Arab_Letter_Classification.ipynb

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   "cell_type": "code",
   "execution_count": 1,
   "id": "7fc61370",
   "metadata": {},
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ilin\\anaconda3\\envs\\pytorch_testing\\Lib\\site-packages\\torchaudio\\backend\\utils.py:74: UserWarning: No audio backend is available.\n",
      "  warnings.warn(\"No audio backend is available.\")\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import cv2\n",
    "import numpy as np\n",
    "import os\n",
    "from matplotlib import pyplot as plt\n",
    "import torchvision.transforms as transforms\n",
    "from torch import nn\n",
    "from torch import optim\n",
    "from torcheval.metrics.functional import multiclass_f1_score\n",
    "from torcheval.metrics.functional import multiclass_accuracy\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f495268-10ec-4a0d-8136-73d48f94567d",
   "metadata": {},
   "source": [
    "The goal of this notebook project is to explore some basic concepts of building good quality neural models with Pytorch. A classic testcase is to use the MNIST dataset, however here I have opted for an additional challenge of trying these concepts out with a different but similar dataset. This dataset contains images of hand written arabic characters and their respective labels. This is a bit more challenging since we have 28 different possible labels rather than the traditional 10 of the MNIST dataset. While the dataset also contains .csv files where the data is given in a much more convenient format, using the actuall image data provides sligthly more of a challenge so let's go with that instead.   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d72f3ffd-5d81-498a-bd43-4a56a4c207b0",
   "metadata": {},
   "source": [
    "Setting up file paths and some other things for converting image data to tensors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "03617556",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_path ='./archive/Train Images 13440x32x32/train/'\n",
    "test_path ='./archive/Test Images 3360x32x32/test/'\n",
    "\n",
    "\n",
    "train_files=os.listdir(training_path)\n",
    "test_files=os.listdir(test_path)\n",
    "transform=transforms.Compose([transforms.ToTensor()])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6dfb36d6-631b-4856-8354-3a8438041c63",
   "metadata": {
    "tags": []
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   "source": [
    "def data_builder(path, files):\n",
    "    '''Function for converting image data to tensors for using in training, along with getting the labels\n",
    "       from the file name.\n",
    "    '''\n",
    "    img_fordim=cv2.imread(os.path.join(path,files[0]), 0)\n",
    "    image_size=torch.flatten(transform(img_fordim))\n",
    "    dim1=image_size.shape[0]\n",
    "    dim0=len(files)\n",
    "    data=torch.zeros((dim0, dim1))\n",
    "    labels=torch.zeros(dim0)\n",
    "    for i, file in enumerate(files):\n",
    "        img=cv2.imread(os.path.join(path,file), 0)\n",
    "        flat_tens=torch.flatten(transform(img))\n",
    "        data[i]=flat_tens\n",
    "        label_png=file.split('_')[-1]\n",
    "        label=label_png.split('.')[0]\n",
    "        labels[i]=int(label)-1\n",
    "           \n",
    "    return data, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "097d523e-579f-44d3-9636-b546c76c2933",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cpu'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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    }
   ],
   "source": [
    "device= 'cuda' if torch.cuda.is_available() else \"cpu\"\n",
    "\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6f8357f6-a244-44e6-b9c2-9d6929cc1bed",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_data, test_labels = data_builder(test_path, test_files)\n",
    "\n",
    "train_data, train_labels = data_builder(training_path, train_files)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0824c8e9-8396-4d05-ae23-f079a573b2e2",
   "metadata": {},
   "source": [
    "Naturally we don't neccessarilly want to have to remake the tensors with the data each and every time we have to restart the notebook, so the code block bellow contains code for saving and opening the tensors with the data as seperate files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "38cdb36d-5867-4b08-9186-323748036967",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
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   "source": [
    "#torch.save(test_data, 'test_data.pt')\n",
    "#torch.save(test_labels, 'test_labels.pt')\n",
    "#torch.save(train_data, 'train_data.pt')\n",
    "#torch.save(train_labels, 'train_labels.pt')\n",
    "\n",
    "#test_data= torch.load('test_data.pt')\n",
    "#test_labels= torch.load('test_labels.pt')\n",
    "#train_data= torch.load('train_data.pt')\n",
    "#train_labels= torch.load('train_labels.pt')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a0e46ec-3b1d-4462-ae42-825ea1d74faf",
   "metadata": {},
   "source": [
    "This is an initial model to test various aspects of Pytorch model bulding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "21937e6c-0159-485c-a19c-499cf7d2e4e2",
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   "source": [
    "class ArabicCharClassifier(nn.Module):\n",
    "    def __init__(self, n_char):\n",
    "        super().__init__()\n",
    "        self.hidden1=nn.Linear(1024, 100)\n",
    "        self.hidden2=nn.Linear(100, 100)\n",
    "        self.hidden3=nn.Linear(100,100)\n",
    "        self.hidden4=nn.Linear(100,100)\n",
    "        self.hidden5=nn.Linear(100,100)\n",
    "        self.hiddenfin=nn.Linear(100, n_char)\n",
    "        self.relu=nn.ReLU()\n",
    "        self.dropout=nn.Dropout()\n",
    "        self.batch1=nn.BatchNorm1d(100)\n",
    "        self.batch2=nn.BatchNorm1d(100)\n",
    "        self.batch3=nn.BatchNorm1d(100)\n",
    "        self.batch4=nn.BatchNorm1d(100)\n",
    "        self.batch5=nn.BatchNorm1d(100)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        X = self.hidden1(x)\n",
    "        X = self.relu(X)\n",
    "        X = self.batch1(X)\n",
    "        X = self.dropout(X)\n",
    "        \n",
    "        X = self.hidden2(X)\n",
    "        X = self.relu(X)\n",
    "        X = self.batch2(X)\n",
    "        X = self.dropout(X)\n",
    "        \n",
    "        X = self.hidden3(X)\n",
    "        X = self.relu(X)\n",
    "        X = self.batch3(X)\n",
    "        X = self.dropout(X)\n",
    "        \n",
    "        X = self.hidden4(X)\n",
    "        X = self.relu(X)\n",
    "        X = self.batch4(X)\n",
    "        X = self.dropout(X)\n",
    "        \n",
    "        X = self.hidden5(X)\n",
    "        X = self.relu(X)\n",
    "        X = self.batch5(X)\n",
    "        X = self.dropout(X)\n",
    "        \n",
    "        \n",
    "        \n",
    "        X = self.hiddenfin(X)\n",
    "        \n",
    "        return X\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "18daa46c-319d-4fe0-b99b-ccc27a133e11",
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "ArabicCharClassifier(\n",
       "  (hidden1): Linear(in_features=1024, out_features=100, bias=True)\n",
       "  (hidden2): Linear(in_features=100, out_features=100, bias=True)\n",
       "  (hidden3): Linear(in_features=100, out_features=100, bias=True)\n",
       "  (hidden4): Linear(in_features=100, out_features=100, bias=True)\n",
       "  (hidden5): Linear(in_features=100, out_features=100, bias=True)\n",
       "  (hiddenfin): Linear(in_features=100, out_features=28, bias=True)\n",
       "  (relu): ReLU()\n",
       "  (dropout): Dropout(p=0.5, inplace=False)\n",
       "  (batch1): BatchNorm1d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (batch2): BatchNorm1d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (batch3): BatchNorm1d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (batch4): BatchNorm1d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (batch5): BatchNorm1d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       ")"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=ArabicCharClassifier(n_char=28).to(device)\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "640e1be1-9b9c-40e5-8f35-650a3572d7d8",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "loss = nn.CrossEntropyLoss()\n",
    "\n",
    "optimization = optim.Adam(params=model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "35706cfd-0dbd-4e09-b42c-3fb230a9545d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def metrics(y_pred, y_true, num_char):\n",
    "    acc = multiclass_accuracy(y_pred, y_true)\n",
    "    f1_score = multiclass_f1_score(y_pred, y_true, num_classes=num_char)\n",
    "    return f1_score, acc "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "06ef1872-a9aa-4ea3-bb2a-e7893cd21d10",
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 | Train loss: 3.67902 | Train accuracy: 3.326% | Train f1 0.033 | Test accuracy 3.571% | Test f1 0.036\n",
      "Epoch: 10 | Train loss: 3.33140 | Train accuracy: 3.981% | Train f1 0.040 | Test accuracy 3.988% | Test f1 0.040\n",
      "Epoch: 20 | Train loss: 3.32583 | Train accuracy: 5.863% | Train f1 0.059 | Test accuracy 6.012% | Test f1 0.060\n",
      "Epoch: 30 | Train loss: 3.29628 | Train accuracy: 9.278% | Train f1 0.093 | Test accuracy 8.780% | Test f1 0.088\n",
      "Epoch: 40 | Train loss: 3.09352 | Train accuracy: 10.722% | Train f1 0.107 | Test accuracy 9.702% | Test f1 0.097\n",
      "Epoch: 50 | Train loss: 2.77245 | Train accuracy: 11.362% | Train f1 0.114 | Test accuracy 12.530% | Test f1 0.125\n",
      "Epoch: 60 | Train loss: 2.50666 | Train accuracy: 21.205% | Train f1 0.212 | Test accuracy 20.774% | Test f1 0.208\n",
      "Epoch: 70 | Train loss: 2.23924 | Train accuracy: 28.266% | Train f1 0.283 | Test accuracy 28.214% | Test f1 0.282\n",
      "Epoch: 80 | Train loss: 2.03597 | Train accuracy: 34.397% | Train f1 0.344 | Test accuracy 33.274% | Test f1 0.333\n",
      "Epoch: 90 | Train loss: 1.83271 | Train accuracy: 40.491% | Train f1 0.405 | Test accuracy 38.720% | Test f1 0.387\n"
     ]
    }
   ],
   "source": [
    "num_epoch = 100\n",
    "\n",
    "train_data, train_labels = train_data.to(device), train_labels.to(device)\n",
    "\n",
    "test_data, test_labels = test_data.to(device), test_labels.to(device)\n",
    "\n",
    "train_metrics={'loss': [], 'accuracy': [], 'f1_score': [], 'epoch': list(range(num_epoch))}\n",
    "\n",
    "test_metrics = {'loss': [], 'accuracy': [], 'f1_score': [], 'epoch': list(range(num_epoch))}\n",
    "\n",
    "for epoch in range(num_epoch):\n",
    "    \n",
    "    train_pred=model(train_data)\n",
    "    model.train()\n",
    "    \n",
    "    \n",
    "    \n",
    "    y_train = train_labels.type(torch.LongTensor) \n",
    "    \n",
    "    optimization.zero_grad()\n",
    "    \n",
    "    loss_epoch = loss(train_pred, y_train)\n",
    "    \n",
    "    f1_train, acc_train = metrics(train_pred, y_train, 28)\n",
    "    \n",
    "    loss_epoch.backward()\n",
    "    \n",
    "    optimization.step()\n",
    "    \n",
    "    train_metrics['loss'].append(loss_epoch.item())\n",
    "    train_metrics['accuracy'].append(acc_train.item()*100)\n",
    "    train_metrics['f1_score'].append(f1_train.item())\n",
    "    \n",
    "    model.eval()\n",
    "    with torch.inference_mode():\n",
    "        test_pred = model(test_data)\n",
    "        y_eval = test_labels.type(torch.LongTensor) \n",
    "        loss_eval=loss(test_pred, y_eval)\n",
    "        f1_test, acc_test = metrics(test_pred, torch.squeeze(test_labels), 28)\n",
    "        \n",
    "        test_metrics['accuracy'].append(acc_test.item()*100)\n",
    "        test_metrics['f1_score'].append(f1_test.item())\n",
    "        test_metrics['loss'].append(loss_eval.item())\n",
    "        \n",
    "        \n",
    "    if epoch%10==0:\n",
    "        print(f'Epoch: {epoch} | Train loss: {loss_epoch:.5f} | Train accuracy: {acc_train*100:.3f}% | Train f1 {f1_train:.3f} | Test accuracy {acc_test*100:.3f}% | Test f1 {f1_test:.3f}')\n",
    "        \n",
    "        \n",
    "    "
   ]
  },
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    {
     "data": {
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       "<matplotlib.legend.Legend at 0x20f3ff3ff10>"
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     "execution_count": 37,
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    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x2000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(3, figsize=(10,20))\n",
    "sns.lineplot(data=train_metrics, x='epoch', y='loss', label='Train data', ax=axs[0])\n",
    "sns.lineplot(data=test_metrics, x='epoch', y='loss', label='Test data', ax=axs[0])\n",
    "sns.lineplot(data=train_metrics, x='epoch', y='accuracy', label='Train data', ax=axs[1])\n",
    "sns.lineplot(data=test_metrics, x='epoch', y='accuracy', label='Test data', ax=axs[1])\n",
    "sns.lineplot(data=train_metrics, x='epoch', y='f1_score', label='Train data', ax=axs[2])\n",
    "sns.lineplot(data=test_metrics, x='epoch', y='f1_score', label='Test data', ax=axs[2])\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "62722057-fa72-448a-8127-f001247ab523",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class ModularNN(nn.Module):\n",
    "    def __init__(self, n_char, input_size, neurons, depth, add_Dropout=True):\n",
    "        super().__init__()\n",
    "        self.layers=nn.ModuleList()\n",
    "        self.input_layer=nn.Linear(input_size, neurons)\n",
    "        for i in range(depth):\n",
    "            self.layers.append(nn.Linear(neurons, neurons))\n",
    "            self.layers.append(nn.ReLU())\n",
    "            self.layers.append(nn.BatchNorm1d(neurons))\n",
    "            if add_Dropout:\n",
    "                self.layers.append(nn.Dropout())\n",
    "        self.final_layer=nn.Linear(neurons, n_char)\n",
    "\n",
    "    def forward(self, X):\n",
    "        x=self.input_layer(X)\n",
    "        for layer in self.layers:\n",
    "            x=layer(x)\n",
    "        x=self.final_layer(x)\n",
    "        \n",
    "        return x\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1620e36b-1775-44ff-bef0-5c024e18ced0",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ModularNN(\n",
       "  (layers): ModuleList(\n",
       "    (0): Linear(in_features=100, out_features=100, bias=True)\n",
       "    (1): ReLU()\n",
       "    (2): BatchNorm1d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (3): Dropout(p=0.5, inplace=False)\n",
       "    (4): Linear(in_features=100, out_features=100, bias=True)\n",
       "    (5): ReLU()\n",
       "    (6): BatchNorm1d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (7): Dropout(p=0.5, inplace=False)\n",
       "    (8): Linear(in_features=100, out_features=100, bias=True)\n",
       "    (9): ReLU()\n",
       "    (10): BatchNorm1d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (11): Dropout(p=0.5, inplace=False)\n",
       "    (12): Linear(in_features=100, out_features=100, bias=True)\n",
       "    (13): ReLU()\n",
       "    (14): BatchNorm1d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (15): Dropout(p=0.5, inplace=False)\n",
       "    (16): Linear(in_features=100, out_features=100, bias=True)\n",
       "    (17): ReLU()\n",
       "    (18): BatchNorm1d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (19): Dropout(p=0.5, inplace=False)\n",
       "  )\n",
       "  (input_layer): Linear(in_features=1024, out_features=100, bias=True)\n",
       "  (final_layer): Linear(in_features=100, out_features=28, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_2=ModularNN(n_char=28, input_size=1024, neurons=100, depth=5).to(device)\n",
    "model_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d5ee5587-9fe1-4fc9-ab96-a4aba84ddb99",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "def SingleTrain(layer_size, depth, num_epochs, learning_rate, printing = False, train_data=train_data, train_labels=train_labels, test_data=test_data, test_labels=test_labels, loss=nn.CrossEntropyLoss()):\n",
    "    model=model_2=ModularNN(n_char=28, input_size=1024, neurons=layer_size, depth=depth).to(device)\n",
    "    loss=loss\n",
    "    optimization= optim.Adam(params=model.parameters(), lr=learning_rate)\n",
    "\n",
    "    train_metrics={'loss': [], 'accuracy': [], 'f1_score': [], 'epoch': list(range(num_epoch))}\n",
    "\n",
    "    test_metrics = {'loss': [], 'accuracy': [], 'f1_score': [], 'epoch': list(range(num_epoch))}\n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "    \n",
    "        train_pred=model(train_data)\n",
    "        model.train()\n",
    "        \n",
    "        y_train = train_labels.type(torch.LongTensor) \n",
    "        \n",
    "        optimization.zero_grad()\n",
    "        \n",
    "        loss_epoch = loss(train_pred, y_train)\n",
    "        \n",
    "        f1_train, acc_train = metrics(train_pred, y_train, 28)\n",
    "        \n",
    "        loss_epoch.backward()\n",
    "        \n",
    "        optimization.step()\n",
    "        \n",
    "        train_metrics['loss'].append(loss_epoch.item())\n",
    "        train_metrics['accuracy'].append(acc_train.item()*100)\n",
    "        train_metrics['f1_score'].append(f1_train.item())\n",
    "    \n",
    "        model.eval()\n",
    "        with torch.inference_mode():\n",
    "            test_pred = model(test_data)\n",
    "            y_eval = test_labels.type(torch.LongTensor) \n",
    "            loss_eval=loss(test_pred, y_eval)\n",
    "            f1_test, acc_test = metrics(test_pred, torch.squeeze(test_labels), 28)\n",
    "            \n",
    "            test_metrics['accuracy'].append(acc_test.item()*100)\n",
    "            test_metrics['f1_score'].append(f1_test.item())\n",
    "            test_metrics['loss'].append(loss_eval.item())\n",
    "\n",
    "        if printing:\n",
    "            if epoch%10==0:\n",
    "                print(f'Epoch: {epoch} | Train loss: {loss_epoch:.5f} | Train accuracy: {acc_train*100:.3f}% | Train f1 {f1_train:.3f} | Test accuracy {acc_test*100:.3f}% | Test f1 {f1_test:.3f}')\n",
    "            \n",
    "\n",
    "    return train_metrics, test_metrics\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3125ba5-7a87-4f23-9db8-f1669ca3cc9c",
   "metadata": {},
   "source": [
    "Since there are quite a few possible parameter combinations to explore, I want to first narow down the range of parameters that I want to gather statistics for. In order to do this I am exploring the parameters in the standard way of randomly selecting the parameter for each training cycle. A quick visual inspection at this stage will tell us what parameter range to take a closer look at."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "26564c6a-bb73-43f6-a566-87e6815348bf",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "depths=[1,2,3,4, 5, 6, 7, 8, 9]\n",
    "layer_sizes=[30, 40, 50, 60, 70, 80, 90, 100, 110]\n",
    "#rates=[0.001, 0.0005, 0.0001]\n",
    "\n",
    "ran_depths = random.choices(depths, k=10)\n",
    "ran_sizes = random.choices(layer_sizes, k=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "94ef1685-5027-4ef3-92e2-742ac378a3a2",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 70\n",
      "Epoch: 0 | Train loss: 3.67240 | Train accuracy: 3.646% | Train f1 0.036 | Test accuracy 3.244% | Test f1 0.032\n",
      "Epoch: 10 | Train loss: 3.33032 | Train accuracy: 3.653% | Train f1 0.037 | Test accuracy 3.929% | Test f1 0.039\n",
      "Epoch: 20 | Train loss: 3.31747 | Train accuracy: 8.504% | Train f1 0.085 | Test accuracy 9.315% | Test f1 0.093\n",
      "Epoch: 30 | Train loss: 3.27022 | Train accuracy: 10.804% | Train f1 0.108 | Test accuracy 9.851% | Test f1 0.099\n",
      "Epoch: 40 | Train loss: 3.03967 | Train accuracy: 9.137% | Train f1 0.091 | Test accuracy 9.256% | Test f1 0.093\n",
      "Epoch: 50 | Train loss: 2.75305 | Train accuracy: 14.829% | Train f1 0.148 | Test accuracy 15.744% | Test f1 0.157\n",
      "Epoch: 60 | Train loss: 2.53738 | Train accuracy: 20.595% | Train f1 0.206 | Test accuracy 19.970% | Test f1 0.200\n",
      "Epoch: 70 | Train loss: 2.37349 | Train accuracy: 24.821% | Train f1 0.248 | Test accuracy 25.565% | Test f1 0.256\n",
      "Epoch: 80 | Train loss: 2.22343 | Train accuracy: 29.985% | Train f1 0.300 | Test accuracy 29.018% | Test f1 0.290\n",
      "Epoch: 90 | Train loss: 2.10133 | Train accuracy: 33.155% | Train f1 0.332 | Test accuracy 31.399% | Test f1 0.314\n",
      "1 down, 9 to go\n",
      "------------------------\n",
      "1 110\n",
      "Epoch: 0 | Train loss: 3.62281 | Train accuracy: 3.757% | Train f1 0.038 | Test accuracy 6.488% | Test f1 0.065\n",
      "Epoch: 10 | Train loss: 3.16937 | Train accuracy: 11.518% | Train f1 0.115 | Test accuracy 11.786% | Test f1 0.118\n",
      "Epoch: 20 | Train loss: 2.88582 | Train accuracy: 16.176% | Train f1 0.162 | Test accuracy 16.339% | Test f1 0.163\n",
      "Epoch: 30 | Train loss: 2.56024 | Train accuracy: 28.199% | Train f1 0.282 | Test accuracy 26.637% | Test f1 0.266\n",
      "Epoch: 40 | Train loss: 2.31518 | Train accuracy: 33.222% | Train f1 0.332 | Test accuracy 31.190% | Test f1 0.312\n",
      "Epoch: 50 | Train loss: 2.15258 | Train accuracy: 38.013% | Train f1 0.380 | Test accuracy 37.173% | Test f1 0.372\n",
      "Epoch: 60 | Train loss: 2.01929 | Train accuracy: 42.701% | Train f1 0.427 | Test accuracy 41.042% | Test f1 0.410\n",
      "Epoch: 70 | Train loss: 1.90275 | Train accuracy: 46.176% | Train f1 0.462 | Test accuracy 43.690% | Test f1 0.437\n",
      "Epoch: 80 | Train loss: 1.78427 | Train accuracy: 49.866% | Train f1 0.499 | Test accuracy 46.280% | Test f1 0.463\n",
      "Epoch: 90 | Train loss: 1.65551 | Train accuracy: 53.259% | Train f1 0.533 | Test accuracy 49.286% | Test f1 0.493\n",
      "2 down, 8 to go\n",
      "------------------------\n",
      "3 70\n",
      "Epoch: 0 | Train loss: 3.63235 | Train accuracy: 3.690% | Train f1 0.037 | Test accuracy 5.506% | Test f1 0.055\n",
      "Epoch: 10 | Train loss: 3.31664 | Train accuracy: 3.616% | Train f1 0.036 | Test accuracy 3.601% | Test f1 0.036\n",
      "Epoch: 20 | Train loss: 3.25986 | Train accuracy: 5.000% | Train f1 0.050 | Test accuracy 5.804% | Test f1 0.058\n",
      "Epoch: 30 | Train loss: 3.07432 | Train accuracy: 10.387% | Train f1 0.104 | Test accuracy 10.179% | Test f1 0.102\n",
      "Epoch: 40 | Train loss: 2.78889 | Train accuracy: 16.481% | Train f1 0.165 | Test accuracy 16.458% | Test f1 0.165\n",
      "Epoch: 50 | Train loss: 2.47814 | Train accuracy: 25.781% | Train f1 0.258 | Test accuracy 24.792% | Test f1 0.248\n",
      "Epoch: 60 | Train loss: 2.23456 | Train accuracy: 32.894% | Train f1 0.329 | Test accuracy 32.351% | Test f1 0.324\n",
      "Epoch: 70 | Train loss: 2.06694 | Train accuracy: 37.463% | Train f1 0.375 | Test accuracy 36.726% | Test f1 0.367\n",
      "Epoch: 80 | Train loss: 1.90833 | Train accuracy: 42.522% | Train f1 0.425 | Test accuracy 41.726% | Test f1 0.417\n",
      "Epoch: 90 | Train loss: 1.75402 | Train accuracy: 47.225% | Train f1 0.472 | Test accuracy 45.595% | Test f1 0.456\n",
      "3 down, 7 to go\n",
      "------------------------\n",
      "6 40\n",
      "Epoch: 0 | Train loss: 3.60673 | Train accuracy: 3.698% | Train f1 0.037 | Test accuracy 3.571% | Test f1 0.036\n",
