      "Epoch 99/100 Done, Total Loss: 0.0003871492476069501\n",
      "Total Time Elapsed: 3.1875 seconds\n",
      "Epoch 100......Step: 1/7....... Average Loss for Epoch: 0.00030526844784617424\n",
      "Epoch 100......Step: 2/7....... Average Loss for Epoch: 0.0005359743372537196\n",
      "Epoch 100......Step: 3/7....... Average Loss for Epoch: 0.00043744960566982627\n",
      "Epoch 100......Step: 4/7....... Average Loss for Epoch: 0.000613272757618688\n",
      "Epoch 100......Step: 5/7....... Average Loss for Epoch: 0.0005860530945938081\n",
      "Epoch 100......Step: 6/7....... Average Loss for Epoch: 0.0005579555969840536\n",
      "Epoch 100......Step: 7/7....... Average Loss for Epoch: 0.0005076810235290655\n",
      "Epoch 100/100 Done, Total Loss: 0.0005076810235290655\n",
      "Total Time Elapsed: 3.171875 seconds\n",
      "Total Training Time: 296.5 seconds\n"
     ]
    }
   ],
   "source": [
    "lr = 0.001\n",
    "gru_model = train(train_loader, lr, model_type=\"GRU\")\n",
    "lstm_model = train(train_loader, lr, model_type=\"LSTM\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "torch.save(gru_model.state_dict(), \"./saved_models/gru_model.pt\")\n",
    "torch.save(lstm_model.state_dict(), \"./saved_models/lstm_model.pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GRU Evaluation for Cell 5\n",
      "Evaluation Time: 0.1875\n",
      "MAE: 0.0020897331557587517\n",
      "RMSE: 0.0023788424710169185\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GRU Evaluation for Cell 6\n",
      "Evaluation Time: 0.25\n",
      "MAE: 0.002183713274023181\n",
      "RMSE: 0.004789298380933013\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GRU Evaluation for Cell 7\n",
      "Evaluation Time: 0.1875\n",
      "MAE: 0.0022697773775133467\n",
      "RMSE: 0.002628050642474416\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GRU Evaluation for Cell 8\n",
      "Evaluation Time: 0.1875\n",
      "MAE: 0.002460641013376912\n",
      "RMSE: 0.0028006312043587977\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(0, 4): #plot both graphs\n",
    "    print(\"GRU Evaluation for Cell {}\".format(i + 5))\n",
    "    gru_outputs, targets, gru_MAE, gru_RMSE = evaluate(gru_model, test_x[i], test_y[i], label_sc)\n",
    "    plt.figure(figsize=(14, 10))\n",
    "    plt.subplot(2, 2, 1)\n",
    "    plt.plot(gru_outputs[0][-100:], \"-o\", color=\"g\", label=\"Predicted\")\n",
    "    plt.plot(targets[0][-100:], color=\"b\", label=\"Actual\")\n",
    "    plt.xlabel('Cycle')\n",
    "    plt.ylabel('SOH')\n",
    "    plt.title(\"Cell {}\".format(i + 5))\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "# MAE and RMSE\n",
    "# predicted curve\n",
    "# performance, numerical results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LSTM Evaluation for Cell 5\n",
      "Evaluation Time: 0.171875\n",
      "MAE: 0.00496651159941313\n",
      "RMSE: 0.0050388560470849145\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LSTM Evaluation for Cell 6\n",
      "Evaluation Time: 0.203125\n",
      "MAE: 0.004287902803103164\n",
      "RMSE: 0.00597403010175092\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LSTM Evaluation for Cell 7\n",
      "Evaluation Time: 0.28125\n",
      "MAE: 0.0020694446310632253\n",
      "RMSE: 0.0024049669618627953\n"
     ]
    },
    {
     "data": {
      "image/png": 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++muO7jly5EhWr17N8uXL2bx5M2fOnLGs4FmQ2DIzzWg2QvXN8FwQ+P2CKak8bw5sTv/X9qJqMREREds5tNA6ODiYpk2b8umnnwLpm8QFBAQwbNgwRo8enaF95cqVeeONNxg6dKjlWNeuXXF3d2fBggU23fPKlSt4e3uzaNEiunXrBsChQ4eoX78+MTExPPjggzbFbo9C66xEHIxgeORwq+1AnHDChMm6YWpJWDMTfusHQM+eJr780olSOVskVUSk2FChddFRqAutU1JS2LVrl2W/FAAnJydCQ0OJiYnJ9Jrk5OQMD+ru7s7WrVttvueuXbtITU21alOvXj2qVq2a5efe+uzExESrV37553TPqe2nZkyIAErchC79ocPLYEhj8WInWrSAP//Mt1BFREQKLYclRefPn8doNOLj42N13MfHh7i4uEyvad++PR999BFHjhzBZDKxYcMGIiIiOHv2rM33jIuLw9XV1bI5nS2fC+m7+t5a9bNs2bKWlTfzy+0z03xK+2Td0AA8+An0a0spr2vs2QONH0hl9RoVYIuIiGTH4YXWOTFt2jRq165NvXr1cHV15aWXXmLAgAE45cMOqWPGjOHKlSuW18mTJ+3+mVnJdlbaLYE/cn1AfaiynatXSvDEEwa6Dz2IKZMOJhEREXFgUlSxYkWcnZ0zzPqKj4/H19c302u8vb1ZtWoVSUlJ/PXXXxw6dIgyZcpQo0YNm+/p6+tLSkpKhqXLs/tcSN+oztPT0+rlKLbMSgOg7CkY8DA0nQ5mJ5Z9Vp8mreO4eDF/4hQRESlMHJYUubq6EhQURFRUlOWYyWQiKiqKkJCQbK8tWbIkVapUIS0tja+//prOnTvbfM+goCBKlChh1ebw4cOcOHHijp9bUNgyK83CJQUeewme7AMu1/l1iy9BQWZum7AnIiKSZwwGA6tWrXJ0GLni0OGzUaNG8cUXXzBv3jwOHjzICy+8QFJSEgMGDACgb9++jBkzxtJ+x44dRERE8Oeff7JlyxY6dOiAyWTi1VdftfmeZcuWZeDAgYwaNYpNmzaxa9cuBgwYQEhIiM0zzwqCrPZLy1KjBTAoBModIzbWQEgIzJyJpu2LiBRiMTExODs789hjj+XousDAQMLDw+0TVCHm4sgP7969OwkJCYwfP564uDgaN25MZGSkpVD6xIkTVvVCN2/eZOzYsfz555+UKVOGRx99lPnz51sVTd/pngBTp07FycmJrl27kpycTPv27fnss8/y7bnzSlj9MDrX7WzZL+1AwgHe2fJO1hf47oXnmnB/zH5+/bEKL7wA338PX34J5cvnX9wiIpI3Zs2axbBhw5g1axZnzpyhcuXKjg6pUNOGsLmUn+sU2So6Npo289rcsd2H/5rK7pUPsXRaY9JSnfD3hwUL4OGH8yFIEZEC5J9r25jNcP26Y2IpVYocbex97do1/Pz8+OWXX5gwYQINGzbk9ddft5xfvXo1b731Fvv27aNMmTK0atWKlStX0rp1azZv3mx1L7PZzJtvvsmqVavYs2eP5Xh4eDjh4eHExsYC8PPPP/P666/z66+/kpqaSuPGjZk6dSoPPPCA5RqDwcDKlSvp0qVLbr4MuVao1ymSvGdLAbaTwYlXNoxkYZkg0gY0wcX7GKdOQZs2MG4cpKXlY8AiIgXM9etQpoxjXjlNxpYtW0a9evWoW7cuvXv3Zvbs2dzq5/juu+948sknefTRR/n111+JioqiWbNmAERERODv789bb73F2bNnLcva2OLq1av069ePrVu3sn37dmrXrs2jjz7K1atXcxZ8AeXQ4TPJW7cKsLst64YBA2YydgKazLfNya/8K2mDGsO6jzHvGcA770BUFCxaBIGB+Ra2iIjkwqxZs+jduzcAHTp04MqVK2zevJnWrVszadIkevTowcSJEy3tGzVqBED58uVxdnbGw8Mj21nXmXnkkUes3v/3v//Fy8uLzZs38/jjj9/lEzmeeoqKmKwKsJ0Nzplf4HYNQ5eBVOgzFE9PMzEx0KgRLF2aD8GKiBQwpUrBtWuOeeVkS6bDhw+zc+dOevbsCYCLiwvdu3dn1qxZAOzZs4e2bdvm+dcnPj6ewYMHU7t2bcqWLYunpyfXrl3jxIkTef5ZjqCeoiLonwXY8UnxjFw/Msv2ZsxcqPkZi9b04ZPXHiQmBnr0gPXr4eOP07t1RUSKA4MBSpd2dBR3NmvWLNLS0qwKq81mM25ubnz66ae4u7vn+J5OTk78s8w4NTXV6n2/fv24cOEC06ZNo1q1ari5uRESEkJKSkruHqSAUU9REWXztiC3MZU9xttfbabLc/sxGMzMmQNBQbB7t52DFRERm6WlpfHVV1/x4YcfsmfPHsvrt99+o3LlyixevJiGDRtarcf3T66urhiN1ts/eXt7ExcXZ5UY3V50DfDTTz/x8ssv8+ijj3Lvvffi5ubG+fPn8/T5HEk9RcWATduCACPXjyThegJUBvo9hPOqxfzxR2UefBDefRdGjIB82FFFRESysWbNGi5dusTAgQMpW7as1bmuXbsya9YsPvjgA9q2bUvNmjXp0aMHaWlprF27ltdeew1IX6foxx9/pEePHri5uVGxYkVat25NQkIC77//Pt26dSMyMpJ169ZZzdaqXbs28+fPp0mTJiQmJvKf//wnV71SBZV+xRUDtm4LknA94e83gT9ifK4B1IsgNRVeeQUefxwSE+0crIiIZGvWrFmEhoZmSIggPSn65ZdfKF++PMuXL+fbb7+lcePGPPLII+zcudPS7q233iI2NpaaNWvi7e0NQP369fnss8+YPn06jRo1YufOnfz73//O8NmXLl3igQceoE+fPrz88stUqlTJvg+cj7ROUS4VxHWKshNxMIJuy7oBZDorLUtmA+V+H82NNZO4edPAAw/A2rXgY9uInIhIgZbd2jZSuGidIrFZVrPSvEt5Z3+hwcyl+6YQvmwX3t7p9UUtWsCff9oxWBEREQdQTVEx8s9ZaX4efpxOPE3vlb3veO2p0t8wevZZ/u/FUI4dc6d5c4iMhMaN7R+3iIhIflBSVMzcmpV2S3RstE3XWfZUe9qXEos3En/mXh5+GL75Blq3zvZSERGRQkHDZ8WcrUXYFh5xpPZtAdWiSUyE9u3h66/tG6OIiEh+UFJUzN3aGgSwPTEqeQV6d8S9wVpSUuCpp+Dzz+0YpIiInWnOUeGXF99DJUWSZRF2tkrc5MaTnXi85xnMZhgyBCZOBP1cEZHCxNk5fQukorIic3F2/X876pYoUSLX91BNkQAZi7APJBz4u44oK04mGg2cgavn00R83oA334Rz59K3BnHOYqs1EZGCxMXFhVKlSpGQkECJEiVw0gq1hY7ZbOb69eucO3cOLy8vS6KbG1qnKJcK2zpFORUdG02beW1sv2Dni7DuEzA70a0bLFgAbm72i09EJK+kpKRw/PhxTCaTo0ORu+Dl5YWvry8GQ8ZSEFt/Z6unSDJ1qwD7dOJp2xZ7bPYZlE6AiPmsWOHGhQuwahUUwXxRRIoYV1dXateurSG0QqxEiRJ31UN0i5IiydStAuxuy7phwGBbYnTvcnC/gGHpN2zaVIaHH4Z168DX1/7xiojcDScnJ61oLSq0lqzlqgC7xg+Y+z1EuQop7NkDzZvDH3/YLUQREZE8o5qiXCrqNUW3M5qMOSvABv4vaBUfDW3HmRPueJZL5bvVTrRsoeprERHJf9r7TPLMrVWwezboSdsabW265r2DgznzdCBU/pnESyVo1TqV0Z/G2DdQERGRu6CkSHLE1hWwE64nQJlz0L811P4O0kry3svNeG7Cr/kTqIiISA4pKZIcyfEK2K7XoUdneOALMDvzxVv3M3qMSYs8iohIgaOkSHIsqwJs71LemV/gbIROz0Hr8QC8964T/fpBaqq9IxUREbGdCq1zqTgVWmfl9gJsPw8/TieepvfK3tlf9Gt/DKu/xGxyJuSR83z/bTnKlFYBtoiI2I8WbxS7u1WAfUt0bPSdL7p/LubS52DZCmJ+qEiFhtv5cvE5+jR7wm5xioiI2ELDZ5JnbC3Cps5a6NMO3K6Q8ueD9O3sz5yt3+VPkCIiIllQUiR5JkdF2NW2ps9MK3UO4h7gubB6HI812j9IERGRLCgpkjyVo1Ww/fbAsy2h7F+kJdQkuHkqhw7ZPUQREZFMOTwpmj59OoGBgZQsWZLg4GB27tyZbfvw8HDq1q2Lu7s7AQEBjBw5kps3b1rOBwYGYjAYMryGDh1qadO6desM54cMGWK3ZyxuwuqHETs8lk39NrEobBFjW43NunHFI/BsC6h4kISzJWnW/CYzVu3CaFKvkYiI5C+HJkVLly5l1KhRTJgwgd27d9OoUSPat2/PuXPnMm2/aNEiRo8ezYQJEzh48CCzZs1i6dKlvP7665Y2P//8M2fPnrW8NmzYAMBTTz1lda/BgwdbtXv//fft96DFUI5WwS57Gga0gso/c/VSSV58qj4+g58j4mBE/gQrIiKCg5Oijz76iMGDBzNgwADuueceZs6cSalSpZg9e3am7bdt20aLFi3o1asXgYGBtGvXjp49e1r1Lnl7e+Pr62t5rVmzhpo1a/Lwww9b3atUqVJW7YrrtPr8YFMBdukL0O8RqLUO0kpxYfYXdH1lkxIjERHJNw5LilJSUti1axehoaF/B+PkRGhoKDExme+R1bx5c3bt2mVJgv7880/Wrl3Lo48+muVnLFiwgGeffRaDwfoX8sKFC6lYsSL33XcfY8aM4fr169nGm5ycTGJiotVLbGNzAbbbNejZCR74L+AE6z6h/4sJpKZpKE1EROzPYUnR+fPnMRqN+Pj4WB338fEhLi4u02t69erFW2+9RcuWLSlRogQ1a9akdevWVsNnt1u1ahWXL1+mf//+Ge6zYMECNm3axJgxY5g/fz69e2e/6OCUKVMoW7as5RUQEGD7w4rtBdjORuj0PLQdA8DV6Of51xMXuHEjH4IUEZFizWErWp85c4YqVaqwbds2QkJCLMdfffVVNm/ezI4dOzJcEx0dTY8ePXjnnXcIDg7m6NGjDB8+nMGDBzNu3LgM7du3b4+rqyurV6/ONpYffviBtm3bcvToUWrWrJlpm+TkZJKTky3vExMTCQgIKNYrWufG7atgH0g4wDtb3sm68d6e8M0cMLrRvDl88w1UrJh/sYqISNFQ4Fe0rlixIs7OzsTHx1sdj4+Px9fXN9Nrxo0bR58+fRg0aBAADRo0ICkpieeee4433ngDJ6e/O77++usvNm7cSETEnWtSgoODAbJNitzc3HBzc7Pp2SRrt6+CHR0bnX1S1HAxeJ6mTMRGtm0rwX0PXOX9OQd5pk0Qzk7aGkRERPKWw4bPXF1dCQoKIioqynLMZDIRFRVl1XN0u+vXr1slPgDOzum/HP/Z4TVnzhwqVarEY489dsdY9uzZA4Cfn19OHkHuki0F2G41d1BqyL/A6zjxJz3o91gdfIf2VgG2iIjkOYfOPhs1ahRffPEF8+bN4+DBg7zwwgskJSUxYMAAAPr27cuYMWMs7Tt16sSMGTNYsmQJx48fZ8OGDYwbN45OnTpZkiNIT67mzJlDv379cHGx7gw7duwYb7/9Nrt27SI2NpZvv/2Wvn378tBDD9GwYcP8eXABbCvATjYmc67UZhjcDAK2QrIX5z+fT9f/bFBiJCIiecqhG8J2796dhIQExo8fT1xcHI0bNyYyMtJSfH3ixAmrnqGxY8diMBgYO3Ysp0+fxtvbm06dOjFp0iSr+27cuJETJ07w7LPPZvhMV1dXNm7cSHh4OElJSQQEBNC1a1fGjs1mgUGxm1sF2MMjh3Mq8ZTluL+HP1eSr3A15Wr6gdLnoV9b+PZL2NsHvptBv8TZPLbRiJurhtJEROTuOazQurCztWhLbHN7Abafhx9Gk5HQ+aEZG5qBLa/DD+mJcHDrC3z/TQX0LRARkazY+jvb4dt8iID1CtitA1tzLinzVc0xAA9Nhqe6gct1dkRX4L6gyyzbFqOtQURE5K4oKZICyc/jDkXv934NAx6CMmc4edSL7h2qUvnVjqozEhGRXFNSJAWSTVuDVNkFgx4E7/1wtQrnPllB18mfKzESEZFcUVIkBZLNW4N4nYRnW0LgJkjxhEVrGPTWTxpKExGRHFNSJAWWzVuDuF+B3h2gwQIwleDSkg8ZOOokmkIgIiI5odlnuaTZZ/nH5q1BzMAP78CWNwB4qPOfjPvgJG1qttQK2CIixZhmn0mRcfvMtLY12mbd0AC0HQuPPweGNH78pgb/ejSJqu/XV52RiIjckZIiKVRsKsBu8gX07Awu1+Hoo5yZPo+uc59TYiQiItlSUiSFis0F2HXWpq+AXfIinAqB2T/y0uL3VIAtIiJZUlIkhY7NBdgB29NnpnmehPP3cHbaCuZH7cqfIEVEpNBRoXUuqdDa8WwuwL7iD/O/h/P1Ke2ZzIZIN0JC8jdWERFxHBVaS5FncwF22VPpPUZVtpOU6EabR4y8On0z0bHRGk4TERELJUVSJNyxALvURcoM7kLJej+QfNOZD4a1oM3wrwicFqgCbBERAZQUSRFhSwH2NeK5+VR7aDQPzC7w7WxOre5P16XdlBiJiIiSIik6sirAruJRBTdnt/Q3zmnQpT+0nJL+ftPb8N10hq8dpaE0EZFiToXWuaRC64Lr9gJsPw8/jCYjofNDMzbc8RKsmwY4Qf2vWb/Sm3Z1H8r3eEVExL5s/Z3tko8xieSLWwXYtyzetzjzhsGfQpk4iFgAB7sy6KlYxs1YQe0qFWlVtZW2BhERKWY0fCZFnp+HX9Yn710BvduD2xVO7gvkubA6tPnsKRVgi4gUQ0qKpMi748y06pthQCsocxbONYSvNnAq7jrdlqkAW0SkOFFSJEWeTVuD+O6Dfm2gdBzEN4b532O+4cWIyBEqwBYRKSaUFEmxYNPWIN6Hod8jUOocnA2C+ZGcPHeFLSe25F+gIiLiMEqKpNgIqx9G7PBYNvXbxKKwRYxtNTZjo0oH0zeSdT8PZ5rBgkiOnU3I/2BFRCTfKSmSYsWmrUF89kPfUCh5EU6FMGlIc+bsWK5tQUREijglRVJsZVuA7fcb9P0XlLzM8b1VeLZ7Jdp8/oRmpYmIFGFKiqTYumMBduXd0LsduF2Bvx6GWTGcinXTrDQRkSJKSZEUa1kVYDsZ/vdPw//n9OJrj9OQcC98sQPz8daalSYiUgRpm49c0jYfRcvtW4PEJ8Uzcv1I6waJfrBkVXrxtVMqdBzGpk97WK2cLSIiBZO2+RDJgdu3Bsl0WxDPszDgYfj2S9j3DHw3k0ll/iDt7c0k3DyDn4eftgYRESnklBSJ/EOW24KUuAlhvaHSPoiazMalddi48yQ8NRRKXcLf059pHaYRVj8sfwMWEZE84fCaounTpxMYGEjJkiUJDg5m586d2bYPDw+nbt26uLu7ExAQwMiRI7l586bl/JtvvonBYLB61atXz+oeN2/eZOjQoVSoUIEyZcrQtWtX4uPj7fJ8UvhkOyvNALR6D3p0AdercLwtfLkDEupyOvG0irBFRAoxhyZFS5cuZdSoUUyYMIHdu3fTqFEj2rdvz7lz5zJtv2jRIkaPHs2ECRM4ePAgs2bNYunSpbz++utW7e69917Onj1reW3dutXq/MiRI1m9ejXLly9n8+bNnDlzhrAw/XUv6WzaFqTeahjYHLyOw8Xa8OV2zEf/BaAibBGRQsqhSdFHH33E4MGDGTBgAPfccw8zZ86kVKlSzJ49O9P227Zto0WLFvTq1YvAwEDatWtHz549M/Quubi44Ovra3lVrFjRcu7KlSvMmjWLjz76iEceeYSgoCDmzJnDtm3b2L59u12fVwqPrGaleZfy/vuNz34Y3AyqboFkL1i4FvP2lzh55aS2BhERKYQclhSlpKSwa9cuQkND/w7GyYnQ0FBiYmIyvaZ58+bs2rXLkgT9+eefrF27lkcffdSq3ZEjR6hcuTI1atTgmWee4cSJE5Zzu3btIjU11epz69WrR9WqVbP8XIDk5GQSExOtXlK0/XNbkE39NjG1/VTrRqXPp69+3Xg2mJ0h8mNYM5OTF+McE7SIiOSawwqtz58/j9FoxMfHx+q4j48Phw4dyvSaXr16cf78eVq2bInZbCYtLY0hQ4ZYDZ8FBwczd+5c6taty9mzZ5k4cSKtWrVi//79eHh4EBcXh6urK15eXhk+Ny4u619kU6ZMYeLEibl/YCmUbp+VBhAdG52xkUsKdB4IlX6H7z+AXc/z1uCT3Aj/mjoBFTQrTUSkkHB4oXVOREdHM3nyZD777DN2795NREQE3333HW+//balTceOHXnqqado2LAh7du3Z+3atVy+fJlly5bd1WePGTOGK1euWF4nT56828eRQijLImwD0Pwj6NUJ3BI5ujuA55+8lzbTu2trEBGRQsJhSVHFihVxdnbOMOsrPj4eX1/fTK8ZN24cffr0YdCgQTRo0IAnn3ySyZMnM2XKFEwmU6bXeHl5UadOHY4ePQqAr68vKSkpXL582ebPBXBzc8PT09PqJcXPHYuw66yFgSHgeQIu1IOvNnLq7E3NShMRKQQclhS5uroSFBREVFSU5ZjJZCIqKoqQkJBMr7l+/TpOTtYhOzunD0tktTD3tWvXOHbsGH5+6WvPBAUFUaJECavPPXz4MCdOnMjyc0Vul1URtiVJqnTg761BzjWA+d9jvuGlWWkiIgWcQxdvHDVqFP369aNJkyY0a9aM8PBwkpKSGDBgAAB9+/alSpUqTJkyBYBOnTrx0Ucfcf/99xMcHMzRo0cZN24cnTp1siRH//73v+nUqRPVqlXjzJkzTJgwAWdnZ3r27AlA2bJlGThwIKNGjaJ8+fJ4enoybNgwQkJCePDBBx3zhZBCJ6x+GJ3rds56a5AKx6BvW5i7GeLuh/mRnOwbypYTW7Q1iIhIAeXQpKh79+4kJCQwfvx44uLiaNy4MZGRkZbi6xMnTlj1DI0dOxaDwcDYsWM5ffo03t7edOrUiUmTJlnanDp1ip49e3LhwgW8vb1p2bIl27dvx9v776nUU6dOxcnJia5du5KcnEz79u357LPP8u/BpUi449Yg3of/lxhFp++ZtnAtx7ucpXVgfkYpIiK20oawuaQNYeV20bHRtJnXJvOTZ+6Hr6LgZjlqPXCSN2bEEFipkmaliYjkE1t/Zxeq2WciBVW2W4NU/hV6dwDX9FlpA3p60WZmF81KExEpYJQUieSBO85K898JzzwKJa7Bn+1gxj5O7amrWWkiIgWIkiKRPJLVrDRnw/+GyKr9BP3bQPkjkBgAX23EvG4qL3/7mmaliYgUAKopyiXVFElWjCZj1rPSAFJKpa98/cuL6e8rHuDzOdd57vEm+R+siEgxoJoiEQe5NSutZ4Oe+JT2ydjA9To8PhSe6QhlzsL5e3ihy/08PWwvUUc3q9dIRMRBlBSJ2JGfh1/WJ2tHwov3wT3LMBmdWf5pQ0IfvUbV9+9RnZGIiAMoKRKxo2xnpQGUughPdYcne4PLdTjyGGc+WUDX2S8qMRIRyWdKikTs6I6z0tJPQKOF6VuDlEqAM03hy20M/WqahtJERPKRkiIRO8tqVloGATtgYHModxQu1yBu2tfMXPVb/gQpIiKafZZbmn0mOXX7rLQDCQd4Z8s7mTe85g2L1sCZZpRwS2PZEhe6dMnXUEVEihTNPhMpYG6flda2RtusG5ZJSF/PqPYaUpNd6NrVzKDxO4mOjdZwmoiIHSkpEnGAOxZgu17HuVc33IMXYDIZmPV2M9oMn6+tQURE7EhJkYgD2FKAbTQkc6NDHwj5MP3At19wautD2hpERMROlBSJOEhWBdj+Hv6UdSub/sYAtPs3NJkBOMHKeZgPPMmIyBEaShMRyWMqtM4lFVpLXrm9ANvPww+jyUjo/FDrRiYDfDMbfusPTinQowubJr1K68DWjghZRKRQsfV3tks+xiQimbhVgH3L4n2LMzZyMkPngZDmDr93h6URfPnAIs62S0+kWlVthbOTc/4FLSJSBCkpEilgstwaxMkEYb0hrSQc7szCMU+z8EB7qLoNf09/pnWYRlj9sPwNVkSkCFFNkUgBk+3MNOc06NYdanwPqWVgQSTs7cXpxNMqwBYRuUtKikQKmDvOTCuRDD26QPUoSPGAiIWYv/4K801PFWCLiNwFJUUiBdAdtwZxvQG920PrCWAwwr7eMPNXTu73Z8uJLfkbrIhIEaHZZ7mk2WeSH2zaGuRECEQshMvVwZDGQ303M3h4Av7lfFWALSKC7b+zlRTlkpIiyW/RsdG0mdcm85M3PeG76ek9RgABW6HzQPxrXFcBtogUe9r7TKSIybYAu2QidO0DYc+AayKcbAmf7efU8hF0/epZFWCLiNhASZFIIWHL1iA0XAQvNII6q8FUAmJegU8OM2jiNlLTVIAtIpIdJUUihcgdC7ABysVCryfgmQ5Q4RAk+XBp6f/RICiJmJh8C1VEpNBRTVEuqaZIHMmmAmyAtBKw8yXYPAGS0/dT690b3nsPKlfOx4BFRBxINUUiRditrUF6NuhJ2xpts27okgrNp8KwOnR8+jQGg5kFC6BmbSOTJpu4eTP/YhYRKeiUFIkUctkWYP+Pu9dV9gU/iHlQU/Dfxs3rzox9w4nAOtf45htQf7GIiJIikULPlgLsG2k3OHX1FFTZBQNbwJO9ocwZ4k+WoUsXaN8eDh7Mx6BFRAoghydF06dPJzAwkJIlSxIcHMzOnTuzbR8eHk7dunVxd3cnICCAkSNHcvO2MYApU6bQtGlTPDw8qFSpEl26dOHw4cNW92jdujUGg8HqNWTIELs8n0h+yKoA29/Dn9IlSls3NgCNFsKwutDyXXBOZsMGaNQI3noLUlLyL24RkYLEoYXWS5cupW/fvsycOZPg4GDCw8NZvnw5hw8fplKlShnaL1q0iGeffZbZs2fTvHlz/vjjD/r370+PHj346KOPAOjQoQM9evSgadOmpKWl8frrr7N//34OHDhA6dLpvxxat25NnTp1eOuttyz3LlWqVI4KplVoLQXR7QXYfh5+GE1GQueHZn/RhZqE/L6dmB8qAtCwIcyZAw88kA8Bi4jkg0KxonVwcDBNmzbl008/BcBkMhEQEMCwYcMYPXp0hvYvvfQSBw8eJCoqynLslVdeYceOHWzdujXTz0hISKBSpUps3ryZhx56CEhPiho3bkx4eHiuY1dSJIXB4n2L6RXR647t3mg5liu/dOSr95qSeKkEzs7w2mswfjy4ueVDoCIidlTgZ5+lpKSwa9cuQkP//ivWycmJ0NBQYrJYTKV58+bs2rXLMsT2559/snbtWh599NEsP+fKlSsAlC9f3ur4woULqVixIvfddx9jxozh+vXr2cabnJxMYmKi1UukoPPz8LOp3aSt7/DpzRYkDqyCe+PVGI0weTLcfz/s2GHnIEVECggXR33w+fPnMRqN+Pj4WB338fHh0KFDmV7Tq1cvzp8/T8uWLTGbzaSlpTFkyBBef/31TNubTCZGjBhBixYtuO+++6zuU61aNSpXrszevXt57bXXOHz4MBERWW+FMGXKFCZOnJiLJxVxnFsz004nnsaMDZ3CZRK42aUz1HkSr6iFHDxYkubNYe3a9GJsEZGizOGF1jkRHR3N5MmT+eyzz9i9ezcRERF89913vP3225m2Hzp0KPv372fJkiVWx5977jnat29PgwYNeOaZZ/jqq69YuXIlx44dy/Kzx4wZw5UrVyyvkydP5umzidiDTVuD/IMZM4Z7VlJqRFM6dzZjMsF//gMmkz0jFRFxPIclRRUrVsTZ2Zn4+Hir4/Hx8fj6+mZ6zbhx4+jTpw+DBg2iQYMGPPnkk0yePJkpU6Zg+sdP7Jdeeok1a9awadMm/P39s40lODgYgKNHj2bZxs3NDU9PT6uXSGFg09Yg/2DGzBnjfp598yc8PWHfPlixwo5BiogUAA5LilxdXQkKCrIqmjaZTERFRRESEpLpNdevX8fJyTpkZ2dnAG7Vi5vNZl566SVWrlzJDz/8QPXq1e8Yy549ewDw87Ot/kKksAmrH0bs8Fg29dvEorBFjG011qbrfr64nna99wIwYYIZo/aUFZEizKHDZ6NGjeKLL75g3rx5HDx4kBdeeIGkpCQGDBgAQN++fRkzZoylfadOnZgxYwZLlizh+PHjbNiwgXHjxtGpUydLcjR06FAWLFjAokWL8PDwIC4ujri4OG7cuAHAsWPHePvtt9m1axexsbF8++239O3bl4ceeoiGDRvm/xdBJJ/YvDXIbd7Z8g4rPFuC+wUOHTIw8v9+tnOUIiKO47BCa4Du3buTkJDA+PHjiYuLo3HjxkRGRlqKr0+cOGHVMzR27FgMBgNjx47l9OnTeHt706lTJyZNmmRpM2PGDCB92v3t5syZQ//+/XF1dWXjxo2Eh4eTlJREQEAAXbt2ZexY2/5yFikKclSAXfIqtHgfNr7HJ++Vp2XHlTzd8Mn8CVREJB85dJ2iwkzrFElhF3Ewgm7LugHcOTFKKQXT/oQkH8o9/SoJi6fg7OScD1GKiNy9Ar9OkYg4Vo4KsF2vp28JAlyKfInvD28mOjaaxfsWEx0bjdGkYiMRKfzUU5RL6imSouL2rUEOJBzgnS3vZN4wtSR8fBSuVqFU51e5fv8HllP+nv5M6zCNsPph+RS1iIjtCsU2H4WZkiIpiqJjo2kzr03WDXa+AGs/gzJnYHhNKJG+GfOtNZDmtv+Gi9s74ewMzz2nLUJEpGDQ8JmI5NitAuwsF3p8YBaUjYVrleHnFyyHzRerY147jf4PP8LIkfDyy3DffbB6NejPLhEpLJQUiYjFHVfAdkmBh/+3gvzWMXD0X7AkAj4+AjuHYU4pTfW61/D1haNH4YknoGNHyGLnHhGRAiVHw2dhYbbVC2S3h1hRoeEzKcoiDkYwPHI4pxJPWY6Vdy/PxRsXwegCnx6ES7WsL6r9HTw4lTf6hVCjdEM2zgvm69kBpKQYcHGBYcNgwgQoWzafH0ZEij271BTdWlTxlkWLFtGpUyc8PDysjs+ZMyeH4RY+SoqkqLu9ANvPww+jyUjo/ND0k/ufhhVLweUGNPoKHgwH74zdQb7JLfHfvoxfNqWvFl+pEsybBx065OODiEixly+F1h4eHvz222/UqFEjt7cotJQUSXFjNBkJnBb494KPZxuB5ykofSHLa24Nwb3ht4XlH7Xg8GEwGNJ7jMaNAycN4ItIPlChtYjkqQz1Rn6/ZZsQwd+LQs5L6snuX40MGZJeeP3mm/DYY3Ah+8tFRPKVkiIRsVmOFnz8HzNmTiaeZGf8FmbMSB8+K1kSIiMhKAh++cWOAYuI5ICSIhHJkbD6YcQOj2VTv00sClvE2Fa27RsY9WcUi/ctpupD0fy0zUjNmvDXX9CiBfz3v5q6LyKOl6MNYb/99lur9yaTiaioKPbv3291/Iknnrj7yESkwHJ2cqZ1YGsgfcHHLFfBvs3tbfw9/Zm88DO+ntKJb76B55+Hfftg2jTVGYmI4+So0NrJhp9WBoMBo7Ho74OkQmuRdBkKsG1wqwB7WbcVHF0dxuuvp/cUPf88fPaZEiMRyVt2KbQ2mUx3fBWHhEhE/nbHBR8zcSt5GvX9CP7zqpG5c9NnpX3+efr2ICaTvaIVEcma/h4Tkbt2NwXY0bHRVH0omhcmb8PJycysWTBwIOjvKxHJb7lap2j58uUsXryYP/74A4A6derQq1cvunXrlucBFlQaPhPJ6PYFHw8kHLCp1siyUjakLwoZsRBMLvTpA3PmgLOznYMWkSLPbsNn3bt3p3v37hw4cIBatWpRq1Ytfv/9d7p3706PHj24i7UgRaSQu1WA3bNBT9rWaGvTNZaECOC+ZdC1JzilMn8+9O0LaWl2ClZE5B9ylBRNmzaNjRs38u2333Lo0CFWrVrFqlWrOHz4MCtXrmTDhg1MmzbNXrGKSCHSqmor/D39ba4zsrh3BTzVHZxSWbQIwsLg1Kk7XyYicrdylBTNmTOHDz74gMcffzzDuSeeeIL333+f2bNn51lwIlJ45aYA26L+Sni6K84uRlavhlq1jYwdZ+LaNTsEKiLyPzlKio4cOUJoaGiW50NDQzly5MhdByUiRUNWBdjl3cvf+eJ6qzEObALVNpN805lJ7zhRtcYN5sxREbaI2EeOkiJ3d3cuX76c5fnExERKlix5tzGJSBHyzxWwN/XbxLJuy2y72G8P9G8NT4dBuWNcSnDn2WehSROIjrZj0CJSLOUoKQoJCWHGjBlZnp8+fTohISF3HZSIFC23F2C3DmxN68DWttcbGYB7VsLQe+Bf/8FQMpE9e6BNG3j7bW0PIiJ5J0dJ0RtvvMGsWbN4+umn2blzJ4mJiVy5coXt27fz1FNPMXv2bN544w17xSoiRUSu6o1cUqDF/2EeVpPHepwBYPx4GDwYUlPtFamIFCc5SoqaN2/O0qVL2bRpEyEhIZQrV47y5cvTvHlzNm3axOLFi2nRooW9YhWRIiQ3Cz4CUPo89w+ewYA3frYs9tipEyQm2idOESk+crV44/Xr11m/fr2lqLpu3bq0a9cOd3f3PA+woNLijSJ54/YFH+OT4hm5fqTtFx9+DMPXyzCnlKJRI/juO6iSwxxLRIo+W39n5ygpiomJ4cKFC1ZT8ufNm8ebb75JUlISXbp04ZNPPsHNze3uoi8ElBSJ5L3cbC7L6SawaA0k+eDvD2vXQoMG9o1TRAoXu6xo/dZbb/H7779b3u/bt4/BgwcTGhrK6NGjWb16NVOmTMl91CJSrOWq1qjKLzAoBJdKRzl1Clq2TO8xEhHJqRwlRXv27KFt27+X7l+yZAnNmjXjiy++YNSoUXz88ccsW2bjVFsRkUzkqtao3HHS+jejQdNLJCbC449DWL9TXEvSgkYiYrscJUWXLl3Cx8fH8n7z5s107NjR8r5p06acPHky76ITkWLpn2sbjW019s4XlbrEqS73wINTAVj5lT/lah7lo5VRdo5WRIqKHCVFPj4+HD9+HICUlBR2797Ngw8+aDl/9epVSpQokaMApk+fTmBgICVLliQ4OJidO3dm2z48PJy6devi7u5OQEAAI0eO5ObNmzm6582bNxk6dCgVKlSgTJkydO3alfj4+BzFLSL2lZvNZS+lxkGHUdC7PZQ5S1p8XV55qiX9X9uLyWTngEWk0MtRUvToo48yevRotmzZwpgxYyhVqhStWrWynN+7dy81a9a0+X5Lly5l1KhRTJgwgd27d9OoUSPat2/PuXPnMm2/aNEiRo8ezYQJEzh48CCzZs1i6dKlvP766zm658iRI1m9ejXLly9n8+bNnDlzhrCwsJx8KUQkH+V4c9la38MLDaHuKjC6Me/9hrRrZ+b0abuGKSKFXI5mn50/f56wsDC2bt1KmTJlmDdvHk8++aTlfNu2bXnwwQeZNGmSTfcLDg6madOmfPrppwCYTCYCAgIYNmwYo0ePztD+pZde4uDBg0RF/d0d/sorr7Bjxw62bt1q0z2vXLmCt7c3ixYtolu3bgAcOnSI+vXrExMTY9XzlR3NPhPJXxEHI+i2LP3frM0z08zArsEQGQ5ppShb4QbvzfmdQY/dj7OTs91iFZGCxS6zzypWrMiPP/7IpUuXuHTpklVCBLB8+XImTJhg071SUlLYtWuX1QazTk5OhIaGEhMTk+k1zZs3Z9euXZbhsD///JO1a9fy6KOP2nzPXbt2kZqaatWmXr16VK1aNcvPBUhOTiYxMdHqJSL5J1ebyxqAJl/AkPvB5zeuXHBnSNd6+AzpT8TBCPsGLCKFjktuLipbtmymx8uXt2Hn6/85f/48RqPRqnAb0uuWDh06lOk1vXr14vz587Rs2RKz2UxaWhpDhgyxDJ/Zcs+4uDhcXV3x8vLK0CYuLi7LeKdMmcLEiRNtfj4RyXth9cPoXLezZbFHPw8/jCYjofNDs7+w4h8woBUs+xr+/BcXvpxD1/jn+frd9HuKiEAOe4ocLTo6msmTJ/PZZ5+xe/duIiIi+O6773j77bft/tljxozhypUrlpdm2Yk4Rq43ly15FXo9Bo3mgdkFvp3FgJGxpBk1bV9E0jksKapYsSLOzs4ZZn3Fx8fj6+ub6TXjxo2jT58+DBo0iAYNGvDkk08yefJkpkyZgslksumevr6+pKSkcPnyZZs/F8DNzQ1PT0+rl4g4Xo4WfHRJhS79oVV63WPi+lG0fPIg83cvITo2GqNJCZJIceawpMjV1ZWgoCCrommTyURUVBQhISGZXnP9+nWcnKxDdnZOL5Y0m8023TMoKIgSJUpYtTl8+DAnTpzI8nNFpGDL0YKPBqDtWHhsCBiM7Fh9H327l6XN9O4ETgtUrZFIMZarmqK8MmrUKPr160eTJk1o1qwZ4eHhJCUlMWDAAAD69u1LlSpVLFuHdOrUiY8++oj777+f4OBgjh49yrhx4+jUqZMlObrTPcuWLcvAgQMZNWoU5cuXx9PTk2HDhhESEmLzzDMRKXj+WW90x81lm34OHmdgxRI42hE+Ocyptm/Q9fLTfN1jmWqNRIohhyZF3bt3JyEhgfHjxxMXF0fjxo2JjIy0FEqfOHHCqmdo7NixGAwGxo4dy+nTp/H29qZTp05WSwDc6Z4AU6dOxcnJia5du5KcnEz79u357LPP8u/BRcQubtUbQfrmsh/GfJj95rL1VsPAFvDtF3C2CaydDr8O4IXEN+n8YWdN2xcpZnK0TpH8TesUiRR8Nq9tZHKCX4ZA1CRI9gJMPNH7LHM/rkK5cvkSqojYkV3WKRIRKUxsrjVyMkGzz+CletBwPuDEtwuqUKN2CiOnblERtkgxoZ6iXFJPkUjhYTQZba81Ajj+MM7rPsd4rm76+yYzqNLtIz5+4j3VGokUQrb+zlZSlEtKikQKJ6PJSOC0wOxrjQDSSsCmt+Cn/2055L0fuvXk62ETlRiJFDIaPhMRyYTN6xq5pMK/xkCff0GZs5BwH3yxk0Hjf9GCjyJFlJIiESl2sqo18i7lnbFxzY0wpBHUWgdp7lxaMZmg0D/5YuvXqjUSKWI0fJZLGj4TKfxurzXy8/DjdOJpeq/snXljkwG2j4CN74LJFTxPQNde+Df4i2kdpmlITaQAs/V3tkPXKRIRcaTb1zUCiI6NzrqxkxmaT4VqP8LXi+FibZi7mVMPv6UFH0WKCA2fiYj8T6uqre68uWyVXfD8A9DwKzA7Q/REmBfFS0ve1VCaSCGnpEhE5H9sLsJ2uwZh/eDJPuB6Ff56mLMfrGPKlwfyKVIRsQclRSIit8nR5rKNFqT3Gvn9AjcqMO75BrTr+QffH/5RvUYihZAKrXNJhdYiRVuOFnxMKwE/TIJt/0l/77MHnwHD+azvcNUZiRQAWrzRzpQUiRQfNi/4eKQ9rPwKrleCEtfgsZf4evITSoxEHEyLN4qI5BGba41qr4cXGkH1KEgtA6vm0qevmctXNJQmUhgoKRIRsYHNtUYecdCnHTzyBhiMXP+lK3UaJDJlWaQWexQp4DR8lksaPhMpnm6vNTqQcIB3tryTdeO/WqSvaZQYAM7J0O7fVAldxccdtdijSH7S8JmIiB3cWvCxZ4OetK3RNvvG1X6CIY2h7jdgdIN1n3D6i4/pOncwEQcj8iVeEbGdkiIRkVyyabHHUhehRxfo8HJ6b9GhJ+Hz3bwwY76G0kQKGCVFIiK5ZHMBtgF48BMYGALlj8CVapz7dDnPvXacH/6MZvG+xao3EikAVFOUS6opEpFbIg5GMDxyOKcST925cXIZWDMD9v1v49kaG9JXxvaIx9/TX5vLitiB1imyMyVFInK7HC32aAb29IO10yG1NHichu5PYvD/BYAVT69QYiSSh5QU2ZmSIhHJis2LPSbUg2UrIOFecL4JnZ7D0HgB/p7+HB9+HGcn5/wLWqQI0+wzEREHsbnWyPsQDHoQ6q4CY0lY9RXm9R9w8tIZPtn5iWqNRPKZeopyST1FInInmdUalXcvz8UbF60bmgwQ/Sb8OD79fc310K0HuF8GUK2RyF3S8JmdKSkSEVvcXmvk5+GH0WQkdH5o5o1/7wqr5qXXGZU/Aj06Q6WDlt4m1RqJ5I6SIjtTUiQiuXHHeqO4hrD4G7gSCK6J0LUX1P0OAwbVGonkkmqKREQKoDvWG/nuheeaQrVoSPGExd/CT//GbDZzMvEkW05syd+ARYoRJUUiIvnsjpvLlj6fvqls0OeAE2z4AFbNgTRXov6MUgG2iJ1o+CyXNHwmInfrjmsbmYGdQyEyHMwu4L8NejwJZc4BKsAWsZVqiuxMSZGI5KVsa42OhcLyZXCzHHiegJ5PgN9vKsAWsVGhqimaPn06gYGBlCxZkuDgYHbu3Jll29atW2MwGDK8HnvsMUubzM4bDAY++OADS5vAwMAM59999127PqeISFayrTWquREGBUOFw5BYFWb/BN+/h/lSNQBGRI7QUJpIHnB4T9HSpUvp27cvM2fOJDg4mPDwcJYvX87hw4epVKlShvYXL14kJSXF8v7ChQs0atSIL7/8kv79+wMQFxdndc26desYOHAgR48epUaNGkB6UjRw4EAGDx5saefh4UHp0qVtils9RSJiD9nuo3bDC1YsgWPt/3fABLXXQdPprH/zFVxLOFum/req2kqz1ET+p9AMnwUHB9O0aVM+/fRTAEwmEwEBAQwbNozRo0ff8frw8HDGjx/P2bNns0xounTpwtWrV4mKirIcCwwMZMSIEYwYMSJXcSspEhF7ub3W6EDCAd7Z8s7fJ00G+ONx+PlFONbBctipfCymBz6DoP+C+xXVG4ncplAkRSkpKZQqVYoVK1bQpUsXy/F+/fpx+fJlvvnmmzveo0GDBoSEhPDf//430/Px8fH4+/szb948evXqZTkeGBjIzZs3SU1NpWrVqvTq1YuRI0fi4uKS6X2Sk5NJTk62vE9MTCQgIEBJkYjYVXRsNG3mtcn85IVa8MsQ+PXZ9HojSN9ctkt/DDXT/whUvZFIIakpOn/+PEajER8fH6vjPj4+GYbAMrNz507279/PoEGDsmwzb948PDw8CAuz/qHw8ssvs2TJEjZt2sTzzz/P5MmTefXVV7O8z5QpUyhbtqzlFRAQcMf4RETuVquqrfD39M98TaMKR6H9v2FUFXjiWSj/B1ytAvM3YI78EHOqm+qNRHLAoT1FZ86coUqVKmzbto2QkBDL8VdffZXNmzezY8eObK9//vnniYmJYe/evVm2qVevHv/617/45JNPsr3X7Nmzef7557l27Rpubm4ZzqunSEQcJeJgBN2WdQPIfBXsW1Lc09c0+nlo+nvv/dD1Gab2G4BPaR/VGkmxVSh6iipWrIizszPx8fFWx+Pj4/H19c322qSkJJYsWcLAgQOzbLNlyxYOHz6cbU/SLcHBwaSlpREbG5vpeTc3Nzw9Pa1eIiL5IavFHsu7l7du6HoDHnsJej0KpeMh4T74YicjJ5yk14pnaDOvDYHTAok4GJGP0YsUHg5NilxdXQkKCrIqgDaZTERFRVn1HGVm+fLlJCcn07t37yzbzJo1i6CgIBo1anTHWPbs2YOTk1OmM95ERBwtrH4YscNj2dRvE4vCFrGp3yaWdVuWeeM66+CFBlD3GzC6wfcfwoJISC7D6cTTdFvWTYmRSCYyryrOR6NGjaJfv340adKEZs2aER4eTlJSEgMGDACgb9++VKlShSlTplhdN2vWLLp06UKFChUyvW9iYiLLly/nww8/zHAuJiaGHTt20KZNGzw8PIiJiWHkyJH07t2bcuXK5f1DiojkAWcnZ1oHtra8N5qM+Hv6Z77gY5kE6NEFdg9KXxH7z3awYB3m3h0xuCUxInIEnet21lCayG0cnhR1796dhIQExo8fT1xcHI0bNyYyMtJSfH3ixAmcnKw7tA4fPszWrVv5/vvvs7zvkiVLMJvN9OzZM8M5Nzc3lixZwptvvklycjLVq1dn5MiRjBo1Km8fTkTEjm4t+NhtWTcMGDImRgYg6Evw/RW+2ggnW1oSo5OJJ/lk5yeqNRK5jcPXKSqstE6RiBQU2S74eMvpoPTEKNkLArZC747gds1yWusaSVFWKNYpKsyUFIlIQXLHzWUhPTGavyF9TaOAn6B3B0tipH3UpChTUmRnSopEpKDKdnPZfyZGz3SEkleB9MTI39Of48OPayhNipRCMSVfRETyXraby1bZBX1DoeQlONkCFqyHPx+BNFfMmDmZeJItJ7Y4IGoRx1NPUS6pp0hECrpsa43OPJBeY3Rre5ASSRAYDTXX8/4LoTRp4EncNW0uK0WDhs/sTEmRiBQG2dYanbsHfnoVjrWDa37WF5aNhZCp0OwT/L2qqAhbCjUlRXampEhECpssa43MQHzD9OToaHs40Sp90UeAwB+gywAMXidVhC2FlpIiO1NSJCKFkU37qKWUgt/6wvcfQGoZcLsCHYfj3+oHYkeoCFsKHyVFdqakSEQKq8xqjbxLeZNwPcG64YWasPIrONU8/X29CN7+KIGa/p6qNZJCRUmRnSkpEpHC7PZaIz8PP04nnqb3ykz2kjQ6w7b/wKaJYHKF0nHQ6Xmo+y3+ZbXgoxQOSorsTEmRiBQl0bHRtJnXJusGZxtBxAJIuC/9fc310P4VDJUOqNZICjytUyQiIjZrVbUV/p7+Gdc1usXvN3iuCbScAs7JcKw9zNiD+btPGbZiIkaTMX8DFrEDJUUiIpL9go+3lEiG0Ndh6D1Q/2swu8DPL3Jm0ma6jtzK/N1LiY6NVoIkhZaGz3JJw2ciUhTZtLnsLccfhshwiG+c/r7CYejSH/97T6nWSAoU1RTZmZIiESmqbNpc9haTE+zpD1GTIckHDGnQ5k1o+S5f91imxEgKBCVFdqakSESKg2w3l73dTU/47jPY90z6+2rR+PV7lZMTYjRtXxxOhdYiInLXbKo1AiiZCGG9oUtfcL0Kf7Xm7PuRvPP570THRrN432LVG0mBp56iXFJPkYgUJzmqNbpQE75eDGeapr8PmgntR4HrDfw9tbaR5D8Nn9mZkiIRKW5yVGuUVgI2vQ0/vZb+3utPeOxFDLW/B9DaRpKvlBTZmZIiESnObK41OtYWvpkDiQHp7+9dAh1G4V/Zmbld5nIu6Zy2DBG7U1JkZ0qKRKS4s2lzWYDkMunbhOwYDmZncLsMoWMg6HNwSr9Ow2piTyq0FhERuwqrH8aKp1dQxbOK1fHy7uWtG7pdgw6vwOCmUPlnSPaC72bArG0Q1wCA04mn6basGxEHI/IpepGM1FOUS+opEhFJ98/NZY0mI6HzQzNvbHKCn1+EqEmQ4gku16FbT6j3LQYM+Hv6c3z4cQ2lSZ7S8JmdKSkSEcmcTfVGiZXhm1lwrANggo4vQ/B0AKa2n4pPaR/VGkmeUVJkZ0qKRESyZlO9kdE5fcHH3c+lv2/+AYS+ZqkzAtUaSd5QTZGIiDhMVvVGVpyN0Ol5eOT19Pfb/gNfL4JUN0sT1RpJflJPUS6pp0hE5M5urzeqVLoS/Vf15/TVTIbVfusN38wGUwmothm6PwmlLgGo1kjumobP7ExJkYhIzmU7rPbnI7A0ApLLQvk/IPhjuOdr8IgDYGOfjTg7OVsKulVvJLZSUmRnSopERHIn2y1D4u+DhWv/XuwRE1T7Ee5djtcDP3DZ5ZClqeqNxFZKiuxMSZGISO5lu2VIUgXY2wd+fwpONb/tKhMEboaW70Kt7y0b1GrLELmTQlVoPX36dAIDAylZsiTBwcHs3Lkzy7atW7fGYDBkeD322GOWNv37989wvkOHDlb3uXjxIs888wyenp54eXkxcOBArl27ZrdnFBGRvzk7OdM6sDU9G/RkWLNh+Hv6W5IcSl+AkHAY1AJGVIV2o6DKdsAJYtvAgvWwbCnmRD8ARkSOwGgyOuxZpOhweFK0dOlSRo0axYQJE9i9ezeNGjWiffv2nDt3LtP2ERERnD171vLav38/zs7OPPXUU1btOnToYNVu8eLFVuefeeYZfv/9dzZs2MCaNWv48ccfee655+z2nCIikjlnJ2emdZgG8HdidIvXSWg+FQaHwIhqEBwOBiMceBo+PYQ55mVOXjrDJzs/YfG+xUTHRitBklxz+PBZcHAwTZs25dNPPwXAZDIREBDAsGHDGD169B2vDw8PZ/z48Zw9e5bSpUsD6T1Fly9fZtWqVZlec/DgQe655x5+/vlnmjRpAkBkZCSPPvoop06donLlynf8XA2fiYjkrcxqjcq7l+fijYvWDc82St8m5FRI+nufPfD4CxCwHVCtkWRUKIbPUlJS2LVrF6Ghfy8H7+TkRGhoKDExMTbdY9asWfTo0cOSEN0SHR1NpUqVqFu3Li+88AIXLlywnIuJicHLy8uSEAGEhobi5OTEjh07Mv2c5ORkEhMTrV4iIpJ3wuqHETs8lk39NrEobBGb+m1iWbdlGRv6/QbPtoBOg6HkRYhvDLNiYF04GJ21tpHkmkOTovPnz2M0GvHx8bE67uPjQ1xc3B2v37lzJ/v372fQoEFWxzt06MBXX31FVFQU7733Hps3b6Zjx44YjeldqnFxcVSqVMnqGhcXF8qXL5/l506ZMoWyZctaXgEBAZm2ExGR3Lu91qh1YGtaB7a2rje6xckMQV/CsLrQeE76sR3DYflyzKmugGqNJOccXlN0N2bNmkWDBg1o1qyZ1fEePXrwxBNP0KBBA7p06cKaNWv4+eefiY6OzvVnjRkzhitXrlheJ0+evMvoRUTkTrKtNwIofR66PAtPh4HzTTj0JCxYj/mGJycTT6rWSHLEoUlRxYoVcXZ2Jj4+3up4fHw8vr6+2V6blJTEkiVLGDhw4B0/p0aNGlSsWJGjR48C4Ovrm6GQOy0tjYsXL2b5uW5ubnh6elq9RETE/mzaMuSeldC7A7hdgb8ehrmb4aovI9ePpFdEL9rMa0PgtEANqUm2HJoUubq6EhQURFRUlOWYyWQiKiqKkJCQbK9dvnw5ycnJ9O7d+46fc+rUKS5cuICfX/r0zZCQEC5fvsyuXbssbX744QdMJhPBwcG5fBoREbGXf9YbTW0/NWOj6pthwENQ5izEN4JZ2+BCLctp1RrJnTh89tnSpUvp168fn3/+Oc2aNSM8PJxly5Zx6NAhfHx86Nu3L1WqVGHKlClW17Vq1YoqVaqwZMkSq+PXrl1j4sSJdO3aFV9fX44dO8arr77K1atX2bdvH25u6RsNduzYkfj4eGbOnElqaioDBgygSZMmLFq0yKa4NftMRMRxjCYjgdMCOZ2YyT5qF6unr2V0sTaUOgfPPApV0v8I1j5qxZOtv7Nd8jGmTHXv3p2EhATGjx9PXFwcjRs3JjIy0lJ8feLECZycrDu0Dh8+zNatW/n+++8z3M/Z2Zm9e/cyb948Ll++TOXKlWnXrh1vv/22JSECWLhwIS+99BJt27bFycmJrl278vHHH9v3YUVEJE/cqjXqtqwbBgzWiVH54+mz0xauhbNN4ItfoMwZKH8Uc/ljnCx3jAlOB6la8yalA45Qpaz2UZN0Du8pKqzUUyQi4njZ7qOWXAa+XgR/dMr6BlW2Q4cR+N97WmsbFWHa+8zOlBSJiBQM2e6jBnC9HFyqCRdrwsVaf//32SBI/d8adw3nQ+gYvh70sRKjIkhJkZ0pKRIRKXiyrTX6p6u+EDUJ9vQHnKBEEp6PzGDRh0EkmuLw89CwWlGhpMjOlBSJiBRMEQcj6LasG8CdEyOA00EQGQ4nW6a/9zwBDRZBqfOUq2Dm+ZbdeLxxCBUqgL8/lCljv9jFPpQU2ZmSIhGRgsvmfdRuMQP7u8OG9yGxapb3dXWFJ56A/v2hfXtwcfh0JbGFkiI7U1IkIlKw3V5r5Ofhh9FkJHR+aPYXpbjDngFwoQ5crwA3KsD1CrgkV6J0WjWuXPl7VW1fX+jdG/r1g/vus/PDyF1RUmRnSopERAqXHNUbZWJq+6ncOFmX3ZH3Ef2tP+fP/50gBQXBxInw2GN5GbHkFSVFdqakSESk8MlxvVEWqpSqTm/3hfzxQwirV0NaWvrxTp1g6lSoWTMvopW8Yuvv7EK9IayIiEhO2LSPmg3OXI/l/Qst6D0pgjNn4D//Sa8vWr0a7r0Xxo2D69fzKGjJN+opyiX1FImIFF631xtVKl2J/qv6c/pqzobVDBio4lGFuV3mci7pHKnnajL/3aZs3Jg+rFa1Knz0EYSFgcFwh5uJXWn4zM6UFImIFB15Nqzm4c8zJZaz5P8e5MSJ9GNPPw0LFkCJEnkRqeSGhs9ERERslGfDaldP88HF5ry78hvGjjPhUsLEsmXQ9okEbiYb8yhasRf1FOWSeopERIqeO24ZYgMDBsq7l8fdxZ1TuxrA0pVgdMO9wVrmLkjm6YZP2iFyyY6Gz+xMSZGISNF2t1P4Lf7oaEmMqP81S5c4KTHKZxo+ExERuQvOTs5M6zANSO/9ybU666D7k+CcDAe70r+PK+sP/8DifYuJjo3GaNKwWkGhpEhERCQLeVVrdHtidGPvY3R48iK9lvelzbw2BE4LJOJgRN4ELHdFw2e5pOEzEZHiIy+m8APWQ2nVN8K9y6HKz1BpP1/3XEJY/TD7PEAxp5oiO1NSJCJSfN3VFP7bE6NbXG7g6r+fx9v44H/vCTo9YaRNzZY