Pruned and Quantized CNN Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d80496c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import wfdb\n",
    "import ast\n",
    "\n",
    "def load_raw_data(df, sampling_rate, path):\n",
    "    if sampling_rate == 100:\n",
    "        data = [wfdb.rdsamp(path+f) for f in df.filename_lr]\n",
    "    else:\n",
    "        data = [wfdb.rdsamp(path+f) for f in df.filename_hr]\n",
    "    data = np.array([signal for signal, meta in data])\n",
    "    return data\n",
    "\n",
    "path = 'C:\\\\Users\\\\sin00\\\\Downloads\\\\ecg_dataset\\\\'\n",
    "sampling_rate=100\n",
    "\n",
    "Y = pd.read_csv(path+'ptbxl_database.csv', index_col='ecg_id')\n",
    "Y.scp_codes = Y.scp_codes.apply(lambda x: ast.literal_eval(x))\n",
    "\n",
    "X = load_raw_data(Y, sampling_rate, path)\n",
    "\n",
    "agg_df = pd.read_csv(path+'scp_statements.csv', index_col=0)\n",
    "agg_df = agg_df[agg_df.diagnostic == 1]\n",
    "\n",
    "def aggregate_diagnostic(y_dic):\n",
    "    tmp = []\n",
    "    for key in y_dic.keys():\n",
    "        if key in agg_df.index:\n",
    "            tmp.append(agg_df.loc[key].diagnostic_class)\n",
    "    return list(set(tmp))\n",
    "\n",
    "Y['diagnostic_superclass'] = Y.scp_codes.apply(aggregate_diagnostic)\n",
    "\n",
    "empty_indices = Y[Y['diagnostic_superclass'].apply(len) == 0].index\n",
    "\n",
    "Y_filtered = Y.drop(empty_indices)\n",
    "\n",
    "empty_indices -= 1\n",
    "\n",
    "X_filtered = np.delete(X, empty_indices, axis=0)\n",
    "\n",
    "from sklearn.preprocessing import MultiLabelBinarizer\n",
    "\n",
    "mlb = MultiLabelBinarizer()\n",
    "Y_data_encoded = mlb.fit_transform(Y_filtered['diagnostic_superclass'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b2333404",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "import torch\n",
    "\n",
    "X_filtered_transposed = X_filtered.transpose(0, 2, 1)\n",
    "\n",
    "X_filtered_tensor = torch.tensor(X_filtered_transposed, dtype=torch.float32)\n",
    "Y_data_encoded_tensor = torch.tensor(Y_data_encoded, dtype=torch.float32)\n",
    "\n",
    "X_train_transposed_tensor, X_test_transposed_tensor, Y_train_tensor, Y_test_tensor = train_test_split(X_filtered_tensor, Y_data_encoded_tensor, test_size=0.2, random_state=42)\n",
    "\n",
    "class_names = ['NORM', 'CD', 'MI', 'STTC', 'HYP']\n",
    "\n",
    "num_classes = Y_data_encoded.shape[1]\n",
    "\n",
    "ECG_sequence_length = X_filtered.shape[1]\n",
    "\n",
    "ECG_channels = X_filtered.shape[2]\n",
    "\n",
    "input_sequence_length = ECG_sequence_length\n",
    "\n",
    "input_length = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "78fcb09d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_filtered shape: (21430, 1000, 12)\n",
      "X_filtered_transposed shape: (21430, 12, 1000)\n",
      "Y_data_encoded shape: (21430, 5)\n",
      "X_filtered_tensor shape: torch.Size([21430, 12, 1000])\n",
      "Y_data_encoded_tensor shape: torch.Size([21430, 5])\n",
      "X_train_tensor shape: torch.Size([17144, 12, 1000])\n",
      "X_test_tensor shape: torch.Size([4286, 12, 1000])\n",
      "Y_train_tensor shape: torch.Size([17144, 5])\n",
      "Y_test_tensor shape: torch.Size([4286, 5])\n"
     ]
    }
   ],
   "source": [
    "print(\"X_filtered shape:\", X_filtered.shape)\n",
    "print(\"X_filtered_transposed shape:\", X_filtered_transposed.shape)\n",
    "print(\"Y_data_encoded shape:\", Y_data_encoded.shape)\n",
    "\n",
    "print(\"X_filtered_tensor shape:\", X_filtered_tensor.shape)\n",
    "print(\"Y_data_encoded_tensor shape:\", Y_data_encoded_tensor.shape)\n",
    "print(\"X_train_tensor shape:\", X_train_transposed_tensor.shape)\n",
    "print(\"X_test_tensor shape:\", X_test_transposed_tensor.shape)\n",
    "print(\"Y_train_tensor shape:\", Y_train_tensor.shape)\n",
    "print(\"Y_test_tensor shape:\", Y_test_tensor.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "53255977",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 1/5 - Train samples: 13723, Validation samples: 3421\n",
      "Epoch 1/200, Loss: 0.3576, Training Accuracy: 0.5291, Validation Loss: 0.3358, Validation Accuracy: 0.5291, Training Time: 129.00 seconds\n",
      "Epoch 2/200, Loss: 0.3136, Training Accuracy: 0.5867, Validation Loss: 0.3035, Validation Accuracy: 0.5867, Training Time: 129.98 seconds\n",
      "Epoch 3/200, Loss: 0.3001, Training Accuracy: 0.5697, Validation Loss: 0.2945, Validation Accuracy: 0.5697, Training Time: 130.27 seconds\n",
      "Epoch 4/200, Loss: 0.2890, Training Accuracy: 0.5896, Validation Loss: 0.2815, Validation Accuracy: 0.5896, Training Time: 129.90 seconds\n",
      "Epoch 5/200, Loss: 0.2848, Training Accuracy: 0.6048, Validation Loss: 0.2773, Validation Accuracy: 0.6048, Training Time: 129.80 seconds\n",
