Car Racing/notebooks/basic_CNN_augmented_edges.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "import os\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision import transforms\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision.datasets import ImageFolder\n",
    "from torch.utils.data import DataLoader, random_split\n",
    "from sklearn.metrics import classification_report\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cpu\n"
     ]
    }
   ],
   "source": [
    "# Imposta il device\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"Using device: {device}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Percorso del dataset (immagini preprocessate con data augmentation)\n",
    "dataset_dir = \"./train_balanced_augmented_edges\"\n",
    "\n",
    "# Trasformazioni: Normalizzazione e Preprocessing finale\n",
    "transform = transforms.Compose([\n",
    "    transforms.Grayscale(),\n",
    "    transforms.ToTensor(),  # Conversione a tensore\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numero di immagini nel train set: 2952\n",
      "Numero di immagini nel test set: 738\n",
      "Classi disponibili: ['0', '1', '2', '3', '4']\n"
     ]
    }
   ],
   "source": [
    "# Creazione del Dataset\n",
    "dataset = ImageFolder(root=dataset_dir, transform=transform)\n",
    "\n",
    "# Suddivisione Train/Test\n",
    "train_size = int(0.8 * len(dataset))\n",
    "test_size = len(dataset) - train_size\n",
    "train_dataset, test_dataset = random_split(dataset, [train_size, test_size])\n",
    "\n",
    "# DataLoaders\n",
    "train_loader = DataLoader(train_dataset, batch_size=32, shuffle= False)\n",
    "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)\n",
    "\n",
    "# Controllo rapido\n",
    "print(f\"Numero di immagini nel train set: {len(train_dataset)}\")\n",
    "print(f\"Numero di immagini nel test set: {len(test_dataset)}\")\n",
    "print(f\"Classi disponibili: {dataset.classes}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SimpleCNN(nn.Module):\n",
    "    def __init__(self, dropout_rate=0.25):\n",
    "        super(SimpleCNN, self).__init__()\n",
    "\n",
    "        # Convolutional Section\n",
    "        self.network = nn.Sequential(\n",
    "            \n",
    "            nn.Conv2d(1, 8, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(8),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(2,2), # 48x48\n",
    "\n",
    "            nn.Conv2d(8, 16, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(16),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(2, 2),   # 24x24\n",
    "\n",
    "            nn.Conv2d(16,32, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(32),\n",
    "            nn.ReLU(),\n",
    "\n",
    "            nn.Dropout(dropout_rate),  # Dropout for regularization\n",
    "        )\n",
    "\n",
    "        # Fully Connected Section\n",
    "        self.fc = nn.Sequential(\n",
    "            nn.AdaptiveAvgPool2d(1),\n",
    "            nn.Flatten(),\n",
    "            nn.Linear(32, 5),  # Fully connected\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.network(x)\n",
    "        x = self.fc(x)\n",
    "        return x\n",
    "    \n",
    "model = SimpleCNN().to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#criterion = nn.CrossEntropyLoss()\n",
    "#optimizer = optim.Adam(model.parameters(), lr=0.001, weight_decay=1e-4)\n",
    "#optimizer = torch.optim.RMSprop(model.parameters(), lr=0.001)\n",
    "#scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=3, gamma=0.5) \n",
    "\n",
    "# Loop di training\n",
    "def train(model, loader, criterion, optimizer):\n",
    "    model.train()\n",
    "    running_loss = 0\n",
    "    correct = 0\n",
    "    for inputs, labels in loader:\n",
    "        inputs, labels = inputs.to(device), labels.to(device)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        #scheduler.step()\n",
    "\n",
    "        running_loss += loss.item()\n",
    "        _, preds = outputs.max(1)\n",
    "        correct += (preds == labels).sum().item()\n",
    "    \n",
    "    accuracy = 100 * correct / len(loader.dataset)\n",
    "    return running_loss / len(loader), accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(model, loader):\n",
    "    model.eval()\n",
    "    y_true = []\n",
    "    y_pred = []\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for inputs, labels in loader:\n",
    "            inputs, labels = inputs.to(device), labels.to(device)\n",
    "            outputs = model(inputs)\n",
    "            _, preds = outputs.max(1)\n",
    "            y_true.extend(labels.cpu().numpy())\n",
    "            y_pred.extend(preds.cpu().numpy())\n",
    "    \n",
    "    return y_true, y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nnum_epochs = 10\\n\\nfor epoch in range(num_epochs):\\n    train_loss, train_acc = train(model, train_loader, criterion, optimizer)\\n    print(f\"Epoch {epoch+1}/{num_epochs}: Loss: {train_loss:.4f}, Accuracy: {train_acc:.2f}%\")\\n\\ntest_set = ImageFolder(root=\\'./test_edges\\', transform=transform)\\ntest_set_loader = DataLoader(test_set, batch_size=32, shuffle=False)\\n\\ny_true, y_pred = evaluate(model, test_set_loader)\\n\\nprint(\"\\nClassification Report:\")\\nprint(classification_report(y_true, y_pred, target_names=train_dataset.dataset.classes))\\n'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "num_epochs = 10\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    train_loss, train_acc = train(model, train_loader, criterion, optimizer)\n",
    "    print(f\"Epoch {epoch+1}/{num_epochs}: Loss: {train_loss:.4f}, Accuracy: {train_acc:.2f}%\")\n",
    "\n",
    "test_set = ImageFolder(root='./test_edges', transform=transform)\n",
    "test_set_loader = DataLoader(test_set, batch_size=32, shuffle=False)\n",
    "\n",
    "y_true, y_pred = evaluate(model, test_set_loader)\n",
    "\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(y_true, y_pred, target_names=train_dataset.dataset.classes))\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameter Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score, recall_score, f1_score, confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_set = ImageFolder(root='./test_edges', transform=transform)\n",
    "test_set_loader = DataLoader(test_set, batch_size=32, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parametri di ricerca\n",
    "epoch_list = [5, 10, 15, 20]\n",
    "learning_rate_list = [0.01, 0.001, 0.0001]\n",
    "dropout_list = [0, 0.25, 0.5]\n",
    "weight_decay_list = [0.01, 0.001, 0.0001]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training with dropout 0, lr 0.01, wd 0.01, epochs 5...\n",
      "Epoch 1/5, Loss: 145.9217, Accuracy: 26.90%\n",
      "Epoch 2/5, Loss: 144.6713, Accuracy: 29.34%\n",
      "Epoch 3/5, Loss: 144.7264, Accuracy: 28.39%\n",
      "Epoch 4/5, Loss: 144.5814, Accuracy: 28.59%\n",
      "Epoch 5/5, Loss: 144.4250, Accuracy: 28.76%\n",
      "Training with dropout 0, lr 0.01, wd 0.01, epochs 10...\n",
      "Epoch 1/10, Loss: 145.8131, Accuracy: 27.24%\n",
      "Epoch 2/10, Loss: 144.4764, Accuracy: 29.27%\n",
      "Epoch 3/10, Loss: 144.5484, Accuracy: 28.22%\n",
      "Epoch 4/10, Loss: 144.2961, Accuracy: 29.00%\n",
      "Epoch 5/10, Loss: 144.3928, Accuracy: 29.40%\n",
      "Epoch 6/10, Loss: 144.2241, Accuracy: 29.23%\n",
      "Epoch 7/10, Loss: 144.0983, Accuracy: 29.40%\n",
      "Epoch 8/10, Loss: 143.9801, Accuracy: 29.23%\n",
      "Epoch 9/10, Loss: 144.0154, Accuracy: 29.44%\n",
      "Epoch 10/10, Loss: 143.8742, Accuracy: 29.34%\n",
      "Training with dropout 0, lr 0.01, wd 0.01, epochs 15...\n",
      "Epoch 1/15, Loss: 146.2657, Accuracy: 26.80%\n",
      "Epoch 2/15, Loss: 144.9915, Accuracy: 28.59%\n",
      "Epoch 3/15, Loss: 144.6145, Accuracy: 28.79%\n",
      "Epoch 4/15, Loss: 144.4978, Accuracy: 29.20%\n",
      "Epoch 5/15, Loss: 144.5248, Accuracy: 28.90%\n",
      "Epoch 6/15, Loss: 144.5979, Accuracy: 28.46%\n",
      "Epoch 7/15, Loss: 144.6876, Accuracy: 28.56%\n",
      "Epoch 8/15, Loss: 144.4108, Accuracy: 28.90%\n",
      "Epoch 9/15, Loss: 144.4997, Accuracy: 28.79%\n",
      "Epoch 10/15, Loss: 144.5801, Accuracy: 28.69%\n",
      "Epoch 11/15, Loss: 144.5059, Accuracy: 29.03%\n",
      "Epoch 12/15, Loss: 144.4089, Accuracy: 28.52%\n",
      "Epoch 13/15, Loss: 144.5054, Accuracy: 28.39%\n",
      "Epoch 14/15, Loss: 144.4177, Accuracy: 28.56%\n",
      "Epoch 15/15, Loss: 144.5095, Accuracy: 27.98%\n",
      "Training with dropout 0, lr 0.01, wd 0.01, epochs 20...\n",
      "Epoch 1/20, Loss: 145.3956, Accuracy: 26.76%\n",
      "Epoch 2/20, Loss: 144.7384, Accuracy: 28.69%\n",
      "Epoch 3/20, Loss: 144.8114, Accuracy: 28.35%\n",
      "Epoch 4/20, Loss: 144.6362, Accuracy: 28.42%\n",
      "Epoch 5/20, Loss: 144.5444, Accuracy: 28.32%\n",
      "Epoch 6/20, Loss: 144.2591, Accuracy: 29.17%\n",
      "Epoch 7/20, Loss: 144.3319, Accuracy: 28.83%\n",
      "Epoch 8/20, Loss: 144.0501, Accuracy: 29.00%\n",
      "Epoch 9/20, Loss: 144.2207, Accuracy: 28.96%\n",
      "Epoch 10/20, Loss: 144.0845, Accuracy: 29.23%\n",
      "Epoch 11/20, Loss: 144.0580, Accuracy: 29.23%\n",
      "Epoch 12/20, Loss: 144.1678, Accuracy: 29.27%\n",
      "Epoch 13/20, Loss: 144.0464, Accuracy: 29.27%\n",
      "Epoch 14/20, Loss: 144.3532, Accuracy: 29.23%\n",
      "Epoch 15/20, Loss: 144.0245, Accuracy: 29.30%\n",
      "Epoch 16/20, Loss: 143.7640, Accuracy: 29.95%\n",
      "Epoch 17/20, Loss: 143.8817, Accuracy: 29.40%\n",
      "Epoch 18/20, Loss: 143.8301, Accuracy: 29.54%\n",
      "Epoch 19/20, Loss: 143.7316, Accuracy: 29.40%\n",
      "Epoch 20/20, Loss: 144.3052, Accuracy: 28.66%\n",
      "Training with dropout 0, lr 0.01, wd 0.001, epochs 5...\n",
      "Epoch 1/5, Loss: 144.8675, Accuracy: 26.49%\n",
      "Epoch 2/5, Loss: 142.8892, Accuracy: 29.57%\n",
      "Epoch 3/5, Loss: 142.2086, Accuracy: 30.39%\n",
      "Epoch 4/5, Loss: 141.6916, Accuracy: 30.56%\n",
      "Epoch 5/5, Loss: 141.4041, Accuracy: 30.89%\n",
      "Training with dropout 0, lr 0.01, wd 0.001, epochs 10...\n",
      "Epoch 1/10, Loss: 144.8240, Accuracy: 27.07%\n",
      "Epoch 2/10, Loss: 142.9417, Accuracy: 30.12%\n",
      "Epoch 3/10, Loss: 142.5210, Accuracy: 30.08%\n",
      "Epoch 4/10, Loss: 142.0657, Accuracy: 30.28%\n",
      "Epoch 5/10, Loss: 141.5644, Accuracy: 31.03%\n",
      "Epoch 6/10, Loss: 141.3642, Accuracy: 31.37%\n",
      "Epoch 7/10, Loss: 141.2644, Accuracy: 30.79%\n",
      "Epoch 8/10, Loss: 141.1404, Accuracy: 30.93%\n",
      "Epoch 9/10, Loss: 140.9737, Accuracy: 31.06%\n",
      "Epoch 10/10, Loss: 140.9103, Accuracy: 31.23%\n",
      "Training with dropout 0, lr 0.01, wd 0.001, epochs 15...\n",
      "Epoch 1/15, Loss: 144.9826, Accuracy: 26.22%\n",
      "Epoch 2/15, Loss: 143.1968, Accuracy: 29.07%\n",
      "Epoch 3/15, Loss: 142.6063, Accuracy: 29.64%\n",
      "Epoch 4/15, Loss: 142.0626, Accuracy: 29.78%\n",
      "Epoch 5/15, Loss: 141.8583, Accuracy: 30.86%\n",
      "Epoch 6/15, Loss: 141.5232, Accuracy: 30.45%\n",
      "Epoch 7/15, Loss: 141.4381, Accuracy: 30.79%\n",
      "Epoch 8/15, Loss: 141.1819, Accuracy: 31.27%\n",
      "Epoch 9/15, Loss: 141.1236, Accuracy: 30.79%\n",
      "Epoch 10/15, Loss: 140.7150, Accuracy: 31.64%\n",
      "Epoch 11/15, Loss: 140.7202, Accuracy: 30.86%\n",
      "Epoch 12/15, Loss: 140.6225, Accuracy: 30.93%\n",
      "Epoch 13/15, Loss: 140.2774, Accuracy: 30.89%\n",
      "Epoch 14/15, Loss: 140.4546, Accuracy: 30.96%\n",
      "Epoch 15/15, Loss: 140.1167, Accuracy: 31.20%\n",
      "Training with dropout 0, lr 0.01, wd 0.001, epochs 20...\n",
      "Epoch 1/20, Loss: 145.1200, Accuracy: 27.64%\n",
      "Epoch 2/20, Loss: 143.1334, Accuracy: 29.67%\n",
      "Epoch 3/20, Loss: 142.7661, Accuracy: 30.42%\n",
      "Epoch 4/20, Loss: 142.0896, Accuracy: 30.89%\n",
      "Epoch 5/20, Loss: 141.6701, Accuracy: 31.23%\n",
      "Epoch 6/20, Loss: 141.2406, Accuracy: 30.72%\n",
      "Epoch 7/20, Loss: 141.1273, Accuracy: 31.00%\n",
      "Epoch 8/20, Loss: 140.8853, Accuracy: 31.10%\n",
      "Epoch 9/20, Loss: 140.7121, Accuracy: 31.57%\n",
      "Epoch 10/20, Loss: 140.6770, Accuracy: 31.78%\n",
      "Epoch 11/20, Loss: 140.6114, Accuracy: 31.88%\n",
      "Epoch 12/20, Loss: 140.7672, Accuracy: 31.57%\n",
      "Epoch 13/20, Loss: 140.4793, Accuracy: 31.67%\n",
      "Epoch 14/20, Loss: 140.2904, Accuracy: 31.64%\n",
      "Epoch 15/20, Loss: 140.1423, Accuracy: 32.62%\n",
      "Epoch 16/20, Loss: 140.1304, Accuracy: 32.52%\n",
      "Epoch 17/20, Loss: 140.0343, Accuracy: 31.88%\n",
      "Epoch 18/20, Loss: 139.8886, Accuracy: 32.15%\n",
      "Epoch 19/20, Loss: 140.3616, Accuracy: 31.71%\n",
      "Epoch 20/20, Loss: 139.8552, Accuracy: 32.28%\n",
      "Training with dropout 0, lr 0.01, wd 0.0001, epochs 5...\n",
      "Epoch 1/5, Loss: 144.7167, Accuracy: 27.00%\n",
      "Epoch 2/5, Loss: 142.6582, Accuracy: 29.10%\n",
      "Epoch 3/5, Loss: 141.5778, Accuracy: 30.49%\n",
      "Epoch 4/5, Loss: 140.7238, Accuracy: 31.00%\n",
      "Epoch 5/5, Loss: 140.1613, Accuracy: 31.30%\n",
      "Training with dropout 0, lr 0.01, wd 0.0001, epochs 10...\n",
      "Epoch 1/10, Loss: 144.8664, Accuracy: 27.20%\n",
      "Epoch 2/10, Loss: 142.8967, Accuracy: 29.67%\n",
      "Epoch 3/10, Loss: 142.0461, Accuracy: 30.56%\n",
      "Epoch 4/10, Loss: 141.4308, Accuracy: 31.17%\n",
      "Epoch 5/10, Loss: 140.9282, Accuracy: 31.54%\n",
      "Epoch 6/10, Loss: 140.3056, Accuracy: 32.79%\n",
      "Epoch 7/10, Loss: 139.8076, Accuracy: 33.30%\n",
      "Epoch 8/10, Loss: 139.1372, Accuracy: 33.84%\n",
      "Epoch 9/10, Loss: 138.3673, Accuracy: 33.88%\n",
      "Epoch 10/10, Loss: 137.5582, Accuracy: 34.69%\n",
      "Training with dropout 0, lr 0.01, wd 0.0001, epochs 15...\n",
      "Epoch 1/15, Loss: 144.9050, Accuracy: 26.66%\n",
      "Epoch 2/15, Loss: 142.5553, Accuracy: 29.91%\n",
      "Epoch 3/15, Loss: 141.5495, Accuracy: 31.44%\n",
      "Epoch 4/15, Loss: 140.8397, Accuracy: 32.08%\n",
      "Epoch 5/15, Loss: 140.2446, Accuracy: 32.45%\n",
      "Epoch 6/15, Loss: 139.3675, Accuracy: 33.03%\n",
      "Epoch 7/15, Loss: 138.5532, Accuracy: 33.20%\n",
      "Epoch 8/15, Loss: 137.6798, Accuracy: 34.25%\n",
      "Epoch 9/15, Loss: 137.1464, Accuracy: 34.69%\n",
      "Epoch 10/15, Loss: 136.4648, Accuracy: 34.25%\n",
      "Epoch 11/15, Loss: 135.7064, Accuracy: 35.81%\n",
      "Epoch 12/15, Loss: 135.1028, Accuracy: 36.21%\n",
      "Epoch 13/15, Loss: 134.5596, Accuracy: 36.79%\n",
      "Epoch 14/15, Loss: 133.9431, Accuracy: 37.23%\n",
      "Epoch 15/15, Loss: 132.9477, Accuracy: 38.14%\n",
      "Training with dropout 0, lr 0.01, wd 0.0001, epochs 20...\n",
      "Epoch 1/20, Loss: 145.3211, Accuracy: 27.27%\n",
      "Epoch 2/20, Loss: 142.9192, Accuracy: 29.20%\n",
      "Epoch 3/20, Loss: 141.8877, Accuracy: 29.91%\n",
      "Epoch 4/20, Loss: 141.2570, Accuracy: 30.35%\n",
      "Epoch 5/20, Loss: 140.7003, Accuracy: 30.96%\n",
      "Epoch 6/20, Loss: 139.9969, Accuracy: 31.50%\n",
      "Epoch 7/20, Loss: 139.3796, Accuracy: 32.28%\n",
      "Epoch 8/20, Loss: 138.9083, Accuracy: 32.83%\n",
      "Epoch 9/20, Loss: 138.0280, Accuracy: 33.91%\n",
      "Epoch 10/20, Loss: 137.3064, Accuracy: 35.13%\n",
      "Epoch 11/20, Loss: 136.2098, Accuracy: 36.62%\n",
      "Epoch 12/20, Loss: 135.2008, Accuracy: 36.86%\n",
      "Epoch 13/20, Loss: 133.9833, Accuracy: 38.21%\n",
      "Epoch 14/20, Loss: 133.0929, Accuracy: 38.58%\n",
      "Epoch 15/20, Loss: 132.6516, Accuracy: 38.11%\n",
      "Epoch 16/20, Loss: 131.4336, Accuracy: 39.63%\n",
      "Epoch 17/20, Loss: 130.5883, Accuracy: 40.01%\n",
      "Epoch 18/20, Loss: 129.7167, Accuracy: 40.89%\n",