4OznnYdTFl5IiO1NSJCJSvEUcjGB45HBOJZ6yHPP38OdG2g0u3riYfbJ0thEc6Aanm6W/kr2sz/vsoVKv15kxeJB6j/KAkiI7U1IkIiK3D6v5efjRqmorvjn8Tc56kUwGuFjrfwlSMOzrCTcqgiENmn/Eok/q0PP+LvZ9kCJOSZGdKSkSEZGsZNaLZLNr3hA5Dfb3BMDF+08ilwbgXP0nq+RLQ2u2U1JkZ0qKREQkO3ddnH2oE3w3A67+b+ZbkxkQ+hqUvIq/pz/TOkzT0JqNlBTZmZIiERHJiVwVZ9/0hA3vwa4h6e89TkOH4XBPBAYDrHh6hRIjG2jxRhERkQIkV2selUyETi9Av9ZQ/kh6r9HyFbBwDeaL1RkROUKLP+Yh9RTlknqKREQkN3I9rJbqBltHw9Yx6dP5XW7AQ2/zwZu+VPHyVq1RNjR8ZmdKikREJC/keFjtfB347jM43jb9fcUD8PgQCNyiWqMsaPhMRESkEMhqWM27lHfmF1T8A/qGQtgzUDoezt8Dc3+E9R9w6mIC3ZZ107YhuaSeolxST5GIiOSlf6551Ny/OTU/qcnpxGyG1m54wfcfwK+D0t/7/AZhvfGvdZm5XeZyLumchtXQ8JndKSkSERF7s3lo7VAn+HYWXPcG55vwr9eg2SfglH5NcR9W0/CZiIhIIWfzjLV6q+GFBlBrLRhLpi/+uCASEv0AOJ14WsNqNigQSdH06dMJDAykZMmSBAcHs3Pnzizbtm7dGoPBkOH12GOPAZCamsprr71GgwYNKF26NJUrV6Zv376cOXPG6j6BgYEZ7vHuu+/a9TlFRERyKqx+GLHDY9nUbxOLwhYxtf3UzBt6xMMzj8GjL6bPTPuzHczYBz8/j9mY/uteU/iz5/Dhs6VLl9K3b19mzpxJcHAw4eHhLF++nMOHD1OpUqUM7S9evEhKSorl/YULF2jUqBFffvkl/fv358qVK3Tr1o3BgwfTqFEjLl26xPDhwzEajfzyyy+W6wIDAxk4cCCDBw+2HPPw8KB06dI2xa3hMxERcQSjyUjgtMDsa40S6kHEAjgblP7eez+0HwW1NrCxz0acnZyL1ZYhhaamKDg4mKZNm/Lpp58CYDKZCAgIYNiwYYwePfqO14eHhzN+/HjOnj2bZULz888/06xZM/766y+qVq0KpCdFI0aMYMSIEbmKW0mRiIg4ik21RkYX+GUIbJoIN8unH6u9hrJPvMMVjx2WZsWh3qhQ1BSlpKSwa9cuQkNDLcecnJwIDQ0lJibGpnvMmjWLHj16ZNvDc+XKFQwGA15eXlbH3333XSpUqMD999/PBx98QFpaWpb3SE5OJjEx0eolIiLiCDbVGjmnQfCn8HItCA4Hp1Q48jhXpm6BdeFwvRygeqPbOTQpOn/+PEajER8fH6vjPj4+xMXF3fH6nTt3sn//fgYNGpRlm5s3b/Laa6/Rs2dPq+zw5ZdfZsmSJWzatInnn3+eyZMn8+qrr2Z5nylTplC2bFnLKyAgwIYnFBERsY9/1hpt7LMRfw9/DBisG5a6BB1Hwov3QZ1vwVQCdgyH6QdhX3duDRip3sjBw2dnzpyhSpUqbNu2jZCQEMvxV199lc2bN7Njx45srobnn3+emJgY9u7dm+n51NRUunbtyqlTp4iOjs62y2z27Nk8//zzXLt2DTc3twznk5OTSU5OtrxPTEwkICBAw2ciIlJg2DSsdqwtrPs4fdFHgNrfwWMvgtcJprafik9pnyJXa1Qohs8qVqyIs7Mz8fHxVsfj4+Px9fXN9tqkpCSWLFnCwIEDMz2fmprK008/zV9//cWGDRvumLgEBweTlpZGbGxspufd3Nzw9PS0eomIiBQkWQ2rlXcv//ebmlEw5H5oPR6ck+HIYzD9d4gZzsh1r9Arohdt5rUhcFpgsRtSc2hS5OrqSlBQEFFRUZZjJpOJqKgoq56jzCxfvpzk5GR69+6d4dythOjIkSNs3LiRChUq3DGWPXv24OTklOmMNxERkcLin8Nqm/ptYlm3ZdaNXFKg9dswpBFU/RFSy8D6cPhyO8Q1BIpnrZGLowMYNWoU/fr1o0mTJjRr1ozw8HCSkpIYMGAAAH379qVKlSpMmTLF6rpZs2bRpUuXDAlPamoq3bp1Y/fu3axZswaj0WipTypfvjyurq7ExMSwY8cO2rRpg4eHBzExMYwcOZLevXtTrly5/HlwERERO3F2cqZ1YGvLe6PJiL+nf8Zp/N6HoX9r+HVg+nYhZ5rCFzuh3SuYm03HYDAwfN1wyrqVLRZbhjg8KerevTsJCQmMHz+euLg4GjduTGRkpKX4+sSJEzg5WXdoHT58mK1bt/L9999nuN/p06f59ttvAWjcuLHVuU2bNtG6dWvc3NxYsmQJb775JsnJyVSvXp2RI0cyatQo+zykiIiIAzk7OTOtwzS6LeuGAYN1YuRkhqAvoc4aWDMDDneBdZ/CXw9hfmIwpzhF6Py/Z4kX5Sn8Dl+nqLDSOkUiIlLYRByMYHjkcE4lnsq8gRnY8XJ6r5HJFcodhaeehsq/Wprcmt224ukVhSYxKjSLNxZWSopERKQwMpqMbDmxhbNXzxKfFM/I9SMzNjrVFJYvgyuB6cXYHUZAk5ncmu1vwIC/pz/Hhx8vFENpSorsTEmRiIgUdtluGXK9HHwzBw53Tn9/z3J46G3w3WdpUli2DFFSZGdKikREpCjIdm0jMxAzEja+l77oI4DPHmj0FTRYRPlKqVy8cdHSvKDWGykpsjMlRSIiUlTcsdbodBPY+hr80QmM/1vg2JAGNb9PT5DqR4BLaoGtN1JSZGdKikREpCi5vdaoUulK9F/Vn9NX/zGsdr0c/P40/NYPTt22nmC5YxA6Gu5ZgcFgoIpHFeZ2mVtgpvErKbIzJUUiIlKU3XHLkPO1YW8f2D0IrvmlH/OPgXb/hqrbrJo6elitUGzzISIiIgXTHbcMqXgEHhkPw2pD6wlQIim992j2T7B0BVyoZbmmsKyOrZ6iXFJPkYiIFAe3D6v5efhhNBmtFnO0uOoLm96CX58FszM4pULz/4NHxoKTyaHT+DV8ZmdKikREpDjKdho/QPy9sOF9OPpo+vt6KyHsGXC9AcDU9lPxKe2Tr7VGSorsTEmRiIgUV3esNwLY/zSsnAfGklBlO/TqBKXPWzXJr1oj1RSJiIiIXWRVb2TlvmXQNxTcL8DpB+HLGKs6Iyh4tUbqKcol9RSJiEhxZ9M0/vN1YEEkXK4O7ufTe4wCtltO36o1OjrsKNtObbPL6tgaPrMzJUUiIiLWshxWu1YJFq2BM03B5QZ0eg6qboWSl8AtEZzMeJfyJuF6guWSvBxaU1JkZ0qKREREMspydeyUUrBiSfqq2FZMUPJKeoJU8jLcuwxavZenq2MrKbIzJUUiIiKZu31YLT4pnpHrR/7vhDNETYZ9z8CNcpBWKuPFzT6GR4cD5Nk0fiVFdqakSERE5M6yncKf5pqeHN30gpvl0v/b8xT47rNqtqnfJloHts51DLb+znbJ9SeIiIiI3IGzkzPTOkyj27JuGDBYJ0YuKeARn/7KxtmrZ+0cZTpNyRcRERG7ymoKv3cpb5uu9/Pws0dYGWj4LJc0fCYiIpIz/9wypLl/c2p+UjPL1bHzu6ZIw2ciIiKSL5ydnDPUBmU1tHZr9ll4h/B82ytNw2ciIiLiMFkNrfl7+ufJdPyc0PBZLmn4TEREJO/8c2jNEStaa/hMREREHC6zobX8puEzEREREZQUiYiIiABKikREREQAJUUiIiIigJIiEREREUBJkYiIiAigpEhEREQEUFIkIiIiAmjxxly7tRB4YmKigyMRERGR7Nz6XX2nTTyUFOXS1atXAQgICHBwJCIiImKLq1evUrZs2SzPa++zXDKZTJw5cwYPDw8MBkOe3TcxMZGAgABOnjxZ7PZU07MXz2eH4v38enY9u57d/sxmM1evXqVy5co4OWVdOaSeolxycnLC39/fbvf39PQsdv9QbtGzF89nh+L9/Hp2PXtxk9/Pnl0P0S0qtBYRERFBSZGIiIgIoKSowHFzc2PChAm4ubk5OpR8p2cvns8Oxfv59ex69uKmID+7Cq1FREREUE+RiIiICKCkSERERARQUiQiIiICKCkSERERAZQUFTjTp08nMDCQkiVLEhwczM6dOx0dUp778ccf6dSpE5UrV8ZgMLBq1Sqr82azmfHjx+Pn54e7uzuhoaEcOXLEMcHmsSlTptC0aVM8PDyoVKkSXbp04fDhw1Ztbt68ydChQ6lQoQJlypSha9euxMfHOyjivDNjxgwaNmxoWbAtJCSEdevWWc4X1efOzLvvvovBYGDEiBGWY0X1+d98800MBoPVq169epbzRfW5bzl9+jS9e/emQoUKuLu706BBA3755RfL+aL88y4wMDDD995gMDB06FCgYH7vlRQVIEuXLmXUqFFMmDCB3bt306hRI9q3b8+5c+ccHVqeSkpKolGjRkyfPj3T8++//z4ff/wxM2fOZMeOHZQuXZr27dtz8+bNfI40723evJmhQ4eyfft2NmzYQGpqKu3atSMpKcnSZuTIkaxevZrly5ezefNmzpw5Q1hYmAOjzhv+/v68++677Nq1i19++YVHHnmEzp078/vvvwNF97n/6eeff+bzzz+nYcOGVseL8vPfe++9nD171vLaunWr