      "Epoch 6/200, Loss: 0.2784, Training Accuracy: 0.5782, Validation Loss: 0.3498, Validation Accuracy: 0.5782, Training Time: 130.02 seconds\n",
      "Epoch 7/200, Loss: 0.2747, Training Accuracy: 0.6141, Validation Loss: 0.2712, Validation Accuracy: 0.6141, Training Time: 130.41 seconds\n",
      "Epoch 8/200, Loss: 0.2711, Training Accuracy: 0.6080, Validation Loss: 0.2909, Validation Accuracy: 0.6080, Training Time: 129.82 seconds\n",
      "Epoch 9/200, Loss: 0.2639, Training Accuracy: 0.5949, Validation Loss: 0.2963, Validation Accuracy: 0.5949, Training Time: 130.26 seconds\n",
      "Epoch 10/200, Loss: 0.2617, Training Accuracy: 0.6188, Validation Loss: 0.2796, Validation Accuracy: 0.6188, Training Time: 129.83 seconds\n",
      "Epoch 00010: reducing learning rate of group 0 to 5.0000e-04.\n",
      "Epoch 11/200, Loss: 0.2525, Training Accuracy: 0.6261, Validation Loss: 0.2682, Validation Accuracy: 0.6261, Training Time: 129.51 seconds\n",
      "Epoch 12/200, Loss: 0.2484, Training Accuracy: 0.6203, Validation Loss: 0.2862, Validation Accuracy: 0.6203, Training Time: 130.11 seconds\n",
      "Epoch 13/200, Loss: 0.2444, Training Accuracy: 0.6092, Validation Loss: 0.2637, Validation Accuracy: 0.6092, Training Time: 129.67 seconds\n",
      "Epoch 14/200, Loss: 0.2414, Training Accuracy: 0.6296, Validation Loss: 0.2575, Validation Accuracy: 0.6296, Training Time: 129.59 seconds\n",
      "Epoch 15/200, Loss: 0.2397, Training Accuracy: 0.5922, Validation Loss: 0.2721, Validation Accuracy: 0.5922, Training Time: 129.68 seconds\n",
      "Epoch 16/200, Loss: 0.2374, Training Accuracy: 0.6019, Validation Loss: 0.2771, Validation Accuracy: 0.6019, Training Time: 129.61 seconds\n",
      "Epoch 17/200, Loss: 0.2374, Training Accuracy: 0.6346, Validation Loss: 0.2637, Validation Accuracy: 0.6346, Training Time: 129.79 seconds\n",
      "Epoch 00017: reducing learning rate of group 0 to 2.5000e-04.\n",
      "Epoch 18/200, Loss: 0.2255, Training Accuracy: 0.6255, Validation Loss: 0.2620, Validation Accuracy: 0.6255, Training Time: 130.02 seconds\n",
      "Epoch 19/200, Loss: 0.2220, Training Accuracy: 0.6381, Validation Loss: 0.2579, Validation Accuracy: 0.6381, Training Time: 129.74 seconds\n",
      "Epoch 20/200, Loss: 0.2209, Training Accuracy: 0.6121, Validation Loss: 0.2690, Validation Accuracy: 0.6121, Training Time: 129.80 seconds\n",
      "Epoch 00020: reducing learning rate of group 0 to 1.2500e-04.\n",
      "Epoch 21/200, Loss: 0.2157, Training Accuracy: 0.6405, Validation Loss: 0.2542, Validation Accuracy: 0.6405, Training Time: 129.99 seconds\n",
      "Epoch 22/200, Loss: 0.2124, Training Accuracy: 0.6375, Validation Loss: 0.2563, Validation Accuracy: 0.6375, Training Time: 130.21 seconds\n",
      "Epoch 23/200, Loss: 0.2104, Training Accuracy: 0.6390, Validation Loss: 0.2562, Validation Accuracy: 0.6390, Training Time: 130.01 seconds\n",
      "Epoch 24/200, Loss: 0.2082, Training Accuracy: 0.6422, Validation Loss: 0.2549, Validation Accuracy: 0.6422, Training Time: 130.10 seconds\n",
      "Epoch 00024: reducing learning rate of group 0 to 6.2500e-05.\n",
      "Epoch 25/200, Loss: 0.2064, Training Accuracy: 0.6440, Validation Loss: 0.2554, Validation Accuracy: 0.6440, Training Time: 129.98 seconds\n",
      "Epoch 26/200, Loss: 0.2056, Training Accuracy: 0.6378, Validation Loss: 0.2547, Validation Accuracy: 0.6378, Training Time: 129.78 seconds\n",
      "Epoch 27/200, Loss: 0.2034, Training Accuracy: 0.6405, Validation Loss: 0.2581, Validation Accuracy: 0.6405, Training Time: 129.53 seconds\n",
      "Epoch 00027: reducing learning rate of group 0 to 3.1250e-05.\n",
      "Epoch 28/200, Loss: 0.2026, Training Accuracy: 0.6419, Validation Loss: 0.2561, Validation Accuracy: 0.6419, Training Time: 130.26 seconds\n",
      "Epoch 29/200, Loss: 0.2012, Training Accuracy: 0.6416, Validation Loss: 0.2577, Validation Accuracy: 0.6416, Training Time: 129.65 seconds\n",
      "Epoch 30/200, Loss: 0.1992, Training Accuracy: 0.6402, Validation Loss: 0.2564, Validation Accuracy: 0.6402, Training Time: 129.77 seconds\n",
      "Epoch 00030: reducing learning rate of group 0 to 1.5625e-05.\n",
      "Epoch 31/200, Loss: 0.2009, Training Accuracy: 0.6440, Validation Loss: 0.2577, Validation Accuracy: 0.6440, Training Time: 129.70 seconds\n",
      "Epoch 32/200, Loss: 0.1997, Training Accuracy: 0.6440, Validation Loss: 0.2564, Validation Accuracy: 0.6440, Training Time: 130.08 seconds\n",
      "Epoch 33/200, Loss: 0.1991, Training Accuracy: 0.6419, Validation Loss: 0.2572, Validation Accuracy: 0.6419, Training Time: 130.04 seconds\n",
      "Epoch 00033: reducing learning rate of group 0 to 7.8125e-06.\n",
      "Epoch 34/200, Loss: 0.1988, Training Accuracy: 0.6399, Validation Loss: 0.2569, Validation Accuracy: 0.6399, Training Time: 129.88 seconds\n",
      "Epoch 35/200, Loss: 0.1993, Training Accuracy: 0.6407, Validation Loss: 0.2568, Validation Accuracy: 0.6407, Training Time: 129.98 seconds\n",