      "Epoch: 10 | Train loss: 3.33437 | Train accuracy: 3.571% | Train f1 0.036 | Test accuracy 3.571% | Test f1 0.036\n",
      "Epoch: 20 | Train loss: 3.33315 | Train accuracy: 3.571% | Train f1 0.036 | Test accuracy 3.571% | Test f1 0.036\n",
      "Epoch: 30 | Train loss: 3.33236 | Train accuracy: 3.482% | Train f1 0.035 | Test accuracy 3.393% | Test f1 0.034\n",
      "Epoch: 40 | Train loss: 3.33151 | Train accuracy: 3.542% | Train f1 0.035 | Test accuracy 3.601% | Test f1 0.036\n",
      "Epoch: 50 | Train loss: 3.32979 | Train accuracy: 3.728% | Train f1 0.037 | Test accuracy 4.018% | Test f1 0.040\n",
      "Epoch: 60 | Train loss: 3.32433 | Train accuracy: 6.711% | Train f1 0.067 | Test accuracy 6.815% | Test f1 0.068\n",
      "Epoch: 70 | Train loss: 3.30027 | Train accuracy: 6.830% | Train f1 0.068 | Test accuracy 6.429% | Test f1 0.064\n",
      "Epoch: 80 | Train loss: 3.19247 | Train accuracy: 7.113% | Train f1 0.071 | Test accuracy 7.351% | Test f1 0.074\n",
      "Epoch: 90 | Train loss: 2.99173 | Train accuracy: 7.634% | Train f1 0.076 | Test accuracy 8.869% | Test f1 0.089\n",
      "4 down, 6 to go\n",
      "------------------------\n",
      "3 80\n",
      "Epoch: 0 | Train loss: 3.64471 | Train accuracy: 3.467% | Train f1 0.035 | Test accuracy 2.798% | Test f1 0.028\n",
      "Epoch: 10 | Train loss: 3.31760 | Train accuracy: 5.751% | Train f1 0.058 | Test accuracy 5.863% | Test f1 0.059\n",
      "Epoch: 20 | Train loss: 3.24427 | Train accuracy: 7.969% | Train f1 0.080 | Test accuracy 7.857% | Test f1 0.079\n",
      "Epoch: 30 | Train loss: 3.01393 | Train accuracy: 7.686% | Train f1 0.077 | Test accuracy 8.512% | Test f1 0.085\n",
      "Epoch: 40 | Train loss: 2.71011 | Train accuracy: 17.716% | Train f1 0.177 | Test accuracy 17.560% | Test f1 0.176\n",
      "Epoch: 50 | Train loss: 2.46213 | Train accuracy: 24.598% | Train f1 0.246 | Test accuracy 23.750% | Test f1 0.237\n",
      "Epoch: 60 | Train loss: 2.27806 | Train accuracy: 29.978% | Train f1 0.300 | Test accuracy 29.167% | Test f1 0.292\n",
      "Epoch: 70 | Train loss: 2.09158 | Train accuracy: 35.766% | Train f1 0.358 | Test accuracy 34.077% | Test f1 0.341\n",
      "Epoch: 80 | Train loss: 1.90182 | Train accuracy: 41.124% | Train f1 0.411 | Test accuracy 39.554% | Test f1 0.396\n",
      "Epoch: 90 | Train loss: 1.74409 | Train accuracy: 46.079% | Train f1 0.461 | Test accuracy 45.298% | Test f1 0.453\n",
      "5 down, 5 to go\n",
      "------------------------\n",
      "8 110\n",
      "Epoch: 0 | Train loss: 3.63096 | Train accuracy: 3.646% | Train f1 0.036 | Test accuracy 3.571% | Test f1 0.036\n",
      "Epoch: 10 | Train loss: 3.33237 | Train accuracy: 3.571% | Train f1 0.036 | Test accuracy 3.571% | Test f1 0.036\n",
      "Epoch: 20 | Train loss: 3.33221 | Train accuracy: 3.571% | Train f1 0.036 | Test accuracy 3.571% | Test f1 0.036\n",
      "Epoch: 30 | Train loss: 3.33220 | Train accuracy: 3.571% | Train f1 0.036 | Test accuracy 3.571% | Test f1 0.036\n",
      "Epoch: 40 | Train loss: 3.33218 | Train accuracy: 3.571% | Train f1 0.036 | Test accuracy 3.571% | Test f1 0.036\n",
      "Epoch: 50 | Train loss: 3.33215 | Train accuracy: 4.710% | Train f1 0.047 | Test accuracy 4.077% | Test f1 0.041\n",
      "Epoch: 60 | Train loss: 3.33213 | Train accuracy: 3.571% | Train f1 0.036 | Test accuracy 3.571% | Test f1 0.036\n",
      "Epoch: 70 | Train loss: 3.33207 | Train accuracy: 4.881% | Train f1 0.049 | Test accuracy 5.000% | Test f1 0.050\n",
      "Epoch: 80 | Train loss: 3.33193 | Train accuracy: 5.052% | Train f1 0.051 | Test accuracy 4.583% | Test f1 0.046\n",
      "Epoch: 90 | Train loss: 3.33138 | Train accuracy: 6.190% | Train f1 0.062 | Test accuracy 6.339% | Test f1 0.063\n",
      "6 down, 4 to go\n",
      "------------------------\n",
      "9 100\n",
      "Epoch: 0 | Train loss: 3.63598 | Train accuracy: 3.698% | Train f1 0.037 | Test accuracy 3.571% | Test f1 0.036\n",
      "Epoch: 10 | Train loss: 3.33272 | Train accuracy: 3.571% | Train f1 0.036 | Test accuracy 3.571% | Test f1 0.036\n",
      "Epoch: 20 | Train loss: 3.33227 | Train accuracy: 3.571% | Train f1 0.036 | Test accuracy 3.571% | Test f1 0.036\n",
      "Epoch: 30 | Train loss: 3.33221 | Train accuracy: 3.571% | Train f1 0.036 | Test accuracy 3.571% | Test f1 0.036\n",
      "Epoch: 40 | Train loss: 3.33219 | Train accuracy: 3.571% | Train f1 0.036 | Test accuracy 3.571% | Test f1 0.036\n",
      "Epoch: 50 | Train loss: 3.33216 | Train accuracy: 3.571% | Train f1 0.036 | Test accuracy 3.571% | Test f1 0.036\n",
      "Epoch: 60 | Train loss: 3.33213 | Train accuracy: 5.565% | Train f1 0.056 | Test accuracy 4.405% | Test f1 0.044\n",
      "Epoch: 70 | Train loss: 3.33207 | Train accuracy: 3.668% | Train f1 0.037 | Test accuracy 3.869% | Test f1 0.039\n",
      "Epoch: 80 | Train loss: 3.33192 | Train accuracy: 7.664% | Train f1 0.077 | Test accuracy 7.113% | Test f1 0.071\n",
      "Epoch: 90 | Train loss: 3.33131 | Train accuracy: 7.366% | Train f1 0.074 | Test accuracy 7.500% | Test f1 0.075\n",
      "7 down, 3 to go\n",
      "------------------------\n",
      "6 100\n",
      "Epoch: 0 | Train loss: 3.61260 | Train accuracy: 3.646% | Train f1 0.036 | Test accuracy 3.571% | Test f1 0.036\n",
      "Epoch: 10 | Train loss: 3.33273 | Train accuracy: 3.571% | Train f1 0.036 | Test accuracy 3.571% | Test f1 0.036\n",
      "Epoch: 20 | Train loss: 3.33183 | Train accuracy: 3.698% | Train f1 0.037 | Test accuracy 4.375% | Test f1 0.044\n",
      "Epoch: 30 | Train loss: 3.33066 | Train accuracy: 7.783% | Train f1 0.078 | Test accuracy 7.113% | Test f1 0.071\n",
      "Epoch: 40 | Train loss: 3.32533 | Train accuracy: 9.665% | Train f1 0.097 | Test accuracy 10.982% | Test f1 0.110\n",
      "Epoch: 50 | Train loss: 3.28635 | Train accuracy: 9.070% | Train f1 0.091 | Test accuracy 8.869% | Test f1 0.089\n",
      "Epoch: 60 | Train loss: 2.98058 | Train accuracy: 7.374% | Train f1 0.074 | Test accuracy 7.321% | Test f1 0.073\n",
      "Epoch: 70 | Train loss: 2.75515 | Train accuracy: 15.528% | Train f1 0.155 | Test accuracy 16.131% | Test f1 0.161\n",
      "Epoch: 80 | Train loss: 2.50697 | Train accuracy: 19.345% | Train f1 0.193 | Test accuracy 20.565% | Test f1 0.206\n",
      "Epoch: 90 | Train loss: 2.38649 | Train accuracy: 21.949% | Train f1 0.219 | Test accuracy 22.202% | Test f1 0.222\n",
      "8 down, 2 to go\n",
      "------------------------\n",
      "1 100\n",
      "Epoch: 0 | Train loss: 3.64305 | Train accuracy: 3.408% | Train f1 0.034 | Test accuracy 5.744% | Test f1 0.057\n",
      "Epoch: 10 | Train loss: 3.20211 | Train accuracy: 13.847% | Train f1 0.138 | Test accuracy 13.601% | Test f1 0.136\n",
      "Epoch: 20 | Train loss: 2.94527 | Train accuracy: 17.708% | Train f1 0.177 | Test accuracy 17.024% | Test f1 0.170\n",
      "Epoch: 30 | Train loss: 2.61911 | Train accuracy: 25.387% | Train f1 0.254 | Test accuracy 25.179% | Test f1 0.252\n",
      "Epoch: 40 | Train loss: 2.35025 | Train accuracy: 33.333% | Train f1 0.333 | Test accuracy 32.083% | Test f1 0.321\n",
      "Epoch: 50 | Train loss: 2.16914 | Train accuracy: 37.872% | Train f1 0.379 | Test accuracy 36.131% | Test f1 0.361\n",
      "Epoch: 60 | Train loss: 2.03707 | Train accuracy: 41.890% | Train f1 0.419 | Test accuracy 39.821% | Test f1 0.398\n",
      "Epoch: 70 | Train loss: 1.91896 | Train accuracy: 45.409% | Train f1 0.454 | Test accuracy 42.917% | Test f1 0.429\n",
      "Epoch: 80 | Train loss: 1.80360 | Train accuracy: 49.077% | Train f1 0.491 | Test accuracy 45.327% | Test f1 0.453\n",
      "Epoch: 90 | Train loss: 1.68356 | Train accuracy: 52.299% | Train f1 0.523 | Test accuracy 47.470% | Test f1 0.475\n",
      "9 down, 1 to go\n",
      "------------------------\n",
      "5 110\n",
      "Epoch: 0 | Train loss: 3.63955 | Train accuracy: 3.415% | Train f1 0.034 | Test accuracy 3.571% | Test f1 0.036\n",
      "Epoch: 10 | Train loss: 3.33206 | Train accuracy: 3.571% | Train f1 0.036 | Test accuracy 3.571% | Test f1 0.036\n",
      "Epoch: 20 | Train loss: 3.32963 | Train accuracy: 4.278% | Train f1 0.043 | Test accuracy 5.268% | Test f1 0.053\n",
      "Epoch: 30 | Train loss: 3.31945 | Train accuracy: 7.723% | Train f1 0.077 | Test accuracy 7.470% | Test f1 0.075\n",
      "Epoch: 40 | Train loss: 3.22796 | Train accuracy: 7.046% | Train f1 0.070 | Test accuracy 7.024% | Test f1 0.070\n",
      "Epoch: 50 | Train loss: 2.90201 | Train accuracy: 10.885% | Train f1 0.109 | Test accuracy 12.351% | Test f1 0.124\n",
      "Epoch: 60 | Train loss: 2.70104 | Train accuracy: 16.577% | Train f1 0.166 | Test accuracy 17.083% | Test f1 0.171\n",
      "Epoch: 70 | Train loss: 2.46398 | Train accuracy: 21.897% | Train f1 0.219 | Test accuracy 21.994% | Test f1 0.220\n",
      "Epoch: 80 | Train loss: 2.28261 | Train accuracy: 26.875% | Train f1 0.269 | Test accuracy 26.131% | Test f1 0.261\n",
      "Epoch: 90 | Train loss: 2.12375 | Train accuracy: 30.595% | Train f1 0.306 | Test accuracy 31.012% | Test f1 0.310\n",
      "10 down, 0 to go\n",
      "------------------------\n"
     ]
    }
   ],
   "source": [
    "training_data=[]\n",
    "testing_data=[]\n",
    "parameters={'depth':[], 'size':[]}\n",
    "k=0\n",
    "for depth, size in zip(ran_depths, ran_sizes):\n",
    "    print(depth, size)\n",
    "    tr_metric, ts_metric = SingleTrain(layer_size=size, depth=depth, num_epochs=100, learning_rate=0.001, printing=True)\n",
    "    training_data.append(tr_metric)\n",
    "    testing_data.append(ts_metric)\n",
    "    #parameters['rate'].append(rate) initially was looking into a range of learning rates, but very quickly found out that 0.001 is a good balance of speed and stability \n",
    "    parameters['depth'].append(depth)\n",
    "    parameters['size'].append(size)\n",
    "    k+=1\n",
    "    print(f'{k} down, {10-k} to go')\n",
    "    print('------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54f26520-ceb7-4203-a08b-401ab498b8df",
   "metadata": {},
   "source": [
    "These simple tests seem to indicate that a good number of layers is between 1 and 3, and a good neuron range seems to be 80-110 (if not immediatelly clear from a single run of the code I encourage trying 2-3 times and it should become clearer), so we can do even more tests in this narrower range, so that we can do some statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "99f310ba-4d43-47c5-8ae0-9c6fd89daf41",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 110\n",
      "1 down, 99 to go\n",
      "------------------------\n",
      "2 90\n",
      "2 down, 98 to go\n",
      "------------------------\n",
      "1 130\n",
      "3 down, 97 to go\n",
      "------------------------\n",
      "2 140\n",
      "4 down, 96 to go\n",
      "------------------------\n",
      "3 90\n",
      "5 down, 95 to go\n",
      "------------------------\n",
      "3 110\n",
      "6 down, 94 to go\n",
      "------------------------\n",
      "3 130\n",
      "7 down, 93 to go\n",
      "------------------------\n",
      "1 130\n",
      "8 down, 92 to go\n",
      "------------------------\n",
      "1 140\n",
      "9 down, 91 to go\n",
      "------------------------\n",
      "2 110\n",
      "10 down, 90 to go\n",
      "------------------------\n",
      "2 80\n",
      "11 down, 89 to go\n",
      "------------------------\n",
      "3 80\n",
      "12 down, 88 to go\n",
      "------------------------\n",
      "1 80\n",
      "13 down, 87 to go\n",
      "------------------------\n",
      "2 90\n",
      "14 down, 86 to go\n",
      "------------------------\n",
      "1 130\n",
      "15 down, 85 to go\n",
      "------------------------\n",
      "2 130\n",
      "16 down, 84 to go\n",
      "------------------------\n",
      "2 80\n",
      "17 down, 83 to go\n",
      "------------------------\n",
      "3 140\n",
      "18 down, 82 to go\n",
      "------------------------\n",
      "1 90\n",
      "19 down, 81 to go\n",
      "------------------------\n",
      "2 80\n",
      "20 down, 80 to go\n",
      "------------------------\n",
      "3 90\n",
      "21 down, 79 to go\n",
      "------------------------\n",
      "2 130\n",
      "22 down, 78 to go\n",
      "------------------------\n",
      "2 140\n",
      "23 down, 77 to go\n",
      "------------------------\n",
      "1 130\n",
      "24 down, 76 to go\n",
      "------------------------\n",
      "2 120\n",
      "25 down, 75 to go\n",
      "------------------------\n",
      "3 80\n",
      "26 down, 74 to go\n",
      "------------------------\n",
      "3 140\n",
      "27 down, 73 to go\n",
      "------------------------\n",
      "1 80\n",
      "28 down, 72 to go\n",
      "------------------------\n",
      "1 80\n",
      "29 down, 71 to go\n",
      "------------------------\n",
      "2 140\n",
      "30 down, 70 to go\n",
      "------------------------\n",
      "3 140\n",
      "31 down, 69 to go\n",
      "------------------------\n",
      "3 100\n",
      "32 down, 68 to go\n",
      "------------------------\n",
      "3 90\n",
      "33 down, 67 to go\n",
      "------------------------\n",
      "3 120\n",
      "34 down, 66 to go\n",
      "------------------------\n",
      "2 90\n",
      "35 down, 65 to go\n",
      "------------------------\n",
      "1 130\n",
      "36 down, 64 to go\n",
      "------------------------\n",
      "2 120\n",
      "37 down, 63 to go\n",
      "------------------------\n",
      "3 120\n",
      "38 down, 62 to go\n",
      "------------------------\n",
      "1 100\n",
      "39 down, 61 to go\n",
      "------------------------\n",
      "3 100\n",
      "40 down, 60 to go\n",
      "------------------------\n",
      "3 90\n",
      "41 down, 59 to go\n",
      "------------------------\n",
      "1 120\n",
      "42 down, 58 to go\n",
      "------------------------\n",
      "3 130\n",
      "43 down, 57 to go\n",
      "------------------------\n",
      "2 140\n",
      "44 down, 56 to go\n",
      "------------------------\n",
      "1 100\n",
      "45 down, 55 to go\n",
      "------------------------\n",
      "3 80\n",
      "46 down, 54 to go\n",
      "------------------------\n",
      "2 140\n",
      "47 down, 53 to go\n",
      "------------------------\n",
      "2 140\n",
      "48 down, 52 to go\n",
      "------------------------\n",
      "3 90\n",
      "49 down, 51 to go\n",
      "------------------------\n",
      "3 120\n",
      "50 down, 50 to go\n",
      "------------------------\n",
      "3 90\n",
      "51 down, 49 to go\n",
      "------------------------\n",
      "2 100\n",
      "52 down, 48 to go\n",
      "------------------------\n",
      "3 140\n",
      "53 down, 47 to go\n",
      "------------------------\n",
      "1 90\n",
      "54 down, 46 to go\n",
      "------------------------\n",
      "2 130\n",
      "55 down, 45 to go\n",
      "------------------------\n",
      "2 90\n",
      "56 down, 44 to go\n",
      "------------------------\n",
      "2 100\n",
      "57 down, 43 to go\n",
      "------------------------\n",
      "3 100\n",
      "58 down, 42 to go\n",
      "------------------------\n",
      "3 110\n",
      "59 down, 41 to go\n",
      "------------------------\n",
      "1 100\n",
      "60 down, 40 to go\n",
      "------------------------\n",
      "2 110\n",
      "61 down, 39 to go\n",
      "------------------------\n",
      "2 80\n",
      "62 down, 38 to go\n",
      "------------------------\n",
      "2 130\n",
      "63 down, 37 to go\n",
      "------------------------\n",
      "2 80\n",
      "64 down, 36 to go\n",
      "------------------------\n",
      "1 110\n",
      "65 down, 35 to go\n",
      "------------------------\n",
      "3 80\n",
      "66 down, 34 to go\n",
      "------------------------\n",
      "3 110\n",
      "67 down, 33 to go\n",
      "------------------------\n",
      "2 140\n",
      "68 down, 32 to go\n",
      "------------------------\n",
      "1 80\n",
      "69 down, 31 to go\n",
      "------------------------\n",
      "3 90\n",
      "70 down, 30 to go\n",
      "------------------------\n",
      "3 90\n",
      "71 down, 29 to go\n",
      "------------------------\n",
      "1 130\n",
      "72 down, 28 to go\n",
      "------------------------\n",
      "2 90\n",
      "73 down, 27 to go\n",
      "------------------------\n",
      "3 130\n",
      "74 down, 26 to go\n",
      "------------------------\n",
      "3 80\n",
      "75 down, 25 to go\n",
      "------------------------\n",
      "2 90\n",
      "76 down, 24 to go\n",
      "------------------------\n",
      "3 80\n",
      "77 down, 23 to go\n",
      "------------------------\n",
      "1 80\n",
      "78 down, 22 to go\n",
      "------------------------\n",
      "3 120\n",
      "79 down, 21 to go\n",
      "------------------------\n",
      "1 120\n",
      "80 down, 20 to go\n",
      "------------------------\n",
      "2 90\n",
      "81 down, 19 to go\n",
      "------------------------\n",
      "2 130\n",
      "82 down, 18 to go\n",
      "------------------------\n",
      "3 130\n",
      "83 down, 17 to go\n",
      "------------------------\n",
      "1 100\n",
      "84 down, 16 to go\n",
      "------------------------\n",
      "1 90\n",
      "85 down, 15 to go\n",
      "------------------------\n",
      "1 100\n",
      "86 down, 14 to go\n",
      "------------------------\n",
      "2 90\n",
      "87 down, 13 to go\n",
      "------------------------\n",
      "2 80\n",
      "88 down, 12 to go\n",
      "------------------------\n",
      "3 140\n",
      "89 down, 11 to go\n",
      "------------------------\n",
      "2 80\n",
      "90 down, 10 to go\n",
      "------------------------\n",
      "1 110\n",
      "91 down, 9 to go\n",
      "------------------------\n",
      "2 130\n",
      "92 down, 8 to go\n",
      "------------------------\n",
      "3 110\n",
      "93 down, 7 to go\n",
      "------------------------\n",
      "3 90\n",
      "94 down, 6 to go\n",
      "------------------------\n",
      "2 130\n",
      "95 down, 5 to go\n",
      "------------------------\n",
      "2 80\n",
      "96 down, 4 to go\n",
      "------------------------\n",
      "1 100\n",
      "97 down, 3 to go\n",
      "------------------------\n",
      "1 110\n",
      "98 down, 2 to go\n",
      "------------------------\n",
      "2 110\n",
      "99 down, 1 to go\n",
      "------------------------\n",
      "3 140\n",
      "100 down, 0 to go\n",
      "------------------------\n"
     ]
    }
   ],
   "source": [
    "depths_2=[1,2,3]\n",
    "layer_sizes_2=[80, 90, 100, 110, 120, 130, 140]\n",
    "\n",
    "ran_depths_2 = random.choices(depths_2, k=100)\n",
    "ran_sizes_2 = random.choices(layer_sizes_2, k=100)\n",
    "\n",
    "parameters={'loss_train':[], 'accuracy_train':[], 'f1_score_train':[],'loss_test':[], 'accuracy_test':[], 'f1_score_test':[], 'depth':[], 'size':[]}\n",
    "k=0\n",
    "for depth, size in zip(ran_depths_2, ran_sizes_2):\n",
    "    print(depth, size)\n",
    "    tr_metric, ts_metric = SingleTrain(layer_size=size, depth=depth, num_epochs=100, learning_rate=0.001, printing=False)\n",
    "    parameters['loss_train'].append(tr_metric['loss'][-1])\n",
    "    parameters['loss_test'].append(ts_metric['loss'][-1])\n",
    "    parameters['accuracy_train'].append(tr_metric['accuracy'][-1])\n",
    "    parameters['accuracy_test'].append(ts_metric['accuracy'][-1])\n",
    "    parameters['f1_score_train'].append(tr_metric['f1_score'][-1])\n",
    "    parameters['f1_score_test'].append(ts_metric['f1_score'][-1])\n",
    "    parameters['depth'].append(depth)\n",
    "    parameters['size'].append(size)\n",
    "    k+=1\n",
    "    print(f'{k} down, {100-k} to go')\n",
    "    print('------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02027034-3b3b-40a2-8b6c-d20c24ef09a9",
   "metadata": {},
   "source": [
    "Since I am not dealing with a lot of data points, visualizing the results is a bit noisy, so a simple table would actually be a better choice. Regardless this helps narrow down what hyperparameter range we need to look at. One could say this is somewhat overkill, since I managed to find a good value for probably the most important parameter (learning rate) rather quickly, and now we could just increase the number of epochs, and get the result that way. But it's always good develop a model, which will require a minimal number of epochs, since that can speed up retraining if needed, and also if additional data is ever introduced, having a model that trains as efficiently as possible is always beneficial. Further more, in theory deeper and bigger models will almost always produce better results, if enough data is available to avoid overfitting. But since we're dealing with a fixed dataset (though synthesising new data is always possible from existing data, but we're trying to keep things simple), we want to develop a model which can efficiently produce a reasonable results with the available data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "588ee97d-096e-47c0-aaf7-87896c3a94c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>loss_train</th>\n",
       "      <th>accuracy_train</th>\n",
       "      <th>f1_score_train</th>\n",
       "      <th>loss_test</th>\n",
       "      <th>accuracy_test</th>\n",
       "      <th>f1_score_test</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>depth</th>\n",
       "      <th>size</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">1</th>\n",
       "      <th>80</th>\n",
       "      <td>1.713008</td>\n",
       "      <td>51.831846</td>\n",
       "      <td>0.518318</td>\n",
       "      <td>1.802353</td>\n",
       "      <td>47.791666</td>\n",
       "      <td>0.477917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>1.651240</td>\n",
       "      <td>53.670633</td>\n",
       "      <td>0.536706</td>\n",
       "      <td>1.742775</td>\n",
       "      <td>49.265873</td>\n",
       "      <td>0.492659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>1.565843</td>\n",
       "      <td>55.783730</td>\n",
       "      <td>0.557837</td>\n",
       "      <td>1.665348</td>\n",
       "      <td>51.443453</td>\n",
       "      <td>0.514435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>1.520793</td>\n",
       "      <td>56.609003</td>\n",
       "      <td>0.566090</td>\n",
       "      <td>1.626778</td>\n",
       "      <td>51.979166</td>\n",
       "      <td>0.519792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>1.478554</td>\n",
       "      <td>57.857141</td>\n",
       "      <td>0.578571</td>\n",
       "      <td>1.596125</td>\n",
       "      <td>53.050596</td>\n",
       "      <td>0.530506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>1.427315</td>\n",
       "      <td>59.325396</td>\n",
       "      <td>0.593254</td>\n",
       "      <td>1.540667</td>\n",
       "      <td>54.399800</td>\n",
       "      <td>0.543998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>1.380409</td>\n",
       "      <td>60.624999</td>\n",
       "      <td>0.606250</td>\n",
       "      <td>1.509745</td>\n",
       "      <td>55.238098</td>\n",
       "      <td>0.552381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">2</th>\n",
       "      <th>80</th>\n",
       "      <td>1.528916</td>\n",
       "      <td>54.701451</td>\n",
       "      <td>0.547015</td>\n",