      "Epoch 19/20, Loss: 128.9896, Accuracy: 41.67%\n",
      "Epoch 20/20, Loss: 128.1485, Accuracy: 42.24%\n",
      "Training with dropout 0, lr 0.001, wd 0.01, epochs 5...\n",
      "Epoch 1/5, Loss: 145.7029, Accuracy: 28.66%\n",
      "Epoch 2/5, Loss: 142.7895, Accuracy: 31.47%\n",
      "Epoch 3/5, Loss: 142.1476, Accuracy: 31.50%\n",
      "Epoch 4/5, Loss: 141.7511, Accuracy: 31.00%\n",
      "Epoch 5/5, Loss: 141.5866, Accuracy: 30.93%\n",
      "Training with dropout 0, lr 0.001, wd 0.01, epochs 10...\n",
      "Epoch 1/10, Loss: 145.2249, Accuracy: 29.13%\n",
      "Epoch 2/10, Loss: 142.6776, Accuracy: 31.78%\n",
      "Epoch 3/10, Loss: 142.1101, Accuracy: 32.01%\n",
      "Epoch 4/10, Loss: 141.8535, Accuracy: 31.91%\n",
      "Epoch 5/10, Loss: 141.6706, Accuracy: 32.22%\n",
      "Epoch 6/10, Loss: 141.5237, Accuracy: 32.32%\n",
      "Epoch 7/10, Loss: 141.4424, Accuracy: 32.18%\n",
      "Epoch 8/10, Loss: 141.2845, Accuracy: 32.15%\n",
      "Epoch 9/10, Loss: 141.2587, Accuracy: 32.18%\n",
      "Epoch 10/10, Loss: 141.1553, Accuracy: 32.35%\n",
      "Training with dropout 0, lr 0.001, wd 0.01, epochs 15...\n",
      "Epoch 1/15, Loss: 146.6805, Accuracy: 28.96%\n",
      "Epoch 2/15, Loss: 143.1627, Accuracy: 31.10%\n",
      "Epoch 3/15, Loss: 142.1315, Accuracy: 31.44%\n",
      "Epoch 4/15, Loss: 141.6793, Accuracy: 31.78%\n",
      "Epoch 5/15, Loss: 141.3693, Accuracy: 32.38%\n",
      "Epoch 6/15, Loss: 141.1910, Accuracy: 32.45%\n",
      "Epoch 7/15, Loss: 141.0101, Accuracy: 32.86%\n",
      "Epoch 8/15, Loss: 140.9129, Accuracy: 33.20%\n",
      "Epoch 9/15, Loss: 140.7704, Accuracy: 32.62%\n",
      "Epoch 10/15, Loss: 140.6397, Accuracy: 32.93%\n",
      "Epoch 11/15, Loss: 140.5418, Accuracy: 32.79%\n",
      "Epoch 12/15, Loss: 140.4537, Accuracy: 32.86%\n",
      "Epoch 13/15, Loss: 140.3757, Accuracy: 32.99%\n",
      "Epoch 14/15, Loss: 140.2973, Accuracy: 32.89%\n",
      "Epoch 15/15, Loss: 140.2584, Accuracy: 32.79%\n",
      "Training with dropout 0, lr 0.001, wd 0.01, epochs 20...\n",
      "Epoch 1/20, Loss: 145.6329, Accuracy: 29.37%\n",
      "Epoch 2/20, Loss: 142.6711, Accuracy: 31.54%\n",
      "Epoch 3/20, Loss: 141.9209, Accuracy: 31.50%\n",
      "Epoch 4/20, Loss: 141.5926, Accuracy: 31.54%\n",
      "Epoch 5/20, Loss: 141.4239, Accuracy: 31.54%\n",
      "Epoch 6/20, Loss: 141.2022, Accuracy: 32.01%\n",
      "Epoch 7/20, Loss: 141.0739, Accuracy: 31.98%\n",
      "Epoch 8/20, Loss: 140.9692, Accuracy: 32.22%\n",
      "Epoch 9/20, Loss: 140.9189, Accuracy: 32.28%\n",
      "Epoch 10/20, Loss: 140.8624, Accuracy: 32.22%\n",
      "Epoch 11/20, Loss: 140.7959, Accuracy: 32.25%\n",
      "Epoch 12/20, Loss: 140.7054, Accuracy: 32.52%\n",
      "Epoch 13/20, Loss: 140.6975, Accuracy: 32.52%\n",
      "Epoch 14/20, Loss: 140.6623, Accuracy: 32.45%\n",
      "Epoch 15/20, Loss: 140.6142, Accuracy: 32.69%\n",
      "Epoch 16/20, Loss: 140.5627, Accuracy: 32.69%\n",
      "Epoch 17/20, Loss: 140.5257, Accuracy: 32.66%\n",
      "Epoch 18/20, Loss: 140.4703, Accuracy: 32.93%\n",
      "Epoch 19/20, Loss: 140.3961, Accuracy: 32.96%\n",
      "Epoch 20/20, Loss: 140.3580, Accuracy: 33.16%\n",
      "Training with dropout 0, lr 0.001, wd 0.001, epochs 5...\n",
      "Epoch 1/5, Loss: 145.8737, Accuracy: 28.76%\n",
      "Epoch 2/5, Loss: 142.4101, Accuracy: 31.54%\n",
      "Epoch 3/5, Loss: 141.3228, Accuracy: 32.15%\n",
      "Epoch 4/5, Loss: 140.7001, Accuracy: 32.52%\n",
      "Epoch 5/5, Loss: 140.2285, Accuracy: 32.83%\n",
      "Training with dropout 0, lr 0.001, wd 0.001, epochs 10...\n",
      "Epoch 1/10, Loss: 148.6865, Accuracy: 24.25%\n",
      "Epoch 2/10, Loss: 143.2401, Accuracy: 30.32%\n",
      "Epoch 3/10, Loss: 141.4832, Accuracy: 31.57%\n",
      "Epoch 4/10, Loss: 140.7239, Accuracy: 32.28%\n",
      "Epoch 5/10, Loss: 140.1815, Accuracy: 32.79%\n",
      "Epoch 6/10, Loss: 139.6885, Accuracy: 33.47%\n",
      "Epoch 7/10, Loss: 139.2617, Accuracy: 34.11%\n",
      "Epoch 8/10, Loss: 138.7702, Accuracy: 35.06%\n",
      "Epoch 9/10, Loss: 138.3125, Accuracy: 35.60%\n",
      "Epoch 10/10, Loss: 137.8257, Accuracy: 36.14%\n",
      "Training with dropout 0, lr 0.001, wd 0.001, epochs 15...\n",
      "Epoch 1/15, Loss: 145.7083, Accuracy: 29.91%\n",
      "Epoch 2/15, Loss: 142.6189, Accuracy: 31.94%\n",
      "Epoch 3/15, Loss: 141.6551, Accuracy: 32.15%\n",
      "Epoch 4/15, Loss: 140.9763, Accuracy: 32.89%\n",
      "Epoch 5/15, Loss: 140.4573, Accuracy: 33.37%\n",
      "Epoch 6/15, Loss: 139.9895, Accuracy: 33.74%\n",
      "Epoch 7/15, Loss: 139.5590, Accuracy: 34.15%\n",
      "Epoch 8/15, Loss: 139.1366, Accuracy: 34.38%\n",
      "Epoch 9/15, Loss: 138.7530, Accuracy: 34.93%\n",
      "Epoch 10/15, Loss: 138.3972, Accuracy: 35.16%\n",
      "Epoch 11/15, Loss: 137.9736, Accuracy: 35.81%\n",
      "Epoch 12/15, Loss: 137.5374, Accuracy: 36.01%\n",
      "Epoch 13/15, Loss: 137.0785, Accuracy: 36.38%\n",
      "Epoch 14/15, Loss: 136.6165, Accuracy: 36.69%\n",
      "Epoch 15/15, Loss: 136.1014, Accuracy: 37.20%\n",
      "Training with dropout 0, lr 0.001, wd 0.001, epochs 20...\n",
      "Epoch 1/20, Loss: 145.6627, Accuracy: 27.30%\n",
      "Epoch 2/20, Loss: 142.3152, Accuracy: 31.84%\n",
      "Epoch 3/20, Loss: 141.5438, Accuracy: 31.81%\n",
      "Epoch 4/20, Loss: 141.0225, Accuracy: 31.57%\n",
      "Epoch 5/20, Loss: 140.6102, Accuracy: 31.84%\n",
      "Epoch 6/20, Loss: 140.2596, Accuracy: 32.35%\n",
      "Epoch 7/20, Loss: 139.9406, Accuracy: 32.42%\n",
      "Epoch 8/20, Loss: 139.6088, Accuracy: 32.86%\n",
      "Epoch 9/20, Loss: 139.2421, Accuracy: 33.47%\n",
      "Epoch 10/20, Loss: 138.8982, Accuracy: 33.77%\n",
      "Epoch 11/20, Loss: 138.4993, Accuracy: 33.74%\n",
      "Epoch 12/20, Loss: 138.1851, Accuracy: 34.18%\n",
      "Epoch 13/20, Loss: 137.8557, Accuracy: 34.32%\n",
      "Epoch 14/20, Loss: 137.5288, Accuracy: 34.52%\n",
      "Epoch 15/20, Loss: 137.1930, Accuracy: 34.52%\n",
      "Epoch 16/20, Loss: 136.8497, Accuracy: 34.55%\n",
      "Epoch 17/20, Loss: 136.5144, Accuracy: 35.03%\n",
      "Epoch 18/20, Loss: 136.1713, Accuracy: 35.47%\n",
      "Epoch 19/20, Loss: 135.8866, Accuracy: 35.64%\n",
      "Epoch 20/20, Loss: 135.5229, Accuracy: 36.35%\n",
      "Training with dropout 0, lr 0.001, wd 0.0001, epochs 5...\n",
      "Epoch 1/5, Loss: 146.3391, Accuracy: 28.32%\n",
      "Epoch 2/5, Loss: 142.6667, Accuracy: 31.94%\n",
      "Epoch 3/5, Loss: 141.3430, Accuracy: 32.52%\n",
      "Epoch 4/5, Loss: 140.6621, Accuracy: 32.15%\n",
      "Epoch 5/5, Loss: 140.1592, Accuracy: 32.38%\n",
      "Training with dropout 0, lr 0.001, wd 0.0001, epochs 10...\n",
      "Epoch 1/10, Loss: 145.8534, Accuracy: 28.46%\n",
      "Epoch 2/10, Loss: 142.7002, Accuracy: 31.78%\n",
      "Epoch 3/10, Loss: 141.4881, Accuracy: 31.40%\n",
      "Epoch 4/10, Loss: 140.5889, Accuracy: 31.98%\n",
      "Epoch 5/10, Loss: 139.8948, Accuracy: 32.18%\n",
      "Epoch 6/10, Loss: 139.3335, Accuracy: 32.83%\n",
      "Epoch 7/10, Loss: 138.8213, Accuracy: 33.47%\n",
      "Epoch 8/10, Loss: 138.3576, Accuracy: 34.15%\n",
      "Epoch 9/10, Loss: 137.9012, Accuracy: 34.59%\n",
      "Epoch 10/10, Loss: 137.4498, Accuracy: 35.09%\n",
      "Training with dropout 0, lr 0.001, wd 0.0001, epochs 15...\n",
      "Epoch 1/15, Loss: 146.3037, Accuracy: 28.69%\n",
      "Epoch 2/15, Loss: 142.3825, Accuracy: 31.88%\n",
      "Epoch 3/15, Loss: 141.0464, Accuracy: 32.35%\n",
      "Epoch 4/15, Loss: 140.2577, Accuracy: 32.96%\n",
      "Epoch 5/15, Loss: 139.5681, Accuracy: 33.91%\n",
      "Epoch 6/15, Loss: 138.8536, Accuracy: 34.62%\n",
      "Epoch 7/15, Loss: 138.1465, Accuracy: 35.20%\n",
      "Epoch 8/15, Loss: 137.4491, Accuracy: 35.84%\n",
      "Epoch 9/15, Loss: 136.7333, Accuracy: 36.01%\n",
      "Epoch 10/15, Loss: 135.9501, Accuracy: 36.48%\n",
      "Epoch 11/15, Loss: 135.1498, Accuracy: 37.09%\n",
      "Epoch 12/15, Loss: 134.3799, Accuracy: 37.80%\n",
      "Epoch 13/15, Loss: 133.5539, Accuracy: 38.45%\n",
      "Epoch 14/15, Loss: 132.7771, Accuracy: 39.09%\n",
      "Epoch 15/15, Loss: 131.9749, Accuracy: 39.30%\n",
      "Training with dropout 0, lr 0.001, wd 0.0001, epochs 20...\n",
      "Epoch 1/20, Loss: 147.0839, Accuracy: 27.13%\n",
      "Epoch 2/20, Loss: 142.9033, Accuracy: 31.64%\n",
      "Epoch 3/20, Loss: 141.4982, Accuracy: 31.81%\n",
      "Epoch 4/20, Loss: 140.8201, Accuracy: 32.45%\n",
      "Epoch 5/20, Loss: 140.3149, Accuracy: 32.52%\n",
      "Epoch 6/20, Loss: 139.9101, Accuracy: 32.96%\n",
      "Epoch 7/20, Loss: 139.4964, Accuracy: 32.93%\n",
      "Epoch 8/20, Loss: 139.1271, Accuracy: 33.16%\n",
      "Epoch 9/20, Loss: 138.7082, Accuracy: 33.81%\n",
      "Epoch 10/20, Loss: 138.2755, Accuracy: 33.77%\n",
      "Epoch 11/20, Loss: 137.8429, Accuracy: 34.76%\n",
      "Epoch 12/20, Loss: 137.3856, Accuracy: 34.86%\n",
      "Epoch 13/20, Loss: 136.9208, Accuracy: 34.99%\n",
      "Epoch 14/20, Loss: 136.4554, Accuracy: 35.13%\n",
      "Epoch 15/20, Loss: 135.9577, Accuracy: 35.37%\n",
      "Epoch 16/20, Loss: 135.4218, Accuracy: 35.81%\n",
      "Epoch 17/20, Loss: 134.9362, Accuracy: 36.21%\n",
      "Epoch 18/20, Loss: 134.4393, Accuracy: 36.79%\n",
      "Epoch 19/20, Loss: 133.9089, Accuracy: 37.20%\n",
      "Epoch 20/20, Loss: 133.3838, Accuracy: 38.08%\n",
      "Training with dropout 0, lr 0.0001, wd 0.01, epochs 5...\n",
      "Epoch 1/5, Loss: 151.2123, Accuracy: 20.90%\n",
      "Epoch 2/5, Loss: 149.4524, Accuracy: 24.36%\n",
      "Epoch 3/5, Loss: 148.1659, Accuracy: 25.98%\n",
      "Epoch 4/5, Loss: 147.0748, Accuracy: 26.22%\n",
      "Epoch 5/5, Loss: 146.1266, Accuracy: 28.56%\n",
      "Training with dropout 0, lr 0.0001, wd 0.01, epochs 10...\n",
      "Epoch 1/10, Loss: 149.8500, Accuracy: 20.70%\n",
      "Epoch 2/10, Loss: 148.1530, Accuracy: 23.71%\n",
      "Epoch 3/10, Loss: 146.9233, Accuracy: 25.51%\n",
      "Epoch 4/10, Loss: 145.9273, Accuracy: 26.93%\n",
      "Epoch 5/10, Loss: 145.1886, Accuracy: 28.29%\n",
      "Epoch 6/10, Loss: 144.5744, Accuracy: 29.88%\n",
      "Epoch 7/10, Loss: 144.0351, Accuracy: 31.06%\n",
      "Epoch 8/10, Loss: 143.5838, Accuracy: 31.17%\n",
      "Epoch 9/10, Loss: 143.2040, Accuracy: 31.44%\n",
      "Epoch 10/10, Loss: 142.8817, Accuracy: 31.71%\n",
      "Training with dropout 0, lr 0.0001, wd 0.01, epochs 15...\n",
      "Epoch 1/15, Loss: 149.9046, Accuracy: 20.22%\n",
      "Epoch 2/15, Loss: 147.5755, Accuracy: 20.60%\n",
      "Epoch 3/15, Loss: 145.9249, Accuracy: 24.05%\n",
      "Epoch 4/15, Loss: 144.7935, Accuracy: 28.83%\n",
      "Epoch 5/15, Loss: 144.0682, Accuracy: 30.18%\n",
      "Epoch 6/15, Loss: 143.5763, Accuracy: 30.66%\n",
      "Epoch 7/15, Loss: 143.2063, Accuracy: 30.83%\n",
      "Epoch 8/15, Loss: 142.9094, Accuracy: 30.89%\n",
      "Epoch 9/15, Loss: 142.6580, Accuracy: 31.20%\n",
      "Epoch 10/15, Loss: 142.4374, Accuracy: 31.17%\n",
      "Epoch 11/15, Loss: 142.2456, Accuracy: 31.00%\n",
      "Epoch 12/15, Loss: 142.0788, Accuracy: 30.89%\n",
      "Epoch 13/15, Loss: 141.9341, Accuracy: 30.62%\n",
      "Epoch 14/15, Loss: 141.8058, Accuracy: 30.59%\n",
      "Epoch 15/15, Loss: 141.6913, Accuracy: 31.17%\n",
      "Training with dropout 0, lr 0.0001, wd 0.01, epochs 20...\n",
      "Epoch 1/20, Loss: 148.7938, Accuracy: 20.83%\n",
      "Epoch 2/20, Loss: 147.1454, Accuracy: 28.22%\n",
      "Epoch 3/20, Loss: 146.0901, Accuracy: 30.86%\n",
      "Epoch 4/20, Loss: 145.1779, Accuracy: 31.37%\n",
      "Epoch 5/20, Loss: 144.4584, Accuracy: 31.33%\n",
      "Epoch 6/20, Loss: 143.9134, Accuracy: 31.33%\n",
      "Epoch 7/20, Loss: 143.4586, Accuracy: 31.54%\n",
      "Epoch 8/20, Loss: 143.0780, Accuracy: 31.88%\n",
      "Epoch 9/20, Loss: 142.7786, Accuracy: 32.08%\n",
      "Epoch 10/20, Loss: 142.5382, Accuracy: 32.05%\n",
      "Epoch 11/20, Loss: 142.3306, Accuracy: 31.94%\n",
      "Epoch 12/20, Loss: 142.1526, Accuracy: 31.98%\n",
      "Epoch 13/20, Loss: 141.9934, Accuracy: 32.05%\n",
      "Epoch 14/20, Loss: 141.8471, Accuracy: 32.01%\n",
      "Epoch 15/20, Loss: 141.7133, Accuracy: 31.74%\n",
      "Epoch 16/20, Loss: 141.5917, Accuracy: 31.91%\n",
      "Epoch 17/20, Loss: 141.4809, Accuracy: 32.01%\n",
      "Epoch 18/20, Loss: 141.3774, Accuracy: 32.25%\n",
      "Epoch 19/20, Loss: 141.2784, Accuracy: 32.38%\n",
      "Epoch 20/20, Loss: 141.1861, Accuracy: 32.52%\n",
      "Training with dropout 0, lr 0.0001, wd 0.001, epochs 5...\n",
      "Epoch 1/5, Loss: 150.7617, Accuracy: 21.04%\n",
      "Epoch 2/5, Loss: 148.2424, Accuracy: 22.83%\n",
      "Epoch 3/5, Loss: 146.5227, Accuracy: 23.71%\n",
      "Epoch 4/5, Loss: 145.2385, Accuracy: 24.93%\n",
      "Epoch 5/5, Loss: 144.3776, Accuracy: 28.08%\n",
      "Training with dropout 0, lr 0.0001, wd 0.001, epochs 10...\n",
      "Epoch 1/10, Loss: 150.3300, Accuracy: 20.22%\n",
      "Epoch 2/10, Loss: 148.3637, Accuracy: 20.39%\n",
      "Epoch 3/10, Loss: 146.8555, Accuracy: 23.92%\n",
      "Epoch 4/10, Loss: 145.6695, Accuracy: 29.13%\n",
      "Epoch 5/10, Loss: 144.8192, Accuracy: 30.62%\n",
      "Epoch 6/10, Loss: 144.1740, Accuracy: 31.30%\n",
      "Epoch 7/10, Loss: 143.6443, Accuracy: 31.78%\n",
      "Epoch 8/10, Loss: 143.2232, Accuracy: 31.98%\n",
      "Epoch 9/10, Loss: 142.8736, Accuracy: 32.62%\n",
      "Epoch 10/10, Loss: 142.5707, Accuracy: 32.32%\n",
      "Training with dropout 0, lr 0.0001, wd 0.001, epochs 15...\n",
      "Epoch 1/15, Loss: 149.5244, Accuracy: 20.43%\n",
      "Epoch 2/15, Loss: 147.6215, Accuracy: 21.17%\n",
      "Epoch 3/15, Loss: 146.1798, Accuracy: 26.05%\n",
      "Epoch 4/15, Loss: 145.0843, Accuracy: 30.42%\n",
      "Epoch 5/15, Loss: 144.2655, Accuracy: 29.91%\n",
      "Epoch 6/15, Loss: 143.6574, Accuracy: 30.52%\n",
      "Epoch 7/15, Loss: 143.2122, Accuracy: 30.45%\n",
      "Epoch 8/15, Loss: 142.8601, Accuracy: 31.10%\n",
      "Epoch 9/15, Loss: 142.5621, Accuracy: 31.47%\n",
      "Epoch 10/15, Loss: 142.3011, Accuracy: 31.64%\n",
      "Epoch 11/15, Loss: 142.0688, Accuracy: 31.67%\n",
      "Epoch 12/15, Loss: 141.8597, Accuracy: 31.84%\n",
      "Epoch 13/15, Loss: 141.6712, Accuracy: 31.91%\n",
      "Epoch 14/15, Loss: 141.5016, Accuracy: 31.98%\n",
      "Epoch 15/15, Loss: 141.3479, Accuracy: 31.91%\n",
      "Training with dropout 0, lr 0.0001, wd 0.001, epochs 20...\n",
      "Epoch 1/20, Loss: 151.7653, Accuracy: 19.99%\n",
      "Epoch 2/20, Loss: 148.5276, Accuracy: 20.53%\n",
      "Epoch 3/20, Loss: 146.5302, Accuracy: 24.56%\n",
      "Epoch 4/20, Loss: 145.3600, Accuracy: 25.64%\n",
      "Epoch 5/20, Loss: 144.5775, Accuracy: 28.01%\n",
      "Epoch 6/20, Loss: 144.0027, Accuracy: 29.27%\n",
      "Epoch 7/20, Loss: 143.5816, Accuracy: 30.05%\n",
      "Epoch 8/20, Loss: 143.2515, Accuracy: 30.28%\n",
      "Epoch 9/20, Loss: 142.9804, Accuracy: 30.42%\n",
      "Epoch 10/20, Loss: 142.7500, Accuracy: 31.50%\n",
      "Epoch 11/20, Loss: 142.5428, Accuracy: 31.44%\n",
      "Epoch 12/20, Loss: 142.3583, Accuracy: 31.37%\n",
      "Epoch 13/20, Loss: 142.1885, Accuracy: 31.40%\n",
      "Epoch 14/20, Loss: 142.0346, Accuracy: 31.67%\n",
      "Epoch 15/20, Loss: 141.8886, Accuracy: 31.67%\n",
      "Epoch 16/20, Loss: 141.7420, Accuracy: 31.64%\n",
      "Epoch 17/20, Loss: 141.6145, Accuracy: 31.64%\n",
      "Epoch 18/20, Loss: 141.4988, Accuracy: 31.64%\n",
      "Epoch 19/20, Loss: 141.3884, Accuracy: 31.64%\n",
      "Epoch 20/20, Loss: 141.2857, Accuracy: 31.61%\n",
      "Training with dropout 0, lr 0.0001, wd 0.0001, epochs 5...\n",
      "Epoch 1/5, Loss: 149.1698, Accuracy: 24.76%\n",
      "Epoch 2/5, Loss: 147.3484, Accuracy: 29.20%\n",
      "Epoch 3/5, Loss: 146.1057, Accuracy: 30.35%\n",
      "Epoch 4/5, Loss: 145.1294, Accuracy: 30.83%\n",