      "Epoch 36/200, Loss: 0.1984, Training Accuracy: 0.6402, Validation Loss: 0.2568, Validation Accuracy: 0.6402, Training Time: 129.63 seconds\n",
      "Epoch 00036: reducing learning rate of group 0 to 3.9063e-06.\n",
      "Epoch 37/200, Loss: 0.1988, Training Accuracy: 0.6405, Validation Loss: 0.2556, Validation Accuracy: 0.6405, Training Time: 129.71 seconds\n",
      "Epoch 38/200, Loss: 0.1984, Training Accuracy: 0.6402, Validation Loss: 0.2585, Validation Accuracy: 0.6402, Training Time: 129.67 seconds\n",
      "Epoch 39/200, Loss: 0.1988, Training Accuracy: 0.6431, Validation Loss: 0.2583, Validation Accuracy: 0.6431, Training Time: 129.87 seconds\n",
      "Epoch 00039: reducing learning rate of group 0 to 1.9531e-06.\n",
      "Epoch 40/200, Loss: 0.1976, Training Accuracy: 0.6399, Validation Loss: 0.2574, Validation Accuracy: 0.6399, Training Time: 129.97 seconds\n",
      "Epoch 41/200, Loss: 0.2002, Training Accuracy: 0.6425, Validation Loss: 0.2580, Validation Accuracy: 0.6425, Training Time: 130.21 seconds\n",
      "Epoch 42/200, Loss: 0.1977, Training Accuracy: 0.6393, Validation Loss: 0.2578, Validation Accuracy: 0.6393, Training Time: 130.01 seconds\n",
      "Epoch 00042: reducing learning rate of group 0 to 9.7656e-07.\n",
      "Epoch 43/200, Loss: 0.1968, Training Accuracy: 0.6405, Validation Loss: 0.2575, Validation Accuracy: 0.6405, Training Time: 129.84 seconds\n",
      "Epoch 44/200, Loss: 0.1983, Training Accuracy: 0.6393, Validation Loss: 0.2590, Validation Accuracy: 0.6393, Training Time: 130.01 seconds\n",
      "Epoch 45/200, Loss: 0.1955, Training Accuracy: 0.6393, Validation Loss: 0.2573, Validation Accuracy: 0.6393, Training Time: 130.24 seconds\n",
      "Epoch 00045: reducing learning rate of group 0 to 4.8828e-07.\n",
      "Epoch 46/200, Loss: 0.1981, Training Accuracy: 0.6431, Validation Loss: 0.2586, Validation Accuracy: 0.6431, Training Time: 129.91 seconds\n",
      "Epoch 47/200, Loss: 0.1993, Training Accuracy: 0.6393, Validation Loss: 0.2588, Validation Accuracy: 0.6393, Training Time: 129.87 seconds\n",
      "Epoch 48/200, Loss: 0.1978, Training Accuracy: 0.6416, Validation Loss: 0.2582, Validation Accuracy: 0.6416, Training Time: 129.87 seconds\n",
      "Epoch 00048: reducing learning rate of group 0 to 2.4414e-07.\n",
      "Epoch 49/200, Loss: 0.1981, Training Accuracy: 0.6399, Validation Loss: 0.2581, Validation Accuracy: 0.6399, Training Time: 129.74 seconds\n",
      "Epoch 50/200, Loss: 0.1992, Training Accuracy: 0.6407, Validation Loss: 0.2589, Validation Accuracy: 0.6407, Training Time: 129.79 seconds\n",
      "Early stopping at epoch 50 due to no improvement in validation loss.\n",
      "Fold 1 - Validation Loss: 0.2589, Validation Accuracy: 0.6262\n",
      "Model Size: 0.53 MB\n",
      "Fold 2/5 - Train samples: 13710, Validation samples: 3434\n",
      "Epoch 1/200, Loss: 0.3662, Training Accuracy: 0.5585, Validation Loss: 0.3258, Validation Accuracy: 0.5585, Training Time: 129.97 seconds\n",
      "Epoch 2/200, Loss: 0.3189, Training Accuracy: 0.5434, Validation Loss: 0.3098, Validation Accuracy: 0.5434, Training Time: 129.68 seconds\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/200, Loss: 0.3049, Training Accuracy: 0.5973, Validation Loss: 0.2909, Validation Accuracy: 0.5973, Training Time: 129.48 seconds\n",
      "Epoch 4/200, Loss: 0.2976, Training Accuracy: 0.6089, Validation Loss: 0.2806, Validation Accuracy: 0.6089, Training Time: 129.70 seconds\n",
      "Epoch 5/200, Loss: 0.2867, Training Accuracy: 0.5859, Validation Loss: 0.2916, Validation Accuracy: 0.5859, Training Time: 128.96 seconds\n",
      "Epoch 6/200, Loss: 0.2810, Training Accuracy: 0.5996, Validation Loss: 0.2950, Validation Accuracy: 0.5996, Training Time: 129.36 seconds\n",
      "Epoch 7/200, Loss: 0.2771, Training Accuracy: 0.6092, Validation Loss: 0.2753, Validation Accuracy: 0.6092, Training Time: 129.45 seconds\n",
      "Epoch 8/200, Loss: 0.2709, Training Accuracy: 0.6232, Validation Loss: 0.2640, Validation Accuracy: 0.6232, Training Time: 129.14 seconds\n",
      "Epoch 9/200, Loss: 0.2706, Training Accuracy: 0.6168, Validation Loss: 0.2794, Validation Accuracy: 0.6168, Training Time: 129.09 seconds\n",
      "Epoch 10/200, Loss: 0.2655, Training Accuracy: 0.5731, Validation Loss: 0.2865, Validation Accuracy: 0.5731, Training Time: 129.58 seconds\n",
      "Epoch 11/200, Loss: 0.2641, Training Accuracy: 0.6214, Validation Loss: 0.2793, Validation Accuracy: 0.6214, Training Time: 129.46 seconds\n",
      "Epoch 00011: reducing learning rate of group 0 to 5.0000e-04.\n",
      "Epoch 12/200, Loss: 0.2513, Training Accuracy: 0.6287, Validation Loss: 0.2613, Validation Accuracy: 0.6287, Training Time: 129.00 seconds\n",
      "Epoch 13/200, Loss: 0.2467, Training Accuracy: 0.6241, Validation Loss: 0.2751, Validation Accuracy: 0.6241, Training Time: 129.28 seconds\n",
      "Epoch 14/200, Loss: 0.2456, Training Accuracy: 0.6095, Validation Loss: 0.2684, Validation Accuracy: 0.6095, Training Time: 129.15 seconds\n",
      "Epoch 15/200, Loss: 0.2417, Training Accuracy: 0.6345, Validation Loss: 0.2554, Validation Accuracy: 0.6345, Training Time: 128.94 seconds\n",
      "Epoch 16/200, Loss: 0.2405, Training Accuracy: 0.6369, Validation Loss: 0.2585, Validation Accuracy: 0.6369, Training Time: 129.42 seconds\n",
      "Epoch 17/200, Loss: 0.2389, Training Accuracy: 0.6348, Validation Loss: 0.2558, Validation Accuracy: 0.6348, Training Time: 129.33 seconds\n",
      "Epoch 18/200, Loss: 0.2354, Training Accuracy: 0.6366, Validation Loss: 0.2610, Validation Accuracy: 0.6366, Training Time: 129.30 seconds\n",
      "Epoch 00018: reducing learning rate of group 0 to 2.5000e-04.\n",
      "Epoch 19/200, Loss: 0.2260, Training Accuracy: 0.6456, Validation Loss: 0.2544, Validation Accuracy: 0.6456, Training Time: 129.31 seconds\n",
      "Epoch 20/200, Loss: 0.2239, Training Accuracy: 0.6211, Validation Loss: 0.2654, Validation Accuracy: 0.6211, Training Time: 129.42 seconds\n",
      "Epoch 21/200, Loss: 0.2218, Training Accuracy: 0.6412, Validation Loss: 0.2621, Validation Accuracy: 0.6412, Training Time: 129.34 seconds\n",
      "Epoch 22/200, Loss: 0.2208, Training Accuracy: 0.6412, Validation Loss: 0.2607, Validation Accuracy: 0.6412, Training Time: 129.68 seconds\n",
      "Epoch 00022: reducing learning rate of group 0 to 1.2500e-04.\n",
      "Epoch 23/200, Loss: 0.2149, Training Accuracy: 0.6439, Validation Loss: 0.2562, Validation Accuracy: 0.6439, Training Time: 129.64 seconds\n",
      "Epoch 24/200, Loss: 0.2125, Training Accuracy: 0.6465, Validation Loss: 0.2550, Validation Accuracy: 0.6465, Training Time: 129.49 seconds\n",
      "Epoch 25/200, Loss: 0.2119, Training Accuracy: 0.6456, Validation Loss: 0.2603, Validation Accuracy: 0.6456, Training Time: 129.51 seconds\n",
      "Epoch 00025: reducing learning rate of group 0 to 6.2500e-05.\n",
      "Epoch 26/200, Loss: 0.2066, Training Accuracy: 0.6433, Validation Loss: 0.2587, Validation Accuracy: 0.6433, Training Time: 129.68 seconds\n",
      "Epoch 27/200, Loss: 0.2078, Training Accuracy: 0.6491, Validation Loss: 0.2578, Validation Accuracy: 0.6491, Training Time: 129.73 seconds\n",
      "Epoch 28/200, Loss: 0.2060, Training Accuracy: 0.6439, Validation Loss: 0.2576, Validation Accuracy: 0.6439, Training Time: 129.36 seconds\n",
      "Epoch 00028: reducing learning rate of group 0 to 3.1250e-05.\n",
      "Epoch 29/200, Loss: 0.2056, Training Accuracy: 0.6418, Validation Loss: 0.2555, Validation Accuracy: 0.6418, Training Time: 129.48 seconds\n",
      "Epoch 30/200, Loss: 0.2035, Training Accuracy: 0.6485, Validation Loss: 0.2570, Validation Accuracy: 0.6485, Training Time: 129.50 seconds\n",
      "Epoch 31/200, Loss: 0.2030, Training Accuracy: 0.6485, Validation Loss: 0.2555, Validation Accuracy: 0.6485, Training Time: 129.26 seconds\n",
      "Epoch 00031: reducing learning rate of group 0 to 1.5625e-05.\n",
      "Epoch 32/200, Loss: 0.2023, Training Accuracy: 0.6436, Validation Loss: 0.2577, Validation Accuracy: 0.6436, Training Time: 129.60 seconds\n",
      "Epoch 33/200, Loss: 0.2022, Training Accuracy: 0.6450, Validation Loss: 0.2561, Validation Accuracy: 0.6450, Training Time: 129.54 seconds\n",
      "Epoch 34/200, Loss: 0.2019, Training Accuracy: 0.6453, Validation Loss: 0.2569, Validation Accuracy: 0.6453, Training Time: 129.73 seconds\n",
      "Epoch 00034: reducing learning rate of group 0 to 7.8125e-06.\n",
      "Epoch 35/200, Loss: 0.2025, Training Accuracy: 0.6474, Validation Loss: 0.2560, Validation Accuracy: 0.6474, Training Time: 129.71 seconds\n",
      "Epoch 36/200, Loss: 0.1998, Training Accuracy: 0.6456, Validation Loss: 0.2575, Validation Accuracy: 0.6456, Training Time: 129.88 seconds\n",
      "Epoch 37/200, Loss: 0.2012, Training Accuracy: 0.6450, Validation Loss: 0.2581, Validation Accuracy: 0.6450, Training Time: 129.60 seconds\n",
      "Epoch 00037: reducing learning rate of group 0 to 3.9063e-06.\n",
      "Epoch 38/200, Loss: 0.1992, Training Accuracy: 0.6456, Validation Loss: 0.2574, Validation Accuracy: 0.6456, Training Time: 129.56 seconds\n",
      "Epoch 39/200, Loss: 0.2016, Training Accuracy: 0.6433, Validation Loss: 0.2565, Validation Accuracy: 0.6433, Training Time: 129.50 seconds\n",
      "Epoch 40/200, Loss: 0.2014, Training Accuracy: 0.6494, Validation Loss: 0.2568, Validation Accuracy: 0.6494, Training Time: 129.64 seconds\n",
      "Epoch 00040: reducing learning rate of group 0 to 1.9531e-06.\n",
      "Epoch 41/200, Loss: 0.1992, Training Accuracy: 0.6479, Validation Loss: 0.2580, Validation Accuracy: 0.6479, Training Time: 129.60 seconds\n",
      "Epoch 42/200, Loss: 0.2002, Training Accuracy: 0.6421, Validation Loss: 0.2572, Validation Accuracy: 0.6421, Training Time: 129.59 seconds\n",