       "      <td>1.597025</td>\n",
       "      <td>52.012648</td>\n",
       "      <td>0.520126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>1.420249</td>\n",
       "      <td>57.348401</td>\n",
       "      <td>0.573484</td>\n",
       "      <td>1.502172</td>\n",
       "      <td>54.029017</td>\n",
       "      <td>0.540290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>1.339303</td>\n",
       "      <td>59.627977</td>\n",
       "      <td>0.596280</td>\n",
       "      <td>1.430699</td>\n",
       "      <td>55.654761</td>\n",
       "      <td>0.556548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>1.262838</td>\n",
       "      <td>61.718750</td>\n",
       "      <td>0.617188</td>\n",
       "      <td>1.368352</td>\n",
       "      <td>57.410713</td>\n",
       "      <td>0.574107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>1.196852</td>\n",
       "      <td>63.716519</td>\n",
       "      <td>0.637165</td>\n",
       "      <td>1.311917</td>\n",
       "      <td>59.107143</td>\n",
       "      <td>0.591071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>1.167203</td>\n",
       "      <td>64.346301</td>\n",
       "      <td>0.643463</td>\n",
       "      <td>1.298827</td>\n",
       "      <td>59.387754</td>\n",
       "      <td>0.593878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>1.104501</td>\n",
       "      <td>66.014031</td>\n",
       "      <td>0.660140</td>\n",
       "      <td>1.237867</td>\n",
       "      <td>60.918367</td>\n",
       "      <td>0.609184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">3</th>\n",
       "      <th>80</th>\n",
       "      <td>1.579390</td>\n",
       "      <td>51.143353</td>\n",
       "      <td>0.511434</td>\n",
       "      <td>1.640107</td>\n",
       "      <td>49.062500</td>\n",
       "      <td>0.490625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>1.498795</td>\n",
       "      <td>53.503639</td>\n",
       "      <td>0.535036</td>\n",
       "      <td>1.565714</td>\n",
       "      <td>51.216931</td>\n",
       "      <td>0.512169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>1.421589</td>\n",
       "      <td>55.451391</td>\n",
       "      <td>0.554514</td>\n",
       "      <td>1.504344</td>\n",
       "      <td>52.708334</td>\n",
       "      <td>0.527083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>1.365904</td>\n",
       "      <td>57.157737</td>\n",
       "      <td>0.571577</td>\n",
       "      <td>1.444664</td>\n",
       "      <td>54.352678</td>\n",
       "      <td>0.543527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>1.348036</td>\n",
       "      <td>57.602306</td>\n",
       "      <td>0.576023</td>\n",
       "      <td>1.438153</td>\n",
       "      <td>54.374999</td>\n",
       "      <td>0.543750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>1.259434</td>\n",
       "      <td>59.832589</td>\n",
       "      <td>0.598326</td>\n",
       "      <td>1.361953</td>\n",
       "      <td>56.197917</td>\n",
       "      <td>0.561979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>1.183805</td>\n",
       "      <td>61.949405</td>\n",
       "      <td>0.619494</td>\n",
       "      <td>1.298999</td>\n",
       "      <td>57.857142</td>\n",
       "      <td>0.578571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            loss_train  accuracy_train  f1_score_train  loss_test  \\\n",
       "depth size                                                          \n",
       "1     80      1.713008       51.831846        0.518318   1.802353   \n",
       "      90      1.651240       53.670633        0.536706   1.742775   \n",
       "      100     1.565843       55.783730        0.557837   1.665348   \n",
       "      110     1.520793       56.609003        0.566090   1.626778   \n",
       "      120     1.478554       57.857141        0.578571   1.596125   \n",
       "      130     1.427315       59.325396        0.593254   1.540667   \n",
       "      140     1.380409       60.624999        0.606250   1.509745   \n",
       "2     80      1.528916       54.701451        0.547015   1.597025   \n",
       "      90      1.420249       57.348401        0.573484   1.502172   \n",
       "      100     1.339303       59.627977        0.596280   1.430699   \n",
       "      110     1.262838       61.718750        0.617188   1.368352   \n",
       "      120     1.196852       63.716519        0.637165   1.311917   \n",
       "      130     1.167203       64.346301        0.643463   1.298827   \n",
       "      140     1.104501       66.014031        0.660140   1.237867   \n",
       "3     80      1.579390       51.143353        0.511434   1.640107   \n",
       "      90      1.498795       53.503639        0.535036   1.565714   \n",
       "      100     1.421589       55.451391        0.554514   1.504344   \n",
       "      110     1.365904       57.157737        0.571577   1.444664   \n",
       "      120     1.348036       57.602306        0.576023   1.438153   \n",
       "      130     1.259434       59.832589        0.598326   1.361953   \n",
       "      140     1.183805       61.949405        0.619494   1.298999   \n",
       "\n",
       "            accuracy_test  f1_score_test  \n",
       "depth size                                \n",
       "1     80        47.791666       0.477917  \n",
       "      90        49.265873       0.492659  \n",
       "      100       51.443453       0.514435  \n",
       "      110       51.979166       0.519792  \n",
       "      120       53.050596       0.530506  \n",
       "      130       54.399800       0.543998  \n",
       "      140       55.238098       0.552381  \n",
       "2     80        52.012648       0.520126  \n",
       "      90        54.029017       0.540290  \n",
       "      100       55.654761       0.556548  \n",
       "      110       57.410713       0.574107  \n",
       "      120       59.107143       0.591071  \n",
       "      130       59.387754       0.593878  \n",
       "      140       60.918367       0.609184  \n",
       "3     80        49.062500       0.490625  \n",
       "      90        51.216931       0.512169  \n",
       "      100       52.708334       0.527083  \n",
       "      110       54.352678       0.543527  \n",
       "      120       54.374999       0.543750  \n",
       "      130       56.197917       0.561979  \n",
       "      140       57.857142       0.578571  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters_df=pd.DataFrame(parameters)\n",
    "grouped=parameters_df.groupby(['depth', 'size']).mean()\n",
    "\n",
    "grouped"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f5df3cd-3b63-47a9-87ce-15be13d4698c",
   "metadata": {},
   "source": [
    "Looking at the result it's at least clear that 2 layers is the way to go, keeping in mind that this means 4 hidden layers total, including the output layer and the input layer. Given that it also seems that the model favours large amounts of neurons, we must be carefull to avoid overfitting, as that would cause more headaches since we would need more data. However knowing a rough area for playing with the overall architecture of the model is a good jumping off point for more fine tunning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e302a600-6c43-41dc-b645-70e7d5642e74",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 | Train loss: 3.65952 | Train accuracy: 3.237% | Train f1 0.032 | Test accuracy 4.196% | Test f1 0.042\n",
      "Epoch: 10 | Train loss: 3.26845 | Train accuracy: 12.031% | Train f1 0.120 | Test accuracy 11.935% | Test f1 0.119\n",
      "Epoch: 20 | Train loss: 3.02180 | Train accuracy: 15.357% | Train f1 0.154 | Test accuracy 15.506% | Test f1 0.155\n",
      "Epoch: 30 | Train loss: 2.62172 | Train accuracy: 22.307% | Train f1 0.223 | Test accuracy 22.798% | Test f1 0.228\n",
      "Epoch: 40 | Train loss: 2.32616 | Train accuracy: 31.615% | Train f1 0.316 | Test accuracy 32.292% | Test f1 0.323\n",
      "Epoch: 50 | Train loss: 2.13015 | Train accuracy: 38.207% | Train f1 0.382 | Test accuracy 37.173% | Test f1 0.372\n",
      "Epoch: 60 | Train loss: 1.96442 | Train accuracy: 42.850% | Train f1 0.428 | Test accuracy 41.726% | Test f1 0.417\n",
      "Epoch: 70 | Train loss: 1.79044 | Train accuracy: 48.021% | Train f1 0.480 | Test accuracy 45.744% | Test f1 0.457\n",
      "Epoch: 80 | Train loss: 1.60035 | Train accuracy: 52.932% | Train f1 0.529 | Test accuracy 50.446% | Test f1 0.504\n",
      "Epoch: 90 | Train loss: 1.40400 | Train accuracy: 57.649% | Train f1 0.576 | Test accuracy 54.018% | Test f1 0.540\n",
      "Epoch: 100 | Train loss: 1.21972 | Train accuracy: 63.118% | Train f1 0.631 | Test accuracy 57.798% | Test f1 0.578\n",
      "Epoch: 110 | Train loss: 1.07758 | Train accuracy: 67.046% | Train f1 0.670 | Test accuracy 60.030% | Test f1 0.600\n",
      "Epoch: 120 | Train loss: 0.95382 | Train accuracy: 70.134% | Train f1 0.701 | Test accuracy 63.512% | Test f1 0.635\n",
      "Epoch: 130 | Train loss: 0.85170 | Train accuracy: 73.237% | Train f1 0.732 | Test accuracy 65.238% | Test f1 0.652\n",
      "Epoch: 140 | Train loss: 0.76642 | Train accuracy: 75.848% | Train f1 0.758 | Test accuracy 67.113% | Test f1 0.671\n",
      "Epoch: 150 | Train loss: 0.69656 | Train accuracy: 78.013% | Train f1 0.780 | Test accuracy 68.482% | Test f1 0.685\n",
      "Epoch: 160 | Train loss: 0.63408 | Train accuracy: 80.030% | Train f1 0.800 | Test accuracy 69.405% | Test f1 0.694\n",
      "Epoch: 170 | Train loss: 0.57949 | Train accuracy: 82.001% | Train f1 0.820 | Test accuracy 69.881% | Test f1 0.699\n",
      "Epoch: 180 | Train loss: 0.53064 | Train accuracy: 83.631% | Train f1 0.836 | Test accuracy 70.506% | Test f1 0.705\n",
      "Epoch: 190 | Train loss: 0.48818 | Train accuracy: 84.866% | Train f1 0.849 | Test accuracy 71.220% | Test f1 0.712\n",
      "1 down, 9 to go\n",
      "------------------------\n",
      "Epoch: 0 | Train loss: 3.67055 | Train accuracy: 3.311% | Train f1 0.033 | Test accuracy 6.429% | Test f1 0.064\n",
      "Epoch: 10 | Train loss: 3.26855 | Train accuracy: 12.098% | Train f1 0.121 | Test accuracy 12.321% | Test f1 0.123\n",
      "Epoch: 20 | Train loss: 3.02868 | Train accuracy: 13.415% | Train f1 0.134 | Test accuracy 14.494% | Test f1 0.145\n",
      "Epoch: 30 | Train loss: 2.63737 | Train accuracy: 23.237% | Train f1 0.232 | Test accuracy 23.065% | Test f1 0.231\n",
      "Epoch: 40 | Train loss: 2.35106 | Train accuracy: 31.287% | Train f1 0.313 | Test accuracy 30.476% | Test f1 0.305\n",
      "Epoch: 50 | Train loss: 2.13829 | Train accuracy: 37.612% | Train f1 0.376 | Test accuracy 36.577% | Test f1 0.366\n",
      "Epoch: 60 | Train loss: 1.94888 | Train accuracy: 43.936% | Train f1 0.439 | Test accuracy 41.905% | Test f1 0.419\n",
      "Epoch: 70 | Train loss: 1.76525 | Train accuracy: 48.504% | Train f1 0.485 | Test accuracy 47.440% | Test f1 0.474\n",
      "Epoch: 80 | Train loss: 1.57945 | Train accuracy: 53.423% | Train f1 0.534 | Test accuracy 51.042% | Test f1 0.510\n",
      "Epoch: 90 | Train loss: 1.41219 | Train accuracy: 57.076% | Train f1 0.571 | Test accuracy 54.583% | Test f1 0.546\n",
      "Epoch: 100 | Train loss: 1.25545 | Train accuracy: 61.875% | Train f1 0.619 | Test accuracy 57.202% | Test f1 0.572\n",
      "Epoch: 110 | Train loss: 1.12064 | Train accuracy: 65.737% | Train f1 0.657 | Test accuracy 60.119% | Test f1 0.601\n",
      "Epoch: 120 | Train loss: 1.00056 | Train accuracy: 69.397% | Train f1 0.694 | Test accuracy 62.917% | Test f1 0.629\n",
      "Epoch: 130 | Train loss: 0.91267 | Train accuracy: 71.629% | Train f1 0.716 | Test accuracy 64.196% | Test f1 0.642\n",
      "Epoch: 140 | Train loss: 0.81704 | Train accuracy: 74.219% | Train f1 0.742 | Test accuracy 66.369% | Test f1 0.664\n",
      "Epoch: 150 | Train loss: 0.74261 | Train accuracy: 76.897% | Train f1 0.769 | Test accuracy 67.560% | Test f1 0.676\n",
      "Epoch: 160 | Train loss: 0.67919 | Train accuracy: 78.914% | Train f1 0.789 | Test accuracy 68.720% | Test f1 0.687\n",
      "Epoch: 170 | Train loss: 0.62157 | Train accuracy: 80.938% | Train f1 0.809 | Test accuracy 69.196% | Test f1 0.692\n",
      "Epoch: 180 | Train loss: 0.59198 | Train accuracy: 81.332% | Train f1 0.813 | Test accuracy 68.988% | Test f1 0.690\n",
      "Epoch: 190 | Train loss: 0.52772 | Train accuracy: 84.010% | Train f1 0.840 | Test accuracy 69.821% | Test f1 0.698\n",
      "2 down, 8 to go\n",
      "------------------------\n",
      "Epoch: 0 | Train loss: 3.61684 | Train accuracy: 4.115% | Train f1 0.041 | Test accuracy 4.940% | Test f1 0.049\n",
      "Epoch: 10 | Train loss: 3.25360 | Train accuracy: 10.439% | Train f1 0.104 | Test accuracy 10.446% | Test f1 0.104\n",
      "Epoch: 20 | Train loss: 2.95915 | Train accuracy: 12.388% | Train f1 0.124 | Test accuracy 12.708% | Test f1 0.127\n",
      "Epoch: 30 | Train loss: 2.56343 | Train accuracy: 24.888% | Train f1 0.249 | Test accuracy 24.107% | Test f1 0.241\n",
      "Epoch: 40 | Train loss: 2.28706 | Train accuracy: 33.199% | Train f1 0.332 | Test accuracy 31.607% | Test f1 0.316\n",
      "Epoch: 50 | Train loss: 2.09445 | Train accuracy: 39.040% | Train f1 0.390 | Test accuracy 36.994% | Test f1 0.370\n",
      "Epoch: 60 | Train loss: 1.92329 | Train accuracy: 44.390% | Train f1 0.444 | Test accuracy 42.202% | Test f1 0.422\n",
      "Epoch: 70 | Train loss: 1.72733 | Train accuracy: 49.628% | Train f1 0.496 | Test accuracy 47.827% | Test f1 0.478\n",
      "Epoch: 80 | Train loss: 1.51884 | Train accuracy: 55.253% | Train f1 0.553 | Test accuracy 52.679% | Test f1 0.527\n",
      "Epoch: 90 | Train loss: 1.32067 | Train accuracy: 59.963% | Train f1 0.600 | Test accuracy 57.143% | Test f1 0.571\n",
      "Epoch: 100 | Train loss: 1.16515 | Train accuracy: 64.747% | Train f1 0.647 | Test accuracy 59.911% | Test f1 0.599\n",
      "Epoch: 110 | Train loss: 1.02674 | Train accuracy: 68.586% | Train f1 0.686 | Test accuracy 63.065% | Test f1 0.631\n",
      "Epoch: 120 | Train loss: 0.90657 | Train accuracy: 71.808% | Train f1 0.718 | Test accuracy 65.119% | Test f1 0.651\n",
      "Epoch: 130 | Train loss: 0.80319 | Train accuracy: 75.015% | Train f1 0.750 | Test accuracy 67.083% | Test f1 0.671\n",
      "Epoch: 140 | Train loss: 0.73103 | Train accuracy: 77.143% | Train f1 0.771 | Test accuracy 68.631% | Test f1 0.686\n",
      "Epoch: 150 | Train loss: 0.65830 | Train accuracy: 79.487% | Train f1 0.795 | Test accuracy 68.958% | Test f1 0.690\n",
      "Epoch: 160 | Train loss: 0.60066 | Train accuracy: 81.592% | Train f1 0.816 | Test accuracy 70.000% | Test f1 0.700\n",
      "Epoch: 170 | Train loss: 0.54863 | Train accuracy: 83.103% | Train f1 0.831 | Test accuracy 70.298% | Test f1 0.703\n",
      "Epoch: 180 | Train loss: 0.50314 | Train accuracy: 84.896% | Train f1 0.849 | Test accuracy 70.298% | Test f1 0.703\n",
      "Epoch: 190 | Train loss: 0.46353 | Train accuracy: 86.034% | Train f1 0.860 | Test accuracy 70.446% | Test f1 0.704\n",
      "3 down, 7 to go\n",
      "------------------------\n",
      "Epoch: 0 | Train loss: 3.65934 | Train accuracy: 3.728% | Train f1 0.037 | Test accuracy 3.601% | Test f1 0.036\n",
      "Epoch: 10 | Train loss: 3.25019 | Train accuracy: 9.390% | Train f1 0.094 | Test accuracy 9.732% | Test f1 0.097\n",
      "Epoch: 20 | Train loss: 2.94813 | Train accuracy: 11.607% | Train f1 0.116 | Test accuracy 12.768% | Test f1 0.128\n",
      "Epoch: 30 | Train loss: 2.53742 | Train accuracy: 27.708% | Train f1 0.277 | Test accuracy 27.232% | Test f1 0.272\n",
      "Epoch: 40 | Train loss: 2.22452 | Train accuracy: 35.365% | Train f1 0.354 | Test accuracy 35.268% | Test f1 0.353\n",
      "Epoch: 50 | Train loss: 2.00957 | Train accuracy: 42.001% | Train f1 0.420 | Test accuracy 40.030% | Test f1 0.400\n",
      "Epoch: 60 | Train loss: 1.80859 | Train accuracy: 47.649% | Train f1 0.476 | Test accuracy 45.863% | Test f1 0.459\n",
      "Epoch: 70 | Train loss: 1.60174 | Train accuracy: 52.902% | Train f1 0.529 | Test accuracy 50.595% | Test f1 0.506\n",
      "Epoch: 80 | Train loss: 1.40102 | Train accuracy: 58.080% | Train f1 0.581 | Test accuracy 54.970% | Test f1 0.550\n",
      "Epoch: 90 | Train loss: 1.23754 | Train accuracy: 62.240% | Train f1 0.622 | Test accuracy 57.649% | Test f1 0.576\n",
      "Epoch: 100 | Train loss: 1.09967 | Train accuracy: 66.168% | Train f1 0.662 | Test accuracy 60.000% | Test f1 0.600\n",
      "Epoch: 110 | Train loss: 0.97068 | Train accuracy: 69.851% | Train f1 0.699 | Test accuracy 62.857% | Test f1 0.629\n",
      "Epoch: 120 | Train loss: 0.85746 | Train accuracy: 73.199% | Train f1 0.732 | Test accuracy 65.327% | Test f1 0.653\n",
      "Epoch: 130 | Train loss: 0.76811 | Train accuracy: 75.618% | Train f1 0.756 | Test accuracy 66.756% | Test f1 0.668\n",
      "Epoch: 140 | Train loss: 0.69774 | Train accuracy: 77.946% | Train f1 0.779 | Test accuracy 68.274% | Test f1 0.683\n",
      "Epoch: 150 | Train loss: 0.63157 | Train accuracy: 80.350% | Train f1 0.803 | Test accuracy 69.345% | Test f1 0.693\n",
      "Epoch: 160 | Train loss: 0.57890 | Train accuracy: 82.083% | Train f1 0.821 | Test accuracy 69.494% | Test f1 0.695\n",
      "Epoch: 170 | Train loss: 0.53456 | Train accuracy: 83.259% | Train f1 0.833 | Test accuracy 70.179% | Test f1 0.702\n",
      "Epoch: 180 | Train loss: 0.48816 | Train accuracy: 84.851% | Train f1 0.849 | Test accuracy 70.595% | Test f1 0.706\n",
      "Epoch: 190 | Train loss: 0.44805 | Train accuracy: 86.436% | Train f1 0.864 | Test accuracy 71.280% | Test f1 0.713\n",
      "4 down, 6 to go\n",
      "------------------------\n",
      "Epoch: 0 | Train loss: 3.65099 | Train accuracy: 3.698% | Train f1 0.037 | Test accuracy 7.113% | Test f1 0.071\n",
      "Epoch: 10 | Train loss: 3.23247 | Train accuracy: 12.031% | Train f1 0.120 | Test accuracy 11.964% | Test f1 0.120\n",
      "Epoch: 20 | Train loss: 2.88720 | Train accuracy: 14.479% | Train f1 0.145 | Test accuracy 14.821% | Test f1 0.148\n",
      "Epoch: 30 | Train loss: 2.46422 | Train accuracy: 27.277% | Train f1 0.273 | Test accuracy 26.161% | Test f1 0.262\n",
      "Epoch: 40 | Train loss: 2.19434 | Train accuracy: 36.057% | Train f1 0.361 | Test accuracy 35.476% | Test f1 0.355\n",
      "Epoch: 50 | Train loss: 1.97798 | Train accuracy: 43.043% | Train f1 0.430 | Test accuracy 41.310% | Test f1 0.413\n",
      "Epoch: 60 | Train loss: 1.77498 | Train accuracy: 48.430% | Train f1 0.484 | Test accuracy 46.756% | Test f1 0.468\n",
      "Epoch: 70 | Train loss: 1.55501 | Train accuracy: 54.293% | Train f1 0.543 | Test accuracy 51.607% | Test f1 0.516\n",
      "Epoch: 80 | Train loss: 1.37452 | Train accuracy: 59.025% | Train f1 0.590 | Test accuracy 55.952% | Test f1 0.560\n",
      "Epoch: 90 | Train loss: 1.20417 | Train accuracy: 62.612% | Train f1 0.626 | Test accuracy 58.720% | Test f1 0.587\n",
      "Epoch: 100 | Train loss: 1.06478 | Train accuracy: 67.016% | Train f1 0.670 | Test accuracy 61.280% | Test f1 0.613\n",
      "Epoch: 110 | Train loss: 0.94248 | Train accuracy: 70.647% | Train f1 0.706 | Test accuracy 64.673% | Test f1 0.647\n",
      "Epoch: 120 | Train loss: 0.83458 | Train accuracy: 73.951% | Train f1 0.740 | Test accuracy 66.518% | Test f1 0.665\n",
      "Epoch: 130 | Train loss: 0.74145 | Train accuracy: 76.726% | Train f1 0.767 | Test accuracy 67.917% | Test f1 0.679\n",
      "Epoch: 140 | Train loss: 0.67082 | Train accuracy: 78.765% | Train f1 0.788 | Test accuracy 68.899% | Test f1 0.689\n",
      "Epoch: 150 | Train loss: 0.60408 | Train accuracy: 81.280% | Train f1 0.813 | Test accuracy 70.030% | Test f1 0.700\n",
      "Epoch: 160 | Train loss: 0.54581 | Train accuracy: 83.229% | Train f1 0.832 | Test accuracy 70.714% | Test f1 0.707\n",
      "Epoch: 170 | Train loss: 0.49714 | Train accuracy: 84.859% | Train f1 0.849 | Test accuracy 70.982% | Test f1 0.710\n",
      "Epoch: 180 | Train loss: 0.47988 | Train accuracy: 84.896% | Train f1 0.849 | Test accuracy 71.577% | Test f1 0.716\n",
      "Epoch: 190 | Train loss: 0.41872 | Train accuracy: 87.693% | Train f1 0.877 | Test accuracy 71.964% | Test f1 0.720\n",
      "5 down, 5 to go\n",
      "------------------------\n",
      "Epoch: 0 | Train loss: 3.60068 | Train accuracy: 3.810% | Train f1 0.038 | Test accuracy 5.030% | Test f1 0.050\n",
      "Epoch: 10 | Train loss: 3.22212 | Train accuracy: 9.159% | Train f1 0.092 | Test accuracy 8.690% | Test f1 0.087\n",
      "Epoch: 20 | Train loss: 2.86346 | Train accuracy: 15.379% | Train f1 0.154 | Test accuracy 17.560% | Test f1 0.176\n",
      "Epoch: 30 | Train loss: 2.45298 | Train accuracy: 29.234% | Train f1 0.292 | Test accuracy 28.899% | Test f1 0.289\n",
      "Epoch: 40 | Train loss: 2.16724 | Train accuracy: 36.815% | Train f1 0.368 | Test accuracy 36.964% | Test f1 0.370\n",
      "Epoch: 50 | Train loss: 1.95688 | Train accuracy: 43.527% | Train f1 0.435 | Test accuracy 42.232% | Test f1 0.422\n",
      "Epoch: 60 | Train loss: 1.75409 | Train accuracy: 48.400% | Train f1 0.484 | Test accuracy 47.470% | Test f1 0.475\n",
      "Epoch: 70 | Train loss: 1.53977 | Train accuracy: 54.055% | Train f1 0.541 | Test accuracy 51.696% | Test f1 0.517\n",
      "Epoch: 80 | Train loss: 1.36656 | Train accuracy: 59.241% | Train f1 0.592 | Test accuracy 56.101% | Test f1 0.561\n",
      "Epoch: 90 | Train loss: 1.19783 | Train accuracy: 63.281% | Train f1 0.633 | Test accuracy 59.256% | Test f1 0.593\n",
      "Epoch: 100 | Train loss: 1.05316 | Train accuracy: 67.463% | Train f1 0.675 | Test accuracy 61.905% | Test f1 0.619\n",
      "Epoch: 110 | Train loss: 0.92646 | Train accuracy: 71.324% | Train f1 0.713 | Test accuracy 64.583% | Test f1 0.646\n",
      "Epoch: 120 | Train loss: 0.82862 | Train accuracy: 74.115% | Train f1 0.741 | Test accuracy 65.893% | Test f1 0.659\n",
      "Epoch: 130 | Train loss: 0.73250 | Train accuracy: 77.612% | Train f1 0.776 | Test accuracy 67.976% | Test f1 0.680\n",
      "Epoch: 140 | Train loss: 0.66178 | Train accuracy: 79.621% | Train f1 0.796 | Test accuracy 69.167% | Test f1 0.692\n",
      "Epoch: 150 | Train loss: 0.59971 | Train accuracy: 81.622% | Train f1 0.816 | Test accuracy 69.970% | Test f1 0.700\n",
      "Epoch: 160 | Train loss: 0.54928 | Train accuracy: 83.185% | Train f1 0.832 | Test accuracy 71.042% | Test f1 0.710\n",
      "Epoch: 170 | Train loss: 0.49447 | Train accuracy: 85.201% | Train f1 0.852 | Test accuracy 71.429% | Test f1 0.714\n",
      "Epoch: 180 | Train loss: 0.44996 | Train accuracy: 86.749% | Train f1 0.867 | Test accuracy 71.429% | Test f1 0.714\n",
      "Epoch: 190 | Train loss: 0.41278 | Train accuracy: 87.999% | Train f1 0.880 | Test accuracy 72.173% | Test f1 0.722\n",
      "6 down, 4 to go\n",
      "------------------------\n",
      "Epoch: 0 | Train loss: 3.66024 | Train accuracy: 3.400% | Train f1 0.034 | Test accuracy 4.524% | Test f1 0.045\n",
      "Epoch: 10 | Train loss: 3.21423 | Train accuracy: 11.347% | Train f1 0.113 | Test accuracy 12.530% | Test f1 0.125\n",
      "Epoch: 20 | Train loss: 2.80455 | Train accuracy: 17.798% | Train f1 0.178 | Test accuracy 19.405% | Test f1 0.194\n",
      "Epoch: 30 | Train loss: 2.37190 | Train accuracy: 31.391% | Train f1 0.314 | Test accuracy 30.893% | Test f1 0.309\n",
      "Epoch: 40 | Train loss: 2.12110 | Train accuracy: 38.490% | Train f1 0.385 | Test accuracy 37.560% | Test f1 0.376\n",