      "Epoch 5/5, Loss: 144.3338, Accuracy: 31.03%\n",
      "Training with dropout 0, lr 0.0001, wd 0.0001, epochs 10...\n",
      "Epoch 1/10, Loss: 151.2712, Accuracy: 20.22%\n",
      "Epoch 2/10, Loss: 148.6309, Accuracy: 22.39%\n",
      "Epoch 3/10, Loss: 147.1599, Accuracy: 25.85%\n",
      "Epoch 4/10, Loss: 146.0802, Accuracy: 28.25%\n",
      "Epoch 5/10, Loss: 145.2378, Accuracy: 28.86%\n",
      "Epoch 6/10, Loss: 144.5906, Accuracy: 29.91%\n",
      "Epoch 7/10, Loss: 144.0660, Accuracy: 30.59%\n",
      "Epoch 8/10, Loss: 143.6187, Accuracy: 31.00%\n",
      "Epoch 9/10, Loss: 143.2245, Accuracy: 31.54%\n",
      "Epoch 10/10, Loss: 142.8945, Accuracy: 31.47%\n",
      "Training with dropout 0, lr 0.0001, wd 0.0001, epochs 15...\n",
      "Epoch 1/15, Loss: 152.1195, Accuracy: 20.93%\n",
      "Epoch 2/15, Loss: 149.5437, Accuracy: 25.95%\n",
      "Epoch 3/15, Loss: 147.7436, Accuracy: 30.52%\n",
      "Epoch 4/15, Loss: 146.4024, Accuracy: 31.98%\n",
      "Epoch 5/15, Loss: 145.4931, Accuracy: 31.71%\n",
      "Epoch 6/15, Loss: 144.7966, Accuracy: 31.74%\n",
      "Epoch 7/15, Loss: 144.2292, Accuracy: 31.71%\n",
      "Epoch 8/15, Loss: 143.7545, Accuracy: 31.78%\n",
      "Epoch 9/15, Loss: 143.3543, Accuracy: 31.78%\n",
      "Epoch 10/15, Loss: 143.0058, Accuracy: 32.08%\n",
      "Epoch 11/15, Loss: 142.7021, Accuracy: 32.18%\n",
      "Epoch 12/15, Loss: 142.4347, Accuracy: 32.18%\n",
      "Epoch 13/15, Loss: 142.1993, Accuracy: 32.25%\n",
      "Epoch 14/15, Loss: 141.9884, Accuracy: 32.38%\n",
      "Epoch 15/15, Loss: 141.8002, Accuracy: 32.22%\n",
      "Training with dropout 0, lr 0.0001, wd 0.0001, epochs 20...\n",
      "Epoch 1/20, Loss: 150.5871, Accuracy: 19.61%\n",
      "Epoch 2/20, Loss: 148.3732, Accuracy: 22.09%\n",
      "Epoch 3/20, Loss: 147.1799, Accuracy: 27.61%\n",
      "Epoch 4/20, Loss: 146.3255, Accuracy: 30.56%\n",
      "Epoch 5/20, Loss: 145.6360, Accuracy: 31.50%\n",
      "Epoch 6/20, Loss: 145.0446, Accuracy: 31.61%\n",
      "Epoch 7/20, Loss: 144.5129, Accuracy: 32.11%\n",
      "Epoch 8/20, Loss: 144.0385, Accuracy: 32.28%\n",
      "Epoch 9/20, Loss: 143.6229, Accuracy: 32.08%\n",
      "Epoch 10/20, Loss: 143.2553, Accuracy: 32.32%\n",
      "Epoch 11/20, Loss: 142.9284, Accuracy: 32.35%\n",
      "Epoch 12/20, Loss: 142.6362, Accuracy: 32.28%\n",
      "Epoch 13/20, Loss: 142.3784, Accuracy: 32.32%\n",
      "Epoch 14/20, Loss: 142.1445, Accuracy: 32.49%\n",
      "Epoch 15/20, Loss: 141.9291, Accuracy: 32.66%\n",
      "Epoch 16/20, Loss: 141.7333, Accuracy: 32.52%\n",
      "Epoch 17/20, Loss: 141.5578, Accuracy: 32.59%\n",
      "Epoch 18/20, Loss: 141.3987, Accuracy: 32.62%\n",
      "Epoch 19/20, Loss: 141.2522, Accuracy: 32.62%\n",
      "Epoch 20/20, Loss: 141.1160, Accuracy: 32.55%\n",
      "Training with dropout 0.25, lr 0.01, wd 0.01, epochs 5...\n",
      "Epoch 1/5, Loss: 145.1706, Accuracy: 27.54%\n",
      "Epoch 2/5, Loss: 144.5263, Accuracy: 28.25%\n",
      "Epoch 3/5, Loss: 144.5371, Accuracy: 28.56%\n",
      "Epoch 4/5, Loss: 144.4300, Accuracy: 28.69%\n",
      "Epoch 5/5, Loss: 144.7241, Accuracy: 28.12%\n",
      "Training with dropout 0.25, lr 0.01, wd 0.01, epochs 10...\n",
      "Epoch 1/10, Loss: 146.4449, Accuracy: 25.98%\n",
      "Epoch 2/10, Loss: 144.8050, Accuracy: 29.23%\n",
      "Epoch 3/10, Loss: 144.6727, Accuracy: 29.00%\n",
      "Epoch 4/10, Loss: 144.5706, Accuracy: 29.20%\n",
      "Epoch 5/10, Loss: 144.8227, Accuracy: 28.73%\n",
      "Epoch 6/10, Loss: 144.5511, Accuracy: 28.01%\n",
      "Epoch 7/10, Loss: 144.4798, Accuracy: 29.13%\n",
      "Epoch 8/10, Loss: 144.4931, Accuracy: 28.83%\n",
      "Epoch 9/10, Loss: 144.6921, Accuracy: 28.56%\n",
      "Epoch 10/10, Loss: 144.3962, Accuracy: 28.90%\n",
      "Training with dropout 0.25, lr 0.01, wd 0.01, epochs 15...\n",
      "Epoch 1/15, Loss: 145.6355, Accuracy: 27.81%\n",
      "Epoch 2/15, Loss: 144.7157, Accuracy: 29.03%\n",
      "Epoch 3/15, Loss: 145.0717, Accuracy: 28.93%\n",
      "Epoch 4/15, Loss: 145.0036, Accuracy: 28.32%\n",
      "Epoch 5/15, Loss: 144.6899, Accuracy: 28.18%\n",
      "Epoch 6/15, Loss: 144.8503, Accuracy: 28.32%\n",
      "Epoch 7/15, Loss: 144.6333, Accuracy: 28.66%\n",
      "Epoch 8/15, Loss: 144.6813, Accuracy: 28.39%\n",
      "Epoch 9/15, Loss: 144.7715, Accuracy: 28.46%\n",
      "Epoch 10/15, Loss: 144.3896, Accuracy: 28.76%\n",
      "Epoch 11/15, Loss: 144.5336, Accuracy: 28.52%\n",
      "Epoch 12/15, Loss: 144.6049, Accuracy: 29.07%\n",
      "Epoch 13/15, Loss: 144.4917, Accuracy: 28.69%\n",
      "Epoch 14/15, Loss: 144.4628, Accuracy: 29.07%\n",
      "Epoch 15/15, Loss: 144.5531, Accuracy: 28.79%\n",
      "Training with dropout 0.25, lr 0.01, wd 0.01, epochs 20...\n",
      "Epoch 1/20, Loss: 145.2755, Accuracy: 27.07%\n",
      "Epoch 2/20, Loss: 144.5362, Accuracy: 28.62%\n",
      "Epoch 3/20, Loss: 144.6166, Accuracy: 28.59%\n",
      "Epoch 4/20, Loss: 144.5224, Accuracy: 28.39%\n",
      "Epoch 5/20, Loss: 144.6421, Accuracy: 28.62%\n",
      "Epoch 6/20, Loss: 144.4313, Accuracy: 28.49%\n",
      "Epoch 7/20, Loss: 144.4088, Accuracy: 28.79%\n",
      "Epoch 8/20, Loss: 144.4265, Accuracy: 28.79%\n",
      "Epoch 9/20, Loss: 144.4712, Accuracy: 28.29%\n",
      "Epoch 10/20, Loss: 144.4507, Accuracy: 28.46%\n",
      "Epoch 11/20, Loss: 144.2689, Accuracy: 28.66%\n",
      "Epoch 12/20, Loss: 144.6448, Accuracy: 28.42%\n",
      "Epoch 13/20, Loss: 144.3827, Accuracy: 28.49%\n",
      "Epoch 14/20, Loss: 144.3580, Accuracy: 28.59%\n",
      "Epoch 15/20, Loss: 144.3723, Accuracy: 28.83%\n",
      "Epoch 16/20, Loss: 144.4236, Accuracy: 29.07%\n",
      "Epoch 17/20, Loss: 144.3190, Accuracy: 28.66%\n",
      "Epoch 18/20, Loss: 144.2662, Accuracy: 28.93%\n",
      "Epoch 19/20, Loss: 144.2577, Accuracy: 28.79%\n",
      "Epoch 20/20, Loss: 144.3299, Accuracy: 28.69%\n",
      "Training with dropout 0.25, lr 0.01, wd 0.001, epochs 5...\n",
      "Epoch 1/5, Loss: 145.0503, Accuracy: 26.69%\n",
      "Epoch 2/5, Loss: 142.9787, Accuracy: 30.15%\n",
      "Epoch 3/5, Loss: 142.4363, Accuracy: 29.95%\n",
      "Epoch 4/5, Loss: 141.7709, Accuracy: 30.25%\n",
      "Epoch 5/5, Loss: 141.5285, Accuracy: 30.52%\n",
      "Training with dropout 0.25, lr 0.01, wd 0.001, epochs 10...\n",
      "Epoch 1/10, Loss: 145.3537, Accuracy: 27.00%\n",
      "Epoch 2/10, Loss: 143.2112, Accuracy: 29.84%\n",
      "Epoch 3/10, Loss: 142.4376, Accuracy: 29.61%\n",
      "Epoch 4/10, Loss: 142.0435, Accuracy: 30.12%\n",
      "Epoch 5/10, Loss: 141.8603, Accuracy: 29.98%\n",
      "Epoch 6/10, Loss: 141.5014, Accuracy: 30.49%\n",
      "Epoch 7/10, Loss: 141.4310, Accuracy: 30.32%\n",
      "Epoch 8/10, Loss: 141.2631, Accuracy: 31.10%\n",
      "Epoch 9/10, Loss: 141.0705, Accuracy: 30.69%\n",
      "Epoch 10/10, Loss: 141.1071, Accuracy: 31.30%\n",
      "Training with dropout 0.25, lr 0.01, wd 0.001, epochs 15...\n",
      "Epoch 1/15, Loss: 145.1811, Accuracy: 26.63%\n",
      "Epoch 2/15, Loss: 143.4577, Accuracy: 29.40%\n",
      "Epoch 3/15, Loss: 142.6995, Accuracy: 29.54%\n",
      "Epoch 4/15, Loss: 142.3173, Accuracy: 30.52%\n",
      "Epoch 5/15, Loss: 142.0076, Accuracy: 30.28%\n",
      "Epoch 6/15, Loss: 141.7855, Accuracy: 30.96%\n",
      "Epoch 7/15, Loss: 141.7188, Accuracy: 30.05%\n",
      "Epoch 8/15, Loss: 141.5222, Accuracy: 30.66%\n",
      "Epoch 9/15, Loss: 141.5009, Accuracy: 30.83%\n",
      "Epoch 10/15, Loss: 141.3528, Accuracy: 30.69%\n",
      "Epoch 11/15, Loss: 141.3118, Accuracy: 30.89%\n",
      "Epoch 12/15, Loss: 141.0224, Accuracy: 30.39%\n",
      "Epoch 13/15, Loss: 141.1128, Accuracy: 31.17%\n",
      "Epoch 14/15, Loss: 140.8331, Accuracy: 30.86%\n",
      "Epoch 15/15, Loss: 140.9147, Accuracy: 30.86%\n",
      "Training with dropout 0.25, lr 0.01, wd 0.001, epochs 20...\n",
      "Epoch 1/20, Loss: 145.3639, Accuracy: 26.96%\n",
      "Epoch 2/20, Loss: 143.2452, Accuracy: 28.90%\n",
      "Epoch 3/20, Loss: 142.4246, Accuracy: 29.07%\n",
      "Epoch 4/20, Loss: 141.8639, Accuracy: 30.52%\n",
      "Epoch 5/20, Loss: 141.6822, Accuracy: 29.51%\n",
      "Epoch 6/20, Loss: 141.3680, Accuracy: 30.25%\n",
      "Epoch 7/20, Loss: 141.3054, Accuracy: 30.76%\n",
      "Epoch 8/20, Loss: 141.0869, Accuracy: 31.44%\n",
      "Epoch 9/20, Loss: 140.9928, Accuracy: 31.27%\n",
      "Epoch 10/20, Loss: 140.6229, Accuracy: 32.01%\n",
      "Epoch 11/20, Loss: 140.5985, Accuracy: 31.84%\n",
      "Epoch 12/20, Loss: 140.4770, Accuracy: 31.98%\n",
      "Epoch 13/20, Loss: 140.2991, Accuracy: 31.84%\n",
      "Epoch 14/20, Loss: 140.0521, Accuracy: 31.37%\n",
      "Epoch 15/20, Loss: 139.9516, Accuracy: 31.47%\n",
      "Epoch 16/20, Loss: 139.7168, Accuracy: 31.54%\n",
      "Epoch 17/20, Loss: 139.4677, Accuracy: 31.94%\n",
      "Epoch 18/20, Loss: 139.2452, Accuracy: 32.28%\n",
      "Epoch 19/20, Loss: 139.0220, Accuracy: 31.88%\n",
      "Epoch 20/20, Loss: 139.1619, Accuracy: 32.45%\n",
      "Training with dropout 0.25, lr 0.01, wd 0.0001, epochs 5...\n",
      "Epoch 1/5, Loss: 145.1290, Accuracy: 25.78%\n",
      "Epoch 2/5, Loss: 142.5108, Accuracy: 29.84%\n",
      "Epoch 3/5, Loss: 141.4364, Accuracy: 30.45%\n",
      "Epoch 4/5, Loss: 140.8780, Accuracy: 30.76%\n",
      "Epoch 5/5, Loss: 139.8506, Accuracy: 31.54%\n",
      "Training with dropout 0.25, lr 0.01, wd 0.0001, epochs 10...\n",
      "Epoch 1/10, Loss: 145.1679, Accuracy: 26.49%\n",
      "Epoch 2/10, Loss: 143.1083, Accuracy: 29.47%\n",
      "Epoch 3/10, Loss: 142.1791, Accuracy: 29.95%\n",
      "Epoch 4/10, Loss: 141.7203, Accuracy: 30.66%\n",
      "Epoch 5/10, Loss: 141.0251, Accuracy: 31.37%\n",
      "Epoch 6/10, Loss: 140.6389, Accuracy: 31.20%\n",
      "Epoch 7/10, Loss: 140.2722, Accuracy: 31.67%\n",
      "Epoch 8/10, Loss: 139.4667, Accuracy: 32.86%\n",
      "Epoch 9/10, Loss: 139.1351, Accuracy: 32.52%\n",
      "Epoch 10/10, Loss: 138.4167, Accuracy: 33.50%\n",
      "Training with dropout 0.25, lr 0.01, wd 0.0001, epochs 15...\n",
      "Epoch 1/15, Loss: 145.0488, Accuracy: 27.24%\n",
      "Epoch 2/15, Loss: 142.6827, Accuracy: 30.69%\n",
      "Epoch 3/15, Loss: 141.9279, Accuracy: 30.79%\n",
      "Epoch 4/15, Loss: 141.2724, Accuracy: 31.23%\n",
      "Epoch 5/15, Loss: 141.1314, Accuracy: 31.23%\n",
      "Epoch 6/15, Loss: 140.5160, Accuracy: 31.10%\n",
      "Epoch 7/15, Loss: 140.1624, Accuracy: 31.74%\n",
      "Epoch 8/15, Loss: 139.7488, Accuracy: 32.22%\n",
      "Epoch 9/15, Loss: 139.1367, Accuracy: 33.84%\n",
      "Epoch 10/15, Loss: 138.6318, Accuracy: 35.06%\n",
      "Epoch 11/15, Loss: 138.0523, Accuracy: 34.45%\n",
      "Epoch 12/15, Loss: 137.7609, Accuracy: 34.59%\n",
      "Epoch 13/15, Loss: 136.7618, Accuracy: 35.40%\n",
      "Epoch 14/15, Loss: 136.2643, Accuracy: 35.77%\n",
      "Epoch 15/15, Loss: 135.8171, Accuracy: 35.67%\n",
      "Training with dropout 0.25, lr 0.01, wd 0.0001, epochs 20...\n",
      "Epoch 1/20, Loss: 144.9310, Accuracy: 27.10%\n",
      "Epoch 2/20, Loss: 142.9293, Accuracy: 29.88%\n",
      "Epoch 3/20, Loss: 141.9789, Accuracy: 30.42%\n",
      "Epoch 4/20, Loss: 141.4547, Accuracy: 30.39%\n",
      "Epoch 5/20, Loss: 140.9340, Accuracy: 30.89%\n",
      "Epoch 6/20, Loss: 140.3933, Accuracy: 31.10%\n",
      "Epoch 7/20, Loss: 139.8925, Accuracy: 31.00%\n",
      "Epoch 8/20, Loss: 139.7519, Accuracy: 31.71%\n",
      "Epoch 9/20, Loss: 139.3913, Accuracy: 32.18%\n",
      "Epoch 10/20, Loss: 138.6783, Accuracy: 32.79%\n",
      "Epoch 11/20, Loss: 138.0568, Accuracy: 34.21%\n",
      "Epoch 12/20, Loss: 137.0858, Accuracy: 34.82%\n",
      "Epoch 13/20, Loss: 136.1894, Accuracy: 35.74%\n",
      "Epoch 14/20, Loss: 135.3507, Accuracy: 36.35%\n",
      "Epoch 15/20, Loss: 134.5363, Accuracy: 37.80%\n",
      "Epoch 16/20, Loss: 133.9911, Accuracy: 37.94%\n",
      "Epoch 17/20, Loss: 133.2181, Accuracy: 38.18%\n",
      "Epoch 18/20, Loss: 132.2351, Accuracy: 39.23%\n",
      "Epoch 19/20, Loss: 131.8378, Accuracy: 39.97%\n",
      "Epoch 20/20, Loss: 131.1069, Accuracy: 40.01%\n",
      "Training with dropout 0.25, lr 0.001, wd 0.01, epochs 5...\n",
      "Epoch 1/5, Loss: 146.1810, Accuracy: 28.52%\n",
      "Epoch 2/5, Loss: 142.9250, Accuracy: 31.81%\n",
      "Epoch 3/5, Loss: 142.0566, Accuracy: 31.84%\n",
      "Epoch 4/5, Loss: 141.6550, Accuracy: 31.44%\n",
      "Epoch 5/5, Loss: 141.3753, Accuracy: 32.11%\n",
      "Training with dropout 0.25, lr 0.001, wd 0.01, epochs 10...\n",
      "Epoch 1/10, Loss: 146.0775, Accuracy: 28.93%\n",
      "Epoch 2/10, Loss: 142.7574, Accuracy: 31.78%\n",
      "Epoch 3/10, Loss: 142.0265, Accuracy: 31.74%\n",
      "Epoch 4/10, Loss: 141.6867, Accuracy: 32.15%\n",
      "Epoch 5/10, Loss: 141.4315, Accuracy: 32.18%\n",
      "Epoch 6/10, Loss: 141.2713, Accuracy: 32.25%\n",
      "Epoch 7/10, Loss: 141.1717, Accuracy: 32.22%\n",
      "Epoch 8/10, Loss: 140.9834, Accuracy: 32.59%\n",
      "Epoch 9/10, Loss: 140.9078, Accuracy: 32.28%\n",
      "Epoch 10/10, Loss: 140.7891, Accuracy: 32.76%\n",
      "Training with dropout 0.25, lr 0.001, wd 0.01, epochs 15...\n",
      "Epoch 1/15, Loss: 145.5933, Accuracy: 29.64%\n",
      "Epoch 2/15, Loss: 142.7139, Accuracy: 32.15%\n",
      "Epoch 3/15, Loss: 141.8678, Accuracy: 32.08%\n",
      "Epoch 4/15, Loss: 141.4460, Accuracy: 31.81%\n",
      "Epoch 5/15, Loss: 141.1582, Accuracy: 31.91%\n",
      "Epoch 6/15, Loss: 141.0624, Accuracy: 32.32%\n",
      "Epoch 7/15, Loss: 140.9032, Accuracy: 32.86%\n",
      "Epoch 8/15, Loss: 140.7008, Accuracy: 32.45%\n",
      "Epoch 9/15, Loss: 140.6303, Accuracy: 32.86%\n",
      "Epoch 10/15, Loss: 140.5120, Accuracy: 32.96%\n",
      "Epoch 11/15, Loss: 140.3676, Accuracy: 33.13%\n",
      "Epoch 12/15, Loss: 140.2796, Accuracy: 33.16%\n",
      "Epoch 13/15, Loss: 140.3043, Accuracy: 32.83%\n",
      "Epoch 14/15, Loss: 140.1472, Accuracy: 33.16%\n",
      "Epoch 15/15, Loss: 140.0811, Accuracy: 33.13%\n",
      "Training with dropout 0.25, lr 0.001, wd 0.01, epochs 20...\n",
      "Epoch 1/20, Loss: 147.3581, Accuracy: 26.86%\n",
      "Epoch 2/20, Loss: 143.1448, Accuracy: 30.35%\n",
      "Epoch 3/20, Loss: 142.0941, Accuracy: 31.10%\n",
      "Epoch 4/20, Loss: 141.7448, Accuracy: 31.37%\n",
      "Epoch 5/20, Loss: 141.3388, Accuracy: 32.01%\n",
      "Epoch 6/20, Loss: 141.2254, Accuracy: 32.38%\n",
      "Epoch 7/20, Loss: 141.0724, Accuracy: 32.32%\n",
      "Epoch 8/20, Loss: 140.9223, Accuracy: 32.89%\n",
      "Epoch 9/20, Loss: 140.8092, Accuracy: 32.79%\n",
      "Epoch 10/20, Loss: 140.7217, Accuracy: 33.33%\n",
      "Epoch 11/20, Loss: 140.5644, Accuracy: 33.37%\n",
      "Epoch 12/20, Loss: 140.5258, Accuracy: 33.33%\n",
      "Epoch 13/20, Loss: 140.5217, Accuracy: 33.47%\n",
      "Epoch 14/20, Loss: 140.4256, Accuracy: 32.96%\n",
      "Epoch 15/20, Loss: 140.3586, Accuracy: 33.88%\n",
      "Epoch 16/20, Loss: 140.2685, Accuracy: 33.37%\n",
      "Epoch 17/20, Loss: 140.2368, Accuracy: 33.88%\n",
      "Epoch 18/20, Loss: 140.1110, Accuracy: 33.64%\n",
      "Epoch 19/20, Loss: 140.0705, Accuracy: 33.47%\n",
      "Epoch 20/20, Loss: 140.0054, Accuracy: 33.91%\n",
      "Training with dropout 0.25, lr 0.001, wd 0.001, epochs 5...\n",
      "Epoch 1/5, Loss: 146.3199, Accuracy: 28.69%\n",
      "Epoch 2/5, Loss: 142.1697, Accuracy: 31.81%\n",
      "Epoch 3/5, Loss: 141.1726, Accuracy: 31.61%\n",
      "Epoch 4/5, Loss: 140.6261, Accuracy: 32.11%\n",
      "Epoch 5/5, Loss: 140.2535, Accuracy: 32.45%\n",
      "Training with dropout 0.25, lr 0.001, wd 0.001, epochs 10...\n",
      "Epoch 1/10, Loss: 145.3816, Accuracy: 27.81%\n",
      "Epoch 2/10, Loss: 142.2690, Accuracy: 31.33%\n",
      "Epoch 3/10, Loss: 141.3461, Accuracy: 31.47%\n",