      "Epoch 43/200, Loss: 0.2013, Training Accuracy: 0.6465, Validation Loss: 0.2563, Validation Accuracy: 0.6465, Training Time: 129.67 seconds\n",
      "Epoch 00043: reducing learning rate of group 0 to 9.7656e-07.\n",
      "Epoch 44/200, Loss: 0.1994, Training Accuracy: 0.6459, Validation Loss: 0.2563, Validation Accuracy: 0.6459, Training Time: 129.45 seconds\n",
      "Epoch 45/200, Loss: 0.2007, Training Accuracy: 0.6409, Validation Loss: 0.2570, Validation Accuracy: 0.6409, Training Time: 129.53 seconds\n",
      "Epoch 46/200, Loss: 0.2013, Training Accuracy: 0.6453, Validation Loss: 0.2574, Validation Accuracy: 0.6453, Training Time: 129.81 seconds\n",
      "Epoch 00046: reducing learning rate of group 0 to 4.8828e-07.\n",
      "Early stopping at epoch 46 due to no improvement in validation loss.\n",
      "Fold 2 - Validation Loss: 0.2574, Validation Accuracy: 0.6300\n",
      "Model Size: 0.53 MB\n",
      "Fold 3/5 - Train samples: 13706, Validation samples: 3438\n",
      "Epoch 1/200, Loss: 0.3588, Training Accuracy: 0.5398, Validation Loss: 0.3546, Validation Accuracy: 0.5398, Training Time: 129.73 seconds\n",
      "Epoch 2/200, Loss: 0.3138, Training Accuracy: 0.5689, Validation Loss: 0.3156, Validation Accuracy: 0.5689, Training Time: 129.75 seconds\n",
      "Epoch 3/200, Loss: 0.2996, Training Accuracy: 0.5704, Validation Loss: 0.3160, Validation Accuracy: 0.5704, Training Time: 129.53 seconds\n",
      "Epoch 4/200, Loss: 0.2919, Training Accuracy: 0.5465, Validation Loss: 0.3053, Validation Accuracy: 0.5465, Training Time: 129.36 seconds\n",
      "Epoch 5/200, Loss: 0.2848, Training Accuracy: 0.6024, Validation Loss: 0.2939, Validation Accuracy: 0.6024, Training Time: 129.62 seconds\n",
      "Epoch 6/200, Loss: 0.2798, Training Accuracy: 0.5704, Validation Loss: 0.2887, Validation Accuracy: 0.5704, Training Time: 129.39 seconds\n",
      "Epoch 7/200, Loss: 0.2730, Training Accuracy: 0.6131, Validation Loss: 0.2918, Validation Accuracy: 0.6131, Training Time: 129.06 seconds\n",
      "Epoch 8/200, Loss: 0.2704, Training Accuracy: 0.5791, Validation Loss: 0.2863, Validation Accuracy: 0.5791, Training Time: 129.24 seconds\n",
      "Epoch 9/200, Loss: 0.2680, Training Accuracy: 0.6094, Validation Loss: 0.2843, Validation Accuracy: 0.6094, Training Time: 129.19 seconds\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/200, Loss: 0.2607, Training Accuracy: 0.5029, Validation Loss: 0.3456, Validation Accuracy: 0.5029, Training Time: 129.35 seconds\n",
      "Epoch 11/200, Loss: 0.2598, Training Accuracy: 0.5835, Validation Loss: 0.3259, Validation Accuracy: 0.5835, Training Time: 129.08 seconds\n",
      "Epoch 12/200, Loss: 0.2565, Training Accuracy: 0.6114, Validation Loss: 0.2839, Validation Accuracy: 0.6114, Training Time: 129.47 seconds\n",
      "Epoch 13/200, Loss: 0.2546, Training Accuracy: 0.6265, Validation Loss: 0.2802, Validation Accuracy: 0.6265, Training Time: 129.48 seconds\n",
      "Epoch 14/200, Loss: 0.2505, Training Accuracy: 0.6172, Validation Loss: 0.2754, Validation Accuracy: 0.6172, Training Time: 129.83 seconds\n",
      "Epoch 15/200, Loss: 0.2466, Training Accuracy: 0.6213, Validation Loss: 0.2712, Validation Accuracy: 0.6213, Training Time: 129.49 seconds\n",
      "Epoch 16/200, Loss: 0.2443, Training Accuracy: 0.5765, Validation Loss: 0.2877, Validation Accuracy: 0.5765, Training Time: 129.51 seconds\n",
      "Epoch 17/200, Loss: 0.2402, Training Accuracy: 0.6166, Validation Loss: 0.2899, Validation Accuracy: 0.6166, Training Time: 129.62 seconds\n",
      "Epoch 18/200, Loss: 0.2398, Training Accuracy: 0.6216, Validation Loss: 0.2703, Validation Accuracy: 0.6216, Training Time: 129.58 seconds\n",
      "Epoch 19/200, Loss: 0.2393, Training Accuracy: 0.5887, Validation Loss: 0.2974, Validation Accuracy: 0.5887, Training Time: 129.18 seconds\n",
      "Epoch 20/200, Loss: 0.2377, Training Accuracy: 0.6291, Validation Loss: 0.2678, Validation Accuracy: 0.6291, Training Time: 129.30 seconds\n",
      "Epoch 21/200, Loss: 0.2344, Training Accuracy: 0.5829, Validation Loss: 0.2926, Validation Accuracy: 0.5829, Training Time: 129.27 seconds\n",
      "Epoch 22/200, Loss: 0.2317, Training Accuracy: 0.6355, Validation Loss: 0.2614, Validation Accuracy: 0.6355, Training Time: 129.60 seconds\n",
      "Epoch 23/200, Loss: 0.2283, Training Accuracy: 0.5989, Validation Loss: 0.2963, Validation Accuracy: 0.5989, Training Time: 129.61 seconds\n",
      "Epoch 24/200, Loss: 0.2263, Training Accuracy: 0.6099, Validation Loss: 0.3032, Validation Accuracy: 0.6099, Training Time: 129.54 seconds\n",
      "Epoch 25/200, Loss: 0.2251, Training Accuracy: 0.5919, Validation Loss: 0.2850, Validation Accuracy: 0.5919, Training Time: 129.22 seconds\n",
      "Epoch 00025: reducing learning rate of group 0 to 5.0000e-04.\n",