      "Epoch: 50 | Train loss: 1.90420 | Train accuracy: 44.509% | Train f1 0.445 | Test accuracy 43.333% | Test f1 0.433\n",
      "Epoch: 60 | Train loss: 1.67662 | Train accuracy: 50.789% | Train f1 0.508 | Test accuracy 48.750% | Test f1 0.488\n",
      "Epoch: 70 | Train loss: 1.43394 | Train accuracy: 56.853% | Train f1 0.569 | Test accuracy 53.958% | Test f1 0.540\n",
      "Epoch: 80 | Train loss: 1.25343 | Train accuracy: 60.350% | Train f1 0.603 | Test accuracy 56.845% | Test f1 0.568\n",
      "Epoch: 90 | Train loss: 1.10352 | Train accuracy: 65.655% | Train f1 0.657 | Test accuracy 60.238% | Test f1 0.602\n",
      "Epoch: 100 | Train loss: 0.97015 | Train accuracy: 69.665% | Train f1 0.697 | Test accuracy 62.887% | Test f1 0.629\n",
      "Epoch: 110 | Train loss: 0.85839 | Train accuracy: 73.296% | Train f1 0.733 | Test accuracy 65.327% | Test f1 0.653\n",
      "Epoch: 120 | Train loss: 0.76018 | Train accuracy: 76.257% | Train f1 0.763 | Test accuracy 67.292% | Test f1 0.673\n",
      "Epoch: 130 | Train loss: 0.68778 | Train accuracy: 77.939% | Train f1 0.779 | Test accuracy 67.738% | Test f1 0.677\n",
      "Epoch: 140 | Train loss: 0.61928 | Train accuracy: 80.975% | Train f1 0.810 | Test accuracy 69.137% | Test f1 0.691\n",
      "Epoch: 150 | Train loss: 0.56530 | Train accuracy: 82.612% | Train f1 0.826 | Test accuracy 70.060% | Test f1 0.701\n",
      "Epoch: 160 | Train loss: 0.51620 | Train accuracy: 84.278% | Train f1 0.843 | Test accuracy 70.893% | Test f1 0.709\n",
      "Epoch: 170 | Train loss: 0.47178 | Train accuracy: 85.722% | Train f1 0.857 | Test accuracy 71.071% | Test f1 0.711\n",
      "Epoch: 180 | Train loss: 0.43056 | Train accuracy: 87.321% | Train f1 0.873 | Test accuracy 71.369% | Test f1 0.714\n",
      "Epoch: 190 | Train loss: 0.40573 | Train accuracy: 87.842% | Train f1 0.878 | Test accuracy 71.756% | Test f1 0.718\n",
      "7 down, 3 to go\n",
      "------------------------\n",
      "Epoch: 0 | Train loss: 3.63845 | Train accuracy: 3.973% | Train f1 0.040 | Test accuracy 6.071% | Test f1 0.061\n",
      "Epoch: 10 | Train loss: 3.19371 | Train accuracy: 12.872% | Train f1 0.129 | Test accuracy 11.905% | Test f1 0.119\n",
      "Epoch: 20 | Train loss: 2.77308 | Train accuracy: 19.688% | Train f1 0.197 | Test accuracy 21.399% | Test f1 0.214\n",
      "Epoch: 30 | Train loss: 2.33249 | Train accuracy: 32.619% | Train f1 0.326 | Test accuracy 33.006% | Test f1 0.330\n",
      "Epoch: 40 | Train loss: 2.09354 | Train accuracy: 39.680% | Train f1 0.397 | Test accuracy 39.048% | Test f1 0.390\n",
      "Epoch: 50 | Train loss: 1.88866 | Train accuracy: 45.632% | Train f1 0.456 | Test accuracy 44.524% | Test f1 0.445\n",
      "Epoch: 60 | Train loss: 1.66056 | Train accuracy: 51.436% | Train f1 0.514 | Test accuracy 49.732% | Test f1 0.497\n",
      "Epoch: 70 | Train loss: 1.44153 | Train accuracy: 55.640% | Train f1 0.556 | Test accuracy 54.554% | Test f1 0.546\n",
      "Epoch: 80 | Train loss: 1.23730 | Train accuracy: 62.188% | Train f1 0.622 | Test accuracy 58.482% | Test f1 0.585\n",
      "Epoch: 90 | Train loss: 1.06891 | Train accuracy: 67.307% | Train f1 0.673 | Test accuracy 62.351% | Test f1 0.624\n",
      "Epoch: 100 | Train loss: 0.94969 | Train accuracy: 69.353% | Train f1 0.694 | Test accuracy 64.018% | Test f1 0.640\n",
      "Epoch: 110 | Train loss: 0.82896 | Train accuracy: 73.653% | Train f1 0.737 | Test accuracy 66.518% | Test f1 0.665\n",
      "Epoch: 120 | Train loss: 0.73864 | Train accuracy: 76.704% | Train f1 0.767 | Test accuracy 68.274% | Test f1 0.683\n",
      "Epoch: 130 | Train loss: 0.66361 | Train accuracy: 79.249% | Train f1 0.792 | Test accuracy 69.613% | Test f1 0.696\n",
      "Epoch: 140 | Train loss: 0.60279 | Train accuracy: 81.094% | Train f1 0.811 | Test accuracy 70.565% | Test f1 0.706\n",
      "Epoch: 150 | Train loss: 0.54957 | Train accuracy: 83.199% | Train f1 0.832 | Test accuracy 70.595% | Test f1 0.706\n",
      "Epoch: 160 | Train loss: 0.50001 | Train accuracy: 85.112% | Train f1 0.851 | Test accuracy 71.845% | Test f1 0.718\n",
      "Epoch: 170 | Train loss: 0.45610 | Train accuracy: 86.213% | Train f1 0.862 | Test accuracy 72.321% | Test f1 0.723\n",
      "Epoch: 180 | Train loss: 0.41416 | Train accuracy: 87.619% | Train f1 0.876 | Test accuracy 72.470% | Test f1 0.725\n",
      "Epoch: 190 | Train loss: 0.38028 | Train accuracy: 88.661% | Train f1 0.887 | Test accuracy 72.292% | Test f1 0.723\n",
      "8 down, 2 to go\n",
      "------------------------\n",
      "Epoch: 0 | Train loss: 3.64671 | Train accuracy: 3.460% | Train f1 0.035 | Test accuracy 4.405% | Test f1 0.044\n",
      "Epoch: 10 | Train loss: 3.19659 | Train accuracy: 12.307% | Train f1 0.123 | Test accuracy 12.351% | Test f1 0.124\n",
      "Epoch: 20 | Train loss: 2.78083 | Train accuracy: 16.562% | Train f1 0.166 | Test accuracy 16.905% | Test f1 0.169\n",
      "Epoch: 30 | Train loss: 2.37331 | Train accuracy: 30.513% | Train f1 0.305 | Test accuracy 30.119% | Test f1 0.301\n",
      "Epoch: 40 | Train loss: 2.12251 | Train accuracy: 38.757% | Train f1 0.388 | Test accuracy 37.649% | Test f1 0.376\n",
      "Epoch: 50 | Train loss: 1.89656 | Train accuracy: 45.446% | Train f1 0.454 | Test accuracy 43.869% | Test f1 0.439\n",
      "Epoch: 60 | Train loss: 1.65585 | Train accuracy: 51.912% | Train f1 0.519 | Test accuracy 50.179% | Test f1 0.502\n",
      "Epoch: 70 | Train loss: 1.41199 | Train accuracy: 57.537% | Train f1 0.575 | Test accuracy 54.970% | Test f1 0.550\n",
      "Epoch: 80 | Train loss: 1.22551 | Train accuracy: 62.753% | Train f1 0.628 | Test accuracy 58.006% | Test f1 0.580\n",
      "Epoch: 90 | Train loss: 1.06221 | Train accuracy: 66.815% | Train f1 0.668 | Test accuracy 62.024% | Test f1 0.620\n",
      "Epoch: 100 | Train loss: 0.92280 | Train accuracy: 71.451% | Train f1 0.715 | Test accuracy 65.268% | Test f1 0.653\n",
      "Epoch: 110 | Train loss: 0.80622 | Train accuracy: 75.060% | Train f1 0.751 | Test accuracy 67.619% | Test f1 0.676\n",
      "Epoch: 120 | Train loss: 0.71819 | Train accuracy: 77.188% | Train f1 0.772 | Test accuracy 69.048% | Test f1 0.690\n",
      "Epoch: 130 | Train loss: 0.63937 | Train accuracy: 80.074% | Train f1 0.801 | Test accuracy 70.417% | Test f1 0.704\n",
      "Epoch: 140 | Train loss: 0.56913 | Train accuracy: 82.560% | Train f1 0.826 | Test accuracy 71.488% | Test f1 0.715\n",
      "Epoch: 150 | Train loss: 0.51248 | Train accuracy: 84.256% | Train f1 0.843 | Test accuracy 72.083% | Test f1 0.721\n",
      "Epoch: 160 | Train loss: 0.46120 | Train accuracy: 85.990% | Train f1 0.860 | Test accuracy 72.560% | Test f1 0.726\n",
      "Epoch: 170 | Train loss: 0.41714 | Train accuracy: 87.307% | Train f1 0.873 | Test accuracy 72.798% | Test f1 0.728\n",
      "Epoch: 180 | Train loss: 0.38039 | Train accuracy: 88.482% | Train f1 0.885 | Test accuracy 73.482% | Test f1 0.735\n",
      "Epoch: 190 | Train loss: 0.34141 | Train accuracy: 90.060% | Train f1 0.901 | Test accuracy 73.780% | Test f1 0.738\n",
      "9 down, 1 to go\n",
      "------------------------\n",
      "Epoch: 0 | Train loss: 3.66430 | Train accuracy: 3.698% | Train f1 0.037 | Test accuracy 6.458% | Test f1 0.065\n",
      "Epoch: 10 | Train loss: 3.16690 | Train accuracy: 8.720% | Train f1 0.087 | Test accuracy 8.690% | Test f1 0.087\n",
      "Epoch: 20 | Train loss: 2.74478 | Train accuracy: 20.305% | Train f1 0.203 | Test accuracy 21.905% | Test f1 0.219\n",
      "Epoch: 30 | Train loss: 2.34855 | Train accuracy: 31.176% | Train f1 0.312 | Test accuracy 31.310% | Test f1 0.313\n",
      "Epoch: 40 | Train loss: 2.08826 | Train accuracy: 39.591% | Train f1 0.396 | Test accuracy 38.631% | Test f1 0.386\n",
      "Epoch: 50 | Train loss: 1.85415 | Train accuracy: 46.629% | Train f1 0.466 | Test accuracy 45.238% | Test f1 0.452\n",
      "Epoch: 60 | Train loss: 1.61170 | Train accuracy: 52.842% | Train f1 0.528 | Test accuracy 51.250% | Test f1 0.512\n",
      "Epoch: 70 | Train loss: 1.39208 | Train accuracy: 58.683% | Train f1 0.587 | Test accuracy 55.268% | Test f1 0.553\n",
      "Epoch: 80 | Train loss: 1.19223 | Train accuracy: 63.973% | Train f1 0.640 | Test accuracy 59.554% | Test f1 0.596\n",
      "Epoch: 90 | Train loss: 1.02544 | Train accuracy: 68.579% | Train f1 0.686 | Test accuracy 62.798% | Test f1 0.628\n",
      "Epoch: 100 | Train loss: 0.88659 | Train accuracy: 72.567% | Train f1 0.726 | Test accuracy 65.595% | Test f1 0.656\n",
      "Epoch: 110 | Train loss: 0.80222 | Train accuracy: 74.464% | Train f1 0.745 | Test accuracy 67.500% | Test f1 0.675\n",
      "Epoch: 120 | Train loss: 0.69156 | Train accuracy: 78.638% | Train f1 0.786 | Test accuracy 69.554% | Test f1 0.696\n",
      "Epoch: 130 | Train loss: 0.62699 | Train accuracy: 80.885% | Train f1 0.809 | Test accuracy 69.524% | Test f1 0.695\n",
      "Epoch: 140 | Train loss: 0.56433 | Train accuracy: 82.894% | Train f1 0.829 | Test accuracy 70.476% | Test f1 0.705\n",
      "Epoch: 150 | Train loss: 0.51528 | Train accuracy: 84.717% | Train f1 0.847 | Test accuracy 71.458% | Test f1 0.715\n",
      "Epoch: 160 | Train loss: 0.46709 | Train accuracy: 86.339% | Train f1 0.863 | Test accuracy 72.411% | Test f1 0.724\n",
      "Epoch: 170 | Train loss: 0.42427 | Train accuracy: 87.805% | Train f1 0.878 | Test accuracy 72.143% | Test f1 0.721\n",
      "Epoch: 180 | Train loss: 0.38394 | Train accuracy: 89.211% | Train f1 0.892 | Test accuracy 72.798% | Test f1 0.728\n",
      "Epoch: 190 | Train loss: 0.39580 | Train accuracy: 87.307% | Train f1 0.873 | Test accuracy 73.095% | Test f1 0.731\n",
      "10 down, 0 to go\n",
      "------------------------\n"
     ]
    }
   ],
   "source": [
    "layer_sizes_2=[110, 120, 130, 140, 150, 160, 170, 180, 190, 200]\n",
    "\n",
    "\n",
    "parameters_2={'loss_train':[], 'accuracy_train':[], 'f1_score_train':[],'loss_test':[], 'accuracy_test':[], 'f1_score_test':[], 'variance_check':[],'size':[]}\n",
    "k=0\n",
    "for size in layer_sizes_2:\n",
    "    tr_metric, ts_metric = SingleTrain(layer_size=size, depth=2, num_epochs=200, learning_rate=0.001, printing=True)\n",
    "    parameters_2['loss_train'].append(tr_metric['loss'][-1])\n",
    "    parameters_2['loss_test'].append(ts_metric['loss'][-1])\n",
    "    parameters_2['accuracy_train'].append(tr_metric['accuracy'][-1])\n",
    "    parameters_2['accuracy_test'].append(ts_metric['accuracy'][-1])\n",
    "    parameters_2['f1_score_train'].append(tr_metric['f1_score'][-1])\n",
    "    parameters_2['f1_score_test'].append(ts_metric['f1_score'][-1])\n",
    "    parameters_2['variance_check'].append(tr_metric['accuracy'][-1]-ts_metric['accuracy'][-1])\n",
    "    parameters_2['size'].append(size)\n",
    "    k+=1\n",
    "    print(f'{k} down, {len(layer_sizes_2)-k} to go')\n",
    "    print('------------------------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c4421615-c1ca-4532-a0f2-df6fff8a86d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loss_train</th>\n",
       "      <th>accuracy_train</th>\n",
       "      <th>f1_score_train</th>\n",
       "      <th>loss_test</th>\n",
       "      <th>accuracy_test</th>\n",
       "      <th>f1_score_test</th>\n",
       "      <th>variance_check</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.450455</td>\n",
       "      <td>86.175597</td>\n",
       "      <td>0.861756</td>\n",
       "      <td>0.936069</td>\n",
       "      <td>71.369046</td>\n",
       "      <td>0.713690</td>\n",
       "      <td>0.148066</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.494317</td>\n",
       "      <td>85.193455</td>\n",
       "      <td>0.851935</td>\n",
       "      <td>0.960649</td>\n",
       "      <td>70.654762</td>\n",
       "      <td>0.706548</td>\n",
       "      <td>0.145387</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.430709</td>\n",
       "      <td>87.046129</td>\n",
       "      <td>0.870461</td>\n",
       "      <td>0.931809</td>\n",
       "      <td>71.041667</td>\n",
       "      <td>0.710417</td>\n",
       "      <td>0.160045</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.414896</td>\n",
       "      <td>87.663692</td>\n",
       "      <td>0.876637</td>\n",
       "      <td>0.919671</td>\n",
       "      <td>71.577382</td>\n",
       "      <td>0.715774</td>\n",
       "      <td>0.160863</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.390486</td>\n",
       "      <td>88.340771</td>\n",
       "      <td>0.883408</td>\n",
       "      <td>0.904446</td>\n",
       "      <td>72.113097</td>\n",
       "      <td>0.721131</td>\n",
       "      <td>0.162277</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.378702</td>\n",
       "      <td>88.898808</td>\n",
       "      <td>0.888988</td>\n",
       "      <td>0.932353</td>\n",
       "      <td>72.351193</td>\n",
       "      <td>0.723512</td>\n",
       "      <td>0.165476</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.363621</td>\n",
       "      <td>89.389879</td>\n",
       "      <td>0.893899</td>\n",
       "      <td>0.905567</td>\n",
       "      <td>72.113097</td>\n",
       "      <td>0.721131</td>\n",
       "      <td>0.172768</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.353388</td>\n",
       "      <td>89.568454</td>\n",
       "      <td>0.895685</td>\n",
       "      <td>0.908956</td>\n",
       "      <td>73.273808</td>\n",
       "      <td>0.732738</td>\n",
       "      <td>0.162946</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.311376</td>\n",
       "      <td>91.227680</td>\n",
       "      <td>0.912277</td>\n",
       "      <td>0.879120</td>\n",
       "      <td>74.255955</td>\n",
       "      <td>0.742560</td>\n",
       "      <td>0.169717</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.337385</td>\n",
       "      <td>89.866072</td>\n",
       "      <td>0.898661</td>\n",
       "      <td>0.892732</td>\n",
       "      <td>73.363096</td>\n",
       "      <td>0.733631</td>\n",
       "      <td>0.165030</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   loss_train  accuracy_train  f1_score_train  loss_test  accuracy_test  \\\n",
       "0    0.450455       86.175597        0.861756   0.936069      71.369046   \n",
       "1    0.494317       85.193455        0.851935   0.960649      70.654762   \n",
       "2    0.430709       87.046129        0.870461   0.931809      71.041667   \n",
       "3    0.414896       87.663692        0.876637   0.919671      71.577382   \n",
       "4    0.390486       88.340771        0.883408   0.904446      72.113097   \n",
       "5    0.378702       88.898808        0.888988   0.932353      72.351193   \n",
       "6    0.363621       89.389879        0.893899   0.905567      72.113097   \n",
       "7    0.353388       89.568454        0.895685   0.908956      73.273808   \n",
       "8    0.311376       91.227680        0.912277   0.879120      74.255955   \n",
       "9    0.337385       89.866072        0.898661   0.892732      73.363096   \n",
       "\n",
       "   f1_score_test  variance_check  size  \n",
       "0       0.713690        0.148066   110  \n",
       "1       0.706548        0.145387   120  \n",
       "2       0.710417        0.160045   130  \n",
       "3       0.715774        0.160863   140  \n",
       "4       0.721131        0.162277   150  \n",
       "5       0.723512        0.165476   160  \n",
       "6       0.721131        0.172768   170  \n",
       "7       0.732738        0.162946   180  \n",
       "8       0.742560        0.169717   190  \n",
       "9       0.733631        0.165030   200  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters_layer=pd.DataFrame(parameters_2)\n",
    "parameters_layer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "095fb511-17f2-4cfd-bd92-5ce04d6bce41",
   "metadata": {},
   "source": [
    "As can be clearly seen, while the performance might be increasing, the difference in the f1 scores between the training and test set seems to suggest, that overfitting starts to become a greater concern for higher node numbers. Therefore, I'll go with a model with 2 inner hidden layers (so 3 total) and 160 nodes per inner layer. Since the results gave us a rather shallow network, this result might change for the final layer, since we will test how the size of the final layer affects the result, which we will investigate next."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1a8bb1c1-8548-4d23-a52c-11c974354194",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "class TestingTails(nn.Module):\n",
    "    def __init__(self, n_char, nodes_inner, nodes_outer):\n",
    "        super().__init__()\n",
    "        self.inner=nn.Linear(1024, nodes_inner)\n",
    "        self.hidden1=nn.Linear(nodes_inner, 160)\n",
    "        self.hidden2=nn.Linear(160,nodes_outer)\n",
    "        self.outer=nn.Linear(nodes_outer, n_char)\n",
    "        self.relu=nn.ReLU()\n",
    "        self.dropout=nn.Dropout()\n",
    "        self.batch1=nn.BatchNorm1d(nodes_inner)\n",
    "        self.batch2=nn.BatchNorm1d(160)\n",
    "        self.batch3=nn.BatchNorm1d(nodes_outer)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        X = self.inner(x)\n",
    "        X = self.relu(X)\n",
    "        X = self.batch1(X)\n",
    "        X = self.dropout(X)\n",
    "        \n",
    "        X = self.hidden1(X)\n",
    "        X = self.relu(X)\n",
    "        X = self.batch2(X)\n",
    "        X = self.dropout(X)\n",
    "        \n",
    "        X = self.hidden2(X)\n",
    "        X = self.relu(X)\n",
    "        X = self.batch3(X)\n",
    "        X = self.dropout(X)\n",
    "        \n",
    "        \n",
    "        X = self.outer(X)\n",
    "        \n",
    "        return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cff2c4b7-b1f5-4dde-bae6-db972fb0bbb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TestingTails(\n",
       "  (inner): Linear(in_features=1024, out_features=10, bias=True)\n",
       "  (hidden1): Linear(in_features=10, out_features=160, bias=True)\n",
       "  (hidden2): Linear(in_features=160, out_features=160, bias=True)\n",
       "  (outer): Linear(in_features=160, out_features=28, bias=True)\n",
       "  (relu): ReLU()\n",
       "  (dropout): Dropout(p=0.5, inplace=False)\n",
       "  (batch1): BatchNorm1d(10, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (batch2): BatchNorm1d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (batch3): BatchNorm1d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       ")"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_3=TestingTails(n_char=28, nodes_inner=10, nodes_outer=160).to(device)\n",
    "model_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "95db9177-8a4f-4294-a118-83826c49727b",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "def SingleTrain_tails(nodes_inner, nodes_outer, num_epochs, learning_rate,printing = False, train_data=train_data, train_labels=train_labels, test_data=test_data, test_labels=test_labels, loss=nn.CrossEntropyLoss()):\n",
    "    model=model_2=TestingTails(n_char=28, nodes_inner=nodes_inner, nodes_outer=nodes_outer).to(device)\n",
    "    loss=loss\n",
    "    optimization= optim.Adam(params=model.parameters(), lr=learning_rate)\n",
    "\n",
    "    train_metrics={'loss': [], 'accuracy': [], 'f1_score': [], 'epoch': list(range(num_epoch))}\n",
    "\n",
    "    test_metrics = {'loss': [], 'accuracy': [], 'f1_score': [], 'epoch': list(range(num_epoch))}\n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "    \n",
    "        train_pred=model(train_data)\n",
    "        model.train()\n",
    "        \n",
    "        y_train = torch.squeeze(train_labels).type(torch.LongTensor) \n",
    "        \n",
    "        optimization.zero_grad()\n",
    "        \n",
    "        loss_epoch = loss(train_pred, y_train)\n",
    "        \n",
    "        f1_train, acc_train = metrics(train_pred, y_train, 28)\n",
    "        \n",
    "        loss_epoch.backward()\n",
    "        \n",
    "        optimization.step()\n",
    "        \n",
    "        train_metrics['loss'].append(loss_epoch.item())\n",
    "        train_metrics['accuracy'].append(acc_train.item()*100)\n",
    "        train_metrics['f1_score'].append(f1_train.item())\n",
    "    \n",
    "        model.eval()\n",
    "        with torch.inference_mode():\n",
    "            test_pred = model(test_data)\n",
    "            y_eval = torch.squeeze(test_labels).type(torch.LongTensor) \n",
    "            loss_eval=loss(test_pred, y_eval)\n",
    "            f1_test, acc_test = metrics(test_pred, torch.squeeze(test_labels), 28)\n",
    "            \n",
    "            test_metrics['accuracy'].append(acc_test.item()*100)\n",
    "            test_metrics['f1_score'].append(f1_test.item())\n",
    "            test_metrics['loss'].append(loss_eval.item())\n",
    "\n",
    "        if printing:\n",
    "            if epoch%10==0:\n",
    "                print(f'Epoch: {epoch} | Train loss: {loss_epoch:.5f} | Train accuracy: {acc_train*100:.3f}% | Train f1 {f1_train:.3f} | Test accuracy {acc_test*100:.3f}% | Test f1 {f1_test:.3f}')\n",
    "            \n",
    "\n",
    "    return train_metrics, test_metrics\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3ea4bb90-5bd9-424f-8905-47c2b9efa18b",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 | Train loss: 3.64819 | Train accuracy: 3.624% | Train f1 0.036 | Test accuracy 3.720% | Test f1 0.037\n",
      "Epoch: 10 | Train loss: 3.30349 | Train accuracy: 5.521% | Train f1 0.055 | Test accuracy 5.863% | Test f1 0.059\n",
      "Epoch: 20 | Train loss: 3.16840 | Train accuracy: 9.010% | Train f1 0.090 | Test accuracy 9.435% | Test f1 0.094\n",
      "Epoch: 30 | Train loss: 2.84542 | Train accuracy: 14.725% | Train f1 0.147 | Test accuracy 16.280% | Test f1 0.163\n",
      "Epoch: 40 | Train loss: 2.45138 | Train accuracy: 29.829% | Train f1 0.298 | Test accuracy 28.810% | Test f1 0.288\n",
      "Epoch: 50 | Train loss: 2.14234 | Train accuracy: 37.016% | Train f1 0.370 | Test accuracy 35.387% | Test f1 0.354\n",
      "Epoch: 60 | Train loss: 1.89744 | Train accuracy: 43.929% | Train f1 0.439 | Test accuracy 42.411% | Test f1 0.424\n",
      "Epoch: 70 | Train loss: 1.67490 | Train accuracy: 49.792% | Train f1 0.498 | Test accuracy 48.155% | Test f1 0.482\n",
      "Epoch: 80 | Train loss: 1.47785 | Train accuracy: 54.955% | Train f1 0.550 | Test accuracy 52.560% | Test f1 0.526\n",
      "Epoch: 90 | Train loss: 1.29759 | Train accuracy: 58.862% | Train f1 0.589 | Test accuracy 55.565% | Test f1 0.556\n"
     ]
    }
   ],
   "source": [
    "tr, ts = SingleTrain_tails(nodes_inner=40, nodes_outer=160, num_epochs=100, learning_rate = 0.001,printing = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "03660b4f-5d13-446a-a1de-0624e9cc5816",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "Epoch: 0 | Train loss: 3.65152 | Train accuracy: 3.676% | Train f1 0.037 | Test accuracy 3.839% | Test f1 0.038\n",
      "Epoch: 10 | Train loss: 3.31459 | Train accuracy: 5.022% | Train f1 0.050 | Test accuracy 4.732% | Test f1 0.047\n",
      "Epoch: 20 | Train loss: 3.22307 | Train accuracy: 7.351% | Train f1 0.074 | Test accuracy 7.827% | Test f1 0.078\n",
      "Epoch: 30 | Train loss: 3.01646 | Train accuracy: 11.890% | Train f1 0.119 | Test accuracy 12.113% | Test f1 0.121\n",
      "Epoch: 40 | Train loss: 2.72906 | Train accuracy: 19.077% | Train f1 0.191 | Test accuracy 17.887% | Test f1 0.179\n",