      "Epoch 4/10, Loss: 140.7779, Accuracy: 31.88%\n",
      "Epoch 5/10, Loss: 140.2777, Accuracy: 32.59%\n",
      "Epoch 6/10, Loss: 139.7687, Accuracy: 33.30%\n",
      "Epoch 7/10, Loss: 139.3301, Accuracy: 33.64%\n",
      "Epoch 8/10, Loss: 138.7324, Accuracy: 34.18%\n",
      "Epoch 9/10, Loss: 138.2710, Accuracy: 34.86%\n",
      "Epoch 10/10, Loss: 137.8365, Accuracy: 35.09%\n",
      "Training with dropout 0.25, lr 0.001, wd 0.001, epochs 15...\n",
      "Epoch 1/15, Loss: 145.8433, Accuracy: 29.40%\n",
      "Epoch 2/15, Loss: 142.4669, Accuracy: 32.22%\n",
      "Epoch 3/15, Loss: 141.4445, Accuracy: 31.94%\n",
      "Epoch 4/15, Loss: 140.8001, Accuracy: 32.32%\n",
      "Epoch 5/15, Loss: 140.3598, Accuracy: 32.38%\n",
      "Epoch 6/15, Loss: 139.8898, Accuracy: 32.86%\n",
      "Epoch 7/15, Loss: 139.4796, Accuracy: 33.03%\n",
      "Epoch 8/15, Loss: 139.1122, Accuracy: 33.03%\n",
      "Epoch 9/15, Loss: 138.7000, Accuracy: 33.94%\n",
      "Epoch 10/15, Loss: 138.2820, Accuracy: 33.67%\n",
      "Epoch 11/15, Loss: 137.8614, Accuracy: 34.38%\n",
      "Epoch 12/15, Loss: 137.4204, Accuracy: 35.30%\n",
      "Epoch 13/15, Loss: 137.0976, Accuracy: 35.06%\n",
      "Epoch 14/15, Loss: 136.6278, Accuracy: 35.81%\n",
      "Epoch 15/15, Loss: 136.1874, Accuracy: 35.81%\n",
      "Training with dropout 0.25, lr 0.001, wd 0.001, epochs 20...\n",
      "Epoch 1/20, Loss: 145.8068, Accuracy: 28.83%\n",
      "Epoch 2/20, Loss: 142.6234, Accuracy: 31.84%\n",
      "Epoch 3/20, Loss: 141.6142, Accuracy: 31.98%\n",
      "Epoch 4/20, Loss: 141.0506, Accuracy: 31.50%\n",
      "Epoch 5/20, Loss: 140.6141, Accuracy: 31.67%\n",
      "Epoch 6/20, Loss: 140.2341, Accuracy: 32.22%\n",
      "Epoch 7/20, Loss: 139.9055, Accuracy: 32.18%\n",
      "Epoch 8/20, Loss: 139.6196, Accuracy: 32.76%\n",
      "Epoch 9/20, Loss: 139.2987, Accuracy: 33.16%\n",
      "Epoch 10/20, Loss: 138.9930, Accuracy: 33.10%\n",
      "Epoch 11/20, Loss: 138.6384, Accuracy: 33.67%\n",
      "Epoch 12/20, Loss: 138.2164, Accuracy: 34.01%\n",
      "Epoch 13/20, Loss: 137.8576, Accuracy: 34.72%\n",
      "Epoch 14/20, Loss: 137.4886, Accuracy: 34.89%\n",
      "Epoch 15/20, Loss: 137.1442, Accuracy: 35.09%\n",
      "Epoch 16/20, Loss: 136.7135, Accuracy: 35.70%\n",
      "Epoch 17/20, Loss: 136.3232, Accuracy: 36.01%\n",
      "Epoch 18/20, Loss: 135.9430, Accuracy: 36.14%\n",
      "Epoch 19/20, Loss: 135.6498, Accuracy: 36.28%\n",
      "Epoch 20/20, Loss: 135.2235, Accuracy: 36.65%\n",
      "Training with dropout 0.25, lr 0.001, wd 0.0001, epochs 5...\n",
      "Epoch 1/5, Loss: 145.4959, Accuracy: 28.66%\n",
      "Epoch 2/5, Loss: 142.5771, Accuracy: 31.33%\n",
      "Epoch 3/5, Loss: 141.5618, Accuracy: 31.91%\n",
      "Epoch 4/5, Loss: 140.7743, Accuracy: 32.11%\n",
      "Epoch 5/5, Loss: 140.2268, Accuracy: 31.74%\n",
      "Training with dropout 0.25, lr 0.001, wd 0.0001, epochs 10...\n",
      "Epoch 1/10, Loss: 145.7683, Accuracy: 29.30%\n",
      "Epoch 2/10, Loss: 142.6154, Accuracy: 31.20%\n",
      "Epoch 3/10, Loss: 141.5200, Accuracy: 31.20%\n",
      "Epoch 4/10, Loss: 140.8353, Accuracy: 31.40%\n",
      "Epoch 5/10, Loss: 140.2862, Accuracy: 31.98%\n",
      "Epoch 6/10, Loss: 139.9169, Accuracy: 32.01%\n",
      "Epoch 7/10, Loss: 139.5365, Accuracy: 32.69%\n",
      "Epoch 8/10, Loss: 139.2026, Accuracy: 32.69%\n",
      "Epoch 9/10, Loss: 138.8213, Accuracy: 33.27%\n",
      "Epoch 10/10, Loss: 138.4206, Accuracy: 33.47%\n",
      "Training with dropout 0.25, lr 0.001, wd 0.0001, epochs 15...\n",
      "Epoch 1/15, Loss: 146.1327, Accuracy: 25.75%\n",
      "Epoch 2/15, Loss: 142.2460, Accuracy: 31.57%\n",
      "Epoch 3/15, Loss: 141.2070, Accuracy: 31.81%\n",
      "Epoch 4/15, Loss: 140.6424, Accuracy: 31.88%\n",
      "Epoch 5/15, Loss: 140.2028, Accuracy: 32.25%\n",
      "Epoch 6/15, Loss: 139.6989, Accuracy: 32.22%\n",
      "Epoch 7/15, Loss: 139.2450, Accuracy: 33.06%\n",
      "Epoch 8/15, Loss: 138.7865, Accuracy: 34.18%\n",
      "Epoch 9/15, Loss: 138.3142, Accuracy: 34.52%\n",
      "Epoch 10/15, Loss: 137.8161, Accuracy: 34.86%\n",
      "Epoch 11/15, Loss: 137.1922, Accuracy: 35.77%\n",
      "Epoch 12/15, Loss: 136.5849, Accuracy: 36.52%\n",
      "Epoch 13/15, Loss: 135.9607, Accuracy: 37.53%\n",
      "Epoch 14/15, Loss: 135.3780, Accuracy: 37.47%\n",
      "Epoch 15/15, Loss: 134.7443, Accuracy: 38.52%\n",
      "Training with dropout 0.25, lr 0.001, wd 0.0001, epochs 20...\n",
      "Epoch 1/20, Loss: 145.9971, Accuracy: 28.39%\n",
      "Epoch 2/20, Loss: 142.7427, Accuracy: 31.10%\n",
      "Epoch 3/20, Loss: 141.5681, Accuracy: 31.40%\n",
      "Epoch 4/20, Loss: 140.8804, Accuracy: 31.91%\n",
      "Epoch 5/20, Loss: 140.4477, Accuracy: 31.88%\n",
      "Epoch 6/20, Loss: 139.9713, Accuracy: 32.96%\n",
      "Epoch 7/20, Loss: 139.5400, Accuracy: 32.96%\n",
      "Epoch 8/20, Loss: 139.1019, Accuracy: 33.06%\n",
      "Epoch 9/20, Loss: 138.7305, Accuracy: 33.23%\n",
      "Epoch 10/20, Loss: 138.4117, Accuracy: 34.01%\n",
      "Epoch 11/20, Loss: 138.0339, Accuracy: 34.45%\n",
      "Epoch 12/20, Loss: 137.6508, Accuracy: 35.16%\n",
      "Epoch 13/20, Loss: 137.2612, Accuracy: 34.93%\n",
      "Epoch 14/20, Loss: 136.8273, Accuracy: 35.57%\n",
      "Epoch 15/20, Loss: 136.5052, Accuracy: 36.21%\n",
      "Epoch 16/20, Loss: 136.1667, Accuracy: 36.69%\n",
      "Epoch 17/20, Loss: 135.7309, Accuracy: 36.75%\n",
      "Epoch 18/20, Loss: 135.3690, Accuracy: 37.13%\n",
      "Epoch 19/20, Loss: 134.9235, Accuracy: 37.33%\n",
      "Epoch 20/20, Loss: 134.4540, Accuracy: 37.26%\n",
      "Training with dropout 0.25, lr 0.0001, wd 0.01, epochs 5...\n",
      "Epoch 1/5, Loss: 149.5743, Accuracy: 20.66%\n",
      "Epoch 2/5, Loss: 148.1860, Accuracy: 24.25%\n",
      "Epoch 3/5, Loss: 146.8483, Accuracy: 29.00%\n",
      "Epoch 4/5, Loss: 145.7332, Accuracy: 30.52%\n",
      "Epoch 5/5, Loss: 144.9009, Accuracy: 31.84%\n",
      "Training with dropout 0.25, lr 0.0001, wd 0.01, epochs 10...\n",
      "Epoch 1/10, Loss: 149.5887, Accuracy: 24.39%\n",
      "Epoch 2/10, Loss: 147.5758, Accuracy: 27.81%\n",
      "Epoch 3/10, Loss: 146.2065, Accuracy: 29.64%\n",
      "Epoch 4/10, Loss: 145.3020, Accuracy: 30.35%\n",
      "Epoch 5/10, Loss: 144.5559, Accuracy: 30.86%\n",
      "Epoch 6/10, Loss: 143.9385, Accuracy: 30.96%\n",
      "Epoch 7/10, Loss: 143.4947, Accuracy: 31.10%\n",
      "Epoch 8/10, Loss: 143.1543, Accuracy: 31.06%\n",
      "Epoch 9/10, Loss: 142.9036, Accuracy: 31.44%\n",
      "Epoch 10/10, Loss: 142.6679, Accuracy: 31.30%\n",
      "Training with dropout 0.25, lr 0.0001, wd 0.01, epochs 15...\n",
      "Epoch 1/15, Loss: 149.0353, Accuracy: 22.36%\n",
      "Epoch 2/15, Loss: 147.1492, Accuracy: 27.27%\n",
      "Epoch 3/15, Loss: 146.0776, Accuracy: 29.27%\n",
      "Epoch 4/15, Loss: 145.3664, Accuracy: 30.39%\n",
      "Epoch 5/15, Loss: 144.7903, Accuracy: 30.66%\n",
      "Epoch 6/15, Loss: 144.2493, Accuracy: 31.27%\n",
      "Epoch 7/15, Loss: 143.8187, Accuracy: 31.37%\n",
      "Epoch 8/15, Loss: 143.4646, Accuracy: 31.33%\n",
      "Epoch 9/15, Loss: 143.1944, Accuracy: 31.40%\n",
      "Epoch 10/15, Loss: 142.9079, Accuracy: 31.27%\n",
      "Epoch 11/15, Loss: 142.7090, Accuracy: 31.61%\n",
      "Epoch 12/15, Loss: 142.5066, Accuracy: 31.61%\n",
      "Epoch 13/15, Loss: 142.3216, Accuracy: 31.84%\n",
      "Epoch 14/15, Loss: 142.1636, Accuracy: 32.08%\n",
      "Epoch 15/15, Loss: 142.0606, Accuracy: 31.78%\n",
      "Training with dropout 0.25, lr 0.0001, wd 0.01, epochs 20...\n",
      "Epoch 1/20, Loss: 150.4952, Accuracy: 19.07%\n",
      "Epoch 2/20, Loss: 148.5893, Accuracy: 25.03%\n",
      "Epoch 3/20, Loss: 147.2359, Accuracy: 28.29%\n",
      "Epoch 4/20, Loss: 146.1360, Accuracy: 30.42%\n",
      "Epoch 5/20, Loss: 145.3180, Accuracy: 31.54%\n",
      "Epoch 6/20, Loss: 144.6787, Accuracy: 31.57%\n",
      "Epoch 7/20, Loss: 144.1299, Accuracy: 32.66%\n",
      "Epoch 8/20, Loss: 143.7023, Accuracy: 32.52%\n",
      "Epoch 9/20, Loss: 143.3492, Accuracy: 32.52%\n",
      "Epoch 10/20, Loss: 143.0157, Accuracy: 32.01%\n",
      "Epoch 11/20, Loss: 142.7012, Accuracy: 32.79%\n",
      "Epoch 12/20, Loss: 142.4982, Accuracy: 32.32%\n",
      "Epoch 13/20, Loss: 142.2940, Accuracy: 32.52%\n",
      "Epoch 14/20, Loss: 142.1247, Accuracy: 32.45%\n",
      "Epoch 15/20, Loss: 141.9645, Accuracy: 31.98%\n",
      "Epoch 16/20, Loss: 141.8304, Accuracy: 32.28%\n",
      "Epoch 17/20, Loss: 141.7032, Accuracy: 32.62%\n",
      "Epoch 18/20, Loss: 141.5653, Accuracy: 32.32%\n",
      "Epoch 19/20, Loss: 141.4944, Accuracy: 32.52%\n",
      "Epoch 20/20, Loss: 141.4040, Accuracy: 32.49%\n",
      "Training with dropout 0.25, lr 0.0001, wd 0.001, epochs 5...\n",
      "Epoch 1/5, Loss: 149.4789, Accuracy: 21.04%\n",
      "Epoch 2/5, Loss: 147.6619, Accuracy: 27.44%\n",
      "Epoch 3/5, Loss: 146.1292, Accuracy: 31.17%\n",
      "Epoch 4/5, Loss: 144.9507, Accuracy: 31.23%\n",
      "Epoch 5/5, Loss: 144.0779, Accuracy: 31.91%\n",
      "Training with dropout 0.25, lr 0.0001, wd 0.001, epochs 10...\n",
      "Epoch 1/10, Loss: 149.7333, Accuracy: 20.12%\n",
      "Epoch 2/10, Loss: 147.5882, Accuracy: 23.14%\n",
      "Epoch 3/10, Loss: 146.0677, Accuracy: 27.91%\n",
      "Epoch 4/10, Loss: 145.0322, Accuracy: 30.39%\n",
      "Epoch 5/10, Loss: 144.3558, Accuracy: 31.06%\n",
      "Epoch 6/10, Loss: 143.8236, Accuracy: 31.47%\n",
      "Epoch 7/10, Loss: 143.3641, Accuracy: 31.94%\n",
      "Epoch 8/10, Loss: 142.9330, Accuracy: 31.81%\n",
      "Epoch 9/10, Loss: 142.6058, Accuracy: 32.45%\n",
      "Epoch 10/10, Loss: 142.3067, Accuracy: 32.66%\n",
      "Training with dropout 0.25, lr 0.0001, wd 0.001, epochs 15...\n",
      "Epoch 1/15, Loss: 149.4550, Accuracy: 24.73%\n",
      "Epoch 2/15, Loss: 147.3528, Accuracy: 25.88%\n",
      "Epoch 3/15, Loss: 145.9014, Accuracy: 29.40%\n",
      "Epoch 4/15, Loss: 144.8344, Accuracy: 30.25%\n",
      "Epoch 5/15, Loss: 144.0188, Accuracy: 30.86%\n",
      "Epoch 6/15, Loss: 143.4364, Accuracy: 31.47%\n",
      "Epoch 7/15, Loss: 143.0096, Accuracy: 31.74%\n",
      "Epoch 8/15, Loss: 142.7235, Accuracy: 31.33%\n",
      "Epoch 9/15, Loss: 142.4653, Accuracy: 31.61%\n",
      "Epoch 10/15, Loss: 142.2261, Accuracy: 31.88%\n",
      "Epoch 11/15, Loss: 142.0141, Accuracy: 32.22%\n",
      "Epoch 12/15, Loss: 141.8606, Accuracy: 31.88%\n",
      "Epoch 13/15, Loss: 141.7090, Accuracy: 32.05%\n",
      "Epoch 14/15, Loss: 141.5376, Accuracy: 32.32%\n",
      "Epoch 15/15, Loss: 141.4514, Accuracy: 32.38%\n",
      "Training with dropout 0.25, lr 0.0001, wd 0.001, epochs 20...\n",
      "Epoch 1/20, Loss: 149.7526, Accuracy: 20.02%\n",
      "Epoch 2/20, Loss: 147.7940, Accuracy: 25.85%\n",
      "Epoch 3/20, Loss: 146.4514, Accuracy: 28.05%\n",
      "Epoch 4/20, Loss: 145.5526, Accuracy: 30.45%\n",
      "Epoch 5/20, Loss: 144.8659, Accuracy: 31.30%\n",
      "Epoch 6/20, Loss: 144.2113, Accuracy: 31.78%\n",
      "Epoch 7/20, Loss: 143.6843, Accuracy: 32.22%\n",
      "Epoch 8/20, Loss: 143.2442, Accuracy: 32.22%\n",
      "Epoch 9/20, Loss: 142.7741, Accuracy: 32.32%\n",
      "Epoch 10/20, Loss: 142.4239, Accuracy: 32.62%\n",
      "Epoch 11/20, Loss: 142.1027, Accuracy: 32.55%\n",
      "Epoch 12/20, Loss: 141.8121, Accuracy: 32.76%\n",
      "Epoch 13/20, Loss: 141.5895, Accuracy: 33.33%\n",
      "Epoch 14/20, Loss: 141.3480, Accuracy: 33.06%\n",
      "Epoch 15/20, Loss: 141.1879, Accuracy: 32.99%\n",
      "Epoch 16/20, Loss: 141.0288, Accuracy: 32.79%\n",
      "Epoch 17/20, Loss: 140.8454, Accuracy: 32.79%\n",
      "Epoch 18/20, Loss: 140.7218, Accuracy: 32.96%\n",
      "Epoch 19/20, Loss: 140.5813, Accuracy: 33.20%\n",
      "Epoch 20/20, Loss: 140.4842, Accuracy: 33.23%\n",
      "Training with dropout 0.25, lr 0.0001, wd 0.0001, epochs 5...\n",
      "Epoch 1/5, Loss: 149.6581, Accuracy: 24.59%\n",
      "Epoch 2/5, Loss: 148.3303, Accuracy: 25.58%\n",
      "Epoch 3/5, Loss: 147.2756, Accuracy: 27.37%\n",
      "Epoch 4/5, Loss: 146.0837, Accuracy: 30.96%\n",
      "Epoch 5/5, Loss: 145.0122, Accuracy: 31.50%\n",
      "Training with dropout 0.25, lr 0.0001, wd 0.0001, epochs 10...\n",
      "Epoch 1/10, Loss: 151.5010, Accuracy: 20.05%\n",
      "Epoch 2/10, Loss: 149.0622, Accuracy: 22.19%\n",
      "Epoch 3/10, Loss: 147.3790, Accuracy: 26.46%\n",
      "Epoch 4/10, Loss: 146.2050, Accuracy: 27.88%\n",
      "Epoch 5/10, Loss: 145.3925, Accuracy: 27.85%\n",
      "Epoch 6/10, Loss: 144.7299, Accuracy: 28.93%\n",
      "Epoch 7/10, Loss: 144.0616, Accuracy: 30.05%\n",
      "Epoch 8/10, Loss: 143.5560, Accuracy: 30.42%\n",
      "Epoch 9/10, Loss: 143.1519, Accuracy: 30.45%\n",
      "Epoch 10/10, Loss: 142.7772, Accuracy: 31.47%\n",
      "Training with dropout 0.25, lr 0.0001, wd 0.0001, epochs 15...\n",
      "Epoch 1/15, Loss: 150.0142, Accuracy: 20.05%\n",
      "Epoch 2/15, Loss: 148.0440, Accuracy: 22.59%\n",
      "Epoch 3/15, Loss: 146.5559, Accuracy: 27.37%\n",
      "Epoch 4/15, Loss: 145.3147, Accuracy: 29.91%\n",
      "Epoch 5/15, Loss: 144.5054, Accuracy: 30.01%\n",
      "Epoch 6/15, Loss: 143.9296, Accuracy: 30.01%\n",
      "Epoch 7/15, Loss: 143.5119, Accuracy: 30.32%\n",
      "Epoch 8/15, Loss: 143.1480, Accuracy: 30.96%\n",
      "Epoch 9/15, Loss: 142.8439, Accuracy: 30.86%\n",
      "Epoch 10/15, Loss: 142.5667, Accuracy: 31.40%\n",
      "Epoch 11/15, Loss: 142.3485, Accuracy: 31.44%\n",
      "Epoch 12/15, Loss: 142.1365, Accuracy: 31.54%\n",
      "Epoch 13/15, Loss: 141.9615, Accuracy: 31.78%\n",
      "Epoch 14/15, Loss: 141.7927, Accuracy: 31.78%\n",
      "Epoch 15/15, Loss: 141.6349, Accuracy: 31.98%\n",
      "Training with dropout 0.25, lr 0.0001, wd 0.0001, epochs 20...\n",
      "Epoch 1/20, Loss: 149.4114, Accuracy: 26.15%\n",
      "Epoch 2/20, Loss: 147.6946, Accuracy: 28.69%\n",
      "Epoch 3/20, Loss: 146.4588, Accuracy: 29.88%\n",
      "Epoch 4/20, Loss: 145.4078, Accuracy: 30.96%\n",
      "Epoch 5/20, Loss: 144.5155, Accuracy: 31.03%\n",
      "Epoch 6/20, Loss: 143.8041, Accuracy: 31.37%\n",
      "Epoch 7/20, Loss: 143.2676, Accuracy: 31.44%\n",
      "Epoch 8/20, Loss: 142.8942, Accuracy: 31.78%\n",
      "Epoch 9/20, Loss: 142.5762, Accuracy: 32.11%\n",
      "Epoch 10/20, Loss: 142.2703, Accuracy: 32.45%\n",
      "Epoch 11/20, Loss: 142.0432, Accuracy: 32.28%\n",
      "Epoch 12/20, Loss: 141.7861, Accuracy: 32.25%\n",
      "Epoch 13/20, Loss: 141.6135, Accuracy: 31.98%\n",
      "Epoch 14/20, Loss: 141.4147, Accuracy: 32.55%\n",
      "Epoch 15/20, Loss: 141.2258, Accuracy: 32.32%\n",
      "Epoch 16/20, Loss: 141.1150, Accuracy: 32.35%\n",
      "Epoch 17/20, Loss: 140.9324, Accuracy: 32.52%\n",
      "Epoch 18/20, Loss: 140.7946, Accuracy: 32.42%\n",
      "Epoch 19/20, Loss: 140.6692, Accuracy: 32.59%\n",
      "Epoch 20/20, Loss: 140.5365, Accuracy: 32.86%\n",
      "Training with dropout 0.5, lr 0.01, wd 0.01, epochs 5...\n",
      "Epoch 1/5, Loss: 145.5961, Accuracy: 26.29%\n",
      "Epoch 2/5, Loss: 144.5397, Accuracy: 28.29%\n",
      "Epoch 3/5, Loss: 144.6390, Accuracy: 28.96%\n",
      "Epoch 4/5, Loss: 144.3781, Accuracy: 28.62%\n",
      "Epoch 5/5, Loss: 144.4298, Accuracy: 28.42%\n",
      "Training with dropout 0.5, lr 0.01, wd 0.01, epochs 10...\n",
      "Epoch 1/10, Loss: 145.8233, Accuracy: 27.17%\n",
      "Epoch 2/10, Loss: 144.6966, Accuracy: 28.42%\n",
      "Epoch 3/10, Loss: 144.5167, Accuracy: 28.93%\n",
      "Epoch 4/10, Loss: 144.4925, Accuracy: 28.46%\n",
      "Epoch 5/10, Loss: 144.6412, Accuracy: 28.46%\n",
      "Epoch 6/10, Loss: 144.7293, Accuracy: 28.42%\n",
      "Epoch 7/10, Loss: 144.8063, Accuracy: 28.49%\n",
      "Epoch 8/10, Loss: 144.7490, Accuracy: 28.42%\n",
      "Epoch 9/10, Loss: 144.5564, Accuracy: 28.83%\n",