      "Epoch 26/200, Loss: 0.2106, Training Accuracy: 0.6120, Validation Loss: 0.2709, Validation Accuracy: 0.6120, Training Time: 129.40 seconds\n",
      "Epoch 27/200, Loss: 0.2067, Training Accuracy: 0.6379, Validation Loss: 0.2812, Validation Accuracy: 0.6379, Training Time: 129.64 seconds\n",
      "Epoch 28/200, Loss: 0.2039, Training Accuracy: 0.5817, Validation Loss: 0.2928, Validation Accuracy: 0.5817, Training Time: 129.23 seconds\n",
      "Epoch 00028: reducing learning rate of group 0 to 2.5000e-04.\n",
      "Epoch 29/200, Loss: 0.1965, Training Accuracy: 0.6321, Validation Loss: 0.2661, Validation Accuracy: 0.6321, Training Time: 129.59 seconds\n",
      "Epoch 30/200, Loss: 0.1933, Training Accuracy: 0.6085, Validation Loss: 0.2748, Validation Accuracy: 0.6085, Training Time: 129.58 seconds\n",
      "Epoch 31/200, Loss: 0.1907, Training Accuracy: 0.6347, Validation Loss: 0.2765, Validation Accuracy: 0.6347, Training Time: 129.45 seconds\n",
      "Epoch 00031: reducing learning rate of group 0 to 1.2500e-04.\n",
      "Epoch 32/200, Loss: 0.1846, Training Accuracy: 0.6280, Validation Loss: 0.2697, Validation Accuracy: 0.6280, Training Time: 129.33 seconds\n",
      "Epoch 33/200, Loss: 0.1833, Training Accuracy: 0.6347, Validation Loss: 0.2705, Validation Accuracy: 0.6347, Training Time: 129.61 seconds\n",
      "Epoch 34/200, Loss: 0.1814, Training Accuracy: 0.6178, Validation Loss: 0.2753, Validation Accuracy: 0.6178, Training Time: 129.77 seconds\n",
      "Epoch 00034: reducing learning rate of group 0 to 6.2500e-05.\n",
      "Epoch 35/200, Loss: 0.1784, Training Accuracy: 0.6321, Validation Loss: 0.2719, Validation Accuracy: 0.6321, Training Time: 129.68 seconds\n",
      "Epoch 36/200, Loss: 0.1780, Training Accuracy: 0.6323, Validation Loss: 0.2687, Validation Accuracy: 0.6323, Training Time: 129.08 seconds\n",
      "Epoch 37/200, Loss: 0.1758, Training Accuracy: 0.6326, Validation Loss: 0.2702, Validation Accuracy: 0.6326, Training Time: 129.55 seconds\n",
      "Epoch 00037: reducing learning rate of group 0 to 3.1250e-05.\n",
      "Epoch 38/200, Loss: 0.1760, Training Accuracy: 0.6233, Validation Loss: 0.2708, Validation Accuracy: 0.6233, Training Time: 129.33 seconds\n",
      "Epoch 39/200, Loss: 0.1753, Training Accuracy: 0.6323, Validation Loss: 0.2674, Validation Accuracy: 0.6323, Training Time: 129.63 seconds\n",
      "Epoch 40/200, Loss: 0.1758, Training Accuracy: 0.6405, Validation Loss: 0.2726, Validation Accuracy: 0.6405, Training Time: 129.68 seconds\n",
      "Epoch 00040: reducing learning rate of group 0 to 1.5625e-05.\n",
      "Epoch 41/200, Loss: 0.1742, Training Accuracy: 0.6318, Validation Loss: 0.2709, Validation Accuracy: 0.6318, Training Time: 130.00 seconds\n",
      "Epoch 42/200, Loss: 0.1741, Training Accuracy: 0.6323, Validation Loss: 0.2751, Validation Accuracy: 0.6323, Training Time: 129.89 seconds\n",
      "Epoch 43/200, Loss: 0.1744, Training Accuracy: 0.6315, Validation Loss: 0.2728, Validation Accuracy: 0.6315, Training Time: 129.67 seconds\n",
      "Epoch 00043: reducing learning rate of group 0 to 7.8125e-06.\n",
      "Epoch 44/200, Loss: 0.1728, Training Accuracy: 0.6387, Validation Loss: 0.2731, Validation Accuracy: 0.6387, Training Time: 129.55 seconds\n",
      "Epoch 45/200, Loss: 0.1740, Training Accuracy: 0.6283, Validation Loss: 0.2700, Validation Accuracy: 0.6283, Training Time: 129.60 seconds\n",
      "Epoch 46/200, Loss: 0.1721, Training Accuracy: 0.6309, Validation Loss: 0.2720, Validation Accuracy: 0.6309, Training Time: 129.74 seconds\n",
      "Epoch 00046: reducing learning rate of group 0 to 3.9063e-06.\n",
      "Epoch 47/200, Loss: 0.1719, Training Accuracy: 0.6318, Validation Loss: 0.2740, Validation Accuracy: 0.6318, Training Time: 129.65 seconds\n",
      "Epoch 48/200, Loss: 0.1727, Training Accuracy: 0.6341, Validation Loss: 0.2713, Validation Accuracy: 0.6341, Training Time: 130.07 seconds\n",
      "Epoch 49/200, Loss: 0.1728, Training Accuracy: 0.6315, Validation Loss: 0.2718, Validation Accuracy: 0.6315, Training Time: 129.37 seconds\n",
      "Epoch 00049: reducing learning rate of group 0 to 1.9531e-06.\n",
      "Epoch 50/200, Loss: 0.1726, Training Accuracy: 0.6306, Validation Loss: 0.2718, Validation Accuracy: 0.6306, Training Time: 130.08 seconds\n",
      "Epoch 51/200, Loss: 0.1729, Training Accuracy: 0.6315, Validation Loss: 0.2727, Validation Accuracy: 0.6315, Training Time: 129.63 seconds\n",
      "Epoch 52/200, Loss: 0.1722, Training Accuracy: 0.6341, Validation Loss: 0.2713, Validation Accuracy: 0.6341, Training Time: 129.84 seconds\n",
      "Epoch 00052: reducing learning rate of group 0 to 9.7656e-07.\n",
      "Early stopping at epoch 52 due to no improvement in validation loss.\n",
      "Fold 3 - Validation Loss: 0.2713, Validation Accuracy: 0.6112\n",
      "Model Size: 0.53 MB\n",
      "Fold 4/5 - Train samples: 13709, Validation samples: 3435\n",