      "Epoch: 50 | Train loss: 2.46083 | Train accuracy: 27.188% | Train f1 0.272 | Test accuracy 26.042% | Test f1 0.260\n",
      "Epoch: 60 | Train loss: 2.22889 | Train accuracy: 33.073% | Train f1 0.331 | Test accuracy 33.482% | Test f1 0.335\n",
      "Epoch: 70 | Train loss: 2.00596 | Train accuracy: 38.720% | Train f1 0.387 | Test accuracy 38.393% | Test f1 0.384\n",
      "Epoch: 80 | Train loss: 1.79985 | Train accuracy: 44.978% | Train f1 0.450 | Test accuracy 43.571% | Test f1 0.436\n",
      "Epoch: 90 | Train loss: 1.63013 | Train accuracy: 49.137% | Train f1 0.491 | Test accuracy 47.679% | Test f1 0.477\n",
      "1 down, 14 to go\n",
      "------------------------\n",
      "20\n",
      "Epoch: 0 | Train loss: 3.64017 | Train accuracy: 3.497% | Train f1 0.035 | Test accuracy 4.077% | Test f1 0.041\n",
      "Epoch: 10 | Train loss: 3.29779 | Train accuracy: 6.659% | Train f1 0.067 | Test accuracy 6.935% | Test f1 0.069\n",
      "Epoch: 20 | Train loss: 3.15332 | Train accuracy: 8.185% | Train f1 0.082 | Test accuracy 7.917% | Test f1 0.079\n",
      "Epoch: 30 | Train loss: 2.86386 | Train accuracy: 14.702% | Train f1 0.147 | Test accuracy 15.685% | Test f1 0.157\n",
      "Epoch: 40 | Train loss: 2.53794 | Train accuracy: 26.332% | Train f1 0.263 | Test accuracy 25.595% | Test f1 0.256\n",
      "Epoch: 50 | Train loss: 2.24092 | Train accuracy: 33.393% | Train f1 0.334 | Test accuracy 32.411% | Test f1 0.324\n",
      "Epoch: 60 | Train loss: 2.01291 | Train accuracy: 39.799% | Train f1 0.398 | Test accuracy 38.185% | Test f1 0.382\n",
      "Epoch: 70 | Train loss: 1.80783 | Train accuracy: 45.551% | Train f1 0.456 | Test accuracy 44.911% | Test f1 0.449\n",
      "Epoch: 80 | Train loss: 1.62102 | Train accuracy: 50.871% | Train f1 0.509 | Test accuracy 49.554% | Test f1 0.496\n",
      "Epoch: 90 | Train loss: 1.46700 | Train accuracy: 54.821% | Train f1 0.548 | Test accuracy 52.173% | Test f1 0.522\n",
      "2 down, 13 to go\n",
      "------------------------\n",
      "30\n",
      "Epoch: 0 | Train loss: 3.63634 | Train accuracy: 3.720% | Train f1 0.037 | Test accuracy 3.899% | Test f1 0.039\n",
      "Epoch: 10 | Train loss: 3.31150 | Train accuracy: 10.930% | Train f1 0.109 | Test accuracy 11.637% | Test f1 0.116\n",
      "Epoch: 20 | Train loss: 3.20786 | Train accuracy: 9.926% | Train f1 0.099 | Test accuracy 9.792% | Test f1 0.098\n",
      "Epoch: 30 | Train loss: 2.88134 | Train accuracy: 14.092% | Train f1 0.141 | Test accuracy 13.750% | Test f1 0.138\n",
      "Epoch: 40 | Train loss: 2.52566 | Train accuracy: 24.539% | Train f1 0.245 | Test accuracy 25.030% | Test f1 0.250\n",
      "Epoch: 50 | Train loss: 2.25473 | Train accuracy: 33.073% | Train f1 0.331 | Test accuracy 32.500% | Test f1 0.325\n",
      "Epoch: 60 | Train loss: 2.00046 | Train accuracy: 39.866% | Train f1 0.399 | Test accuracy 39.077% | Test f1 0.391\n",
      "Epoch: 70 | Train loss: 1.78847 | Train accuracy: 46.243% | Train f1 0.462 | Test accuracy 44.881% | Test f1 0.449\n",
      "Epoch: 80 | Train loss: 1.58546 | Train accuracy: 51.830% | Train f1 0.518 | Test accuracy 50.863% | Test f1 0.509\n",
      "Epoch: 90 | Train loss: 1.39298 | Train accuracy: 57.225% | Train f1 0.572 | Test accuracy 55.298% | Test f1 0.553\n",
      "3 down, 12 to go\n",
      "------------------------\n",
      "40\n",
      "Epoch: 0 | Train loss: 3.63362 | Train accuracy: 4.055% | Train f1 0.041 | Test accuracy 4.554% | Test f1 0.046\n",
      "Epoch: 10 | Train loss: 3.28484 | Train accuracy: 7.507% | Train f1 0.075 | Test accuracy 7.292% | Test f1 0.073\n",
      "Epoch: 20 | Train loss: 3.08682 | Train accuracy: 8.028% | Train f1 0.080 | Test accuracy 8.274% | Test f1 0.083\n",
      "Epoch: 30 | Train loss: 2.77953 | Train accuracy: 19.613% | Train f1 0.196 | Test accuracy 20.446% | Test f1 0.204\n",
      "Epoch: 40 | Train loss: 2.44329 | Train accuracy: 29.725% | Train f1 0.297 | Test accuracy 29.375% | Test f1 0.294\n",
      "Epoch: 50 | Train loss: 2.11649 | Train accuracy: 38.080% | Train f1 0.381 | Test accuracy 37.113% | Test f1 0.371\n",
      "Epoch: 60 | Train loss: 1.85675 | Train accuracy: 44.501% | Train f1 0.445 | Test accuracy 43.095% | Test f1 0.431\n",
      "Epoch: 70 | Train loss: 1.65557 | Train accuracy: 50.037% | Train f1 0.500 | Test accuracy 47.440% | Test f1 0.474\n",
      "Epoch: 80 | Train loss: 1.47719 | Train accuracy: 54.933% | Train f1 0.549 | Test accuracy 52.083% | Test f1 0.521\n",
      "Epoch: 90 | Train loss: 1.31223 | Train accuracy: 59.129% | Train f1 0.591 | Test accuracy 55.685% | Test f1 0.557\n",
      "4 down, 11 to go\n",
      "------------------------\n",
      "50\n",
      "Epoch: 0 | Train loss: 3.65076 | Train accuracy: 3.586% | Train f1 0.036 | Test accuracy 2.649% | Test f1 0.026\n",
      "Epoch: 10 | Train loss: 3.30652 | Train accuracy: 13.638% | Train f1 0.136 | Test accuracy 13.869% | Test f1 0.139\n",
      "Epoch: 20 | Train loss: 3.16505 | Train accuracy: 9.010% | Train f1 0.090 | Test accuracy 8.750% | Test f1 0.087\n",
      "Epoch: 30 | Train loss: 2.81043 | Train accuracy: 14.792% | Train f1 0.148 | Test accuracy 16.935% | Test f1 0.169\n",
      "Epoch: 40 | Train loss: 2.45291 | Train accuracy: 26.815% | Train f1 0.268 | Test accuracy 26.310% | Test f1 0.263\n",
      "Epoch: 50 | Train loss: 2.16754 | Train accuracy: 35.670% | Train f1 0.357 | Test accuracy 34.464% | Test f1 0.345\n",
      "Epoch: 60 | Train loss: 1.90526 | Train accuracy: 42.961% | Train f1 0.430 | Test accuracy 41.369% | Test f1 0.414\n",
      "Epoch: 70 | Train loss: 1.66480 | Train accuracy: 49.747% | Train f1 0.497 | Test accuracy 48.363% | Test f1 0.484\n",
      "Epoch: 80 | Train loss: 1.46832 | Train accuracy: 54.405% | Train f1 0.544 | Test accuracy 52.411% | Test f1 0.524\n",
      "Epoch: 90 | Train loss: 1.28822 | Train accuracy: 59.821% | Train f1 0.598 | Test accuracy 56.696% | Test f1 0.567\n",
      "5 down, 10 to go\n",
      "------------------------\n",
      "60\n",
      "Epoch: 0 | Train loss: 3.60568 | Train accuracy: 3.757% | Train f1 0.038 | Test accuracy 4.673% | Test f1 0.047\n",
      "Epoch: 10 | Train loss: 3.28495 | Train accuracy: 6.600% | Train f1 0.066 | Test accuracy 7.262% | Test f1 0.073\n",
      "Epoch: 20 | Train loss: 3.05949 | Train accuracy: 11.674% | Train f1 0.117 | Test accuracy 11.696% | Test f1 0.117\n",
      "Epoch: 30 | Train loss: 2.67853 | Train accuracy: 19.888% | Train f1 0.199 | Test accuracy 20.238% | Test f1 0.202\n",
      "Epoch: 40 | Train loss: 2.34141 | Train accuracy: 31.592% | Train f1 0.316 | Test accuracy 31.577% | Test f1 0.316\n",
      "Epoch: 50 | Train loss: 2.06862 | Train accuracy: 38.638% | Train f1 0.386 | Test accuracy 38.006% | Test f1 0.380\n",
      "Epoch: 60 | Train loss: 1.82348 | Train accuracy: 45.067% | Train f1 0.451 | Test accuracy 44.494% | Test f1 0.445\n",
      "Epoch: 70 | Train loss: 1.58402 | Train accuracy: 52.217% | Train f1 0.522 | Test accuracy 50.565% | Test f1 0.506\n",
      "Epoch: 80 | Train loss: 1.37564 | Train accuracy: 57.493% | Train f1 0.575 | Test accuracy 55.238% | Test f1 0.552\n",
      "Epoch: 90 | Train loss: 1.19840 | Train accuracy: 62.396% | Train f1 0.624 | Test accuracy 58.185% | Test f1 0.582\n",
      "6 down, 9 to go\n",
      "------------------------\n",
      "70\n",
      "Epoch: 0 | Train loss: 3.64137 | Train accuracy: 3.772% | Train f1 0.038 | Test accuracy 3.482% | Test f1 0.035\n",
      "Epoch: 10 | Train loss: 3.29253 | Train accuracy: 10.871% | Train f1 0.109 | Test accuracy 11.310% | Test f1 0.113\n",
      "Epoch: 20 | Train loss: 3.08767 | Train accuracy: 10.923% | Train f1 0.109 | Test accuracy 11.548% | Test f1 0.115\n",
      "Epoch: 30 | Train loss: 2.71418 | Train accuracy: 21.287% | Train f1 0.213 | Test accuracy 21.220% | Test f1 0.212\n",
      "Epoch: 40 | Train loss: 2.33864 | Train accuracy: 31.071% | Train f1 0.311 | Test accuracy 29.940% | Test f1 0.299\n",
      "Epoch: 50 | Train loss: 2.05528 | Train accuracy: 39.040% | Train f1 0.390 | Test accuracy 37.827% | Test f1 0.378\n",
      "Epoch: 60 | Train loss: 1.79857 | Train accuracy: 46.443% | Train f1 0.464 | Test accuracy 44.762% | Test f1 0.448\n",
      "Epoch: 70 | Train loss: 1.54895 | Train accuracy: 53.519% | Train f1 0.535 | Test accuracy 51.042% | Test f1 0.510\n",
      "Epoch: 80 | Train loss: 1.33493 | Train accuracy: 58.936% | Train f1 0.589 | Test accuracy 54.851% | Test f1 0.549\n",
      "Epoch: 90 | Train loss: 1.15698 | Train accuracy: 63.728% | Train f1 0.637 | Test accuracy 58.423% | Test f1 0.584\n",
      "7 down, 8 to go\n",
      "------------------------\n",
      "80\n",
      "Epoch: 0 | Train loss: 3.64471 | Train accuracy: 3.795% | Train f1 0.038 | Test accuracy 3.393% | Test f1 0.034\n",
      "Epoch: 10 | Train loss: 3.30091 | Train accuracy: 13.311% | Train f1 0.133 | Test accuracy 13.214% | Test f1 0.132\n",
      "Epoch: 20 | Train loss: 3.11387 | Train accuracy: 10.551% | Train f1 0.106 | Test accuracy 10.714% | Test f1 0.107\n",
      "Epoch: 30 | Train loss: 2.70845 | Train accuracy: 19.591% | Train f1 0.196 | Test accuracy 19.167% | Test f1 0.192\n",
      "Epoch: 40 | Train loss: 2.36649 | Train accuracy: 29.866% | Train f1 0.299 | Test accuracy 29.940% | Test f1 0.299\n",
      "Epoch: 50 | Train loss: 2.08082 | Train accuracy: 38.467% | Train f1 0.385 | Test accuracy 38.363% | Test f1 0.384\n",
      "Epoch: 60 | Train loss: 1.81130 | Train accuracy: 46.176% | Train f1 0.462 | Test accuracy 44.286% | Test f1 0.443\n",
      "Epoch: 70 | Train loss: 1.56853 | Train accuracy: 52.574% | Train f1 0.526 | Test accuracy 50.268% | Test f1 0.503\n",
      "Epoch: 80 | Train loss: 1.35350 | Train accuracy: 58.185% | Train f1 0.582 | Test accuracy 53.929% | Test f1 0.539\n",
      "Epoch: 90 | Train loss: 1.16616 | Train accuracy: 63.065% | Train f1 0.631 | Test accuracy 58.393% | Test f1 0.584\n",
      "8 down, 7 to go\n",
      "------------------------\n",
      "90\n",
      "Epoch: 0 | Train loss: 3.65002 | Train accuracy: 3.214% | Train f1 0.032 | Test accuracy 3.571% | Test f1 0.036\n",
      "Epoch: 10 | Train loss: 3.29335 | Train accuracy: 11.027% | Train f1 0.110 | Test accuracy 9.940% | Test f1 0.099\n",
      "Epoch: 20 | Train loss: 3.08874 | Train accuracy: 10.461% | Train f1 0.105 | Test accuracy 10.595% | Test f1 0.106\n",
      "Epoch: 30 | Train loss: 2.66863 | Train accuracy: 20.670% | Train f1 0.207 | Test accuracy 20.179% | Test f1 0.202\n",
      "Epoch: 40 | Train loss: 2.33226 | Train accuracy: 30.685% | Train f1 0.307 | Test accuracy 30.476% | Test f1 0.305\n",
      "Epoch: 50 | Train loss: 2.04783 | Train accuracy: 39.301% | Train f1 0.393 | Test accuracy 38.333% | Test f1 0.383\n",
      "Epoch: 60 | Train loss: 1.77797 | Train accuracy: 46.711% | Train f1 0.467 | Test accuracy 45.863% | Test f1 0.459\n",
      "Epoch: 70 | Train loss: 1.53317 | Train accuracy: 53.943% | Train f1 0.539 | Test accuracy 51.250% | Test f1 0.512\n",
      "Epoch: 80 | Train loss: 1.32769 | Train accuracy: 59.412% | Train f1 0.594 | Test accuracy 55.357% | Test f1 0.554\n",
      "Epoch: 90 | Train loss: 1.15825 | Train accuracy: 63.170% | Train f1 0.632 | Test accuracy 59.435% | Test f1 0.594\n",
      "9 down, 6 to go\n",
      "------------------------\n",
      "100\n",
      "Epoch: 0 | Train loss: 3.64070 | Train accuracy: 3.296% | Train f1 0.033 | Test accuracy 4.435% | Test f1 0.044\n",
      "Epoch: 10 | Train loss: 3.29881 | Train accuracy: 11.057% | Train f1 0.111 | Test accuracy 10.804% | Test f1 0.108\n",
      "Epoch: 20 | Train loss: 3.10297 | Train accuracy: 8.051% | Train f1 0.081 | Test accuracy 8.482% | Test f1 0.085\n",
      "Epoch: 30 | Train loss: 2.76144 | Train accuracy: 16.562% | Train f1 0.166 | Test accuracy 17.560% | Test f1 0.176\n",
      "Epoch: 40 | Train loss: 2.42727 | Train accuracy: 27.775% | Train f1 0.278 | Test accuracy 27.143% | Test f1 0.271\n",
      "Epoch: 50 | Train loss: 2.12134 | Train accuracy: 37.366% | Train f1 0.374 | Test accuracy 36.577% | Test f1 0.366\n",
      "Epoch: 60 | Train loss: 1.82773 | Train accuracy: 45.134% | Train f1 0.451 | Test accuracy 43.690% | Test f1 0.437\n",
      "Epoch: 70 | Train loss: 1.56966 | Train accuracy: 52.106% | Train f1 0.521 | Test accuracy 50.000% | Test f1 0.500\n",
      "Epoch: 80 | Train loss: 1.34001 | Train accuracy: 58.847% | Train f1 0.588 | Test accuracy 55.536% | Test f1 0.555\n",
      "Epoch: 90 | Train loss: 1.13660 | Train accuracy: 64.085% | Train f1 0.641 | Test accuracy 59.940% | Test f1 0.599\n",
      "10 down, 5 to go\n",
      "------------------------\n",
      "110\n",
      "Epoch: 0 | Train loss: 3.64877 | Train accuracy: 3.259% | Train f1 0.033 | Test accuracy 4.643% | Test f1 0.046\n",
      "Epoch: 10 | Train loss: 3.28818 | Train accuracy: 9.970% | Train f1 0.100 | Test accuracy 10.030% | Test f1 0.100\n",
      "Epoch: 20 | Train loss: 3.03480 | Train accuracy: 9.479% | Train f1 0.095 | Test accuracy 9.792% | Test f1 0.098\n",
      "Epoch: 30 | Train loss: 2.67467 | Train accuracy: 22.850% | Train f1 0.228 | Test accuracy 21.786% | Test f1 0.218\n",
      "Epoch: 40 | Train loss: 2.31787 | Train accuracy: 32.842% | Train f1 0.328 | Test accuracy 32.083% | Test f1 0.321\n",
      "Epoch: 50 | Train loss: 2.01961 | Train accuracy: 40.022% | Train f1 0.400 | Test accuracy 38.899% | Test f1 0.389\n",
      "Epoch: 60 | Train loss: 1.74730 | Train accuracy: 47.746% | Train f1 0.477 | Test accuracy 46.101% | Test f1 0.461\n",
      "Epoch: 70 | Train loss: 1.49402 | Train accuracy: 54.978% | Train f1 0.550 | Test accuracy 52.411% | Test f1 0.524\n",
      "Epoch: 80 | Train loss: 1.26021 | Train accuracy: 60.990% | Train f1 0.610 | Test accuracy 57.470% | Test f1 0.575\n",
      "Epoch: 90 | Train loss: 1.06888 | Train accuracy: 66.354% | Train f1 0.664 | Test accuracy 61.339% | Test f1 0.613\n",
      "11 down, 4 to go\n",
      "------------------------\n",
      "120\n",
      "Epoch: 0 | Train loss: 3.63689 | Train accuracy: 3.661% | Train f1 0.037 | Test accuracy 4.167% | Test f1 0.042\n",
      "Epoch: 10 | Train loss: 3.27885 | Train accuracy: 16.094% | Train f1 0.161 | Test accuracy 14.464% | Test f1 0.145\n",
      "Epoch: 20 | Train loss: 2.98689 | Train accuracy: 10.975% | Train f1 0.110 | Test accuracy 11.131% | Test f1 0.111\n",
      "Epoch: 30 | Train loss: 2.57087 | Train accuracy: 22.984% | Train f1 0.230 | Test accuracy 22.619% | Test f1 0.226\n",
      "Epoch: 40 | Train loss: 2.23537 | Train accuracy: 34.412% | Train f1 0.344 | Test accuracy 33.452% | Test f1 0.335\n",
      "Epoch: 50 | Train loss: 1.95520 | Train accuracy: 42.217% | Train f1 0.422 | Test accuracy 40.208% | Test f1 0.402\n",
      "Epoch: 60 | Train loss: 1.67588 | Train accuracy: 49.926% | Train f1 0.499 | Test accuracy 47.827% | Test f1 0.478\n",
      "Epoch: 70 | Train loss: 1.43054 | Train accuracy: 56.436% | Train f1 0.564 | Test accuracy 53.155% | Test f1 0.532\n",
      "Epoch: 80 | Train loss: 1.21915 | Train accuracy: 62.284% | Train f1 0.623 | Test accuracy 57.887% | Test f1 0.579\n",
      "Epoch: 90 | Train loss: 1.04152 | Train accuracy: 67.135% | Train f1 0.671 | Test accuracy 61.220% | Test f1 0.612\n",
      "12 down, 3 to go\n",
      "------------------------\n",
      "130\n",
      "Epoch: 0 | Train loss: 3.66262 | Train accuracy: 3.229% | Train f1 0.032 | Test accuracy 3.125% | Test f1 0.031\n",
      "Epoch: 10 | Train loss: 3.29248 | Train accuracy: 12.567% | Train f1 0.126 | Test accuracy 12.946% | Test f1 0.129\n",
      "Epoch: 20 | Train loss: 3.04866 | Train accuracy: 11.533% | Train f1 0.115 | Test accuracy 12.024% | Test f1 0.120\n",
      "Epoch: 30 | Train loss: 2.60065 | Train accuracy: 25.499% | Train f1 0.255 | Test accuracy 25.298% | Test f1 0.253\n",
      "Epoch: 40 | Train loss: 2.24352 | Train accuracy: 33.981% | Train f1 0.340 | Test accuracy 33.274% | Test f1 0.333\n",
      "Epoch: 50 | Train loss: 1.96385 | Train accuracy: 41.562% | Train f1 0.416 | Test accuracy 40.208% | Test f1 0.402\n",
      "Epoch: 60 | Train loss: 1.68925 | Train accuracy: 49.323% | Train f1 0.493 | Test accuracy 48.423% | Test f1 0.484\n",
      "Epoch: 70 | Train loss: 1.44433 | Train accuracy: 55.818% | Train f1 0.558 | Test accuracy 53.690% | Test f1 0.537\n",
      "Epoch: 80 | Train loss: 1.24093 | Train accuracy: 60.871% | Train f1 0.609 | Test accuracy 58.185% | Test f1 0.582\n",
      "Epoch: 90 | Train loss: 1.05751 | Train accuracy: 66.540% | Train f1 0.665 | Test accuracy 61.577% | Test f1 0.616\n",
      "13 down, 2 to go\n",
      "------------------------\n",
      "140\n",
      "Epoch: 0 | Train loss: 3.66331 | Train accuracy: 3.371% | Train f1 0.034 | Test accuracy 4.881% | Test f1 0.049\n",
      "Epoch: 10 | Train loss: 3.29516 | Train accuracy: 10.997% | Train f1 0.110 | Test accuracy 12.798% | Test f1 0.128\n",
      "Epoch: 20 | Train loss: 3.07254 | Train accuracy: 15.060% | Train f1 0.151 | Test accuracy 15.863% | Test f1 0.159\n",
      "Epoch: 30 | Train loss: 2.58518 | Train accuracy: 25.432% | Train f1 0.254 | Test accuracy 25.923% | Test f1 0.259\n",
      "Epoch: 40 | Train loss: 2.20168 | Train accuracy: 34.695% | Train f1 0.347 | Test accuracy 34.405% | Test f1 0.344\n",
      "Epoch: 50 | Train loss: 1.88723 | Train accuracy: 43.676% | Train f1 0.437 | Test accuracy 42.589% | Test f1 0.426\n",
      "Epoch: 60 | Train loss: 1.61523 | Train accuracy: 51.086% | Train f1 0.511 | Test accuracy 50.387% | Test f1 0.504\n",
      "Epoch: 70 | Train loss: 1.36147 | Train accuracy: 58.281% | Train f1 0.583 | Test accuracy 55.476% | Test f1 0.555\n",
      "Epoch: 80 | Train loss: 1.15514 | Train accuracy: 63.534% | Train f1 0.635 | Test accuracy 59.554% | Test f1 0.596\n",
      "Epoch: 90 | Train loss: 0.98405 | Train accuracy: 69.524% | Train f1 0.695 | Test accuracy 62.619% | Test f1 0.626\n",
      "14 down, 1 to go\n",
      "------------------------\n",
      "150\n",
      "Epoch: 0 | Train loss: 3.66851 | Train accuracy: 3.631% | Train f1 0.036 | Test accuracy 3.810% | Test f1 0.038\n",
      "Epoch: 10 | Train loss: 3.27929 | Train accuracy: 12.083% | Train f1 0.121 | Test accuracy 12.202% | Test f1 0.122\n",
      "Epoch: 20 | Train loss: 2.98026 | Train accuracy: 9.725% | Train f1 0.097 | Test accuracy 11.429% | Test f1 0.114\n",
      "Epoch: 30 | Train loss: 2.53494 | Train accuracy: 25.439% | Train f1 0.254 | Test accuracy 26.250% | Test f1 0.262\n",
      "Epoch: 40 | Train loss: 2.19351 | Train accuracy: 35.647% | Train f1 0.356 | Test accuracy 35.268% | Test f1 0.353\n",
      "Epoch: 50 | Train loss: 1.91263 | Train accuracy: 43.713% | Train f1 0.437 | Test accuracy 43.006% | Test f1 0.430\n",
      "Epoch: 60 | Train loss: 1.63190 | Train accuracy: 51.741% | Train f1 0.517 | Test accuracy 50.000% | Test f1 0.500\n",
      "Epoch: 70 | Train loss: 1.38770 | Train accuracy: 57.187% | Train f1 0.572 | Test accuracy 55.179% | Test f1 0.552\n",
      "Epoch: 80 | Train loss: 1.18000 | Train accuracy: 63.981% | Train f1 0.640 | Test accuracy 59.643% | Test f1 0.596\n",
      "Epoch: 90 | Train loss: 1.01043 | Train accuracy: 68.460% | Train f1 0.685 | Test accuracy 62.232% | Test f1 0.622\n",
      "15 down, 0 to go\n",
      "------------------------\n",
      "160\n",
      "Epoch: 0 | Train loss: 3.66535 | Train accuracy: 3.638% | Train f1 0.036 | Test accuracy 3.780% | Test f1 0.038\n",
      "Epoch: 10 | Train loss: 3.27380 | Train accuracy: 8.676% | Train f1 0.087 | Test accuracy 8.393% | Test f1 0.084\n",
      "Epoch: 20 | Train loss: 2.99367 | Train accuracy: 12.202% | Train f1 0.122 | Test accuracy 12.560% | Test f1 0.126\n",
      "Epoch: 30 | Train loss: 2.51607 | Train accuracy: 26.845% | Train f1 0.268 | Test accuracy 26.488% | Test f1 0.265\n",
      "Epoch: 40 | Train loss: 2.15802 | Train accuracy: 36.310% | Train f1 0.363 | Test accuracy 35.565% | Test f1 0.356\n",
      "Epoch: 50 | Train loss: 1.88299 | Train accuracy: 44.182% | Train f1 0.442 | Test accuracy 43.125% | Test f1 0.431\n",
      "Epoch: 60 | Train loss: 1.62290 | Train accuracy: 51.384% | Train f1 0.514 | Test accuracy 49.583% | Test f1 0.496\n",
      "Epoch: 70 | Train loss: 1.37217 | Train accuracy: 57.604% | Train f1 0.576 | Test accuracy 55.327% | Test f1 0.553\n",
      "Epoch: 80 | Train loss: 1.16412 | Train accuracy: 63.683% | Train f1 0.637 | Test accuracy 58.750% | Test f1 0.587\n",
      "Epoch: 90 | Train loss: 0.99525 | Train accuracy: 68.891% | Train f1 0.689 | Test accuracy 62.470% | Test f1 0.625\n",
      "16 down, -1 to go\n",
      "------------------------\n"
     ]
    }
   ],
   "source": [
    "layer_size=[x for x in range(10, 170, 10)]\n",
    "\n",
    "\n",
    "parameters_3={'loss_train':[], 'accuracy_train':[], 'f1_score_train':[],'loss_test':[], 'accuracy_test':[], 'f1_score_test':[], 'variance_check':[],'first_layer':[]}\n",
    "k=0\n",
    "for layer in layer_size:\n",
    "    print(layer)\n",
    "    tr_metric, ts_metric = SingleTrain_tails(nodes_inner=layer, nodes_outer=160, num_epochs=100, learning_rate = 0.001,printing = True)\n",
    "    parameters_3['loss_train'].append(tr_metric['loss'][-1])\n",
    "    parameters_3['loss_test'].append(ts_metric['loss'][-1])\n",
    "    parameters_3['accuracy_train'].append(tr_metric['accuracy'][-1])\n",
    "    parameters_3['accuracy_test'].append(ts_metric['accuracy'][-1])\n",
    "    parameters_3['f1_score_train'].append(tr_metric['f1_score'][-1])\n",
    "    parameters_3['f1_score_test'].append(ts_metric['f1_score'][-1])\n",
    "    parameters_3['variance_check'].append(tr_metric['accuracy'][-1]-ts_metric['accuracy'][-1])\n",
    "    parameters_3['first_layer'].append(layer)\n",
    "    k+=1\n",
    "    print(f'{k} down, {len(layer_size)-k} to go')\n",
    "    print('------------------------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "605001c1-c640-49b4-a5db-d27e1e9421ce",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "parameters_inner=pd.DataFrame(parameters_3).set_index('first_layer')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3441c25f-4e29-4dc5-8e79-b44954e1f86e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='first_layer', ylabel='variance_check'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x2000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(4, figsize=(10,20))\n",
    "sns.lineplot(data=parameters_inner[['accuracy_train', 'accuracy_test']], ax=axs[0])\n",
    "sns.lineplot(data=parameters_inner[['loss_test', 'loss_train']], ax=axs[1])\n",
    "sns.lineplot(data=parameters_inner[['f1_score_train', 'f1_score_test']], ax=axs[2])\n",
    "sns.lineplot(data=parameters_inner['variance_check'], ax=axs[3])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "837584a8-26ff-44d7-a5b5-cde42fab3b97",
   "metadata": {},
   "source": [
    "Looking at the graphs, it seems the most rapid change seems to happen until the layer size resches 60-70, and then the gains in accuracy/f1_score start decreasing, so I will select this as the initial layer size. Next we want to investigate if the last layer i.e. the layer that produces the final output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4539e035-7d6e-48e0-b873-22f8b17326dc",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "Epoch: 0 | Train loss: 3.62764 | Train accuracy: 3.780% | Train f1 0.038 | Test accuracy 3.571% | Test f1 0.036\n",
      "Epoch: 10 | Train loss: 3.32481 | Train accuracy: 6.711% | Train f1 0.067 | Test accuracy 7.917% | Test f1 0.079\n",
      "Epoch: 20 | Train loss: 3.28224 | Train accuracy: 9.420% | Train f1 0.094 | Test accuracy 9.970% | Test f1 0.100\n",