      "Epoch 10/10, Loss: 144.5563, Accuracy: 28.86%\n",
      "Training with dropout 0.5, lr 0.01, wd 0.01, epochs 15...\n",
      "Epoch 1/15, Loss: 145.7428, Accuracy: 26.42%\n",
      "Epoch 2/15, Loss: 144.7697, Accuracy: 29.00%\n",
      "Epoch 3/15, Loss: 144.7295, Accuracy: 28.52%\n",
      "Epoch 4/15, Loss: 144.4055, Accuracy: 28.49%\n",
      "Epoch 5/15, Loss: 144.6982, Accuracy: 28.69%\n",
      "Epoch 6/15, Loss: 144.3738, Accuracy: 28.46%\n",
      "Epoch 7/15, Loss: 145.0152, Accuracy: 28.29%\n",
      "Epoch 8/15, Loss: 144.6301, Accuracy: 28.62%\n",
      "Epoch 9/15, Loss: 144.3886, Accuracy: 28.15%\n",
      "Epoch 10/15, Loss: 144.3939, Accuracy: 28.42%\n",
      "Epoch 11/15, Loss: 144.2083, Accuracy: 28.83%\n",
      "Epoch 12/15, Loss: 144.8275, Accuracy: 28.86%\n",
      "Epoch 13/15, Loss: 144.8500, Accuracy: 28.59%\n",
      "Epoch 14/15, Loss: 144.5572, Accuracy: 28.46%\n",
      "Epoch 15/15, Loss: 144.5445, Accuracy: 28.56%\n",
      "Training with dropout 0.5, lr 0.01, wd 0.01, epochs 20...\n",
      "Epoch 1/20, Loss: 145.5181, Accuracy: 27.47%\n",
      "Epoch 2/20, Loss: 145.0185, Accuracy: 28.39%\n",
      "Epoch 3/20, Loss: 144.7404, Accuracy: 28.32%\n",
      "Epoch 4/20, Loss: 144.6407, Accuracy: 28.66%\n",
      "Epoch 5/20, Loss: 144.4894, Accuracy: 29.03%\n",
      "Epoch 6/20, Loss: 144.5923, Accuracy: 29.40%\n",
      "Epoch 7/20, Loss: 144.4208, Accuracy: 29.47%\n",
      "Epoch 8/20, Loss: 144.3909, Accuracy: 28.73%\n",
      "Epoch 9/20, Loss: 144.5017, Accuracy: 28.90%\n",
      "Epoch 10/20, Loss: 144.5011, Accuracy: 28.59%\n",
      "Epoch 11/20, Loss: 144.2685, Accuracy: 29.37%\n",
      "Epoch 12/20, Loss: 144.4814, Accuracy: 29.23%\n",
      "Epoch 13/20, Loss: 144.4343, Accuracy: 29.00%\n",
      "Epoch 14/20, Loss: 144.5716, Accuracy: 28.18%\n",
      "Epoch 15/20, Loss: 144.5180, Accuracy: 28.62%\n",
      "Epoch 16/20, Loss: 144.4416, Accuracy: 29.00%\n",
      "Epoch 17/20, Loss: 144.4947, Accuracy: 28.12%\n",
      "Epoch 18/20, Loss: 144.4041, Accuracy: 28.59%\n",
      "Epoch 19/20, Loss: 144.4566, Accuracy: 28.79%\n",
      "Epoch 20/20, Loss: 144.4019, Accuracy: 29.30%\n",
      "Training with dropout 0.5, lr 0.01, wd 0.001, epochs 5...\n",
      "Epoch 1/5, Loss: 145.2310, Accuracy: 26.19%\n",
      "Epoch 2/5, Loss: 143.0175, Accuracy: 29.17%\n",
      "Epoch 3/5, Loss: 142.3338, Accuracy: 28.96%\n",
      "Epoch 4/5, Loss: 141.9102, Accuracy: 29.47%\n",
      "Epoch 5/5, Loss: 141.4507, Accuracy: 29.84%\n",
      "Training with dropout 0.5, lr 0.01, wd 0.001, epochs 10...\n",
      "Epoch 1/10, Loss: 145.9407, Accuracy: 26.36%\n",
      "Epoch 2/10, Loss: 143.7236, Accuracy: 28.93%\n",
      "Epoch 3/10, Loss: 142.7219, Accuracy: 29.44%\n",
      "Epoch 4/10, Loss: 142.1912, Accuracy: 30.39%\n",
      "Epoch 5/10, Loss: 141.7154, Accuracy: 30.72%\n",
      "Epoch 6/10, Loss: 141.5732, Accuracy: 31.03%\n",
      "Epoch 7/10, Loss: 141.2620, Accuracy: 31.13%\n",
      "Epoch 8/10, Loss: 141.3323, Accuracy: 30.76%\n",
      "Epoch 9/10, Loss: 141.0867, Accuracy: 31.10%\n",
      "Epoch 10/10, Loss: 141.1502, Accuracy: 31.27%\n",
      "Training with dropout 0.5, lr 0.01, wd 0.001, epochs 15...\n",
      "Epoch 1/15, Loss: 145.0649, Accuracy: 27.03%\n",
      "Epoch 2/15, Loss: 142.8786, Accuracy: 29.03%\n",
      "Epoch 3/15, Loss: 142.1659, Accuracy: 29.64%\n",
      "Epoch 4/15, Loss: 141.7707, Accuracy: 29.47%\n",
      "Epoch 5/15, Loss: 141.5330, Accuracy: 30.15%\n",
      "Epoch 6/15, Loss: 141.2508, Accuracy: 30.01%\n",
      "Epoch 7/15, Loss: 140.9637, Accuracy: 31.10%\n",
      "Epoch 8/15, Loss: 140.7745, Accuracy: 30.79%\n",
      "Epoch 9/15, Loss: 140.6784, Accuracy: 31.57%\n",
      "Epoch 10/15, Loss: 140.5298, Accuracy: 31.74%\n",
      "Epoch 11/15, Loss: 140.3942, Accuracy: 31.27%\n",
      "Epoch 12/15, Loss: 140.1745, Accuracy: 31.44%\n",
      "Epoch 13/15, Loss: 140.1793, Accuracy: 30.96%\n",
      "Epoch 14/15, Loss: 139.7771, Accuracy: 31.27%\n",
      "Epoch 15/15, Loss: 139.8253, Accuracy: 31.47%\n",
      "Training with dropout 0.5, lr 0.01, wd 0.001, epochs 20...\n",
      "Epoch 1/20, Loss: 144.8418, Accuracy: 27.17%\n",
      "Epoch 2/20, Loss: 143.0505, Accuracy: 28.90%\n",
      "Epoch 3/20, Loss: 142.6939, Accuracy: 29.91%\n",
      "Epoch 4/20, Loss: 142.2725, Accuracy: 30.42%\n",
      "Epoch 5/20, Loss: 141.7700, Accuracy: 30.52%\n",
      "Epoch 6/20, Loss: 141.5974, Accuracy: 30.76%\n",
      "Epoch 7/20, Loss: 141.5394, Accuracy: 30.76%\n",
      "Epoch 8/20, Loss: 141.2792, Accuracy: 30.62%\n",
      "Epoch 9/20, Loss: 141.3627, Accuracy: 31.10%\n",
      "Epoch 10/20, Loss: 141.0582, Accuracy: 30.93%\n",
      "Epoch 11/20, Loss: 141.0537, Accuracy: 31.44%\n",
      "Epoch 12/20, Loss: 140.9560, Accuracy: 31.88%\n",
      "Epoch 13/20, Loss: 140.8352, Accuracy: 31.84%\n",
      "Epoch 14/20, Loss: 140.6803, Accuracy: 31.71%\n",
      "Epoch 15/20, Loss: 140.4970, Accuracy: 31.57%\n",
      "Epoch 16/20, Loss: 140.2802, Accuracy: 31.98%\n",
      "Epoch 17/20, Loss: 140.0913, Accuracy: 32.15%\n",
      "Epoch 18/20, Loss: 139.9591, Accuracy: 31.57%\n",
      "Epoch 19/20, Loss: 139.7099, Accuracy: 31.94%\n",
      "Epoch 20/20, Loss: 139.6062, Accuracy: 32.01%\n",
      "Training with dropout 0.5, lr 0.01, wd 0.0001, epochs 5...\n",
      "Epoch 1/5, Loss: 145.0258, Accuracy: 26.15%\n",
      "Epoch 2/5, Loss: 142.7494, Accuracy: 28.96%\n",
      "Epoch 3/5, Loss: 141.6380, Accuracy: 30.28%\n",
      "Epoch 4/5, Loss: 141.2298, Accuracy: 31.23%\n",
      "Epoch 5/5, Loss: 140.4979, Accuracy: 31.98%\n",
      "Training with dropout 0.5, lr 0.01, wd 0.0001, epochs 10...\n",
      "Epoch 1/10, Loss: 145.1263, Accuracy: 25.98%\n",
      "Epoch 2/10, Loss: 143.0648, Accuracy: 28.96%\n",
      "Epoch 3/10, Loss: 142.3180, Accuracy: 29.71%\n",
      "Epoch 4/10, Loss: 141.8126, Accuracy: 30.15%\n",
      "Epoch 5/10, Loss: 141.4535, Accuracy: 30.28%\n",
      "Epoch 6/10, Loss: 140.8429, Accuracy: 31.27%\n",
      "Epoch 7/10, Loss: 140.4483, Accuracy: 31.27%\n",
      "Epoch 8/10, Loss: 140.0687, Accuracy: 31.74%\n",
      "Epoch 9/10, Loss: 139.5075, Accuracy: 31.94%\n",
      "Epoch 10/10, Loss: 138.6616, Accuracy: 33.16%\n",
      "Training with dropout 0.5, lr 0.01, wd 0.0001, epochs 15...\n",
      "Epoch 1/15, Loss: 145.4673, Accuracy: 26.80%\n",
      "Epoch 2/15, Loss: 143.0651, Accuracy: 29.47%\n",
      "Epoch 3/15, Loss: 141.9632, Accuracy: 29.37%\n",
      "Epoch 4/15, Loss: 141.2488, Accuracy: 29.95%\n",
      "Epoch 5/15, Loss: 140.5501, Accuracy: 30.49%\n",
      "Epoch 6/15, Loss: 140.0563, Accuracy: 30.69%\n",
      "Epoch 7/15, Loss: 139.5264, Accuracy: 31.30%\n",
      "Epoch 8/15, Loss: 139.0182, Accuracy: 32.25%\n",
      "Epoch 9/15, Loss: 137.9945, Accuracy: 33.27%\n",
      "Epoch 10/15, Loss: 137.2490, Accuracy: 34.21%\n",
      "Epoch 11/15, Loss: 136.5353, Accuracy: 35.98%\n",
      "Epoch 12/15, Loss: 135.6747, Accuracy: 36.48%\n",
      "Epoch 13/15, Loss: 134.7977, Accuracy: 36.59%\n",
      "Epoch 14/15, Loss: 133.4656, Accuracy: 38.21%\n",
      "Epoch 15/15, Loss: 132.8523, Accuracy: 39.43%\n",
      "Training with dropout 0.5, lr 0.01, wd 0.0001, epochs 20...\n",
      "Epoch 1/20, Loss: 144.7408, Accuracy: 26.56%\n",
      "Epoch 2/20, Loss: 142.7514, Accuracy: 29.67%\n",
      "Epoch 3/20, Loss: 141.6875, Accuracy: 29.95%\n",
      "Epoch 4/20, Loss: 141.1885, Accuracy: 29.64%\n",
      "Epoch 5/20, Loss: 140.6112, Accuracy: 30.28%\n",
      "Epoch 6/20, Loss: 140.2369, Accuracy: 31.44%\n",
      "Epoch 7/20, Loss: 139.9047, Accuracy: 31.44%\n",
      "Epoch 8/20, Loss: 139.4156, Accuracy: 31.33%\n",
      "Epoch 9/20, Loss: 138.8423, Accuracy: 31.94%\n",
      "Epoch 10/20, Loss: 138.2723, Accuracy: 33.23%\n",
      "Epoch 11/20, Loss: 137.8540, Accuracy: 33.77%\n",
      "Epoch 12/20, Loss: 136.8741, Accuracy: 34.82%\n",
      "Epoch 13/20, Loss: 135.9833, Accuracy: 34.42%\n",
      "Epoch 14/20, Loss: 135.1354, Accuracy: 35.54%\n",
      "Epoch 15/20, Loss: 133.8839, Accuracy: 38.08%\n",
      "Epoch 16/20, Loss: 133.0570, Accuracy: 38.52%\n",
      "Epoch 17/20, Loss: 132.0622, Accuracy: 39.46%\n",
      "Epoch 18/20, Loss: 131.3699, Accuracy: 40.14%\n",
      "Epoch 19/20, Loss: 130.7084, Accuracy: 40.35%\n",
      "Epoch 20/20, Loss: 130.1867, Accuracy: 40.11%\n",
      "Training with dropout 0.5, lr 0.001, wd 0.01, epochs 5...\n",
      "Epoch 1/5, Loss: 147.4742, Accuracy: 26.08%\n",
      "Epoch 2/5, Loss: 143.5697, Accuracy: 31.23%\n",
      "Epoch 3/5, Loss: 142.3893, Accuracy: 31.71%\n",
      "Epoch 4/5, Loss: 141.9032, Accuracy: 32.05%\n",
      "Epoch 5/5, Loss: 141.5984, Accuracy: 32.18%\n",
      "Training with dropout 0.5, lr 0.001, wd 0.01, epochs 10...\n",
      "Epoch 1/10, Loss: 146.6802, Accuracy: 28.35%\n",
      "Epoch 2/10, Loss: 143.2229, Accuracy: 32.01%\n",
      "Epoch 3/10, Loss: 142.1381, Accuracy: 31.88%\n",
      "Epoch 4/10, Loss: 141.5967, Accuracy: 31.44%\n",
      "Epoch 5/10, Loss: 141.3136, Accuracy: 31.71%\n",
      "Epoch 6/10, Loss: 141.1419, Accuracy: 32.52%\n",
      "Epoch 7/10, Loss: 141.0043, Accuracy: 32.62%\n",
      "Epoch 8/10, Loss: 140.8281, Accuracy: 33.16%\n",
      "Epoch 9/10, Loss: 140.7636, Accuracy: 33.30%\n",
      "Epoch 10/10, Loss: 140.6479, Accuracy: 33.33%\n",
      "Training with dropout 0.5, lr 0.001, wd 0.01, epochs 15...\n",
      "Epoch 1/15, Loss: 146.4401, Accuracy: 28.76%\n",
      "Epoch 2/15, Loss: 142.9117, Accuracy: 31.57%\n",
      "Epoch 3/15, Loss: 142.1220, Accuracy: 31.91%\n",
      "Epoch 4/15, Loss: 141.7785, Accuracy: 31.98%\n",
      "Epoch 5/15, Loss: 141.5669, Accuracy: 31.81%\n",
      "Epoch 6/15, Loss: 141.4684, Accuracy: 32.01%\n",
      "Epoch 7/15, Loss: 141.4124, Accuracy: 32.18%\n",
      "Epoch 8/15, Loss: 141.3285, Accuracy: 32.11%\n",
      "Epoch 9/15, Loss: 141.3046, Accuracy: 32.59%\n",
      "Epoch 10/15, Loss: 141.2122, Accuracy: 32.38%\n",
      "Epoch 11/15, Loss: 141.2147, Accuracy: 32.15%\n",
      "Epoch 12/15, Loss: 141.2471, Accuracy: 32.05%\n",
      "Epoch 13/15, Loss: 141.1708, Accuracy: 32.66%\n",
      "Epoch 14/15, Loss: 141.1142, Accuracy: 32.42%\n",
      "Epoch 15/15, Loss: 141.0727, Accuracy: 32.55%\n",
      "Training with dropout 0.5, lr 0.001, wd 0.01, epochs 20...\n",
      "Epoch 1/20, Loss: 147.3436, Accuracy: 25.30%\n",
      "Epoch 2/20, Loss: 142.9065, Accuracy: 30.79%\n",
      "Epoch 3/20, Loss: 142.1128, Accuracy: 31.27%\n",
      "Epoch 4/20, Loss: 141.7657, Accuracy: 31.13%\n",
      "Epoch 5/20, Loss: 141.5602, Accuracy: 31.54%\n",
      "Epoch 6/20, Loss: 141.4035, Accuracy: 32.01%\n",
      "Epoch 7/20, Loss: 141.3017, Accuracy: 31.74%\n",
      "Epoch 8/20, Loss: 141.2226, Accuracy: 31.84%\n",
      "Epoch 9/20, Loss: 141.1562, Accuracy: 32.01%\n",
      "Epoch 10/20, Loss: 141.0704, Accuracy: 31.84%\n",
      "Epoch 11/20, Loss: 140.9509, Accuracy: 32.22%\n",
      "Epoch 12/20, Loss: 140.9335, Accuracy: 32.45%\n",
      "Epoch 13/20, Loss: 140.8191, Accuracy: 32.28%\n",
      "Epoch 14/20, Loss: 140.7840, Accuracy: 32.76%\n",
      "Epoch 15/20, Loss: 140.7439, Accuracy: 33.06%\n",
      "Epoch 16/20, Loss: 140.6206, Accuracy: 32.96%\n",
      "Epoch 17/20, Loss: 140.5354, Accuracy: 32.55%\n",
      "Epoch 18/20, Loss: 140.4408, Accuracy: 33.03%\n",
      "Epoch 19/20, Loss: 140.3244, Accuracy: 33.03%\n",
      "Epoch 20/20, Loss: 140.2704, Accuracy: 33.33%\n",
      "Training with dropout 0.5, lr 0.001, wd 0.001, epochs 5...\n",
      "Epoch 1/5, Loss: 146.5044, Accuracy: 28.12%\n",
      "Epoch 2/5, Loss: 142.8632, Accuracy: 31.54%\n",
      "Epoch 3/5, Loss: 141.5088, Accuracy: 31.13%\n",
      "Epoch 4/5, Loss: 140.7797, Accuracy: 31.74%\n",
      "Epoch 5/5, Loss: 140.2627, Accuracy: 32.89%\n",
      "Training with dropout 0.5, lr 0.001, wd 0.001, epochs 10...\n",
      "Epoch 1/10, Loss: 145.7111, Accuracy: 28.12%\n",
      "Epoch 2/10, Loss: 142.4677, Accuracy: 31.33%\n",
      "Epoch 3/10, Loss: 141.3398, Accuracy: 31.44%\n",
      "Epoch 4/10, Loss: 140.6928, Accuracy: 31.94%\n",
      "Epoch 5/10, Loss: 140.2694, Accuracy: 32.01%\n",
      "Epoch 6/10, Loss: 139.8548, Accuracy: 32.72%\n",
      "Epoch 7/10, Loss: 139.4986, Accuracy: 32.66%\n",
      "Epoch 8/10, Loss: 139.1669, Accuracy: 33.03%\n",
      "Epoch 9/10, Loss: 138.8380, Accuracy: 33.27%\n",
      "Epoch 10/10, Loss: 138.4835, Accuracy: 33.84%\n",
      "Training with dropout 0.5, lr 0.001, wd 0.001, epochs 15...\n",
      "Epoch 1/15, Loss: 145.4749, Accuracy: 27.17%\n",
      "Epoch 2/15, Loss: 142.6606, Accuracy: 32.01%\n",
      "Epoch 3/15, Loss: 141.7445, Accuracy: 31.94%\n",
      "Epoch 4/15, Loss: 141.0781, Accuracy: 31.91%\n",
      "Epoch 5/15, Loss: 140.6194, Accuracy: 32.08%\n",
      "Epoch 6/15, Loss: 140.1449, Accuracy: 32.49%\n",
      "Epoch 7/15, Loss: 139.6271, Accuracy: 32.66%\n",
      "Epoch 8/15, Loss: 139.0441, Accuracy: 33.40%\n",
      "Epoch 9/15, Loss: 138.4614, Accuracy: 33.03%\n",
      "Epoch 10/15, Loss: 137.8654, Accuracy: 33.47%\n",
      "Epoch 11/15, Loss: 137.2423, Accuracy: 34.89%\n",
      "Epoch 12/15, Loss: 136.6167, Accuracy: 35.37%\n",
      "Epoch 13/15, Loss: 135.8483, Accuracy: 37.13%\n",
      "Epoch 14/15, Loss: 135.3858, Accuracy: 36.21%\n",
      "Epoch 15/15, Loss: 134.5189, Accuracy: 37.57%\n",
      "Training with dropout 0.5, lr 0.001, wd 0.001, epochs 20...\n",
      "Epoch 1/20, Loss: 147.6192, Accuracy: 28.01%\n",
      "Epoch 2/20, Loss: 143.2320, Accuracy: 31.17%\n",
      "Epoch 3/20, Loss: 141.9221, Accuracy: 31.57%\n",
      "Epoch 4/20, Loss: 141.2101, Accuracy: 31.61%\n",
      "Epoch 5/20, Loss: 140.7182, Accuracy: 32.38%\n",
      "Epoch 6/20, Loss: 140.1553, Accuracy: 33.27%\n",
      "Epoch 7/20, Loss: 139.8568, Accuracy: 33.43%\n",
      "Epoch 8/20, Loss: 139.4729, Accuracy: 33.81%\n",
      "Epoch 9/20, Loss: 139.2121, Accuracy: 34.18%\n",
      "Epoch 10/20, Loss: 138.7399, Accuracy: 34.28%\n",
      "Epoch 11/20, Loss: 138.4056, Accuracy: 35.06%\n",
      "Epoch 12/20, Loss: 138.1241, Accuracy: 35.23%\n",
      "Epoch 13/20, Loss: 137.6913, Accuracy: 34.93%\n",
      "Epoch 14/20, Loss: 137.3428, Accuracy: 35.81%\n",
      "Epoch 15/20, Loss: 136.9360, Accuracy: 35.77%\n",
      "Epoch 16/20, Loss: 136.6300, Accuracy: 35.57%\n",
      "Epoch 17/20, Loss: 136.2332, Accuracy: 36.45%\n",
      "Epoch 18/20, Loss: 135.6909, Accuracy: 37.20%\n",
      "Epoch 19/20, Loss: 135.2236, Accuracy: 38.28%\n",
      "Epoch 20/20, Loss: 134.9799, Accuracy: 37.77%\n",
      "Training with dropout 0.5, lr 0.001, wd 0.0001, epochs 5...\n",
      "Epoch 1/5, Loss: 145.8725, Accuracy: 28.56%\n",
      "Epoch 2/5, Loss: 142.4752, Accuracy: 31.57%\n",
      "Epoch 3/5, Loss: 141.4499, Accuracy: 32.05%\n",
      "Epoch 4/5, Loss: 140.8304, Accuracy: 31.91%\n",
      "Epoch 5/5, Loss: 140.5327, Accuracy: 32.69%\n",
      "Training with dropout 0.5, lr 0.001, wd 0.0001, epochs 10...\n",
      "Epoch 1/10, Loss: 146.0897, Accuracy: 27.91%\n",
      "Epoch 2/10, Loss: 143.0356, Accuracy: 30.69%\n",
      "Epoch 3/10, Loss: 141.9259, Accuracy: 31.33%\n",
      "Epoch 4/10, Loss: 141.1405, Accuracy: 32.45%\n",
      "Epoch 5/10, Loss: 140.5129, Accuracy: 32.32%\n",
      "Epoch 6/10, Loss: 140.1115, Accuracy: 32.96%\n",
      "Epoch 7/10, Loss: 139.6703, Accuracy: 32.55%\n",
      "Epoch 8/10, Loss: 139.2803, Accuracy: 33.13%\n",
      "Epoch 9/10, Loss: 138.7695, Accuracy: 33.57%\n",
      "Epoch 10/10, Loss: 138.3111, Accuracy: 34.25%\n",
      "Training with dropout 0.5, lr 0.001, wd 0.0001, epochs 15...\n",
      "Epoch 1/15, Loss: 146.3987, Accuracy: 26.59%\n",
      "Epoch 2/15, Loss: 142.7199, Accuracy: 31.40%\n",
      "Epoch 3/15, Loss: 141.5934, Accuracy: 32.35%\n",
      "Epoch 4/15, Loss: 141.0714, Accuracy: 31.71%\n",