      "Epoch 1/200, Loss: 0.3641, Training Accuracy: 0.5077, Validation Loss: 0.3227, Validation Accuracy: 0.5077, Training Time: 129.73 seconds\n",
      "Epoch 2/200, Loss: 0.3140, Training Accuracy: 0.5744, Validation Loss: 0.3013, Validation Accuracy: 0.5744, Training Time: 129.78 seconds\n",
      "Epoch 3/200, Loss: 0.2993, Training Accuracy: 0.6029, Validation Loss: 0.2923, Validation Accuracy: 0.6029, Training Time: 129.91 seconds\n",
      "Epoch 4/200, Loss: 0.2899, Training Accuracy: 0.5523, Validation Loss: 0.3124, Validation Accuracy: 0.5523, Training Time: 129.81 seconds\n",
      "Epoch 5/200, Loss: 0.2853, Training Accuracy: 0.5948, Validation Loss: 0.2806, Validation Accuracy: 0.5948, Training Time: 129.82 seconds\n",
      "Epoch 6/200, Loss: 0.2784, Training Accuracy: 0.5709, Validation Loss: 0.2947, Validation Accuracy: 0.5709, Training Time: 129.60 seconds\n",
      "Epoch 7/200, Loss: 0.2744, Training Accuracy: 0.5773, Validation Loss: 0.2935, Validation Accuracy: 0.5773, Training Time: 129.30 seconds\n",
      "Epoch 8/200, Loss: 0.2704, Training Accuracy: 0.6038, Validation Loss: 0.2809, Validation Accuracy: 0.6038, Training Time: 129.63 seconds\n",
      "Epoch 00008: reducing learning rate of group 0 to 5.0000e-04.\n",
      "Epoch 9/200, Loss: 0.2564, Training Accuracy: 0.6239, Validation Loss: 0.2700, Validation Accuracy: 0.6239, Training Time: 129.69 seconds\n",
      "Epoch 10/200, Loss: 0.2551, Training Accuracy: 0.6309, Validation Loss: 0.2763, Validation Accuracy: 0.6309, Training Time: 129.72 seconds\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11/200, Loss: 0.2517, Training Accuracy: 0.6119, Validation Loss: 0.2843, Validation Accuracy: 0.6119, Training Time: 129.79 seconds\n",
      "Epoch 12/200, Loss: 0.2475, Training Accuracy: 0.6349, Validation Loss: 0.2655, Validation Accuracy: 0.6349, Training Time: 129.69 seconds\n",
      "Epoch 13/200, Loss: 0.2477, Training Accuracy: 0.6303, Validation Loss: 0.2729, Validation Accuracy: 0.6303, Training Time: 129.79 seconds\n",
      "Epoch 14/200, Loss: 0.2442, Training Accuracy: 0.6151, Validation Loss: 0.2713, Validation Accuracy: 0.6151, Training Time: 129.73 seconds\n",
      "Epoch 15/200, Loss: 0.2410, Training Accuracy: 0.6213, Validation Loss: 0.2751, Validation Accuracy: 0.6213, Training Time: 129.47 seconds\n",
      "Epoch 00015: reducing learning rate of group 0 to 2.5000e-04.\n",
      "Epoch 16/200, Loss: 0.2309, Training Accuracy: 0.6402, Validation Loss: 0.2587, Validation Accuracy: 0.6402, Training Time: 129.56 seconds\n",
      "Epoch 17/200, Loss: 0.2308, Training Accuracy: 0.6413, Validation Loss: 0.2651, Validation Accuracy: 0.6413, Training Time: 129.64 seconds\n",
      "Epoch 18/200, Loss: 0.2269, Training Accuracy: 0.6405, Validation Loss: 0.2859, Validation Accuracy: 0.6405, Training Time: 129.48 seconds\n",
      "Epoch 19/200, Loss: 0.2245, Training Accuracy: 0.6378, Validation Loss: 0.2698, Validation Accuracy: 0.6378, Training Time: 129.76 seconds\n",
      "Epoch 00019: reducing learning rate of group 0 to 1.2500e-04.\n",
      "Epoch 20/200, Loss: 0.2213, Training Accuracy: 0.6425, Validation Loss: 0.2704, Validation Accuracy: 0.6425, Training Time: 129.66 seconds\n",
      "Epoch 21/200, Loss: 0.2183, Training Accuracy: 0.6344, Validation Loss: 0.2624, Validation Accuracy: 0.6344, Training Time: 129.78 seconds\n",
      "Epoch 22/200, Loss: 0.2151, Training Accuracy: 0.6434, Validation Loss: 0.2595, Validation Accuracy: 0.6434, Training Time: 129.89 seconds\n",
      "Epoch 00022: reducing learning rate of group 0 to 6.2500e-05.\n",
      "Epoch 23/200, Loss: 0.2117, Training Accuracy: 0.6445, Validation Loss: 0.2636, Validation Accuracy: 0.6445, Training Time: 129.89 seconds\n",
      "Epoch 24/200, Loss: 0.2100, Training Accuracy: 0.6405, Validation Loss: 0.2615, Validation Accuracy: 0.6405, Training Time: 129.77 seconds\n",
      "Epoch 25/200, Loss: 0.2101, Training Accuracy: 0.6425, Validation Loss: 0.2594, Validation Accuracy: 0.6425, Training Time: 129.58 seconds\n",
      "Epoch 00025: reducing learning rate of group 0 to 3.1250e-05.\n",
      "Epoch 26/200, Loss: 0.2095, Training Accuracy: 0.6431, Validation Loss: 0.2608, Validation Accuracy: 0.6431, Training Time: 129.82 seconds\n",
      "Epoch 27/200, Loss: 0.2086, Training Accuracy: 0.6445, Validation Loss: 0.2599, Validation Accuracy: 0.6445, Training Time: 129.75 seconds\n",
      "Epoch 28/200, Loss: 0.2084, Training Accuracy: 0.6437, Validation Loss: 0.2599, Validation Accuracy: 0.6437, Training Time: 130.25 seconds\n",
      "Epoch 00028: reducing learning rate of group 0 to 1.5625e-05.\n",
      "Epoch 29/200, Loss: 0.2051, Training Accuracy: 0.6466, Validation Loss: 0.2597, Validation Accuracy: 0.6466, Training Time: 130.08 seconds\n",
      "Epoch 30/200, Loss: 0.2046, Training Accuracy: 0.6489, Validation Loss: 0.2610, Validation Accuracy: 0.6489, Training Time: 129.92 seconds\n",