      "Epoch: 30 | Train loss: 3.21089 | Train accuracy: 9.382% | Train f1 0.094 | Test accuracy 9.286% | Test f1 0.093\n",
      "Epoch: 40 | Train loss: 3.11912 | Train accuracy: 12.083% | Train f1 0.121 | Test accuracy 11.548% | Test f1 0.115\n",
      "Epoch: 50 | Train loss: 3.00335 | Train accuracy: 15.484% | Train f1 0.155 | Test accuracy 14.494% | Test f1 0.145\n",
      "Epoch: 60 | Train loss: 2.86700 | Train accuracy: 18.110% | Train f1 0.181 | Test accuracy 17.798% | Test f1 0.178\n",
      "Epoch: 70 | Train loss: 2.71126 | Train accuracy: 21.518% | Train f1 0.215 | Test accuracy 21.280% | Test f1 0.213\n",
      "Epoch: 80 | Train loss: 2.53132 | Train accuracy: 26.585% | Train f1 0.266 | Test accuracy 25.863% | Test f1 0.259\n",
      "Epoch: 90 | Train loss: 2.32385 | Train accuracy: 31.905% | Train f1 0.319 | Test accuracy 29.613% | Test f1 0.296\n",
      "1 down, 15 to go\n",
      "------------------------\n",
      "20\n",
      "Epoch: 0 | Train loss: 3.64127 | Train accuracy: 3.475% | Train f1 0.035 | Test accuracy 4.137% | Test f1 0.041\n",
      "Epoch: 10 | Train loss: 3.31791 | Train accuracy: 3.951% | Train f1 0.040 | Test accuracy 4.018% | Test f1 0.040\n",
      "Epoch: 20 | Train loss: 3.25055 | Train accuracy: 5.558% | Train f1 0.056 | Test accuracy 5.774% | Test f1 0.058\n",
      "Epoch: 30 | Train loss: 3.10104 | Train accuracy: 9.442% | Train f1 0.094 | Test accuracy 9.732% | Test f1 0.097\n",
      "Epoch: 40 | Train loss: 2.89182 | Train accuracy: 14.985% | Train f1 0.150 | Test accuracy 15.357% | Test f1 0.154\n",
      "Epoch: 50 | Train loss: 2.66297 | Train accuracy: 20.513% | Train f1 0.205 | Test accuracy 19.851% | Test f1 0.199\n",
      "Epoch: 60 | Train loss: 2.43480 | Train accuracy: 26.287% | Train f1 0.263 | Test accuracy 25.506% | Test f1 0.255\n",
      "Epoch: 70 | Train loss: 2.22533 | Train accuracy: 33.438% | Train f1 0.334 | Test accuracy 31.577% | Test f1 0.316\n",
      "Epoch: 80 | Train loss: 2.00982 | Train accuracy: 40.089% | Train f1 0.401 | Test accuracy 37.589% | Test f1 0.376\n",
      "Epoch: 90 | Train loss: 1.78960 | Train accuracy: 46.034% | Train f1 0.460 | Test accuracy 42.619% | Test f1 0.426\n",
      "2 down, 14 to go\n",
      "------------------------\n",
      "30\n",
      "Epoch: 0 | Train loss: 3.64457 | Train accuracy: 3.564% | Train f1 0.036 | Test accuracy 3.929% | Test f1 0.039\n",
      "Epoch: 10 | Train loss: 3.31677 | Train accuracy: 6.637% | Train f1 0.066 | Test accuracy 7.262% | Test f1 0.073\n",
      "Epoch: 20 | Train loss: 3.26060 | Train accuracy: 7.426% | Train f1 0.074 | Test accuracy 7.351% | Test f1 0.074\n",
      "Epoch: 30 | Train loss: 3.09442 | Train accuracy: 9.122% | Train f1 0.091 | Test accuracy 9.226% | Test f1 0.092\n",
      "Epoch: 40 | Train loss: 2.79790 | Train accuracy: 16.436% | Train f1 0.164 | Test accuracy 16.399% | Test f1 0.164\n",
      "Epoch: 50 | Train loss: 2.46392 | Train accuracy: 25.424% | Train f1 0.254 | Test accuracy 24.286% | Test f1 0.243\n",
      "Epoch: 60 | Train loss: 2.21295 | Train accuracy: 31.905% | Train f1 0.319 | Test accuracy 31.071% | Test f1 0.311\n",
      "Epoch: 70 | Train loss: 2.00600 | Train accuracy: 39.732% | Train f1 0.397 | Test accuracy 39.107% | Test f1 0.391\n",
      "Epoch: 80 | Train loss: 1.80880 | Train accuracy: 45.640% | Train f1 0.456 | Test accuracy 44.137% | Test f1 0.441\n",
      "Epoch: 90 | Train loss: 1.62342 | Train accuracy: 50.960% | Train f1 0.510 | Test accuracy 48.006% | Test f1 0.480\n",
      "3 down, 13 to go\n",
      "------------------------\n",
      "40\n",
      "Epoch: 0 | Train loss: 3.63629 | Train accuracy: 3.728% | Train f1 0.037 | Test accuracy 5.863% | Test f1 0.059\n",
      "Epoch: 10 | Train loss: 3.30525 | Train accuracy: 6.131% | Train f1 0.061 | Test accuracy 6.429% | Test f1 0.064\n",
      "Epoch: 20 | Train loss: 3.19423 | Train accuracy: 8.438% | Train f1 0.084 | Test accuracy 8.363% | Test f1 0.084\n",
      "Epoch: 30 | Train loss: 2.94282 | Train accuracy: 11.458% | Train f1 0.115 | Test accuracy 11.339% | Test f1 0.113\n",
      "Epoch: 40 | Train loss: 2.66918 | Train accuracy: 19.516% | Train f1 0.195 | Test accuracy 20.417% | Test f1 0.204\n",
      "Epoch: 50 | Train loss: 2.39643 | Train accuracy: 27.984% | Train f1 0.280 | Test accuracy 28.452% | Test f1 0.285\n",
      "Epoch: 60 | Train loss: 2.15313 | Train accuracy: 36.034% | Train f1 0.360 | Test accuracy 34.494% | Test f1 0.345\n",
      "Epoch: 70 | Train loss: 1.90863 | Train accuracy: 43.162% | Train f1 0.432 | Test accuracy 41.607% | Test f1 0.416\n",
      "Epoch: 80 | Train loss: 1.68068 | Train accuracy: 49.435% | Train f1 0.494 | Test accuracy 47.173% | Test f1 0.472\n",
      "Epoch: 90 | Train loss: 1.49106 | Train accuracy: 53.914% | Train f1 0.539 | Test accuracy 51.071% | Test f1 0.511\n",
      "4 down, 12 to go\n",
      "------------------------\n",
      "50\n",
      "Epoch: 0 | Train loss: 3.65821 | Train accuracy: 3.653% | Train f1 0.037 | Test accuracy 4.583% | Test f1 0.046\n",
      "Epoch: 10 | Train loss: 3.30732 | Train accuracy: 8.318% | Train f1 0.083 | Test accuracy 8.690% | Test f1 0.087\n",
      "Epoch: 20 | Train loss: 3.21140 | Train accuracy: 10.253% | Train f1 0.103 | Test accuracy 10.565% | Test f1 0.106\n",
      "Epoch: 30 | Train loss: 2.95672 | Train accuracy: 11.071% | Train f1 0.111 | Test accuracy 11.905% | Test f1 0.119\n",
      "Epoch: 40 | Train loss: 2.64421 | Train accuracy: 22.932% | Train f1 0.229 | Test accuracy 23.631% | Test f1 0.236\n",
      "Epoch: 50 | Train loss: 2.35334 | Train accuracy: 30.900% | Train f1 0.309 | Test accuracy 30.655% | Test f1 0.307\n",
      "Epoch: 60 | Train loss: 2.12596 | Train accuracy: 36.875% | Train f1 0.369 | Test accuracy 36.964% | Test f1 0.370\n",
      "Epoch: 70 | Train loss: 1.90419 | Train accuracy: 42.485% | Train f1 0.425 | Test accuracy 41.458% | Test f1 0.415\n",
      "Epoch: 80 | Train loss: 1.70444 | Train accuracy: 48.229% | Train f1 0.482 | Test accuracy 46.250% | Test f1 0.463\n",
      "Epoch: 90 | Train loss: 1.53374 | Train accuracy: 52.180% | Train f1 0.522 | Test accuracy 49.970% | Test f1 0.500\n",
      "5 down, 11 to go\n",
      "------------------------\n",
      "60\n",
      "Epoch: 0 | Train loss: 3.63142 | Train accuracy: 3.393% | Train f1 0.034 | Test accuracy 4.673% | Test f1 0.047\n",
      "Epoch: 10 | Train loss: 3.30708 | Train accuracy: 9.182% | Train f1 0.092 | Test accuracy 8.274% | Test f1 0.083\n",
      "Epoch: 20 | Train loss: 3.20573 | Train accuracy: 8.199% | Train f1 0.082 | Test accuracy 8.929% | Test f1 0.089\n",
      "Epoch: 30 | Train loss: 2.94633 | Train accuracy: 12.418% | Train f1 0.124 | Test accuracy 13.899% | Test f1 0.139\n",
      "Epoch: 40 | Train loss: 2.57551 | Train accuracy: 22.135% | Train f1 0.221 | Test accuracy 22.351% | Test f1 0.224\n",
      "Epoch: 50 | Train loss: 2.29460 | Train accuracy: 31.190% | Train f1 0.312 | Test accuracy 31.101% | Test f1 0.311\n",
      "Epoch: 60 | Train loss: 2.05521 | Train accuracy: 39.754% | Train f1 0.398 | Test accuracy 38.482% | Test f1 0.385\n",
      "Epoch: 70 | Train loss: 1.80556 | Train accuracy: 46.265% | Train f1 0.463 | Test accuracy 44.673% | Test f1 0.447\n",
      "Epoch: 80 | Train loss: 1.56767 | Train accuracy: 52.515% | Train f1 0.525 | Test accuracy 49.732% | Test f1 0.497\n",
      "Epoch: 90 | Train loss: 1.36254 | Train accuracy: 57.820% | Train f1 0.578 | Test accuracy 55.119% | Test f1 0.551\n",
      "6 down, 10 to go\n",
      "------------------------\n",
      "70\n",
      "Epoch: 0 | Train loss: 3.65289 | Train accuracy: 3.609% | Train f1 0.036 | Test accuracy 3.810% | Test f1 0.038\n",
      "Epoch: 10 | Train loss: 3.31718 | Train accuracy: 6.719% | Train f1 0.067 | Test accuracy 6.726% | Test f1 0.067\n",
      "Epoch: 20 | Train loss: 3.24447 | Train accuracy: 6.905% | Train f1 0.069 | Test accuracy 7.262% | Test f1 0.073\n",
      "Epoch: 30 | Train loss: 3.00315 | Train accuracy: 10.751% | Train f1 0.108 | Test accuracy 12.262% | Test f1 0.123\n",
      "Epoch: 40 | Train loss: 2.66852 | Train accuracy: 20.729% | Train f1 0.207 | Test accuracy 21.042% | Test f1 0.210\n",
      "Epoch: 50 | Train loss: 2.35765 | Train accuracy: 30.022% | Train f1 0.300 | Test accuracy 28.988% | Test f1 0.290\n",
      "Epoch: 60 | Train loss: 2.09303 | Train accuracy: 37.574% | Train f1 0.376 | Test accuracy 36.250% | Test f1 0.363\n",
      "Epoch: 70 | Train loss: 1.84714 | Train accuracy: 44.546% | Train f1 0.445 | Test accuracy 43.274% | Test f1 0.433\n",
      "Epoch: 80 | Train loss: 1.62141 | Train accuracy: 51.488% | Train f1 0.515 | Test accuracy 49.018% | Test f1 0.490\n",
      "Epoch: 90 | Train loss: 1.43228 | Train accuracy: 56.734% | Train f1 0.567 | Test accuracy 53.006% | Test f1 0.530\n",
      "7 down, 9 to go\n",
      "------------------------\n",
      "80\n",
      "Epoch: 0 | Train loss: 3.64227 | Train accuracy: 3.676% | Train f1 0.037 | Test accuracy 3.869% | Test f1 0.039\n",
      "Epoch: 10 | Train loss: 3.30699 | Train accuracy: 8.802% | Train f1 0.088 | Test accuracy 8.423% | Test f1 0.084\n",
      "Epoch: 20 | Train loss: 3.19875 | Train accuracy: 9.650% | Train f1 0.097 | Test accuracy 9.315% | Test f1 0.093\n",
      "Epoch: 30 | Train loss: 2.89958 | Train accuracy: 13.824% | Train f1 0.138 | Test accuracy 15.506% | Test f1 0.155\n",
      "Epoch: 40 | Train loss: 2.53688 | Train accuracy: 24.955% | Train f1 0.250 | Test accuracy 24.702% | Test f1 0.247\n",
      "Epoch: 50 | Train loss: 2.25788 | Train accuracy: 33.021% | Train f1 0.330 | Test accuracy 32.649% | Test f1 0.326\n",
      "Epoch: 60 | Train loss: 2.02512 | Train accuracy: 40.499% | Train f1 0.405 | Test accuracy 38.869% | Test f1 0.389\n",
      "Epoch: 70 | Train loss: 1.77950 | Train accuracy: 47.307% | Train f1 0.473 | Test accuracy 45.417% | Test f1 0.454\n",
      "Epoch: 80 | Train loss: 1.54087 | Train accuracy: 53.222% | Train f1 0.532 | Test accuracy 50.774% | Test f1 0.508\n",
      "Epoch: 90 | Train loss: 1.36146 | Train accuracy: 57.679% | Train f1 0.577 | Test accuracy 54.018% | Test f1 0.540\n",
      "8 down, 8 to go\n",
      "------------------------\n",
      "90\n",
      "Epoch: 0 | Train loss: 3.64142 | Train accuracy: 3.557% | Train f1 0.036 | Test accuracy 3.810% | Test f1 0.038\n",
      "Epoch: 10 | Train loss: 3.30533 | Train accuracy: 6.235% | Train f1 0.062 | Test accuracy 7.113% | Test f1 0.071\n",
      "Epoch: 20 | Train loss: 3.18836 | Train accuracy: 12.061% | Train f1 0.121 | Test accuracy 11.905% | Test f1 0.119\n",
      "Epoch: 30 | Train loss: 2.87649 | Train accuracy: 14.167% | Train f1 0.142 | Test accuracy 15.714% | Test f1 0.157\n",
      "Epoch: 40 | Train loss: 2.51835 | Train accuracy: 27.046% | Train f1 0.270 | Test accuracy 27.738% | Test f1 0.277\n",
      "Epoch: 50 | Train loss: 2.20274 | Train accuracy: 35.283% | Train f1 0.353 | Test accuracy 34.762% | Test f1 0.348\n",
      "Epoch: 60 | Train loss: 1.94750 | Train accuracy: 42.195% | Train f1 0.422 | Test accuracy 41.369% | Test f1 0.414\n",
      "Epoch: 70 | Train loss: 1.70579 | Train accuracy: 48.676% | Train f1 0.487 | Test accuracy 47.173% | Test f1 0.472\n",
      "Epoch: 80 | Train loss: 1.50417 | Train accuracy: 54.278% | Train f1 0.543 | Test accuracy 52.173% | Test f1 0.522\n",
      "Epoch: 90 | Train loss: 1.33219 | Train accuracy: 58.720% | Train f1 0.587 | Test accuracy 55.417% | Test f1 0.554\n",
      "9 down, 7 to go\n",
      "------------------------\n",
      "100\n",
      "Epoch: 0 | Train loss: 3.62663 | Train accuracy: 3.586% | Train f1 0.036 | Test accuracy 2.946% | Test f1 0.029\n",
      "Epoch: 10 | Train loss: 3.30297 | Train accuracy: 7.612% | Train f1 0.076 | Test accuracy 6.786% | Test f1 0.068\n",
      "Epoch: 20 | Train loss: 3.17090 | Train accuracy: 9.144% | Train f1 0.091 | Test accuracy 9.881% | Test f1 0.099\n",
      "Epoch: 30 | Train loss: 2.85949 | Train accuracy: 13.757% | Train f1 0.138 | Test accuracy 15.000% | Test f1 0.150\n",
      "Epoch: 40 | Train loss: 2.50962 | Train accuracy: 27.128% | Train f1 0.271 | Test accuracy 26.429% | Test f1 0.264\n",
      "Epoch: 50 | Train loss: 2.20371 | Train accuracy: 34.412% | Train f1 0.344 | Test accuracy 34.077% | Test f1 0.341\n",
      "Epoch: 60 | Train loss: 1.93558 | Train accuracy: 42.612% | Train f1 0.426 | Test accuracy 41.101% | Test f1 0.411\n",
      "Epoch: 70 | Train loss: 1.69650 | Train accuracy: 49.375% | Train f1 0.494 | Test accuracy 47.024% | Test f1 0.470\n",
      "Epoch: 80 | Train loss: 1.49304 | Train accuracy: 54.814% | Train f1 0.548 | Test accuracy 50.923% | Test f1 0.509\n",
      "Epoch: 90 | Train loss: 1.31352 | Train accuracy: 59.568% | Train f1 0.596 | Test accuracy 55.268% | Test f1 0.553\n",
      "10 down, 6 to go\n",
      "------------------------\n",
      "110\n",
      "Epoch: 0 | Train loss: 3.65252 | Train accuracy: 3.460% | Train f1 0.035 | Test accuracy 4.405% | Test f1 0.044\n",
      "Epoch: 10 | Train loss: 3.31063 | Train accuracy: 9.189% | Train f1 0.092 | Test accuracy 10.238% | Test f1 0.102\n",
      "Epoch: 20 | Train loss: 3.19789 | Train accuracy: 10.789% | Train f1 0.108 | Test accuracy 10.923% | Test f1 0.109\n",
      "Epoch: 30 | Train loss: 2.86915 | Train accuracy: 14.420% | Train f1 0.144 | Test accuracy 15.119% | Test f1 0.151\n",
      "Epoch: 40 | Train loss: 2.49414 | Train accuracy: 26.607% | Train f1 0.266 | Test accuracy 26.369% | Test f1 0.264\n",
      "Epoch: 50 | Train loss: 2.19636 | Train accuracy: 34.583% | Train f1 0.346 | Test accuracy 34.464% | Test f1 0.345\n",
      "Epoch: 60 | Train loss: 1.96329 | Train accuracy: 41.518% | Train f1 0.415 | Test accuracy 40.060% | Test f1 0.401\n",
      "Epoch: 70 | Train loss: 1.73759 | Train accuracy: 48.051% | Train f1 0.481 | Test accuracy 46.310% | Test f1 0.463\n",
      "Epoch: 80 | Train loss: 1.53009 | Train accuracy: 53.929% | Train f1 0.539 | Test accuracy 51.071% | Test f1 0.511\n",
      "Epoch: 90 | Train loss: 1.35008 | Train accuracy: 58.795% | Train f1 0.588 | Test accuracy 55.298% | Test f1 0.553\n",
      "11 down, 5 to go\n",
      "------------------------\n",
      "120\n",
      "Epoch: 0 | Train loss: 3.65168 | Train accuracy: 3.504% | Train f1 0.035 | Test accuracy 3.423% | Test f1 0.034\n",
      "Epoch: 10 | Train loss: 3.29933 | Train accuracy: 9.472% | Train f1 0.095 | Test accuracy 9.554% | Test f1 0.096\n",
      "Epoch: 20 | Train loss: 3.14134 | Train accuracy: 9.740% | Train f1 0.097 | Test accuracy 9.613% | Test f1 0.096\n",
      "Epoch: 30 | Train loss: 2.80284 | Train accuracy: 15.521% | Train f1 0.155 | Test accuracy 17.411% | Test f1 0.174\n",
      "Epoch: 40 | Train loss: 2.44629 | Train accuracy: 27.939% | Train f1 0.279 | Test accuracy 27.024% | Test f1 0.270\n",
      "Epoch: 50 | Train loss: 2.13629 | Train accuracy: 36.749% | Train f1 0.367 | Test accuracy 35.685% | Test f1 0.357\n",
      "Epoch: 60 | Train loss: 1.89060 | Train accuracy: 43.482% | Train f1 0.435 | Test accuracy 41.905% | Test f1 0.419\n",
      "Epoch: 70 | Train loss: 1.66554 | Train accuracy: 49.554% | Train f1 0.496 | Test accuracy 48.065% | Test f1 0.481\n",
      "Epoch: 80 | Train loss: 1.45068 | Train accuracy: 55.446% | Train f1 0.554 | Test accuracy 52.738% | Test f1 0.527\n",
      "Epoch: 90 | Train loss: 1.27133 | Train accuracy: 60.647% | Train f1 0.606 | Test accuracy 56.815% | Test f1 0.568\n",
      "12 down, 4 to go\n",
      "------------------------\n",
      "130\n",
      "Epoch: 0 | Train loss: 3.62812 | Train accuracy: 3.445% | Train f1 0.034 | Test accuracy 4.613% | Test f1 0.046\n",
      "Epoch: 10 | Train loss: 3.30269 | Train accuracy: 10.893% | Train f1 0.109 | Test accuracy 10.327% | Test f1 0.103\n",
      "Epoch: 20 | Train loss: 3.15476 | Train accuracy: 10.841% | Train f1 0.108 | Test accuracy 11.280% | Test f1 0.113\n",
      "Epoch: 30 | Train loss: 2.77804 | Train accuracy: 16.458% | Train f1 0.165 | Test accuracy 17.292% | Test f1 0.173\n",
      "Epoch: 40 | Train loss: 2.39332 | Train accuracy: 30.484% | Train f1 0.305 | Test accuracy 30.327% | Test f1 0.303\n",
      "Epoch: 50 | Train loss: 2.10948 | Train accuracy: 38.095% | Train f1 0.381 | Test accuracy 37.500% | Test f1 0.375\n",
      "Epoch: 60 | Train loss: 1.85713 | Train accuracy: 45.149% | Train f1 0.451 | Test accuracy 44.196% | Test f1 0.442\n",
      "Epoch: 70 | Train loss: 1.62445 | Train accuracy: 51.265% | Train f1 0.513 | Test accuracy 49.315% | Test f1 0.493\n",
      "Epoch: 80 | Train loss: 1.42635 | Train accuracy: 56.295% | Train f1 0.563 | Test accuracy 53.899% | Test f1 0.539\n",
      "Epoch: 90 | Train loss: 1.25170 | Train accuracy: 61.131% | Train f1 0.611 | Test accuracy 57.798% | Test f1 0.578\n",
      "13 down, 3 to go\n",
      "------------------------\n",
      "140\n",
      "Epoch: 0 | Train loss: 3.64932 | Train accuracy: 3.490% | Train f1 0.035 | Test accuracy 4.226% | Test f1 0.042\n",
      "Epoch: 10 | Train loss: 3.31340 | Train accuracy: 9.546% | Train f1 0.095 | Test accuracy 11.131% | Test f1 0.111\n",
      "Epoch: 20 | Train loss: 3.21874 | Train accuracy: 13.690% | Train f1 0.137 | Test accuracy 13.095% | Test f1 0.131\n",
      "Epoch: 30 | Train loss: 2.88142 | Train accuracy: 14.807% | Train f1 0.148 | Test accuracy 16.012% | Test f1 0.160\n",
      "Epoch: 40 | Train loss: 2.48459 | Train accuracy: 26.979% | Train f1 0.270 | Test accuracy 27.292% | Test f1 0.273\n",
      "Epoch: 50 | Train loss: 2.18573 | Train accuracy: 36.042% | Train f1 0.360 | Test accuracy 35.119% | Test f1 0.351\n",
      "Epoch: 60 | Train loss: 1.93017 | Train accuracy: 43.177% | Train f1 0.432 | Test accuracy 41.101% | Test f1 0.411\n",
      "Epoch: 70 | Train loss: 1.70943 | Train accuracy: 48.996% | Train f1 0.490 | Test accuracy 48.065% | Test f1 0.481\n",
      "Epoch: 80 | Train loss: 1.50671 | Train accuracy: 54.226% | Train f1 0.542 | Test accuracy 52.500% | Test f1 0.525\n",
      "Epoch: 90 | Train loss: 1.32463 | Train accuracy: 59.435% | Train f1 0.594 | Test accuracy 56.190% | Test f1 0.562\n",
      "14 down, 2 to go\n",
      "------------------------\n",
      "150\n",
      "Epoch: 0 | Train loss: 3.65576 | Train accuracy: 3.720% | Train f1 0.037 | Test accuracy 4.107% | Test f1 0.041\n",
      "Epoch: 10 | Train loss: 3.31086 | Train accuracy: 8.423% | Train f1 0.084 | Test accuracy 9.673% | Test f1 0.097\n",
      "Epoch: 20 | Train loss: 3.19525 | Train accuracy: 12.106% | Train f1 0.121 | Test accuracy 11.875% | Test f1 0.119\n",
      "Epoch: 30 | Train loss: 2.83870 | Train accuracy: 16.138% | Train f1 0.161 | Test accuracy 16.696% | Test f1 0.167\n",
      "Epoch: 40 | Train loss: 2.45602 | Train accuracy: 29.152% | Train f1 0.292 | Test accuracy 28.690% | Test f1 0.287\n",
      "Epoch: 50 | Train loss: 2.15055 | Train accuracy: 37.210% | Train f1 0.372 | Test accuracy 35.982% | Test f1 0.360\n",
      "Epoch: 60 | Train loss: 1.87884 | Train accuracy: 44.085% | Train f1 0.441 | Test accuracy 42.500% | Test f1 0.425\n",
      "Epoch: 70 | Train loss: 1.64232 | Train accuracy: 50.625% | Train f1 0.506 | Test accuracy 47.530% | Test f1 0.475\n",
      "Epoch: 80 | Train loss: 1.44997 | Train accuracy: 55.082% | Train f1 0.551 | Test accuracy 52.827% | Test f1 0.528\n",
      "Epoch: 90 | Train loss: 1.27273 | Train accuracy: 60.156% | Train f1 0.602 | Test accuracy 56.071% | Test f1 0.561\n",
      "15 down, 1 to go\n",
      "------------------------\n",
      "160\n",
      "Epoch: 0 | Train loss: 3.63262 | Train accuracy: 3.438% | Train f1 0.034 | Test accuracy 3.512% | Test f1 0.035\n",
      "Epoch: 10 | Train loss: 3.29987 | Train accuracy: 8.601% | Train f1 0.086 | Test accuracy 9.048% | Test f1 0.090\n",
      "Epoch: 20 | Train loss: 3.13686 | Train accuracy: 8.147% | Train f1 0.081 | Test accuracy 8.065% | Test f1 0.081\n",
      "Epoch: 30 | Train loss: 2.77930 | Train accuracy: 18.088% | Train f1 0.181 | Test accuracy 18.810% | Test f1 0.188\n",
      "Epoch: 40 | Train loss: 2.37996 | Train accuracy: 30.774% | Train f1 0.308 | Test accuracy 30.685% | Test f1 0.307\n",
      "Epoch: 50 | Train loss: 2.06993 | Train accuracy: 39.472% | Train f1 0.395 | Test accuracy 38.393% | Test f1 0.384\n",
      "Epoch: 60 | Train loss: 1.81226 | Train accuracy: 45.938% | Train f1 0.459 | Test accuracy 44.226% | Test f1 0.442\n",
      "Epoch: 70 | Train loss: 1.58751 | Train accuracy: 52.091% | Train f1 0.521 | Test accuracy 49.286% | Test f1 0.493\n",
      "Epoch: 80 | Train loss: 1.39226 | Train accuracy: 56.942% | Train f1 0.569 | Test accuracy 53.274% | Test f1 0.533\n",
      "Epoch: 90 | Train loss: 1.22935 | Train accuracy: 61.272% | Train f1 0.613 | Test accuracy 56.994% | Test f1 0.570\n",
      "16 down, 0 to go\n",
      "------------------------\n"
     ]
    }
   ],
   "source": [
    "parameters_4={'loss_train':[], 'accuracy_train':[], 'f1_score_train':[],'loss_test':[], 'accuracy_test':[], 'f1_score_test':[], 'variance_check':[],'last_layer':[]}\n",
    "k=0\n",
    "for layer in layer_size:\n",
    "    print(layer)\n",
    "    tr_metric, ts_metric = SingleTrain_tails(nodes_inner=60, nodes_outer=layer, num_epochs=100, learning_rate = 0.001,printing = True)\n",
    "    parameters_4['loss_train'].append(tr_metric['loss'][-1])\n",
    "    parameters_4['loss_test'].append(ts_metric['loss'][-1])\n",
    "    parameters_4['accuracy_train'].append(tr_metric['accuracy'][-1])\n",
    "    parameters_4['accuracy_test'].append(ts_metric['accuracy'][-1])\n",
    "    parameters_4['f1_score_train'].append(tr_metric['f1_score'][-1])\n",
    "    parameters_4['f1_score_test'].append(ts_metric['f1_score'][-1])\n",
    "    parameters_4['variance_check'].append(tr_metric['accuracy'][-1]-ts_metric['accuracy'][-1])\n",
    "    parameters_4['last_layer'].append(layer)\n",
    "    k+=1\n",
    "    print(f'{k} down, {len(layer_size)-k} to go')\n",
    "    print('------------------------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2f5259d3-8aec-46b0-a4a1-18f3191c984f",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "parameters_outer=pd.DataFrame(parameters_4).set_index('last_layer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "18686bd5-1207-49cd-a6f1-c4160102cab7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='last_layer', ylabel='variance_check'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x2000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(4, figsize=(10,20))\n",
    "sns.lineplot(data=parameters_outer[['accuracy_train', 'accuracy_test']], ax=axs[0])\n",
    "sns.lineplot(data=parameters_outer[['loss_test', 'loss_train']], ax=axs[1])\n",
    "sns.lineplot(data=parameters_outer[['f1_score_train', 'f1_score_test']], ax=axs[2])\n",
    "sns.lineplot(data=parameters_outer['variance_check'], ax=axs[3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a63fc025-80df-4990-8f39-b7a1879313c3",
   "metadata": {},
   "source": [
    "As we can see 60-70 seems to be the magic number again. Last thing we want to check, are some more advanced options such as checking how dropout changes the results, as well as adding rgularization to minimize overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c67a6be1-9eb5-4e19-905a-41b8b3e84b02",
   "metadata": {},
   "outputs": [],
   "source": [
    "class TestingDropout(nn.Module):\n",
    "    def __init__(self, n_char, dropout, nodes_inner=60, nodes_outer=60, hidden=160):\n",
    "        super().__init__()\n",