      "Epoch 5/15, Loss: 140.6678, Accuracy: 31.74%\n",
      "Epoch 6/15, Loss: 140.2254, Accuracy: 32.62%\n",
      "Epoch 7/15, Loss: 139.8441, Accuracy: 32.45%\n",
      "Epoch 8/15, Loss: 139.4469, Accuracy: 33.10%\n",
      "Epoch 9/15, Loss: 139.0420, Accuracy: 33.74%\n",
      "Epoch 10/15, Loss: 138.5489, Accuracy: 33.81%\n",
      "Epoch 11/15, Loss: 138.2159, Accuracy: 34.15%\n",
      "Epoch 12/15, Loss: 137.6809, Accuracy: 35.30%\n",
      "Epoch 13/15, Loss: 137.3581, Accuracy: 35.84%\n",
      "Epoch 14/15, Loss: 136.9131, Accuracy: 35.70%\n",
      "Epoch 15/15, Loss: 136.3935, Accuracy: 35.87%\n",
      "Training with dropout 0.5, lr 0.001, wd 0.0001, epochs 20...\n",
      "Epoch 1/20, Loss: 146.1416, Accuracy: 28.29%\n",
      "Epoch 2/20, Loss: 142.6387, Accuracy: 31.33%\n",
      "Epoch 3/20, Loss: 141.4664, Accuracy: 31.47%\n",
      "Epoch 4/20, Loss: 140.6910, Accuracy: 31.74%\n",
      "Epoch 5/20, Loss: 140.1502, Accuracy: 32.18%\n",
      "Epoch 6/20, Loss: 139.6531, Accuracy: 32.69%\n",
      "Epoch 7/20, Loss: 139.1654, Accuracy: 33.27%\n",
      "Epoch 8/20, Loss: 138.7513, Accuracy: 33.71%\n",
      "Epoch 9/20, Loss: 138.0913, Accuracy: 34.82%\n",
      "Epoch 10/20, Loss: 137.5405, Accuracy: 35.47%\n",
      "Epoch 11/20, Loss: 136.9422, Accuracy: 36.62%\n",
      "Epoch 12/20, Loss: 136.3939, Accuracy: 36.21%\n",
      "Epoch 13/20, Loss: 135.9466, Accuracy: 37.13%\n",
      "Epoch 14/20, Loss: 135.4613, Accuracy: 37.20%\n",
      "Epoch 15/20, Loss: 134.7769, Accuracy: 37.77%\n",
      "Epoch 16/20, Loss: 134.2409, Accuracy: 37.84%\n",
      "Epoch 17/20, Loss: 133.7160, Accuracy: 38.55%\n",
      "Epoch 18/20, Loss: 133.0610, Accuracy: 38.48%\n",
      "Epoch 19/20, Loss: 132.6205, Accuracy: 39.09%\n",
      "Epoch 20/20, Loss: 132.1014, Accuracy: 39.67%\n",
      "Training with dropout 0.5, lr 0.0001, wd 0.01, epochs 5...\n",
      "Epoch 1/5, Loss: 149.4133, Accuracy: 20.33%\n",
      "Epoch 2/5, Loss: 147.1106, Accuracy: 25.68%\n",
      "Epoch 3/5, Loss: 145.7240, Accuracy: 30.12%\n",
      "Epoch 4/5, Loss: 144.7328, Accuracy: 32.49%\n",
      "Epoch 5/5, Loss: 144.0877, Accuracy: 32.28%\n",
      "Training with dropout 0.5, lr 0.0001, wd 0.01, epochs 10...\n",
      "Epoch 1/10, Loss: 151.7878, Accuracy: 20.05%\n",
      "Epoch 2/10, Loss: 149.1419, Accuracy: 24.12%\n",
      "Epoch 3/10, Loss: 147.3897, Accuracy: 27.24%\n",
      "Epoch 4/10, Loss: 146.2256, Accuracy: 28.76%\n",
      "Epoch 5/10, Loss: 145.3597, Accuracy: 29.98%\n",
      "Epoch 6/10, Loss: 144.6892, Accuracy: 30.42%\n",
      "Epoch 7/10, Loss: 144.1661, Accuracy: 30.79%\n",
      "Epoch 8/10, Loss: 143.7537, Accuracy: 30.96%\n",
      "Epoch 9/10, Loss: 143.3998, Accuracy: 31.54%\n",
      "Epoch 10/10, Loss: 143.1598, Accuracy: 31.44%\n",
      "Training with dropout 0.5, lr 0.0001, wd 0.01, epochs 15...\n",
      "Epoch 1/15, Loss: 149.0893, Accuracy: 20.49%\n",
      "Epoch 2/15, Loss: 147.3377, Accuracy: 23.88%\n",
      "Epoch 3/15, Loss: 145.9623, Accuracy: 27.91%\n",
      "Epoch 4/15, Loss: 144.9764, Accuracy: 29.95%\n",
      "Epoch 5/15, Loss: 144.2665, Accuracy: 31.40%\n",
      "Epoch 6/15, Loss: 143.7644, Accuracy: 31.44%\n",
      "Epoch 7/15, Loss: 143.3864, Accuracy: 31.40%\n",
      "Epoch 8/15, Loss: 143.0553, Accuracy: 31.10%\n",
      "Epoch 9/15, Loss: 142.8276, Accuracy: 31.54%\n",
      "Epoch 10/15, Loss: 142.5831, Accuracy: 32.08%\n",
      "Epoch 11/15, Loss: 142.3723, Accuracy: 32.22%\n",
      "Epoch 12/15, Loss: 142.1689, Accuracy: 31.94%\n",
      "Epoch 13/15, Loss: 142.0379, Accuracy: 31.94%\n",
      "Epoch 14/15, Loss: 141.9150, Accuracy: 31.98%\n",
      "Epoch 15/15, Loss: 141.7842, Accuracy: 32.15%\n",
      "Training with dropout 0.5, lr 0.0001, wd 0.01, epochs 20...\n",
      "Epoch 1/20, Loss: 150.0018, Accuracy: 20.36%\n",
      "Epoch 2/20, Loss: 148.6279, Accuracy: 22.05%\n",
      "Epoch 3/20, Loss: 147.3371, Accuracy: 26.29%\n",
      "Epoch 4/20, Loss: 146.2310, Accuracy: 28.08%\n",
      "Epoch 5/20, Loss: 145.3362, Accuracy: 29.61%\n",
      "Epoch 6/20, Loss: 144.6120, Accuracy: 30.93%\n",
      "Epoch 7/20, Loss: 144.0794, Accuracy: 31.37%\n",
      "Epoch 8/20, Loss: 143.5823, Accuracy: 32.35%\n",
      "Epoch 9/20, Loss: 143.2242, Accuracy: 31.67%\n",
      "Epoch 10/20, Loss: 142.8738, Accuracy: 31.81%\n",
      "Epoch 11/20, Loss: 142.5171, Accuracy: 32.18%\n",
      "Epoch 12/20, Loss: 142.2383, Accuracy: 32.38%\n",
      "Epoch 13/20, Loss: 141.9888, Accuracy: 32.42%\n",
      "Epoch 14/20, Loss: 141.7964, Accuracy: 31.88%\n",
      "Epoch 15/20, Loss: 141.5647, Accuracy: 32.05%\n",
      "Epoch 16/20, Loss: 141.4237, Accuracy: 32.49%\n",
      "Epoch 17/20, Loss: 141.2780, Accuracy: 32.69%\n",
      "Epoch 18/20, Loss: 141.1131, Accuracy: 32.72%\n",
      "Epoch 19/20, Loss: 140.9702, Accuracy: 32.55%\n",
      "Epoch 20/20, Loss: 140.8795, Accuracy: 32.72%\n",
      "Training with dropout 0.5, lr 0.0001, wd 0.001, epochs 5...\n",
      "Epoch 1/5, Loss: 149.1235, Accuracy: 25.20%\n",
      "Epoch 2/5, Loss: 147.2882, Accuracy: 28.49%\n",
      "Epoch 3/5, Loss: 146.0124, Accuracy: 29.71%\n",
      "Epoch 4/5, Loss: 144.9973, Accuracy: 30.52%\n",
      "Epoch 5/5, Loss: 144.3036, Accuracy: 30.62%\n",
      "Training with dropout 0.5, lr 0.0001, wd 0.001, epochs 10...\n",
      "Epoch 1/10, Loss: 148.9837, Accuracy: 23.24%\n",
      "Epoch 2/10, Loss: 146.8648, Accuracy: 30.25%\n",
      "Epoch 3/10, Loss: 145.4743, Accuracy: 31.78%\n",
      "Epoch 4/10, Loss: 144.6230, Accuracy: 31.57%\n",
      "Epoch 5/10, Loss: 143.9764, Accuracy: 31.88%\n",
      "Epoch 6/10, Loss: 143.5084, Accuracy: 31.74%\n",
      "Epoch 7/10, Loss: 143.1598, Accuracy: 31.98%\n",
      "Epoch 8/10, Loss: 142.7850, Accuracy: 32.01%\n",
      "Epoch 9/10, Loss: 142.4286, Accuracy: 32.32%\n",
      "Epoch 10/10, Loss: 142.2158, Accuracy: 32.28%\n",
      "Training with dropout 0.5, lr 0.0001, wd 0.001, epochs 15...\n",
      "Epoch 1/15, Loss: 151.1197, Accuracy: 21.07%\n",
      "Epoch 2/15, Loss: 149.0949, Accuracy: 25.30%\n",
      "Epoch 3/15, Loss: 147.5497, Accuracy: 27.78%\n",
      "Epoch 4/15, Loss: 146.4383, Accuracy: 29.88%\n",
      "Epoch 5/15, Loss: 145.5939, Accuracy: 30.32%\n",
      "Epoch 6/15, Loss: 144.9512, Accuracy: 30.69%\n",
      "Epoch 7/15, Loss: 144.4436, Accuracy: 30.86%\n",
      "Epoch 8/15, Loss: 143.9549, Accuracy: 31.23%\n",
      "Epoch 9/15, Loss: 143.6370, Accuracy: 31.23%\n",
      "Epoch 10/15, Loss: 143.2427, Accuracy: 31.30%\n",
      "Epoch 11/15, Loss: 142.9451, Accuracy: 31.54%\n",
      "Epoch 12/15, Loss: 142.6712, Accuracy: 31.88%\n",
      "Epoch 13/15, Loss: 142.4231, Accuracy: 31.74%\n",
      "Epoch 14/15, Loss: 142.1420, Accuracy: 32.05%\n",
      "Epoch 15/15, Loss: 142.0365, Accuracy: 31.91%\n",
      "Training with dropout 0.5, lr 0.0001, wd 0.001, epochs 20...\n",
      "Epoch 1/20, Loss: 149.5665, Accuracy: 20.26%\n",
      "Epoch 2/20, Loss: 147.8133, Accuracy: 21.34%\n",
      "Epoch 3/20, Loss: 146.6142, Accuracy: 22.97%\n",
      "Epoch 4/20, Loss: 145.5959, Accuracy: 26.25%\n",
      "Epoch 5/20, Loss: 144.6783, Accuracy: 29.34%\n",
      "Epoch 6/20, Loss: 143.8985, Accuracy: 30.45%\n",
      "Epoch 7/20, Loss: 143.3699, Accuracy: 31.06%\n",
      "Epoch 8/20, Loss: 142.9237, Accuracy: 31.78%\n",
      "Epoch 9/20, Loss: 142.5800, Accuracy: 31.74%\n",
      "Epoch 10/20, Loss: 142.2327, Accuracy: 32.32%\n",
      "Epoch 11/20, Loss: 142.0286, Accuracy: 32.05%\n",
      "Epoch 12/20, Loss: 141.8229, Accuracy: 32.01%\n",
      "Epoch 13/20, Loss: 141.6638, Accuracy: 32.05%\n",
      "Epoch 14/20, Loss: 141.5050, Accuracy: 32.25%\n",
      "Epoch 15/20, Loss: 141.2963, Accuracy: 32.42%\n",
      "Epoch 16/20, Loss: 141.2481, Accuracy: 32.35%\n",
      "Epoch 17/20, Loss: 141.0828, Accuracy: 32.08%\n",
      "Epoch 18/20, Loss: 140.9226, Accuracy: 32.76%\n",
      "Epoch 19/20, Loss: 140.8410, Accuracy: 32.69%\n",
      "Epoch 20/20, Loss: 140.8199, Accuracy: 32.08%\n",
      "Training with dropout 0.5, lr 0.0001, wd 0.0001, epochs 5...\n",
      "Epoch 1/5, Loss: 149.6322, Accuracy: 20.19%\n",
      "Epoch 2/5, Loss: 148.0720, Accuracy: 24.49%\n",
      "Epoch 3/5, Loss: 146.9396, Accuracy: 27.98%\n",
      "Epoch 4/5, Loss: 145.9605, Accuracy: 28.56%\n",
      "Epoch 5/5, Loss: 145.0666, Accuracy: 30.12%\n",
      "Training with dropout 0.5, lr 0.0001, wd 0.0001, epochs 10...\n",
      "Epoch 1/10, Loss: 150.5658, Accuracy: 20.05%\n",
      "Epoch 2/10, Loss: 148.1361, Accuracy: 24.29%\n",
      "Epoch 3/10, Loss: 146.2271, Accuracy: 29.47%\n",
      "Epoch 4/10, Loss: 145.0062, Accuracy: 30.42%\n",
      "Epoch 5/10, Loss: 144.3110, Accuracy: 30.96%\n",
      "Epoch 6/10, Loss: 143.7394, Accuracy: 30.89%\n",
      "Epoch 7/10, Loss: 143.3455, Accuracy: 30.96%\n",
      "Epoch 8/10, Loss: 143.0115, Accuracy: 31.40%\n",
      "Epoch 9/10, Loss: 142.7873, Accuracy: 31.06%\n",
      "Epoch 10/10, Loss: 142.5500, Accuracy: 31.27%\n",
      "Training with dropout 0.5, lr 0.0001, wd 0.0001, epochs 15...\n",
      "Epoch 1/15, Loss: 150.8505, Accuracy: 20.19%\n",
      "Epoch 2/15, Loss: 148.4554, Accuracy: 21.31%\n",
      "Epoch 3/15, Loss: 146.7301, Accuracy: 27.54%\n",
      "Epoch 4/15, Loss: 145.5134, Accuracy: 30.25%\n",
      "Epoch 5/15, Loss: 144.7208, Accuracy: 30.66%\n",
      "Epoch 6/15, Loss: 144.0552, Accuracy: 31.27%\n",
      "Epoch 7/15, Loss: 143.5253, Accuracy: 32.01%\n",
      "Epoch 8/15, Loss: 143.1701, Accuracy: 31.71%\n",
      "Epoch 9/15, Loss: 142.7237, Accuracy: 32.38%\n",
      "Epoch 10/15, Loss: 142.5236, Accuracy: 32.28%\n",
      "Epoch 11/15, Loss: 142.1990, Accuracy: 32.22%\n",
      "Epoch 12/15, Loss: 141.9674, Accuracy: 32.86%\n",
      "Epoch 13/15, Loss: 141.7320, Accuracy: 32.66%\n",
      "Epoch 14/15, Loss: 141.5154, Accuracy: 32.89%\n",
      "Epoch 15/15, Loss: 141.3849, Accuracy: 32.66%\n",
      "Training with dropout 0.5, lr 0.0001, wd 0.0001, epochs 20...\n",
      "Epoch 1/20, Loss: 149.3602, Accuracy: 20.53%\n",
      "Epoch 2/20, Loss: 147.5794, Accuracy: 24.83%\n",
      "Epoch 3/20, Loss: 146.0028, Accuracy: 29.44%\n",
      "Epoch 4/20, Loss: 144.8859, Accuracy: 30.45%\n",
      "Epoch 5/20, Loss: 144.1717, Accuracy: 31.00%\n",
      "Epoch 6/20, Loss: 143.6899, Accuracy: 31.54%\n",
      "Epoch 7/20, Loss: 143.3125, Accuracy: 31.17%\n",
      "Epoch 8/20, Loss: 143.0478, Accuracy: 31.61%\n",
      "Epoch 9/20, Loss: 142.7343, Accuracy: 31.50%\n",
      "Epoch 10/20, Loss: 142.4508, Accuracy: 32.22%\n",
      "Epoch 11/20, Loss: 142.3608, Accuracy: 32.15%\n",
      "Epoch 12/20, Loss: 142.1053, Accuracy: 32.62%\n",
      "Epoch 13/20, Loss: 141.9942, Accuracy: 32.35%\n",
      "Epoch 14/20, Loss: 141.7946, Accuracy: 32.01%\n",
      "Epoch 15/20, Loss: 141.6398, Accuracy: 32.18%\n",
      "Epoch 16/20, Loss: 141.4965, Accuracy: 32.83%\n",
      "Epoch 17/20, Loss: 141.3775, Accuracy: 32.42%\n",
      "Epoch 18/20, Loss: 141.2669, Accuracy: 32.28%\n",
      "Epoch 19/20, Loss: 141.1228, Accuracy: 32.66%\n",
      "Epoch 20/20, Loss: 141.0701, Accuracy: 32.42%\n",
      "Results:\n",
      "{'dropout_rate': 0, 'learning_rate': 0.01, 'weight_decay': 0.01, 'epochs': 5, 'final_accuracy': 28.760162601626018, 'final_loss': 144.42500042915344, 'precision': 0.12423208385794225, 'recall': 0.2994579945799458, 'f1_score': 0.17504744730583716}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.01, 'weight_decay': 0.01, 'epochs': 10, 'final_accuracy': 29.336043360433603, 'final_loss': 143.87417221069336, 'precision': 0.03754562613376811, 'recall': 0.1937669376693767, 'f1_score': 0.06290277431718698}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.01, 'weight_decay': 0.01, 'epochs': 15, 'final_accuracy': 27.981029810298104, 'final_loss': 144.50950932502747, 'precision': 0.3268495595259626, 'recall': 0.25474254742547425, 'f1_score': 0.1727213497640987}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.01, 'weight_decay': 0.01, 'epochs': 20, 'final_accuracy': 28.658536585365855, 'final_loss': 144.30518448352814, 'precision': 0.16164081947378506, 'recall': 0.3048780487804878, 'f1_score': 0.20103958769387859}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.01, 'weight_decay': 0.001, 'epochs': 5, 'final_accuracy': 30.89430894308943, 'final_loss': 141.40411031246185, 'precision': 0.2429215427820682, 'recall': 0.3008130081300813, 'f1_score': 0.23978229066450532}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.01, 'weight_decay': 0.001, 'epochs': 10, 'final_accuracy': 31.233062330623305, 'final_loss': 140.91029715538025, 'precision': 0.27659020605944673, 'recall': 0.3048780487804878, 'f1_score': 0.2565664481239511}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.01, 'weight_decay': 0.001, 'epochs': 15, 'final_accuracy': 31.19918699186992, 'final_loss': 140.11674416065216, 'precision': 0.3078683268845477, 'recall': 0.3062330623306233, 'f1_score': 0.27501081137974853}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.01, 'weight_decay': 0.001, 'epochs': 20, 'final_accuracy': 32.28319783197832, 'final_loss': 139.85519325733185, 'precision': 0.24748816008403274, 'recall': 0.3075880758807588, 'f1_score': 0.26442584022710147}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.01, 'weight_decay': 0.0001, 'epochs': 5, 'final_accuracy': 31.30081300813008, 'final_loss': 140.1612777709961, 'precision': 0.23430345484461337, 'recall': 0.23577235772357724, 'f1_score': 0.11924947993890006}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.01, 'weight_decay': 0.0001, 'epochs': 10, 'final_accuracy': 34.68834688346883, 'final_loss': 137.5582424402237, 'precision': 0.31929986576748565, 'recall': 0.22899728997289973, 'f1_score': 0.15424618057869793}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.01, 'weight_decay': 0.0001, 'epochs': 15, 'final_accuracy': 38.143631436314365, 'final_loss': 132.94766771793365, 'precision': 0.3672994173624697, 'recall': 0.34010840108401086, 'f1_score': 0.2948465379843087}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.01, 'weight_decay': 0.0001, 'epochs': 20, 'final_accuracy': 42.24254742547426, 'final_loss': 128.1484738588333, 'precision': 0.21571347434995147, 'recall': 0.22899728997289973, 'f1_score': 0.12975164164284692}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.001, 'weight_decay': 0.01, 'epochs': 5, 'final_accuracy': 30.928184281842817, 'final_loss': 141.5866459608078, 'precision': 0.23499365230119845, 'recall': 0.2886178861788618, 'f1_score': 0.23702429663567867}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.001, 'weight_decay': 0.01, 'epochs': 10, 'final_accuracy': 32.35094850948509, 'final_loss': 141.15528285503387, 'precision': 0.3263074625897518, 'recall': 0.32791327913279134, 'f1_score': 0.2311152660062851}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.001, 'weight_decay': 0.01, 'epochs': 15, 'final_accuracy': 32.79132791327913, 'final_loss': 140.25842261314392, 'precision': 0.26373245709052046, 'recall': 0.2926829268292683, 'f1_score': 0.22041640394786455}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.001, 'weight_decay': 0.01, 'epochs': 20, 'final_accuracy': 33.16395663956639, 'final_loss': 140.3579946756363, 'precision': 0.24030266681206305, 'recall': 0.2967479674796748, 'f1_score': 0.2213797341392569}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.001, 'weight_decay': 0.001, 'epochs': 5, 'final_accuracy': 32.82520325203252, 'final_loss': 140.22851479053497, 'precision': 0.28557583156880295, 'recall': 0.32791327913279134, 'f1_score': 0.2618834557028486}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.001, 'weight_decay': 0.001, 'epochs': 10, 'final_accuracy': 36.1449864498645, 'final_loss': 137.82565999031067, 'precision': 0.09900786439236942, 'recall': 0.2222222222222222, 