5VxRfu5Lly7RokULSpQowbp16zhw4AAffvgh5cqVs7Qpyj/vfv75Z6vv+4YNGwB46qmngAL6vTdLgdGsWTPz0KFDLe+NRqO5cuXK5ilTpjgwKvsCzCtXrrS8N5lMZl9fX/MHH3xgOXb58mWzm5ubefHixQ6I0L7OnTtnBsybN282m83pz1qiRAnz8uXLLW0OHjxoBswxMTGOCtNuypUrZ/7yyy+LzXNfvXrVXLt2bfOGDRvMDz/8sHn48OFms7lof98nTJhgbtSoUabnivJzm81m82uvvWZu2bJllueL28+74cOHm2vWrGk2mUwF9nuvnqICIiUlhV27dhEaGmo55uTkRGhoKDExMQ6MLH8dP36cuLg4q69D2bJlCQ4OLpJfhytXrgBQvnx5AHbt2kVqaqrV89erV4+qVasWqec3Go0sWbKEpKQkQkJCis1zDx06lMcee8zqOaHof9+PHDlC5cqVqVGjBs888wwnTpwAiv5zf/vttzRp0oSnnnqKSpUqcf/99/PFF19Yzhenn3cpKSksWLCAZ599FoPBUGC/90qKCojz589jNBrx8fGxOu7j40NcXJyDosp/t561OHwdTCYTI0aMoEWLFtx3331A+vO7urri5eVl1baoPP++ffsoU6YMbm5uDBkyhJUrV3LPPfcU+ecGWLJkCbt372bKlCkZzhXl5w8ODmbu3LlERkYyY8YMjh8/TqtWrbh69WqRfm6AP//8kxkzZlC7dm3Wr1/PCy+8wMsvv8y8efOA4vXzbtWqVVy+fJn+/fsDBff/eReHfbJIMTd06FD2799vVV9R1NWtW5c9e/Zw5coVVqxYQb9+/di8ebOjw7K7kydPMnz4cDZs2EDJkiUdHU6+6tixo+W/GzZsSHBwMNWqVWPZsmW4u7s7MDL7M5lMNGnShMmTJwNw//33s3//fmbOnEm/fv0cHF3+mjVrFh07dqRy5cqODiVb6ikqICpWrIizs3OGyvv4+Hh8fX0dFFX+u/WsRf3r8NJLL7FmzRo2bdqEv7+/5bivry8pKSlcvnzZqn1ReX5XV1dq1apFUFAQU6ZMoVGjRkybNq3IP/euXbs4d+4cDzzwAC4uLri4uLB582Y+/vhjXFxc8PHxKdLPfzsvLy/q1KnD0aNHi/z33c/Pj3vuucfqWP369S3Dh8Xl591ff/3Fxo0bGTRokOVYQf3eKykqIFxdXQkKCiIqKspyzGQyERUVRUhIiAMjy1/Vq1fH19fX6uuQmJjIjh07isTXwWw289JLL7Fy5Up++OEHqlevbnU+KCiIEiVKWD3/4cOHOXHiRJF4/n8ymUwkJycX+edu27Yt+/btY8+ePZZXkyZNeOaZZyz/XZSf/3bXrl3j2LFj+Pn5Ffnve4sWLTIsufHHH39QrVo1oOj/vLtlzpw5VKpUiccee8xyrMB+7x1W4i0ZLFmyxOzm5maeO3eu+cCBA+bnnnvO7OXlZY6Li3N0aHnq6tWr5l9//dX866+/mgHzRx99ZP7111/Nf/31l9lsNpvfffdds5eXl/mbb74x792719y5c2dz9erVzTdu3HBw5HfvhRdeMJctW9YcHR1tPnv2rOV1/fp1S5shQ4aYq1atav7hhx/Mv/zyizkkJMQcEhLiwKjzxujRo82bN282Hz9+3Lx3717z6NGjzQaDwfz999+bzeai+9xZuX32mdlcdJ//lVdeMUdHR5uPHz9u/umnn8yhoaHmihUrms+dO2c2m4vuc5vNZvPOnTvNLi4u5kmTJpmPHDliXrhwoblUqVLmBQsWWNoU5Z93ZnP6LOqqVauaX3vttQznCuL3XklRAfPJJ5+Yq1atanZ1dTU3a9bMvH37dkeHlOc2bdpkBjK8+vXrZzab06epjhs3zuzj42N2c3Mzt23b1nz48GHHBp1HMntuwDxnzhxLmxs3bphffPFFc7ly5cylSpUyP/nkk+azZ886Lug88uyzz5qrVatmdnV1NXt7e5vbtm1rSYjM5qL73Fn5Z1JUVJ+/e/fuZj8/P7Orq6u5SpUq5u7du5uPHj1qOV9Un/uW1atXm++77z6zm5ubuV69eub//ve/VueL8s87s9lsXr9+vRnI9JkK4vfeYDabzQ7pohIREREpQFRTJCIiIoKSIhERERFASZGIiIgIoKRIREREBFBSJCIiIgIoKRIREREBlBSJiIiIAEqKRERERAAlRSIiNouNjcVgMLBnzx5HhyIidqCkSESKlLi4OIYNG0aNGjVwc3MjICCATp06WW08KSKSGRdHByAikldiY2Np0aIFXl5efPDBBzRo0IDU1FTWr1/P0KFDOXTokKNDFJECTD1FIlJkvPjiixgMBnbu3EnXrl2pU6cO9957L6NGjWL79u08++yzPP7441bXpKamUqlSJWbNmgWAyWTi/fffp1atWri5uVG1alUmTZqU5Wfu37+fjh07UqZMGXx8fOjTpw/nz5+363OKiH0oKRKRIuHixYtERkYydOhQSpcuneG8l5cXgwYNIjIykrNnz1qOr1mzhuvXr9O9e3cAxowZw7vvvsu4ceM4cOAAixYtwsfHJ9PPvHz5Mo888gj3338/v/zyC5GRkcTHx/P000/b5yFFxK40fCYiRcLRo0cxm83Uq1cvyzbNmzenbt26zJ8/n1dffRWAOXPm8NRTT1GmTBmuXr3KtGnT+PTTT+nXrx8ANWvWpGXLlpne79NPP+X+++9n8uTJlmOzZ88mICCAP/74gzp16uThE4qIvamnSESKBLPZbFO7QYMGMWfOHADi4+NZt24dzz77LAAHDx4kOTmZtm3b2nSv3377jU2bNlGmTBnL61ZSduzYsVw8hYg4knqKRKRIqF27NgaD4Y7F1H379mX06NHExMSwbds2qlevTqtWrQBwd3fP0Wdeu3aNTp068d5772U45+fnl6N7iYjjqadIRIqE8uXL0759e6ZPn05SUlKG85cvXwagQoUKdOnShTlz5jB37lwGDBhgaVO7dm3c3d1tnr7/wAMP8PvvvxMYGEitWrWsXpnVNYlIwaakSESKjOnTp2M0GmnWrBlff/01R44c4eDBg3z88ceEhIRY2g0aNIh58+Zx8OBBS+0QQMmSJXnttdd49dVX+eqrrzh27Bjbt2+3zEz7p6FDh3Lx4kV69uzJzz//zLFjx1i/fj0DBgzAaDTa/XlFJG9p+ExEiowaNWqwe/duJk2axCuvvMLZs2fx9vYmKCiIGTNmWNqFhobi5+fHvffeS+XKla3uMW7cOFxcXBg/fjxnzpzBz8+PIUOGZPp5lStX5qeffuK1116jXbt2JCcnU61aNTp06ICTk/7mFClsDGZbqxNFRIqIa9euUaVKFebMmUNYWJijwxGRAkI9RSJSbJhMJs6fP8+HH36Il5cXTzzxhKNDEpECREmRiBQbJ06coHr16vj7+zN37lxcXPQjUET+puEzERERETT7TERERARQUiQiIiICKCkSERERAZQUiYiIiABKikREREQAJUUiIiIigJIiEREREUBJkYiIiAgA/w+sBHXsPAbzjAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LSTM Evaluation for Cell 8\n",
      "Evaluation Time: 0.21875\n",
      "MAE: 0.0022970852537496245\n",
      "RMSE: 0.0027899224428143744\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(0, 4):\n",
    "    print(\"LSTM Evaluation for Cell {}\".format(i + 5))\n",
    "    lstm_outputs, targets, lstm_MAE, lstm_RMSE = evaluate(lstm_model, test_x[i], test_y[i], label_sc)\n",
    "    plt.figure(figsize=(14, 10))\n",
    "    plt.subplot(2, 2, 1)\n",
    "    plt.plot(lstm_outputs[0][-100:], \"-o\", color=\"g\", label=\"Predicted\")\n",
    "    plt.plot(targets[0][-100:], color=\"b\", label=\"Actual\")\n",
    "    plt.xlabel('Cycle')\n",
    "    plt.ylabel('SOH')\n",
    "    plt.title(\"Cell {}\".format(i + 5))\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "# MAE and RMSE\n",
    "# predicted curve\n",
    "# performance, numerical results"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  },
  "vscode": {
   "interpreter": {
    "hash": "abb986b297a1e39e4a9784cac1041b65609e2eefd57e07e1d90a430925bc2fc5"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}