    "        self.inner=nn.Linear(1024, nodes_inner)\n",
    "        self.hidden1=nn.Linear(nodes_inner, nodes_outer)\n",
    "       # self.hidden2=nn.Linear(hidden, nodes_outer)\n",
    "        self.outer=nn.Linear(nodes_outer, n_char)\n",
    "        self.relu=nn.ReLU()\n",
    "        self.dropout=nn.Dropout(p=dropout)\n",
    "        self.batch1=nn.BatchNorm1d(nodes_inner)\n",
    "        self.batch2=nn.BatchNorm1d(nodes_outer)\n",
    "        #self.batch3=nn.BatchNorm1d(nodes_outer)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        X = self.inner(x)\n",
    "        X = self.relu(X)\n",
    "        X = self.batch1(X)\n",
    "        X = self.dropout(X)\n",
    "        \n",
    "        X = self.hidden1(X)\n",
    "        X = self.relu(X)\n",
    "        X = self.batch2(X)\n",
    "        X = self.dropout(X)\n",
    "\n",
    "       # X = self.hidden2(X)\n",
    "       # X = self.relu(X)\n",
    "       # X = self.batch3(X)\n",
    "       # X = self.dropout(X)\n",
    "        \n",
    "        #X = self.hidden2(X)\n",
    "       # X = self.relu(X)\n",
    "        #X = self.batch3(X)\n",
    "      #  X = self.dropout(X)\n",
    "\n",
    "       # X = self.hidden3(X)\n",
    "       # X = self.relu(X)\n",
    "        #X = self.batch4(X)\n",
    "      #  X = self.dropout(X)\n",
    "\n",
    "        #X = self.hidden4(X)\n",
    "       # X = self.relu(X)\n",
    "      #  X = self.dropout(X)\n",
    "\n",
    "        #X = self.hidden5(X)\n",
    "       # X = self.relu(X)\n",
    "      #  X = self.dropout(X)\n",
    "\n",
    "        #X = self.hidden6(X)\n",
    "       # X = self.relu(X)\n",
    "      #  X = self.dropout(X)\n",
    "\n",
    "        #X = self.hidden7(X)\n",
    "        #X = self.relu(X)\n",
    "        #X = self.dropout(X)\n",
    "\n",
    "        #X = self.hidden8(X)\n",
    "        #X = self.relu(X)\n",
    "        #X = self.dropout(X)\n",
    "        \n",
    "       # X = self.hidden9(X)\n",
    "      #  X = self.relu(X)\n",
    "     #   X = self.dropout(X)\n",
    "        \n",
    "        \n",
    "        \n",
    "        X = self.outer(X)\n",
    "        \n",
    "        return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "73e7b819-21e9-41c2-b8ad-7762918976f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TestingDropout(\n",
       "  (inner): Linear(in_features=1024, out_features=60, bias=True)\n",
       "  (hidden1): Linear(in_features=60, out_features=60, bias=True)\n",
       "  (outer): Linear(in_features=60, out_features=28, bias=True)\n",
       "  (relu): ReLU()\n",
       "  (dropout): Dropout(p=0.5, inplace=False)\n",
       "  (batch1): BatchNorm1d(60, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (batch2): BatchNorm1d(60, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       ")"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_drop=TestingDropout(28, 0.5)\n",
    "model_drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d6bdaeaf-8f77-4ce1-af12-ecd0f9c8dc42",
   "metadata": {},
   "outputs": [],
   "source": [
    "def SingleTrain_drop(prob, num_epochs, learning_rate, inner=60, outer=60, hidden=160, decay = 0, printing = False, train_data=train_data, train_labels=train_labels, test_data=test_data, test_labels=test_labels, loss=nn.CrossEntropyLoss()):\n",
    "    model=model_2=TestingDropout(n_char=28, dropout=prob, nodes_inner=inner, nodes_outer=outer, hidden=hidden).to(device)\n",
    "    loss=loss\n",
    "    optimization= optim.SGD(params=model.parameters(), lr=learning_rate, weight_decay = decay, momentum=0.9)\n",
    "\n",
    "    train_metrics={'loss': [], 'accuracy': [], 'f1_score': [], 'epoch': list(range(num_epochs))}\n",
    "\n",
    "    test_metrics = {'loss': [], 'accuracy': [], 'f1_score': [], 'epoch': list(range(num_epochs))}\n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "    \n",
    "        train_pred=model(train_data)\n",
    "        model.train()\n",
    "        \n",
    "        y_train = train_labels.type(torch.LongTensor) \n",
    "        \n",
    "        optimization.zero_grad()\n",
    "        \n",
    "        loss_epoch = loss(train_pred, y_train)\n",
    "        \n",
    "        f1_train, acc_train = metrics(train_pred, y_train, 28)\n",
    "        \n",
    "        loss_epoch.backward()\n",
    "        \n",
    "        optimization.step()\n",
    "        \n",
    "        train_metrics['loss'].append(loss_epoch.item())\n",
    "        train_metrics['accuracy'].append(acc_train.item()*100)\n",
    "        train_metrics['f1_score'].append(f1_train.item())\n",
    "    \n",
    "        model.eval()\n",
    "        with torch.inference_mode():\n",
    "            test_pred = model(test_data)\n",
    "            y_eval = test_labels.type(torch.LongTensor) \n",
    "            loss_eval=loss(test_pred, y_eval)\n",
    "            f1_test, acc_test = metrics(test_pred, torch.squeeze(test_labels), 28)\n",
    "            \n",
    "            test_metrics['accuracy'].append(acc_test.item()*100)\n",
    "            test_metrics['f1_score'].append(f1_test.item())\n",
    "            test_metrics['loss'].append(loss_eval.item())\n",
    "\n",
    "        if printing:\n",
    "            if epoch%10==0:\n",
    "                print(f'Epoch: {epoch} | Train loss: {loss_epoch:.5f} | Train accuracy: {acc_train*100:.3f}% | Train f1 {f1_train:.3f} | Test accuracy {acc_test*100:.3f}% | Test f1 {f1_test:.3f}')\n",
    "            \n",
    "\n",
    "    return train_metrics, test_metrics\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "267ffeeb-80f0-417b-8d46-c253ff14123a",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "drop_rates=np.arange(0, 1, 0.1)\n",
    "drop_extended=[]\n",
    "for i in range(15):\n",
    "    drop_extended.extend(drop_rates)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "eafcac01-ed07-4193-919c-a3e2b6cb560a",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "parameters_5={'loss_train':[], 'accuracy_train':[], 'f1_score_train':[],'loss_test':[], 'accuracy_test':[], 'f1_score_test':[], 'variance_check':[],'drop_prob':[]}\n",
    "k=0\n",
    "for prob in drop_extended:\n",
    "    print(prob)\n",
    "    tr_metric, ts_metric = SingleTrain_drop(prob=prob, num_epochs=100, learning_rate = 0.001)\n",
    "    parameters_5['loss_train'].append(tr_metric['loss'][-1])\n",
    "    parameters_5['loss_test'].append(ts_metric['loss'][-1])\n",
    "    parameters_5['accuracy_train'].append(tr_metric['accuracy'][-1])\n",
    "    parameters_5['accuracy_test'].append(ts_metric['accuracy'][-1])\n",
    "    parameters_5['f1_score_train'].append(tr_metric['f1_score'][-1])\n",
    "    parameters_5['f1_score_test'].append(ts_metric['f1_score'][-1])\n",
    "    parameters_5['variance_check'].append(tr_metric['accuracy'][-1]-ts_metric['accuracy'][-1])\n",
    "    parameters_5['drop_prob'].append(prob)\n",
    "    k+=1\n",
    "    print(f'{k} down, {len(drop_extended)-k} to go')\n",
    "    print('------------------------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "339e8117-72ca-48cb-8bc2-089ba5145e83",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "#parameters_drop=pd.DataFrame(parameters_5)\n",
    "#grouped_drop=parameters_drop.groupby('drop_prob').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6750ce45-6210-4163-87d5-a61a8c685011",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "#fig, axs = plt.subplots(4, figsize=(10,20))\n",
    "#sns.lineplot(data=grouped_drop[['accuracy_train', 'accuracy_test']], ax=axs[0])\n",
    "#sns.lineplot(data=grouped_drop[['loss_test', 'loss_train']], ax=axs[1])\n",
    "#sns.lineplot(data=grouped_drop[['f1_score_train', 'f1_score_test']], ax=axs[2])\n",
    "#sns.lineplot(data=grouped_drop['variance_check'], ax=axs[3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "327b3908-a359-4fc0-9fcc-1f30b3888a62",
   "metadata": {},
   "source": [
    "It's fairly clear from the results that the choice of dropout makes little difference until it reaches 0.5, after which most parameters change in undesirable ways, so I will stick with 0.4, since at the very least it decreses computing time during training. And now finally we can explore the regularization parameter. First let's check if it's even needed by starting a run with many more epochs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ca97ad5-7222-4baa-939e-647c233ff12f",
   "metadata": {},
   "source": [
    "It's very clear that the accuracy on the test set seems to stop significantly improving around the 200th epoch, implying that regularization is definetly needed. After some manual testing I determined that 0.01 is a reasonable starting value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3299b794-9cdc-4ca4-8a4e-1ed189f74226",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 | Train loss: 3.51452 | Train accuracy: 3.876% | Train f1 0.039 | Test accuracy 4.018% | Test f1 0.040\n",
      "Epoch: 10 | Train loss: 3.31965 | Train accuracy: 7.292% | Train f1 0.073 | Test accuracy 6.607% | Test f1 0.066\n",
      "Epoch: 20 | Train loss: 3.30886 | Train accuracy: 9.591% | Train f1 0.096 | Test accuracy 8.958% | Test f1 0.090\n",
      "Epoch: 30 | Train loss: 3.29695 | Train accuracy: 10.699% | Train f1 0.107 | Test accuracy 10.119% | Test f1 0.101\n",
      "Epoch: 40 | Train loss: 3.28227 | Train accuracy: 11.339% | Train f1 0.113 | Test accuracy 11.429% | Test f1 0.114\n",
      "Epoch: 50 | Train loss: 3.26316 | Train accuracy: 11.496% | Train f1 0.115 | Test accuracy 11.250% | Test f1 0.112\n",
      "Epoch: 60 | Train loss: 3.23769 | Train accuracy: 11.354% | Train f1 0.114 | Test accuracy 10.863% | Test f1 0.109\n",
      "Epoch: 70 | Train loss: 3.20361 | Train accuracy: 10.878% | Train f1 0.109 | Test accuracy 10.506% | Test f1 0.105\n",
      "Epoch: 80 | Train loss: 3.15832 | Train accuracy: 10.469% | Train f1 0.105 | Test accuracy 10.536% | Test f1 0.105\n",
      "Epoch: 90 | Train loss: 3.09985 | Train accuracy: 10.402% | Train f1 0.104 | Test accuracy 10.655% | Test f1 0.107\n",
      "Epoch: 100 | Train loss: 3.02977 | Train accuracy: 11.176% | Train f1 0.112 | Test accuracy 11.429% | Test f1 0.114\n",
      "Epoch: 110 | Train loss: 2.95483 | Train accuracy: 13.326% | Train f1 0.133 | Test accuracy 13.601% | Test f1 0.136\n",
      "Epoch: 120 | Train loss: 2.88145 | Train accuracy: 16.734% | Train f1 0.167 | Test accuracy 16.667% | Test f1 0.167\n",
      "Epoch: 130 | Train loss: 2.81083 | Train accuracy: 20.945% | Train f1 0.209 | Test accuracy 20.387% | Test f1 0.204\n",
      "Epoch: 140 | Train loss: 2.74175 | Train accuracy: 24.323% | Train f1 0.243 | Test accuracy 23.274% | Test f1 0.233\n",
      "Epoch: 150 | Train loss: 2.67299 | Train accuracy: 26.481% | Train f1 0.265 | Test accuracy 25.060% | Test f1 0.251\n",
      "Epoch: 160 | Train loss: 2.60452 | Train accuracy: 28.571% | Train f1 0.286 | Test accuracy 26.905% | Test f1 0.269\n",
      "Epoch: 170 | Train loss: 2.53785 | Train accuracy: 30.580% | Train f1 0.306 | Test accuracy 28.631% | Test f1 0.286\n",
      "Epoch: 180 | Train loss: 2.47471 | Train accuracy: 32.188% | Train f1 0.322 | Test accuracy 29.851% | Test f1 0.299\n",
      "Epoch: 190 | Train loss: 2.41634 | Train accuracy: 33.690% | Train f1 0.337 | Test accuracy 30.833% | Test f1 0.308\n",
      "Epoch: 200 | Train loss: 2.36313 | Train accuracy: 34.717% | Train f1 0.347 | Test accuracy 32.321% | Test f1 0.323\n",
      "Epoch: 210 | Train loss: 2.31481 | Train accuracy: 35.677% | Train f1 0.357 | Test accuracy 33.810% | Test f1 0.338\n",
      "Epoch: 220 | Train loss: 2.27081 | Train accuracy: 36.421% | Train f1 0.364 | Test accuracy 34.554% | Test f1 0.346\n",
      "Epoch: 230 | Train loss: 2.23050 | Train accuracy: 37.314% | Train f1 0.373 | Test accuracy 35.327% | Test f1 0.353\n",
      "Epoch: 240 | Train loss: 2.19324 | Train accuracy: 38.304% | Train f1 0.383 | Test accuracy 36.250% | Test f1 0.363\n",
      "Epoch: 250 | Train loss: 2.15836 | Train accuracy: 39.062% | Train f1 0.391 | Test accuracy 36.875% | Test f1 0.369\n",
      "Epoch: 260 | Train loss: 2.12526 | Train accuracy: 39.821% | Train f1 0.398 | Test accuracy 37.560% | Test f1 0.376\n",
      "Epoch: 270 | Train loss: 2.09345 | Train accuracy: 40.580% | Train f1 0.406 | Test accuracy 38.393% | Test f1 0.384\n",
      "Epoch: 280 | Train loss: 2.06252 | Train accuracy: 41.332% | Train f1 0.413 | Test accuracy 39.196% | Test f1 0.392\n",
      "Epoch: 290 | Train loss: 2.03219 | Train accuracy: 42.121% | Train f1 0.421 | Test accuracy 40.030% | Test f1 0.400\n",
      "Epoch: 300 | Train loss: 2.00226 | Train accuracy: 42.850% | Train f1 0.428 | Test accuracy 41.071% | Test f1 0.411\n",
      "Epoch: 310 | Train loss: 1.97264 | Train accuracy: 43.720% | Train f1 0.437 | Test accuracy 41.577% | Test f1 0.416\n",
      "Epoch: 320 | Train loss: 1.94329 | Train accuracy: 44.501% | Train f1 0.445 | Test accuracy 42.500% | Test f1 0.425\n",
      "Epoch: 330 | Train loss: 1.91421 | Train accuracy: 45.506% | Train f1 0.455 | Test accuracy 43.631% | Test f1 0.436\n",
      "Epoch: 340 | Train loss: 1.88540 | Train accuracy: 46.347% | Train f1 0.463 | Test accuracy 44.554% | Test f1 0.446\n",
      "Epoch: 350 | Train loss: 1.85690 | Train accuracy: 47.106% | Train f1 0.471 | Test accuracy 45.208% | Test f1 0.452\n",
      "Epoch: 360 | Train loss: 1.82879 | Train accuracy: 47.790% | Train f1 0.478 | Test accuracy 46.071% | Test f1 0.461\n",
      "Epoch: 370 | Train loss: 1.80113 | Train accuracy: 48.482% | Train f1 0.485 | Test accuracy 46.607% | Test f1 0.466\n",
      "Epoch: 380 | Train loss: 1.77394 | Train accuracy: 49.152% | Train f1 0.492 | Test accuracy 47.560% | Test f1 0.476\n",
      "Epoch: 390 | Train loss: 1.74723 | Train accuracy: 49.896% | Train f1 0.499 | Test accuracy 48.244% | Test f1 0.482\n",
      "Epoch: 400 | Train loss: 1.72099 | Train accuracy: 50.357% | Train f1 0.504 | Test accuracy 49.048% | Test f1 0.490\n",
      "Epoch: 410 | Train loss: 1.69520 | Train accuracy: 51.079% | Train f1 0.511 | Test accuracy 49.583% | Test f1 0.496\n",
      "Epoch: 420 | Train loss: 1.66987 | Train accuracy: 51.793% | Train f1 0.518 | Test accuracy 49.940% | Test f1 0.499\n",
      "Epoch: 430 | Train loss: 1.64499 | Train accuracy: 52.440% | Train f1 0.524 | Test accuracy 50.506% | Test f1 0.505\n",
      "Epoch: 440 | Train loss: 1.62052 | Train accuracy: 53.147% | Train f1 0.531 | Test accuracy 50.863% | Test f1 0.509\n",
      "Epoch: 450 | Train loss: 1.59643 | Train accuracy: 53.780% | Train f1 0.538 | Test accuracy 51.429% | Test f1 0.514\n",
      "Epoch: 460 | Train loss: 1.57268 | Train accuracy: 54.368% | Train f1 0.544 | Test accuracy 51.756% | Test f1 0.518\n",
      "Epoch: 470 | Train loss: 1.54925 | Train accuracy: 54.963% | Train f1 0.550 | Test accuracy 52.143% | Test f1 0.521\n",
      "Epoch: 480 | Train loss: 1.52611 | Train accuracy: 55.521% | Train f1 0.555 | Test accuracy 52.351% | Test f1 0.524\n",
      "Epoch: 490 | Train loss: 1.50327 | Train accuracy: 56.094% | Train f1 0.561 | Test accuracy 52.917% | Test f1 0.529\n",
      "Epoch: 500 | Train loss: 1.48074 | Train accuracy: 56.652% | Train f1 0.567 | Test accuracy 53.780% | Test f1 0.538\n",
      "Epoch: 510 | Train loss: 1.45853 | Train accuracy: 57.247% | Train f1 0.572 | Test accuracy 54.137% | Test f1 0.541\n",
      "Epoch: 520 | Train loss: 1.43667 | Train accuracy: 57.775% | Train f1 0.578 | Test accuracy 54.643% | Test f1 0.546\n",
      "Epoch: 530 | Train loss: 1.41518 | Train accuracy: 58.281% | Train f1 0.583 | Test accuracy 55.060% | Test f1 0.551\n",
      "Epoch: 540 | Train loss: 1.39406 | Train accuracy: 58.869% | Train f1 0.589 | Test accuracy 55.655% | Test f1 0.557\n",
      "Epoch: 550 | Train loss: 1.37331 | Train accuracy: 59.353% | Train f1 0.594 | Test accuracy 56.220% | Test f1 0.562\n",
      "Epoch: 560 | Train loss: 1.35294 | Train accuracy: 59.933% | Train f1 0.599 | Test accuracy 56.548% | Test f1 0.565\n",
      "Epoch: 570 | Train loss: 1.33298 | Train accuracy: 60.469% | Train f1 0.605 | Test accuracy 56.964% | Test f1 0.570\n",
      "Epoch: 580 | Train loss: 1.31340 | Train accuracy: 61.094% | Train f1 0.611 | Test accuracy 57.440% | Test f1 0.574\n",
      "Epoch: 590 | Train loss: 1.29420 | Train accuracy: 61.488% | Train f1 0.615 | Test accuracy 57.857% | Test f1 0.579\n",
      "Epoch: 600 | Train loss: 1.27539 | Train accuracy: 61.972% | Train f1 0.620 | Test accuracy 58.482% | Test f1 0.585\n",
      "Epoch: 610 | Train loss: 1.25696 | Train accuracy: 62.507% | Train f1 0.625 | Test accuracy 59.048% | Test f1 0.590\n",
      "Epoch: 620 | Train loss: 1.23891 | Train accuracy: 62.961% | Train f1 0.630 | Test accuracy 59.286% | Test f1 0.593\n",
      "Epoch: 630 | Train loss: 1.22124 | Train accuracy: 63.415% | Train f1 0.634 | Test accuracy 59.702% | Test f1 0.597\n",
      "Epoch: 640 | Train loss: 1.20395 | Train accuracy: 63.899% | Train f1 0.639 | Test accuracy 60.060% | Test f1 0.601\n",
      "Epoch: 650 | Train loss: 1.18704 | Train accuracy: 64.368% | Train f1 0.644 | Test accuracy 60.655% | Test f1 0.607\n",
      "Epoch: 660 | Train loss: 1.17050 | Train accuracy: 64.955% | Train f1 0.650 | Test accuracy 61.131% | Test f1 0.611\n",
      "Epoch: 670 | Train loss: 1.15431 | Train accuracy: 65.432% | Train f1 0.654 | Test accuracy 61.310% | Test f1 0.613\n",
      "Epoch: 680 | Train loss: 1.13846 | Train accuracy: 65.863% | Train f1 0.659 | Test accuracy 61.667% | Test f1 0.617\n",
      "Epoch: 690 | Train loss: 1.12293 | Train accuracy: 66.317% | Train f1 0.663 | Test accuracy 61.726% | Test f1 0.617\n",
      "Epoch: 700 | Train loss: 1.10772 | Train accuracy: 66.667% | Train f1 0.667 | Test accuracy 62.143% | Test f1 0.621\n",
      "Epoch: 710 | Train loss: 1.09281 | Train accuracy: 67.292% | Train f1 0.673 | Test accuracy 62.470% | Test f1 0.625\n",
      "Epoch: 720 | Train loss: 1.07820 | Train accuracy: 67.768% | Train f1 0.678 | Test accuracy 63.036% | Test f1 0.630\n",
      "Epoch: 730 | Train loss: 1.06388 | Train accuracy: 68.207% | Train f1 0.682 | Test accuracy 63.452% | Test f1 0.635\n",
      "Epoch: 740 | Train loss: 1.04985 | Train accuracy: 68.624% | Train f1 0.686 | Test accuracy 63.810% | Test f1 0.638\n",
      "Epoch: 750 | Train loss: 1.03607 | Train accuracy: 69.055% | Train f1 0.691 | Test accuracy 63.958% | Test f1 0.640\n",
      "Epoch: 760 | Train loss: 1.02253 | Train accuracy: 69.464% | Train f1 0.695 | Test accuracy 64.196% | Test f1 0.642\n",
      "Epoch: 770 | Train loss: 1.00924 | Train accuracy: 69.896% | Train f1 0.699 | Test accuracy 64.702% | Test f1 0.647\n",
      "Epoch: 780 | Train loss: 0.99619 | Train accuracy: 70.312% | Train f1 0.703 | Test accuracy 65.149% | Test f1 0.651\n",
      "Epoch: 790 | Train loss: 0.98336 | Train accuracy: 70.744% | Train f1 0.707 | Test accuracy 65.387% | Test f1 0.654\n",
      "Epoch: 800 | Train loss: 0.97077 | Train accuracy: 71.116% | Train f1 0.711 | Test accuracy 65.685% | Test f1 0.657\n",
      "Epoch: 810 | Train loss: 0.95839 | Train accuracy: 71.443% | Train f1 0.714 | Test accuracy 66.071% | Test f1 0.661\n",
      "Epoch: 820 | Train loss: 0.94624 | Train accuracy: 71.771% | Train f1 0.718 | Test accuracy 66.310% | Test f1 0.663\n",
      "Epoch: 830 | Train loss: 0.93430 | Train accuracy: 72.016% | Train f1 0.720 | Test accuracy 66.458% | Test f1 0.665\n",
      "Epoch: 840 | Train loss: 0.92257 | Train accuracy: 72.374% | Train f1 0.724 | Test accuracy 66.696% | Test f1 0.667\n",
      "Epoch: 850 | Train loss: 0.91107 | Train accuracy: 72.805% | Train f1 0.728 | Test accuracy 66.935% | Test f1 0.669\n",
      "Epoch: 860 | Train loss: 0.89977 | Train accuracy: 73.199% | Train f1 0.732 | Test accuracy 67.113% | Test f1 0.671\n",
      "Epoch: 870 | Train loss: 0.88867 | Train accuracy: 73.445% | Train f1 0.734 | Test accuracy 67.381% | Test f1 0.674\n",
      "Epoch: 880 | Train loss: 0.87776 | Train accuracy: 73.817% | Train f1 0.738 | Test accuracy 67.679% | Test f1 0.677\n",
      "Epoch: 890 | Train loss: 0.86703 | Train accuracy: 74.107% | Train f1 0.741 | Test accuracy 67.946% | Test f1 0.679\n",
      "Epoch: 900 | Train loss: 0.85648 | Train accuracy: 74.368% | Train f1 0.744 | Test accuracy 68.244% | Test f1 0.682\n",
      "Epoch: 910 | Train loss: 0.84611 | Train accuracy: 74.613% | Train f1 0.746 | Test accuracy 68.393% | Test f1 0.684\n",
      "Epoch: 920 | Train loss: 0.83590 | Train accuracy: 74.948% | Train f1 0.749 | Test accuracy 68.512% | Test f1 0.685\n",
      "Epoch: 930 | Train loss: 0.82586 | Train accuracy: 75.327% | Train f1 0.753 | Test accuracy 68.690% | Test f1 0.687\n",
      "Epoch: 940 | Train loss: 0.81597 | Train accuracy: 75.573% | Train f1 0.756 | Test accuracy 68.839% | Test f1 0.688\n",
      "Epoch: 950 | Train loss: 0.80622 | Train accuracy: 75.789% | Train f1 0.758 | Test accuracy 68.988% | Test f1 0.690\n",
      "Epoch: 960 | Train loss: 0.79663 | Train accuracy: 76.153% | Train f1 0.762 | Test accuracy 69.077% | Test f1 0.691\n",
      "Epoch: 970 | Train loss: 0.78718 | Train accuracy: 76.555% | Train f1 0.766 | Test accuracy 69.286% | Test f1 0.693\n",
      "Epoch: 980 | Train loss: 0.77787 | Train accuracy: 76.875% | Train f1 0.769 | Test accuracy 69.405% | Test f1 0.694\n",
      "Epoch: 990 | Train loss: 0.76870 | Train accuracy: 77.054% | Train f1 0.771 | Test accuracy 69.613% | Test f1 0.696\n",
      "Epoch: 1000 | Train loss: 0.75967 | Train accuracy: 77.396% | Train f1 0.774 | Test accuracy 69.702% | Test f1 0.697\n",
      "Epoch: 1010 | Train loss: 0.75077 | Train accuracy: 77.686% | Train f1 0.777 | Test accuracy 69.851% | Test f1 0.699\n",
      "Epoch: 1020 | Train loss: 0.74200 | Train accuracy: 77.946% | Train f1 0.779 | Test accuracy 69.911% | Test f1 0.699\n",
      "Epoch: 1030 | Train loss: 0.73335 | Train accuracy: 78.251% | Train f1 0.783 | Test accuracy 70.060% | Test f1 0.701\n",
      "Epoch: 1040 | Train loss: 0.72481 | Train accuracy: 78.460% | Train f1 0.785 | Test accuracy 70.149% | Test f1 0.701\n",
      "Epoch: 1050 | Train loss: 0.71640 | Train accuracy: 78.698% | Train f1 0.787 | Test accuracy 70.238% | Test f1 0.702\n",
      "Epoch: 1060 | Train loss: 0.70811 | Train accuracy: 78.966% | Train f1 0.790 | Test accuracy 70.476% | Test f1 0.705\n",
      "Epoch: 1070 | Train loss: 0.69993 | Train accuracy: 79.122% | Train f1 0.791 | Test accuracy 70.685% | Test f1 0.707\n",
      "Epoch: 1080 | Train loss: 0.69186 | Train accuracy: 79.308% | Train f1 0.793 | Test accuracy 70.774% | Test f1 0.708\n",
      "Epoch: 1090 | Train loss: 0.68390 | Train accuracy: 79.568% | Train f1 0.796 | Test accuracy 71.042% | Test f1 0.710\n",
      "Epoch: 1100 | Train loss: 0.67604 | Train accuracy: 79.799% | Train f1 0.798 | Test accuracy 71.101% | Test f1 0.711\n",
      "Epoch: 1110 | Train loss: 0.66828 | Train accuracy: 80.104% | Train f1 0.801 | Test accuracy 71.190% | Test f1 0.712\n",
      "Epoch: 1120 | Train loss: 0.66062 | Train accuracy: 80.312% | Train f1 0.803 | Test accuracy 71.399% | Test f1 0.714\n",
      "Epoch: 1130 | Train loss: 0.65307 | Train accuracy: 80.595% | Train f1 0.806 | Test accuracy 71.548% | Test f1 0.715\n",
      "Epoch: 1140 | Train loss: 0.64561 | Train accuracy: 80.759% | Train f1 0.808 | Test accuracy 71.696% | Test f1 0.717\n",
      "Epoch: 1150 | Train loss: 0.63825 | Train accuracy: 80.960% | Train f1 0.810 | Test accuracy 71.756% | Test f1 0.718\n",
      "Epoch: 1160 | Train loss: 0.63098 | Train accuracy: 81.198% | Train f1 0.812 | Test accuracy 71.845% | Test f1 0.718\n",