'f1_score': 0.11880619119954461}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.001, 'weight_decay': 0.001, 'epochs': 15, 'final_accuracy': 37.19512195121951, 'final_loss': 136.101442694664, 'precision': 0.2724980871512646, 'recall': 0.3089430894308943, 'f1_score': 0.2610448891389951}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.001, 'weight_decay': 0.001, 'epochs': 20, 'final_accuracy': 36.34823848238482, 'final_loss': 135.5228682756424, 'precision': 0.3022751373758797, 'recall': 0.31978319783197834, 'f1_score': 0.23845332779021178}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.001, 'weight_decay': 0.0001, 'epochs': 5, 'final_accuracy': 32.38482384823848, 'final_loss': 140.15919506549835, 'precision': 0.28569888746601574, 'recall': 0.2940379403794038, 'f1_score': 0.283231003360835}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.001, 'weight_decay': 0.0001, 'epochs': 10, 'final_accuracy': 35.09485094850948, 'final_loss': 137.44979810714722, 'precision': 0.36876114905733653, 'recall': 0.2981029810298103, 'f1_score': 0.28640165545290314}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.001, 'weight_decay': 0.0001, 'epochs': 15, 'final_accuracy': 39.295392953929536, 'final_loss': 131.9748581647873, 'precision': 0.5526436782547818, 'recall': 0.2886178861788618, 'f1_score': 0.2232291766803891}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.001, 'weight_decay': 0.0001, 'epochs': 20, 'final_accuracy': 38.075880758807585, 'final_loss': 133.38378083705902, 'precision': 0.2839658714695711, 'recall': 0.3130081300813008, 'f1_score': 0.21616270129908058}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.0001, 'weight_decay': 0.01, 'epochs': 5, 'final_accuracy': 28.556910569105693, 'final_loss': 146.12663185596466, 'precision': 0.3194336525636679, 'recall': 0.28319783197831977, 'f1_score': 0.24796488139307588}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.0001, 'weight_decay': 0.01, 'epochs': 10, 'final_accuracy': 31.70731707317073, 'final_loss': 142.88168597221375, 'precision': 0.29244345264653093, 'recall': 0.3157181571815718, 'f1_score': 0.24327813688290917}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.0001, 'weight_decay': 0.01, 'epochs': 15, 'final_accuracy': 31.165311653116532, 'final_loss': 141.69127464294434, 'precision': 0.26629889309152394, 'recall': 0.2886178861788618, 'f1_score': 0.2475758356772122}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.0001, 'weight_decay': 0.01, 'epochs': 20, 'final_accuracy': 32.520325203252035, 'final_loss': 141.18613576889038, 'precision': 0.24467476398512783, 'recall': 0.3021680216802168, 'f1_score': 0.22726481549383015}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.0001, 'weight_decay': 0.001, 'epochs': 5, 'final_accuracy': 28.082655826558266, 'final_loss': 144.37763559818268, 'precision': 0.2594912436519323, 'recall': 0.2777777777777778, 'f1_score': 0.24021640293527127}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.0001, 'weight_decay': 0.001, 'epochs': 10, 'final_accuracy': 32.31707317073171, 'final_loss': 142.57067203521729, 'precision': 0.26934579994613783, 'recall': 0.3075880758807588, 'f1_score': 0.2345160192793579}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.0001, 'weight_decay': 0.001, 'epochs': 15, 'final_accuracy': 31.910569105691057, 'final_loss': 141.3478900194168, 'precision': 0.25258617408349704, 'recall': 0.2967479674796748, 'f1_score': 0.24596188665299068}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.0001, 'weight_decay': 0.001, 'epochs': 20, 'final_accuracy': 31.60569105691057, 'final_loss': 141.28565800189972, 'precision': 0.2745258038155539, 'recall': 0.3170731707317073, 'f1_score': 0.2449806927830819}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.0001, 'weight_decay': 0.0001, 'epochs': 5, 'final_accuracy': 31.02981029810298, 'final_loss': 144.3337904214859, 'precision': 0.20978594589504046, 'recall': 0.3035230352303523, 'f1_score': 0.2052663503374059}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.0001, 'weight_decay': 0.0001, 'epochs': 10, 'final_accuracy': 31.47018970189702, 'final_loss': 142.89450228214264, 'precision': 0.23267165764293268, 'recall': 0.3075880758807588, 'f1_score': 0.2247236082878397}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.0001, 'weight_decay': 0.0001, 'epochs': 15, 'final_accuracy': 32.21544715447155, 'final_loss': 141.8001868724823, 'precision': 0.2687482422971252, 'recall': 0.31029810298102983, 'f1_score': 0.245337938582617}\n",
      "{'dropout_rate': 0, 'learning_rate': 0.0001, 'weight_decay': 0.0001, 'epochs': 20, 'final_accuracy': 32.55420054200542, 'final_loss': 141.1160078048706, 'precision': 0.29213726484533076, 'recall': 0.3089430894308943, 'f1_score': 0.2558202097978482}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.01, 'weight_decay': 0.01, 'epochs': 5, 'final_accuracy': 28.116531165311653, 'final_loss': 144.72411584854126, 'precision': 0.07638474219017144, 'recall': 0.21951219512195122, 'f1_score': 0.08762309323538119}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.01, 'weight_decay': 0.01, 'epochs': 10, 'final_accuracy': 28.895663956639567, 'final_loss': 144.39615559577942, 'precision': 0.04641747636988565, 'recall': 0.21544715447154472, 'f1_score': 0.07637925877586535}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.01, 'weight_decay': 0.01, 'epochs': 15, 'final_accuracy': 28.794037940379404, 'final_loss': 144.55306732654572, 'precision': 0.04641747636988565, 'recall': 0.21544715447154472, 'f1_score': 0.07637925877586535}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.01, 'weight_decay': 0.01, 'epochs': 20, 'final_accuracy': 28.69241192411924, 'final_loss': 144.3298774957657, 'precision': 0.20879306910835502, 'recall': 0.23577235772357724, 'f1_score': 0.1431356935697408}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.01, 'weight_decay': 0.001, 'epochs': 5, 'final_accuracy': 30.521680216802167, 'final_loss': 141.52853274345398, 'precision': 0.10177880264826877, 'recall': 0.23712737127371275, 'f1_score': 0.1364730875326637}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.01, 'weight_decay': 0.001, 'epochs': 10, 'final_accuracy': 31.30081300813008, 'final_loss': 141.1070555448532, 'precision': 0.14118964305862727, 'recall': 0.25474254742547425, 'f1_score': 0.16382486721039896}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.01, 'weight_decay': 0.001, 'epochs': 15, 'final_accuracy': 30.860433604336045, 'final_loss': 140.91474890708923, 'precision': 0.16385360880086808, 'recall': 0.2750677506775068, 'f1_score': 0.20131261911691614}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.01, 'weight_decay': 0.001, 'epochs': 20, 'final_accuracy': 32.452574525745256, 'final_loss': 139.16187238693237, 'precision': 0.3870217770034843, 'recall': 0.33875338753387535, 'f1_score': 0.2627459355929154}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.01, 'weight_decay': 0.0001, 'epochs': 5, 'final_accuracy': 31.537940379403793, 'final_loss': 139.85061359405518, 'precision': 0.2200583311694015, 'recall': 0.2777777777777778, 'f1_score': 0.22676982635769266}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.01, 'weight_decay': 0.0001, 'epochs': 10, 'final_accuracy': 33.50271002710027, 'final_loss': 138.4167219400406, 'precision': 0.32542254851873376, 'recall': 0.3089430894308943, 'f1_score': 0.27565222788391547}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.01, 'weight_decay': 0.0001, 'epochs': 15, 'final_accuracy': 35.670731707317074, 'final_loss': 135.81709921360016, 'precision': 0.40772260001777827, 'recall': 0.2953929539295393, 'f1_score': 0.2374368716387937}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.01, 'weight_decay': 0.0001, 'epochs': 20, 'final_accuracy': 40.00677506775068, 'final_loss': 131.10694420337677, 'precision': 0.33721961534459177, 'recall': 0.31978319783197834, 'f1_score': 0.2502226932739011}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.001, 'weight_decay': 0.01, 'epochs': 5, 'final_accuracy': 32.113821138211385, 'final_loss': 141.37532114982605, 'precision': 0.22775780593354025, 'recall': 0.2967479674796748, 'f1_score': 0.2532104051272011}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.001, 'weight_decay': 0.01, 'epochs': 10, 'final_accuracy': 32.75745257452574, 'final_loss': 140.78906667232513, 'precision': 0.2349189078658967, 'recall': 0.2886178861788618, 'f1_score': 0.1958640774698241}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.001, 'weight_decay': 0.01, 'epochs': 15, 'final_accuracy': 33.13008130081301, 'final_loss': 140.08114445209503, 'precision': 0.3561474467611566, 'recall': 0.32926829268292684, 'f1_score': 0.27838059880158367}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.001, 'weight_decay': 0.01, 'epochs': 20, 'final_accuracy': 33.90921409214092, 'final_loss': 140.0053894519806, 'precision': 0.207635621373152, 'recall': 0.23983739837398374, 'f1_score': 0.1828476546103291}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.001, 'weight_decay': 0.001, 'epochs': 5, 'final_accuracy': 32.452574525745256, 'final_loss': 140.25352787971497, 'precision': 0.27311892015027367, 'recall': 0.3021680216802168, 'f1_score': 0.25733893472655056}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.001, 'weight_decay': 0.001, 'epochs': 10, 'final_accuracy': 35.09485094850948, 'final_loss': 137.83653724193573, 'precision': 0.38442655802445747, 'recall': 0.34146341463414637, 'f1_score': 0.28265892362727857}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.001, 'weight_decay': 0.001, 'epochs': 15, 'final_accuracy': 35.80623306233063, 'final_loss': 136.18737375736237, 'precision': 0.48072298100283306, 'recall': 0.32926829268292684, 'f1_score': 0.2794419778318757}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.001, 'weight_decay': 0.001, 'epochs': 20, 'final_accuracy': 36.65311653116531, 'final_loss': 135.2234650850296, 'precision': 0.3732996386770146, 'recall': 0.3075880758807588, 'f1_score': 0.23864098598113676}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.001, 'weight_decay': 0.0001, 'epochs': 5, 'final_accuracy': 31.741192411924118, 'final_loss': 140.22679340839386, 'precision': 0.2608939177346578, 'recall': 0.2872628726287263, 'f1_score': 0.24174851781009896}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.001, 'weight_decay': 0.0001, 'epochs': 10, 'final_accuracy': 33.46883468834688, 'final_loss': 138.42059350013733, 'precision': 0.29023764529985335, 'recall': 0.2845528455284553, 'f1_score': 0.25173023115816473}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.001, 'weight_decay': 0.0001, 'epochs': 15, 'final_accuracy': 38.516260162601625, 'final_loss': 134.74430465698242, 'precision': 0.39840638684635754, 'recall': 0.36449864498644985, 'f1_score': 0.31605294314772947}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.001, 'weight_decay': 0.0001, 'epochs': 20, 'final_accuracy': 37.262872628726285, 'final_loss': 134.4540195465088, 'precision': 0.5345126484508775, 'recall': 0.2764227642276423, 'f1_score': 0.2187343087343449}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.0001, 'weight_decay': 0.01, 'epochs': 5, 'final_accuracy': 31.84281842818428, 'final_loss': 144.90087044239044, 'precision': 0.2691894905543454, 'recall': 0.3035230352303523, 'f1_score': 0.24113442803982196}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.0001, 'weight_decay': 0.01, 'epochs': 10, 'final_accuracy': 31.30081300813008, 'final_loss': 142.6678808927536, 'precision': 0.2604978740890166, 'recall': 0.3143631436314363, 'f1_score': 0.24138956711343132}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.0001, 'weight_decay': 0.01, 'epochs': 15, 'final_accuracy': 31.775067750677508, 'final_loss': 142.06064915657043, 'precision': 0.2592895574930785, 'recall': 0.3062330623306233, 'f1_score': 0.2244170398120052}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.0001, 'weight_decay': 0.01, 'epochs': 20, 'final_accuracy': 32.486449864498645, 'final_loss': 141.40401101112366, 'precision': 0.31159502641924436, 'recall': 0.3319783197831978, 'f1_score': 0.29826505937213005}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.0001, 'weight_decay': 0.001, 'epochs': 5, 'final_accuracy': 31.910569105691057, 'final_loss': 144.07792711257935, 'precision': 0.23440286277383401, 'recall': 0.3143631436314363, 'f1_score': 0.23159531127037533}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.0001, 'weight_decay': 0.001, 'epochs': 10, 'final_accuracy': 32.65582655826558, 'final_loss': 142.30665159225464, 'precision': 0.2517134705254078, 'recall': 0.3008130081300813, 'f1_score': 0.24380681675353794}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.0001, 'weight_decay': 0.001, 'epochs': 15, 'final_accuracy': 32.38482384823848, 'final_loss': 141.45138216018677, 'precision': 0.24794503607709292, 'recall': 0.3035230352303523, 'f1_score': 0.23707281916551134}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.0001, 'weight_decay': 0.001, 'epochs': 20, 'final_accuracy': 33.23170731707317, 'final_loss': 140.48416674137115, 'precision': 0.301902652523679, 'recall': 0.32113821138211385, 'f1_score': 0.25681513836543585}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.0001, 'weight_decay': 0.0001, 'epochs': 5, 'final_accuracy': 31.504065040650406, 'final_loss': 145.01218605041504, 'precision': 0.2984534101026349, 'recall': 0.32926829268292684, 'f1_score': 0.2627171880173929}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.0001, 'weight_decay': 0.0001, 'epochs': 10, 'final_accuracy': 31.47018970189702, 'final_loss': 142.7772399187088, 'precision': 0.240266397681248, 'recall': 0.2953929539295393, 'f1_score': 0.2299353814126241}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.0001, 'weight_decay': 0.0001, 'epochs': 15, 'final_accuracy': 31.978319783197833, 'final_loss': 141.63485169410706, 'precision': 0.28498733312616964, 'recall': 0.3075880758807588, 'f1_score': 0.24044527686693795}\n",
      "{'dropout_rate': 0.25, 'learning_rate': 0.0001, 'weight_decay': 0.0001, 'epochs': 20, 'final_accuracy': 32.859078590785906, 'final_loss': 140.53648483753204, 'precision': 0.2888423465694587, 'recall': 0.3143631436314363, 'f1_score': 0.2551751195770284}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.01, 'weight_decay': 0.01, 'epochs': 5, 'final_accuracy': 28.42140921409214, 'final_loss': 144.42978239059448, 'precision': 0.15806092478323025, 'recall': 0.21680216802168023, 'f1_score': 0.08899166049441422}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.01, 'weight_decay': 0.01, 'epochs': 10, 'final_accuracy': 28.86178861788618, 'final_loss': 144.5562573671341, 'precision': 0.07647385360139268, 'recall': 0.2046070460704607, 'f1_score': 0.08118046468118205}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.01, 'weight_decay': 0.01, 'epochs': 15, 'final_accuracy': 28.556910569105693, 'final_loss': 144.54450500011444, 'precision': 0.04641747636988565, 'recall': 0.21544715447154472, 'f1_score': 0.07637925877586535}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.01, 'weight_decay': 0.01, 'epochs': 20, 'final_accuracy': 29.302168021680217, 'final_loss': 144.40194749832153, 'precision': 0.04641747636988565, 'recall': 0.21544715447154472, 'f1_score': 0.07637925877586535}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.01, 'weight_decay': 0.001, 'epochs': 5, 'final_accuracy': 29.844173441734416, 'final_loss': 141.4506560564041, 'precision': 0.10338540521640086, 'recall': 0.26558265582655827, 'f1_score': 0.14881834041012476}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.01, 'weight_decay': 0.001, 'epochs': 10, 'final_accuracy': 31.266937669376695, 'final_loss': 141.15020096302032, 'precision': 0.24509355325633161, 'recall': 0.2981029810298103, 'f1_score': 0.23461095906339444}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.01, 'weight_decay': 0.001, 'epochs': 15, 'final_accuracy': 31.47018970189702, 'final_loss': 139.82528269290924, 'precision': 0.13228707784858926, 'recall': 0.25203252032520324, 'f1_score': 0.14317901309771228}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.01, 'weight_decay': 0.001, 'epochs': 20, 'final_accuracy': 32.01219512195122, 'final_loss': 139.60624754428864, 'precision': 0.2577530835162846, 'recall': 0.24119241192411925, 'f1_score': 0.20063073601289186}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.01, 'weight_decay': 0.0001, 'epochs': 5, 'final_accuracy': 31.978319783197833, 'final_loss': 140.49794828891754, 'precision': 0.142159483238668, 'recall': 0.21951219512195122, 'f1_score': 0.08683997983637215}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.01, 'weight_decay': 0.0001, 'epochs': 10, 'final_accuracy': 33.16395663956639, 'final_loss': 138.66155230998993, 'precision': 0.2913890054172638, 'recall': 0.3143631436314363, 'f1_score': 0.23612514489442502}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.01, 'weight_decay': 0.0001, 'epochs': 15, 'final_accuracy': 39.43089430894309, 'final_loss': 132.85234034061432, 'precision': 0.03967545773018706, 'recall': 0.1991869918699187, 'f1_score': 0.06617059390932892}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.01, 'weight_decay': 0.0001, 'epochs': 20, 'final_accuracy': 40.10840108401084, 'final_loss': 130.1866693496704, 'precision': 0.1199504760534331, 'recall': 0.23035230352303523, 'f1_score': 0.11940124536857631}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.001, 'weight_decay': 0.01, 'epochs': 5, 'final_accuracy': 32.18157181571816, 'final_loss': 141.5983874797821, 'precision': 0.24300273810701317, 'recall': 0.2899728997289973, 'f1_score': 0.22101019353432483}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.001, 'weight_decay': 0.01, 'epochs': 10, 'final_accuracy': 33.333333333333336, 'final_loss': 140.6479296684265, 'precision': 0.18476077322594758, 'recall': 0.31029810298102983, 'f1_score': 0.2283170701442221}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.001, 'weight_decay': 0.01, 'epochs': 15, 'final_accuracy': 32.55420054200542, 'final_loss': 141.0727459192276, 'precision': 0.1866607693703502, 'recall': 0.31029810298102983, 'f1_score': 0.20775758815042616}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.001, 'weight_decay': 0.01, 'epochs': 20, 'final_accuracy': 33.333333333333336, 'final_loss': 140.27038526535034, 'precision': 0.42836430462626823, 'recall': 0.28319783197831977, 'f1_score': 0.2097717620257562}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.001, 'weight_decay': 0.001, 'epochs': 5, 'final_accuracy': 32.892953929539296, 'final_loss': 140.2627170085907, 'precision': 0.37309656646324607, 'recall': 0.23577235772357724, 'f1_score': 0.16002461015458233}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.001, 'weight_decay': 0.001, 'epochs': 10, 'final_accuracy': 33.84146341463415, 'final_loss': 138.48347234725952, 'precision': 0.46833600294933486, 'recall': 0.3130081300813008, 'f1_score': 0.24673613002227088}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.001, 'weight_decay': 0.001, 'epochs': 15, 'final_accuracy': 37.56775067750677, 'final_loss': 134.5189117193222, 'precision': 0.4075409620863488, 'recall': 0.3428184281842818, 'f1_score': 0.3001685927756905}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.001, 'weight_decay': 0.001, 'epochs': 20, 'final_accuracy': 37.7710027100271, 'final_loss': 134.97992646694183, 'precision': 0.3769533551313774, 'recall': 0.3130081300813008, 'f1_score': 0.2664619967963869}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.001, 'weight_decay': 0.0001, 'epochs': 5, 'final_accuracy': 32.68970189701897, 'final_loss': 140.53271329402924, 'precision': 0.2868827566974546, 'recall': 0.2994579945799458, 'f1_score': 0.26257048985265014}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.001, 'weight_decay': 0.0001, 'epochs': 10, 'final_accuracy': 34.2479674796748, 'final_loss': 138.31106185913086, 'precision': 0.29531494798230656, 'recall': 0.2981029810298103, 'f1_score': 0.24514614269508422}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.001, 'weight_decay': 0.0001, 'epochs': 15, 'final_accuracy': 35.8739837398374, 'final_loss': 136.39347457885742, 'precision': 0.3354638689632112, 'recall': 0.3252032520325203, 'f1_score': 0.25418228371621737}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.001, 'weight_decay': 0.0001, 'epochs': 20, 'final_accuracy': 39.6680216802168, 'final_loss': 132.10142934322357, 'precision': 0.4619722710710207, 'recall': 0.38346883468834686, 'f1_score': 0.3584420828918794}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.0001, 'weight_decay': 0.01, 'epochs': 5, 'final_accuracy': 32.28319783197832, 'final_loss': 144.08772921562195, 'precision': 0.2361021053217155, 'recall': 0.31842818428184283, 'f1_score': 0.24676219444744837}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.0001, 'weight_decay': 0.01, 'epochs': 10, 'final_accuracy': 31.43631436314363, 'final_loss': 143.15981149673462, 'precision': 0.18959389097059556, 'recall': 0.3116531165311653, 'f1_score': 0.21472838435899605}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.0001, 'weight_decay': 0.01, 'epochs': 15, 'final_accuracy': 32.14769647696477, 'final_loss': 141.78415870666504, 'precision': 0.27290117493637844, 'recall': 0.31029810298102983, 'f1_score': 0.24024259153188976}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.0001, 'weight_decay': 0.01, 'epochs': 20, 'final_accuracy': 32.72357723577236, 'final_loss': 140.87953758239746, 'precision': 0.2953069378119895, 'recall': 0.31842818428184283, 'f1_score': 0.25738574250597784}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.0001, 'weight_decay': 0.001, 'epochs': 5, 'final_accuracy': 30.62330623306233, 'final_loss': 144.30355036258698, 'precision': 0.21131759432851585, 'recall': 0.3021680216802168, 'f1_score': 0.2053210629139944}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.0001, 'weight_decay': 0.001, 'epochs': 10, 'final_accuracy': 32.28319783197832, 'final_loss': 142.21582293510437, 'precision': 0.2693493956888219, 'recall': 0.3116531165311653, 'f1_score': 0.2358568763681552}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.0001, 'weight_decay': 0.001, 'epochs': 15, 'final_accuracy': 31.910569105691057, 'final_loss': 142.03650498390198, 'precision': 0.21233555668045373, 'recall': 0.3075880758807588, 'f1_score': 0.21618576271380924}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.0001, 'weight_decay': 0.001, 'epochs': 20, 'final_accuracy': 32.079945799457995, 'final_loss': 140.81986927986145, 'precision': 0.26403942632400257, 'recall': 0.3048780487804878, 'f1_score': 0.25646887681588104}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.0001, 'weight_decay': 0.0001, 'epochs': 5, 'final_accuracy': 30.115176151761517, 'final_loss': 145.06659305095673, 'precision': 0.1996552714549422, 'recall': 0.2886178861788618, 'f1_score': 0.21935064902016715}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.0001, 'weight_decay': 0.0001, 'epochs': 10, 'final_accuracy': 31.266937669376695, 'final_loss': 142.549950838089, 'precision': 0.25016434201106996, 'recall': 0.2967479674796748, 'f1_score': 0.22014539023708596}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.0001, 'weight_decay': 0.0001, 'epochs': 15, 'final_accuracy': 32.65582655826558, 'final_loss': 141.3849254846573, 'precision': 0.26876050400027324, 'recall': 0.2953929539295393, 'f1_score': 0.2344548216230671}\n",
      "{'dropout_rate': 0.5, 'learning_rate': 0.0001, 'weight_decay': 0.0001, 'epochs': 20, 'final_accuracy': 32.41869918699187, 'final_loss': 141.07013630867004, 'precision': 0.2684927852601951, 'recall': 0.3116531165311653, 'f1_score': 0.24685088405827155}\n"
     ]
    }
   ],
   "source": [
    "results = []\n",
    "# Inizia la grid search\n",
    "for dropout_rate in dropout_list:\n",
    "    for lr in learning_rate_list:\n",
    "        for weight_decay in weight_decay_list:\n",
    "            for epochs in epoch_list:\n",
    "                print(f\"Training with dropout {dropout_rate}, lr {lr}, wd {weight_decay}, epochs {epochs}...\")\n",
    "                \n",
    "                # Definisci il modello\n",
    "                model = SimpleCNN(dropout_rate=dropout_rate).to(device)\n",
    "                optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay)\n",
    "                criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "                # Training loop\n",
    "                model.train()\n",
    "                all_labels = []  # Per tenere traccia di tutte le etichette\n",
    "                all_predictions = []  # Per tenere traccia di tutte le predizioni\n",
    "\n",
    "                for epoch in range(epochs):\n",
    "                    epoch_loss = 0\n",
    "                    correct = 0\n",
    "                    total = 0\n",
    "                    for images, labels in train_loader:\n",
    "                        images, labels = images.to(device), labels.to(device)\n",
    "\n",
    "                        optimizer.zero_grad()\n",
    "                        outputs = model(images)\n",
    "                        loss = criterion(outputs, labels)\n",
    "                        loss.backward()\n",
    "                        optimizer.step()\n",
    "\n",
    "                        epoch_loss += loss.item()\n",
    "                        _, predicted = outputs.max(1)\n",
    "                        total += labels.size(0)\n",
    "                        correct += predicted.eq(labels).sum().item()\n",
    "                        \n",
    "                        # Aggiungi le etichette e le predizioni alla lista\n",
    "                        all_labels.extend(labels.cpu().numpy())\n",
    "                        all_predictions.extend(predicted.cpu().numpy())\n",
    "                    \n",
    "                    print(f\"Epoch {epoch+1}/{epochs}, Loss: {epoch_loss:.4f}, Accuracy: {100. * correct / total:.2f}%\")\n",
    "\n",
    "                # Ora valutiamo il modello sul test set\n",
    "                model.eval()\n",
    "                all_labels = []  # Per tenere traccia di tutte le etichette\n",
    "                all_predictions = []  # Per tenere traccia di tutte le predizioni\n",
    "\n",
    "                with torch.no_grad():  # Disabilita il calcolo del gradiente per la valutazione\n",
    "                    for images, labels in test_loader:\n",
    "                        images, labels = images.to(device), labels.to(device)\n",
    "\n",
    "                        outputs = model(images)\n",
    "                        _, predicted = outputs.max(1)\n",
    "\n",
    "                        all_labels.extend(labels.cpu().numpy())\n",
    "                        all_predictions.extend(predicted.cpu().numpy())\n",
    "\n",
    "                # Calcola precision, recall, f1 score sul test set\n",
    "                precision = precision_score(all_labels, all_predictions, average='weighted', zero_division=0)\n",
    "                recall = recall_score(all_labels, all_predictions, average='weighted', zero_division=0)\n",
    "                f1 = f1_score(all_labels, all_predictions, average='weighted', zero_division=0)\n",
    "\n",
    "                # Registra risultati finali\n",
    "                results.append({\n",
    "                    \"dropout_rate\": dropout_rate,\n",
    "                    \"learning_rate\": lr,\n",
    "                    \"weight_decay\": weight_decay,\n",
    "                    \"epochs\": epochs,\n",
    "                    \"final_accuracy\": 100. * correct / total,\n",
    "                    \"final_loss\": epoch_loss,\n",
    "                    \"precision\": precision,\n",
    "                    \"recall\": recall,\n",
    "                    \"f1_score\": f1\n",
    "                })\n",
    "\n",
    "# Mostra i risultati\n",
    "print(\"Results:\")\n",
    "for result in results:\n",
    "    print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find best parameters (tradeoff between Accuracy and F1-score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "# Converte la lista dei risultati in un DataFrame pandas\n",
    "results_df = pd.DataFrame(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Combinazione migliore per Accuracy e F1 Score:\n",
      "dropout_rate        0.000000\n",
      "learning_rate       0.010000\n",
      "weight_decay        0.000100\n",
      "epochs             20.000000\n",
      "final_accuracy     42.242547\n",
      "final_loss        128.148474\n",
      "precision           0.215713\n",
      "recall              0.228997\n",
      "f1_score            0.129752\n",
      "tradeoff_score     21.186150\n",
      "Name: 11, dtype: float64\n",
      "\n",
      "Top 5 combinazioni per Accuracy e F1 Score:\n",
      "    dropout_rate  learning_rate  weight_decay  epochs  final_accuracy  \\\n",
      "11          0.00          0.010        0.0001      20       42.242547   \n",
      "47          0.25          0.010        0.0001      20       40.006775   \n",
      "83          0.50          0.010        0.0001      20       40.108401   \n",
      "95          0.50          0.001        0.0001      20       39.668022   \n",
      "22          0.00          0.001        0.0001      15       39.295393   \n",
      "\n",
      "    final_loss  precision    recall  f1_score  tradeoff_score  \n",
      "11  128.148474   0.215713  0.228997  0.129752       21.186150  \n",
      "47  131.106944   0.337220  0.319783  0.250223       20.128499  \n",
      "83  130.186669   0.119950  0.230352  0.119401       20.113901  \n",
      "95  132.101429   0.461972  0.383469  0.358442       20.013232  \n",
      "22  131.974858   0.552644  0.288618  0.223229       19.759311  \n"
     ]
    }
   ],
   "source": [
    "# Calcolo del punteggio di trade-off\n",
    "results_df[\"tradeoff_score\"] = (results_df[\"final_accuracy\"] + results_df[\"f1_score\"]) / 2\n",
    "\n",
    "# Trovare la combinazione con il punteggio di trade-off massimo\n",
    "best_combination = results_df.sort_values(by=\"tradeoff_score\", ascending=False).iloc[0]\n",
    "\n",
    "print(\"Combinazione migliore per Accuracy e F1 Score:\")\n",
    "print(best_combination)\n",
    "\n",
    "# Visualizzare le prime 5 combinazioni\n",
    "top_5_combinations = results_df.sort_values(by=\"tradeoff_score\", ascending=False).head(5)\n",
    "print(\"\\nTop 5 combinazioni per Accuracy e F1 Score:\")\n",
    "print(top_5_combinations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizzare graficamente il trade-off tra Accuracy e F1 Score\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(\n",
    "    data=results_df, \n",
    "    x=\"final_accuracy\", \n",
    "    y=\"f1_score\", \n",
    "    hue=\"tradeoff_score\", \n",
    "    size=\"tradeoff_score\", \n",
    "    palette=\"viridis\", \n",
    "    legend=\"brief\"\n",
    ")\n",
    "plt.title(\"Trade-off tra Accuracy e F1 Score\")\n",
    "plt.xlabel(\"Accuracy (%)\")\n",
    "plt.ylabel(\"F1 Score\")\n",
    "plt.axhline(y=best_combination[\"f1_score\"], color=\"r\", linestyle=\"--\", label=\"Miglior F1 Score\")\n",
    "plt.axvline(x=best_combination[\"final_accuracy\"], color=\"g\", linestyle=\"--\", label=\"Miglior Accuracy\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save({\n",
    "    'model': model,\n",
    "    'model_state_dict': model.state_dict(),\n",
    "    'optimizer_state_dict': optimizer.state_dict(),\n",
    "}, 'basic_CNN_augmented_edges_58_accuracy.pth')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}