      "Epoch: 1170 | Train loss: 0.62380 | Train accuracy: 81.406% | Train f1 0.814 | Test accuracy 72.083% | Test f1 0.721\n",
      "Epoch: 1180 | Train loss: 0.61670 | Train accuracy: 81.637% | Train f1 0.816 | Test accuracy 72.083% | Test f1 0.721\n",
      "Epoch: 1190 | Train loss: 0.60969 | Train accuracy: 81.860% | Train f1 0.819 | Test accuracy 72.381% | Test f1 0.724\n",
      "Epoch: 1200 | Train loss: 0.60275 | Train accuracy: 82.054% | Train f1 0.821 | Test accuracy 72.351% | Test f1 0.724\n",
      "Epoch: 1210 | Train loss: 0.59590 | Train accuracy: 82.284% | Train f1 0.823 | Test accuracy 72.470% | Test f1 0.725\n",
      "Epoch: 1220 | Train loss: 0.58912 | Train accuracy: 82.567% | Train f1 0.826 | Test accuracy 72.649% | Test f1 0.726\n",
      "Epoch: 1230 | Train loss: 0.58243 | Train accuracy: 82.865% | Train f1 0.829 | Test accuracy 72.738% | Test f1 0.727\n",
      "Epoch: 1240 | Train loss: 0.57580 | Train accuracy: 83.058% | Train f1 0.831 | Test accuracy 72.946% | Test f1 0.729\n",
      "Epoch: 1250 | Train loss: 0.56925 | Train accuracy: 83.251% | Train f1 0.833 | Test accuracy 73.036% | Test f1 0.730\n",
      "Epoch: 1260 | Train loss: 0.56278 | Train accuracy: 83.504% | Train f1 0.835 | Test accuracy 73.155% | Test f1 0.732\n",
      "Epoch: 1270 | Train loss: 0.55639 | Train accuracy: 83.728% | Train f1 0.837 | Test accuracy 73.214% | Test f1 0.732\n",
      "Epoch: 1280 | Train loss: 0.55006 | Train accuracy: 83.914% | Train f1 0.839 | Test accuracy 73.185% | Test f1 0.732\n",
      "Epoch: 1290 | Train loss: 0.54380 | Train accuracy: 84.182% | Train f1 0.842 | Test accuracy 73.274% | Test f1 0.733\n",
      "Epoch: 1300 | Train loss: 0.53762 | Train accuracy: 84.353% | Train f1 0.844 | Test accuracy 73.333% | Test f1 0.733\n",
      "Epoch: 1310 | Train loss: 0.53150 | Train accuracy: 84.591% | Train f1 0.846 | Test accuracy 73.423% | Test f1 0.734\n",
      "Epoch: 1320 | Train loss: 0.52546 | Train accuracy: 84.762% | Train f1 0.848 | Test accuracy 73.542% | Test f1 0.735\n",
      "Epoch: 1330 | Train loss: 0.51948 | Train accuracy: 84.955% | Train f1 0.850 | Test accuracy 73.601% | Test f1 0.736\n",
      "Epoch: 1340 | Train loss: 0.51358 | Train accuracy: 85.268% | Train f1 0.853 | Test accuracy 73.601% | Test f1 0.736\n",
      "Epoch: 1350 | Train loss: 0.50773 | Train accuracy: 85.484% | Train f1 0.855 | Test accuracy 73.780% | Test f1 0.738\n",
      "Epoch: 1360 | Train loss: 0.50195 | Train accuracy: 85.707% | Train f1 0.857 | Test accuracy 73.750% | Test f1 0.738\n",
      "Epoch: 1370 | Train loss: 0.49623 | Train accuracy: 85.975% | Train f1 0.860 | Test accuracy 73.750% | Test f1 0.738\n",
      "Epoch: 1380 | Train loss: 0.49056 | Train accuracy: 86.131% | Train f1 0.861 | Test accuracy 73.810% | Test f1 0.738\n",
      "Epoch: 1390 | Train loss: 0.48496 | Train accuracy: 86.347% | Train f1 0.863 | Test accuracy 73.899% | Test f1 0.739\n",
      "Epoch: 1400 | Train loss: 0.47941 | Train accuracy: 86.592% | Train f1 0.866 | Test accuracy 74.018% | Test f1 0.740\n",
      "Epoch: 1410 | Train loss: 0.47394 | Train accuracy: 86.734% | Train f1 0.867 | Test accuracy 74.048% | Test f1 0.740\n",
      "Epoch: 1420 | Train loss: 0.46852 | Train accuracy: 86.890% | Train f1 0.869 | Test accuracy 74.048% | Test f1 0.740\n",
      "Epoch: 1430 | Train loss: 0.46316 | Train accuracy: 87.054% | Train f1 0.871 | Test accuracy 73.988% | Test f1 0.740\n",
      "Epoch: 1440 | Train loss: 0.45786 | Train accuracy: 87.269% | Train f1 0.873 | Test accuracy 73.929% | Test f1 0.739\n",
      "Epoch: 1450 | Train loss: 0.45261 | Train accuracy: 87.388% | Train f1 0.874 | Test accuracy 74.018% | Test f1 0.740\n",
      "Epoch: 1460 | Train loss: 0.44742 | Train accuracy: 87.582% | Train f1 0.876 | Test accuracy 74.226% | Test f1 0.742\n",
      "Epoch: 1470 | Train loss: 0.44229 | Train accuracy: 87.716% | Train f1 0.877 | Test accuracy 74.375% | Test f1 0.744\n",
      "Epoch: 1480 | Train loss: 0.43721 | Train accuracy: 87.887% | Train f1 0.879 | Test accuracy 74.643% | Test f1 0.746\n",
      "Epoch: 1490 | Train loss: 0.43218 | Train accuracy: 88.073% | Train f1 0.881 | Test accuracy 74.702% | Test f1 0.747\n",
      "Epoch: 1500 | Train loss: 0.42721 | Train accuracy: 88.251% | Train f1 0.883 | Test accuracy 74.702% | Test f1 0.747\n",
      "Epoch: 1510 | Train loss: 0.42230 | Train accuracy: 88.400% | Train f1 0.884 | Test accuracy 74.851% | Test f1 0.749\n",
      "Epoch: 1520 | Train loss: 0.41744 | Train accuracy: 88.564% | Train f1 0.886 | Test accuracy 75.000% | Test f1 0.750\n",
      "Epoch: 1530 | Train loss: 0.41263 | Train accuracy: 88.705% | Train f1 0.887 | Test accuracy 75.030% | Test f1 0.750\n",
      "Epoch: 1540 | Train loss: 0.40787 | Train accuracy: 88.884% | Train f1 0.889 | Test accuracy 75.149% | Test f1 0.751\n",
      "Epoch: 1550 | Train loss: 0.40316 | Train accuracy: 89.033% | Train f1 0.890 | Test accuracy 75.268% | Test f1 0.753\n",
      "Epoch: 1560 | Train loss: 0.39850 | Train accuracy: 89.189% | Train f1 0.892 | Test accuracy 75.268% | Test f1 0.753\n",
      "Epoch: 1570 | Train loss: 0.39389 | Train accuracy: 89.382% | Train f1 0.894 | Test accuracy 75.179% | Test f1 0.752\n",
      "Epoch: 1580 | Train loss: 0.38932 | Train accuracy: 89.539% | Train f1 0.895 | Test accuracy 75.238% | Test f1 0.752\n",
      "Epoch: 1590 | Train loss: 0.38480 | Train accuracy: 89.710% | Train f1 0.897 | Test accuracy 75.268% | Test f1 0.753\n",
      "Epoch: 1600 | Train loss: 0.38033 | Train accuracy: 89.836% | Train f1 0.898 | Test accuracy 75.417% | Test f1 0.754\n",
      "Epoch: 1610 | Train loss: 0.37591 | Train accuracy: 90.015% | Train f1 0.900 | Test accuracy 75.476% | Test f1 0.755\n",
      "Epoch: 1620 | Train loss: 0.37154 | Train accuracy: 90.126% | Train f1 0.901 | Test accuracy 75.387% | Test f1 0.754\n",
      "Epoch: 1630 | Train loss: 0.36721 | Train accuracy: 90.312% | Train f1 0.903 | Test accuracy 75.417% | Test f1 0.754\n",
      "Epoch: 1640 | Train loss: 0.36292 | Train accuracy: 90.454% | Train f1 0.905 | Test accuracy 75.506% | Test f1 0.755\n",
      "Epoch: 1650 | Train loss: 0.35868 | Train accuracy: 90.551% | Train f1 0.906 | Test accuracy 75.625% | Test f1 0.756\n",
      "Epoch: 1660 | Train loss: 0.35448 | Train accuracy: 90.670% | Train f1 0.907 | Test accuracy 75.625% | Test f1 0.756\n",
      "Epoch: 1670 | Train loss: 0.35032 | Train accuracy: 90.856% | Train f1 0.909 | Test accuracy 75.685% | Test f1 0.757\n",
      "Epoch: 1680 | Train loss: 0.34621 | Train accuracy: 90.967% | Train f1 0.910 | Test accuracy 75.714% | Test f1 0.757\n",
      "Epoch: 1690 | Train loss: 0.34215 | Train accuracy: 91.094% | Train f1 0.911 | Test accuracy 75.714% | Test f1 0.757\n",
      "Epoch: 1700 | Train loss: 0.33813 | Train accuracy: 91.213% | Train f1 0.912 | Test accuracy 75.744% | Test f1 0.757\n",
      "Epoch: 1710 | Train loss: 0.33415 | Train accuracy: 91.362% | Train f1 0.914 | Test accuracy 75.833% | Test f1 0.758\n",
      "Epoch: 1720 | Train loss: 0.33021 | Train accuracy: 91.451% | Train f1 0.915 | Test accuracy 75.833% | Test f1 0.758\n",
      "Epoch: 1730 | Train loss: 0.32632 | Train accuracy: 91.585% | Train f1 0.916 | Test accuracy 75.804% | Test f1 0.758\n",
      "Epoch: 1740 | Train loss: 0.32247 | Train accuracy: 91.786% | Train f1 0.918 | Test accuracy 75.833% | Test f1 0.758\n",
      "Epoch: 1750 | Train loss: 0.31865 | Train accuracy: 91.927% | Train f1 0.919 | Test accuracy 75.774% | Test f1 0.758\n",
      "Epoch: 1760 | Train loss: 0.31488 | Train accuracy: 92.054% | Train f1 0.921 | Test accuracy 75.833% | Test f1 0.758\n",
      "Epoch: 1770 | Train loss: 0.31115 | Train accuracy: 92.165% | Train f1 0.922 | Test accuracy 75.833% | Test f1 0.758\n",
      "Epoch: 1780 | Train loss: 0.30745 | Train accuracy: 92.284% | Train f1 0.923 | Test accuracy 76.012% | Test f1 0.760\n",
      "Epoch: 1790 | Train loss: 0.30380 | Train accuracy: 92.440% | Train f1 0.924 | Test accuracy 76.161% | Test f1 0.762\n",
      "Epoch: 1800 | Train loss: 0.30019 | Train accuracy: 92.582% | Train f1 0.926 | Test accuracy 76.190% | Test f1 0.762\n",
      "Epoch: 1810 | Train loss: 0.29661 | Train accuracy: 92.723% | Train f1 0.927 | Test accuracy 76.220% | Test f1 0.762\n",
      "Epoch: 1820 | Train loss: 0.29308 | Train accuracy: 92.842% | Train f1 0.928 | Test accuracy 76.220% | Test f1 0.762\n",
      "Epoch: 1830 | Train loss: 0.28958 | Train accuracy: 92.924% | Train f1 0.929 | Test accuracy 76.310% | Test f1 0.763\n",
      "Epoch: 1840 | Train loss: 0.28612 | Train accuracy: 93.058% | Train f1 0.931 | Test accuracy 76.429% | Test f1 0.764\n",
      "Epoch: 1850 | Train loss: 0.28269 | Train accuracy: 93.170% | Train f1 0.932 | Test accuracy 76.488% | Test f1 0.765\n",
      "Epoch: 1860 | Train loss: 0.27931 | Train accuracy: 93.296% | Train f1 0.933 | Test accuracy 76.488% | Test f1 0.765\n",
      "Epoch: 1870 | Train loss: 0.27596 | Train accuracy: 93.363% | Train f1 0.934 | Test accuracy 76.518% | Test f1 0.765\n",
      "Epoch: 1880 | Train loss: 0.27265 | Train accuracy: 93.408% | Train f1 0.934 | Test accuracy 76.458% | Test f1 0.765\n",
      "Epoch: 1890 | Train loss: 0.26938 | Train accuracy: 93.534% | Train f1 0.935 | Test accuracy 76.548% | Test f1 0.765\n",
      "Epoch: 1900 | Train loss: 0.26614 | Train accuracy: 93.668% | Train f1 0.937 | Test accuracy 76.667% | Test f1 0.767\n",
      "Epoch: 1910 | Train loss: 0.26294 | Train accuracy: 93.780% | Train f1 0.938 | Test accuracy 76.637% | Test f1 0.766\n",
      "Epoch: 1920 | Train loss: 0.25978 | Train accuracy: 93.936% | Train f1 0.939 | Test accuracy 76.756% | Test f1 0.768\n",
      "Epoch: 1930 | Train loss: 0.25664 | Train accuracy: 94.055% | Train f1 0.941 | Test accuracy 76.756% | Test f1 0.768\n",
      "Epoch: 1940 | Train loss: 0.25355 | Train accuracy: 94.144% | Train f1 0.941 | Test accuracy 76.726% | Test f1 0.767\n",
      "Epoch: 1950 | Train loss: 0.25049 | Train accuracy: 94.301% | Train f1 0.943 | Test accuracy 76.667% | Test f1 0.767\n",
      "Epoch: 1960 | Train loss: 0.24746 | Train accuracy: 94.368% | Train f1 0.944 | Test accuracy 76.815% | Test f1 0.768\n",
      "Epoch: 1970 | Train loss: 0.24447 | Train accuracy: 94.435% | Train f1 0.944 | Test accuracy 76.905% | Test f1 0.769\n",
      "Epoch: 1980 | Train loss: 0.24151 | Train accuracy: 94.561% | Train f1 0.946 | Test accuracy 76.905% | Test f1 0.769\n",
      "Epoch: 1990 | Train loss: 0.23859 | Train accuracy: 94.702% | Train f1 0.947 | Test accuracy 76.935% | Test f1 0.769\n",
      "Epoch: 2000 | Train loss: 0.23570 | Train accuracy: 94.799% | Train f1 0.948 | Test accuracy 77.024% | Test f1 0.770\n",
      "Epoch: 2010 | Train loss: 0.23284 | Train accuracy: 94.844% | Train f1 0.948 | Test accuracy 77.113% | Test f1 0.771\n",
      "Epoch: 2020 | Train loss: 0.23002 | Train accuracy: 94.993% | Train f1 0.950 | Test accuracy 77.173% | Test f1 0.772\n",
      "Epoch: 2030 | Train loss: 0.22722 | Train accuracy: 95.060% | Train f1 0.951 | Test accuracy 77.173% | Test f1 0.772\n",
      "Epoch: 2040 | Train loss: 0.22446 | Train accuracy: 95.112% | Train f1 0.951 | Test accuracy 77.173% | Test f1 0.772\n",
      "Epoch: 2050 | Train loss: 0.22173 | Train accuracy: 95.201% | Train f1 0.952 | Test accuracy 77.232% | Test f1 0.772\n",
      "Epoch: 2060 | Train loss: 0.21903 | Train accuracy: 95.305% | Train f1 0.953 | Test accuracy 77.262% | Test f1 0.773\n",
      "Epoch: 2070 | Train loss: 0.21636 | Train accuracy: 95.439% | Train f1 0.954 | Test accuracy 77.232% | Test f1 0.772\n",
      "Epoch: 2080 | Train loss: 0.21373 | Train accuracy: 95.558% | Train f1 0.956 | Test accuracy 77.262% | Test f1 0.773\n",
      "Epoch: 2090 | Train loss: 0.21113 | Train accuracy: 95.632% | Train f1 0.956 | Test accuracy 77.262% | Test f1 0.773\n",
      "Epoch: 2100 | Train loss: 0.20855 | Train accuracy: 95.685% | Train f1 0.957 | Test accuracy 77.262% | Test f1 0.773\n",
      "Epoch: 2110 | Train loss: 0.20601 | Train accuracy: 95.751% | Train f1 0.958 | Test accuracy 77.321% | Test f1 0.773\n",
      "Epoch: 2120 | Train loss: 0.20350 | Train accuracy: 95.826% | Train f1 0.958 | Test accuracy 77.321% | Test f1 0.773\n",
      "Epoch: 2130 | Train loss: 0.20102 | Train accuracy: 95.975% | Train f1 0.960 | Test accuracy 77.321% | Test f1 0.773\n",
      "Epoch: 2140 | Train loss: 0.19856 | Train accuracy: 96.094% | Train f1 0.961 | Test accuracy 77.411% | Test f1 0.774\n",
      "Epoch: 2150 | Train loss: 0.19614 | Train accuracy: 96.146% | Train f1 0.961 | Test accuracy 77.351% | Test f1 0.774\n",
      "Epoch: 2160 | Train loss: 0.19374 | Train accuracy: 96.228% | Train f1 0.962 | Test accuracy 77.411% | Test f1 0.774\n",
      "Epoch: 2170 | Train loss: 0.19138 | Train accuracy: 96.354% | Train f1 0.964 | Test accuracy 77.440% | Test f1 0.774\n",
      "Epoch: 2180 | Train loss: 0.18904 | Train accuracy: 96.466% | Train f1 0.965 | Test accuracy 77.500% | Test f1 0.775\n",
      "Epoch: 2190 | Train loss: 0.18673 | Train accuracy: 96.540% | Train f1 0.965 | Test accuracy 77.470% | Test f1 0.775\n",
      "Epoch: 2200 | Train loss: 0.18444 | Train accuracy: 96.622% | Train f1 0.966 | Test accuracy 77.440% | Test f1 0.774\n",
      "Epoch: 2210 | Train loss: 0.18219 | Train accuracy: 96.704% | Train f1 0.967 | Test accuracy 77.530% | Test f1 0.775\n",
      "Epoch: 2220 | Train loss: 0.17997 | Train accuracy: 96.808% | Train f1 0.968 | Test accuracy 77.530% | Test f1 0.775\n",
      "Epoch: 2230 | Train loss: 0.17777 | Train accuracy: 96.890% | Train f1 0.969 | Test accuracy 77.500% | Test f1 0.775\n",
      "Epoch: 2240 | Train loss: 0.17560 | Train accuracy: 96.972% | Train f1 0.970 | Test accuracy 77.500% | Test f1 0.775\n",
      "Epoch: 2250 | Train loss: 0.17345 | Train accuracy: 97.054% | Train f1 0.971 | Test accuracy 77.500% | Test f1 0.775\n",
      "Epoch: 2260 | Train loss: 0.17133 | Train accuracy: 97.113% | Train f1 0.971 | Test accuracy 77.530% | Test f1 0.775\n",
      "Epoch: 2270 | Train loss: 0.16924 | Train accuracy: 97.150% | Train f1 0.972 | Test accuracy 77.440% | Test f1 0.774\n",
      "Epoch: 2280 | Train loss: 0.16718 | Train accuracy: 97.202% | Train f1 0.972 | Test accuracy 77.381% | Test f1 0.774\n",
      "Epoch: 2290 | Train loss: 0.16514 | Train accuracy: 97.262% | Train f1 0.973 | Test accuracy 77.411% | Test f1 0.774\n",
      "Epoch: 2300 | Train loss: 0.16312 | Train accuracy: 97.329% | Train f1 0.973 | Test accuracy 77.440% | Test f1 0.774\n",
      "Epoch: 2310 | Train loss: 0.16114 | Train accuracy: 97.418% | Train f1 0.974 | Test accuracy 77.411% | Test f1 0.774\n",
      "Epoch: 2320 | Train loss: 0.15917 | Train accuracy: 97.485% | Train f1 0.975 | Test accuracy 77.351% | Test f1 0.774\n",
      "Epoch: 2330 | Train loss: 0.15723 | Train accuracy: 97.530% | Train f1 0.975 | Test accuracy 77.321% | Test f1 0.773\n",
      "Epoch: 2340 | Train loss: 0.15532 | Train accuracy: 97.574% | Train f1 0.976 | Test accuracy 77.351% | Test f1 0.774\n",
      "Epoch: 2350 | Train loss: 0.15343 | Train accuracy: 97.589% | Train f1 0.976 | Test accuracy 77.321% | Test f1 0.773\n",
      "Epoch: 2360 | Train loss: 0.15156 | Train accuracy: 97.626% | Train f1 0.976 | Test accuracy 77.321% | Test f1 0.773\n",
      "Epoch: 2370 | Train loss: 0.14972 | Train accuracy: 97.686% | Train f1 0.977 | Test accuracy 77.351% | Test f1 0.774\n",
      "Epoch: 2380 | Train loss: 0.14789 | Train accuracy: 97.753% | Train f1 0.978 | Test accuracy 77.321% | Test f1 0.773\n",
      "Epoch: 2390 | Train loss: 0.14610 | Train accuracy: 97.805% | Train f1 0.978 | Test accuracy 77.351% | Test f1 0.774\n",
      "Epoch: 2400 | Train loss: 0.14432 | Train accuracy: 97.842% | Train f1 0.978 | Test accuracy 77.381% | Test f1 0.774\n",
      "Epoch: 2410 | Train loss: 0.14257 | Train accuracy: 97.872% | Train f1 0.979 | Test accuracy 77.381% | Test f1 0.774\n",
      "Epoch: 2420 | Train loss: 0.14085 | Train accuracy: 97.909% | Train f1 0.979 | Test accuracy 77.351% | Test f1 0.774\n",
      "Epoch: 2430 | Train loss: 0.13914 | Train accuracy: 97.946% | Train f1 0.979 | Test accuracy 77.411% | Test f1 0.774\n",
      "Epoch: 2440 | Train loss: 0.13746 | Train accuracy: 98.036% | Train f1 0.980 | Test accuracy 77.470% | Test f1 0.775\n",
      "Epoch: 2450 | Train loss: 0.13579 | Train accuracy: 98.065% | Train f1 0.981 | Test accuracy 77.470% | Test f1 0.775\n",
      "Epoch: 2460 | Train loss: 0.13415 | Train accuracy: 98.118% | Train f1 0.981 | Test accuracy 77.500% | Test f1 0.775\n",
      "Epoch: 2470 | Train loss: 0.13253 | Train accuracy: 98.140% | Train f1 0.981 | Test accuracy 77.440% | Test f1 0.774\n",
      "Epoch: 2480 | Train loss: 0.13093 | Train accuracy: 98.185% | Train f1 0.982 | Test accuracy 77.440% | Test f1 0.774\n",
      "Epoch: 2490 | Train loss: 0.12935 | Train accuracy: 98.237% | Train f1 0.982 | Test accuracy 77.470% | Test f1 0.775\n"
     ]
    }
   ],
   "source": [
    "tr_metrics, ts_metrics = SingleTrain_drop(prob=0.2, num_epochs=2500, learning_rate = 0.01, decay=0.00005, printing=True, inner=455, outer=645)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ebca62a4-a1a7-403f-ab75-b8a816bf8c4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f301eaff10>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x2000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(3, figsize=(10,20))\n",
    "sns.lineplot(data=tr_metrics, x='epoch', y='loss', label='Train data', ax=axs[0])\n",
    "sns.lineplot(data=ts_metrics, x='epoch', y='loss', label='Test data', ax=axs[0])\n",
    "sns.lineplot(data=tr_metrics, x='epoch', y='accuracy', label='Train data', ax=axs[1])\n",
    "sns.lineplot(data=ts_metrics, x='epoch', y='accuracy', label='Test data', ax=axs[1])\n",
    "sns.lineplot(data=tr_metrics, x='epoch', y='f1_score', label='Train data', ax=axs[2])\n",
    "sns.lineplot(data=ts_metrics, x='epoch', y='f1_score', label='Test data', ax=axs[2])\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eabb5dbe-9b8d-40ed-84b0-5c197ef3122a",
   "metadata": {},
   "source": [
    "As can be seen after some manual testing of the different hyperparameters, the performance of the model has been improved somewhat, with the biggrest factor being the switch to the SGD optimizer rather than the Adam optimizer. This is likely due to the sensitivity of the Adam optimizier to the initial learning rate, so a more detailed exploration of that parameter space would likely also lead good results. However, the biggest hurdle is the amount of training data, since the model is trained on around 16000 images (classic MNIST has around 60000), overfitting is a major problem. This can be somewhat remedied with creating synthetic data, but again the goal of this notebook is to explore Pytorch for a classic problem, so that would likely be overkill. Bellow, I have also implemented the model using the data from the csv files as a sanity check, to make sure there are no significant problems with the data pipeline.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a1bcb8e9-bfd6-46ce-88cd-6403c4aabe2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_train_file='./archive/csvTrainImages 13440x1024.csv'\n",
    "csv_train_labels='./archive/csvTrainLabel 13440x1.csv'\n",
    "csv_test_file= './archive/csvTestImages 3360x1024.csv'\n",
    "csv_test_labels= './archive/csvTestLabel 3360x1.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "65a5391e-902e-41c5-8c46-da5074423302",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_train_data=pd.read_csv(csv_train_file)\n",
    "pd_train_labels=pd.read_csv(csv_train_labels)\n",
    "pd_test_data=pd.read_csv(csv_test_file)\n",
    "pd_test_labels=pd.read_csv(csv_test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "f80392d5-f1bd-4dbd-adf7-af043fe01c1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "tens_test_labels=torch.squeeze(torch.tensor(pd_test_labels.values))-1\n",
    "tens_test_data=torch.tensor(pd_test_data.values).float()\n",
    "tens_train_labels=torch.squeeze(torch.tensor(pd_train_labels.values))-1\n",
    "tens_train_data=torch.tensor(pd_train_data.values).float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b4356f1-45c6-465e-9f16-79b030bcbd26",
   "metadata": {
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     "name": "stdout",
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     "text": [
      "Epoch: 0 | Train loss: 5.46480 | Train accuracy: 3.319% | Train f1 0.033 | Test accuracy 4.734% | Test f1 0.047\n",
      "Epoch: 10 | Train loss: 2.70151 | Train accuracy: 21.832% | Train f1 0.218 | Test accuracy 23.191% | Test f1 0.232\n",
      "Epoch: 20 | Train loss: 1.90082 | Train accuracy: 40.948% | Train f1 0.409 | Test accuracy 39.744% | Test f1 0.397\n",
      "Epoch: 30 | Train loss: 1.26810 | Train accuracy: 59.923% | Train f1 0.599 | Test accuracy 54.242% | Test f1 0.542\n",
      "Epoch: 40 | Train loss: 0.87684 | Train accuracy: 71.583% | Train f1 0.716 | Test accuracy 60.762% | Test f1 0.608\n",
      "Epoch: 50 | Train loss: 0.59473 | Train accuracy: 80.877% | Train f1 0.809 | Test accuracy 66.657% | Test f1 0.667\n",
      "Epoch: 60 | Train loss: 0.40317 | Train accuracy: 88.332% | Train f1 0.883 | Test accuracy 70.497% | Test f1 0.705\n",
      "Epoch: 70 | Train loss: 0.44051 | Train accuracy: 82.588% | Train f1 0.826 | Test accuracy 70.944% | Test f1 0.709\n",
      "Epoch: 80 | Train loss: 0.24317 | Train accuracy: 93.876% | Train f1 0.939 | Test accuracy 71.450% | Test f1 0.714\n",
      "Epoch: 90 | Train loss: 0.16389 | Train accuracy: 96.852% | Train f1 0.969 | Test accuracy 73.415% | Test f1 0.734\n",
      "Epoch: 100 | Train loss: 0.11324 | Train accuracy: 98.385% | Train f1 0.984 | Test accuracy 73.415% | Test f1 0.734\n",
      "Epoch: 110 | Train loss: 0.07773 | Train accuracy: 99.315% | Train f1 0.993 | Test accuracy 74.278% | Test f1 0.743\n",
      "Epoch: 120 | Train loss: 0.05514 | Train accuracy: 99.747% | Train f1 0.997 | Test accuracy 74.248% | Test f1 0.742\n",
      "Epoch: 130 | Train loss: 0.04028 | Train accuracy: 99.963% | Train f1 1.000 | Test accuracy 74.219% | Test f1 0.742\n",
      "Epoch: 140 | Train loss: 0.03100 | Train accuracy: 99.985% | Train f1 1.000 | Test accuracy 74.248% | Test f1 0.742\n",
      "Epoch: 150 | Train loss: 0.02513 | Train accuracy: 99.993% | Train f1 1.000 | Test accuracy 74.278% | Test f1 0.743\n",
      "Epoch: 160 | Train loss: 8.21307 | Train accuracy: 30.851% | Train f1 0.309 | Test accuracy 27.330% | Test f1 0.273\n",
      "Epoch: 170 | Train loss: 1.54108 | Train accuracy: 55.153% | Train f1 0.552 | Test accuracy 47.216% | Test f1 0.472\n",
      "Epoch: 180 | Train loss: 1.11212 | Train accuracy: 70.630% | Train f1 0.706 | Test accuracy 60.941% | Test f1 0.609\n"
     ]
    }
   ],
   "source": [
    "tr_metrics_csv, ts_metrics_csv = SingleTrain_drop(prob=0.2, num_epochs=500, learning_rate = 0.0007, \n",
    "                                                  decay=0.005, printing=True, inner=512, hidden=512, outer=256,\n",
    "                                                 train_data=tens_train_data, train_labels=tens_train_labels, \n",
    "                                                  test_data=tens_test_data, test_labels=tens_test_labels)"
   ]
  },
  {
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   "id": "75cdadb5-d17c-4f86-99db-5aae9cb33e6c",
   "metadata": {},
   "outputs": [],
   "source": []
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