      "Epoch 31/200, Loss: 0.2060, Training Accuracy: 0.6469, Validation Loss: 0.2618, Validation Accuracy: 0.6469, Training Time: 130.16 seconds\n",
      "Epoch 00031: reducing learning rate of group 0 to 7.8125e-06.\n",
      "Epoch 32/200, Loss: 0.2059, Training Accuracy: 0.6434, Validation Loss: 0.2620, Validation Accuracy: 0.6434, Training Time: 130.00 seconds\n",
      "Epoch 33/200, Loss: 0.2045, Training Accuracy: 0.6469, Validation Loss: 0.2611, Validation Accuracy: 0.6469, Training Time: 130.28 seconds\n",
      "Epoch 34/200, Loss: 0.2033, Training Accuracy: 0.6469, Validation Loss: 0.2638, Validation Accuracy: 0.6469, Training Time: 129.97 seconds\n",
      "Epoch 00034: reducing learning rate of group 0 to 3.9063e-06.\n",
      "Epoch 35/200, Loss: 0.2049, Training Accuracy: 0.6466, Validation Loss: 0.2611, Validation Accuracy: 0.6466, Training Time: 129.92 seconds\n",
      "Epoch 36/200, Loss: 0.2042, Training Accuracy: 0.6434, Validation Loss: 0.2609, Validation Accuracy: 0.6434, Training Time: 130.05 seconds\n",
      "Epoch 37/200, Loss: 0.2058, Training Accuracy: 0.6405, Validation Loss: 0.2607, Validation Accuracy: 0.6405, Training Time: 130.07 seconds\n",
      "Epoch 00037: reducing learning rate of group 0 to 1.9531e-06.\n",
      "Epoch 38/200, Loss: 0.2045, Training Accuracy: 0.6445, Validation Loss: 0.2613, Validation Accuracy: 0.6445, Training Time: 130.15 seconds\n",
      "Epoch 39/200, Loss: 0.2039, Training Accuracy: 0.6483, Validation Loss: 0.2608, Validation Accuracy: 0.6483, Training Time: 134.87 seconds\n",
      "Early stopping at epoch 39 due to no improvement in validation loss.\n",
      "Fold 4 - Validation Loss: 0.2608, Validation Accuracy: 0.6255\n",
      "Model Size: 0.53 MB\n",
      "Fold 5/5 - Train samples: 13728, Validation samples: 3416\n",
      "Epoch 1/200, Loss: 0.3652, Training Accuracy: 0.5413, Validation Loss: 0.3430, Validation Accuracy: 0.5413, Training Time: 131.70 seconds\n",
      "Epoch 2/200, Loss: 0.3150, Training Accuracy: 0.5606, Validation Loss: 0.3126, Validation Accuracy: 0.5606, Training Time: 131.09 seconds\n",
      "Epoch 3/200, Loss: 0.3007, Training Accuracy: 0.5919, Validation Loss: 0.2889, Validation Accuracy: 0.5919, Training Time: 130.57 seconds\n",
      "Epoch 4/200, Loss: 0.2916, Training Accuracy: 0.6001, Validation Loss: 0.2913, Validation Accuracy: 0.6001, Training Time: 130.67 seconds\n",
      "Epoch 5/200, Loss: 0.2819, Training Accuracy: 0.5738, Validation Loss: 0.2920, Validation Accuracy: 0.5738, Training Time: 130.47 seconds\n",
      "Epoch 6/200, Loss: 0.2800, Training Accuracy: 0.5972, Validation Loss: 0.3053, Validation Accuracy: 0.5972, Training Time: 130.45 seconds\n",
      "Epoch 00006: reducing learning rate of group 0 to 5.0000e-04.\n",
      "Epoch 7/200, Loss: 0.2662, Training Accuracy: 0.6171, Validation Loss: 0.2709, Validation Accuracy: 0.6171, Training Time: 130.64 seconds\n",
      "Epoch 8/200, Loss: 0.2626, Training Accuracy: 0.6230, Validation Loss: 0.2681, Validation Accuracy: 0.6230, Training Time: 130.42 seconds\n",
      "Epoch 9/200, Loss: 0.2600, Training Accuracy: 0.5325, Validation Loss: 0.3277, Validation Accuracy: 0.5325, Training Time: 130.39 seconds\n",
      "Epoch 10/200, Loss: 0.2563, Training Accuracy: 0.6177, Validation Loss: 0.2727, Validation Accuracy: 0.6177, Training Time: 130.37 seconds\n",
      "Epoch 11/200, Loss: 0.2546, Training Accuracy: 0.6121, Validation Loss: 0.2858, Validation Accuracy: 0.6121, Training Time: 130.49 seconds\n",
      "Epoch 00011: reducing learning rate of group 0 to 2.5000e-04.\n",
      "Epoch 12/200, Loss: 0.2448, Training Accuracy: 0.6180, Validation Loss: 0.2655, Validation Accuracy: 0.6180, Training Time: 130.30 seconds\n",
      "Epoch 13/200, Loss: 0.2425, Training Accuracy: 0.6150, Validation Loss: 0.2626, Validation Accuracy: 0.6150, Training Time: 130.51 seconds\n",
      "Epoch 14/200, Loss: 0.2408, Training Accuracy: 0.6361, Validation Loss: 0.2630, Validation Accuracy: 0.6361, Training Time: 130.43 seconds\n",
      "Epoch 15/200, Loss: 0.2382, Training Accuracy: 0.6186, Validation Loss: 0.2632, Validation Accuracy: 0.6186, Training Time: 130.15 seconds\n",
      "Epoch 16/200, Loss: 0.2353, Training Accuracy: 0.6215, Validation Loss: 0.2754, Validation Accuracy: 0.6215, Training Time: 130.21 seconds\n",
      "Epoch 00016: reducing learning rate of group 0 to 1.2500e-04.\n",
      "Epoch 17/200, Loss: 0.2291, Training Accuracy: 0.6276, Validation Loss: 0.2602, Validation Accuracy: 0.6276, Training Time: 130.05 seconds\n",
      "Epoch 18/200, Loss: 0.2284, Training Accuracy: 0.6341, Validation Loss: 0.2588, Validation Accuracy: 0.6341, Training Time: 131.53 seconds\n",
      "Epoch 19/200, Loss: 0.2261, Training Accuracy: 0.6314, Validation Loss: 0.2653, Validation Accuracy: 0.6314, Training Time: 130.65 seconds\n",
      "Epoch 20/200, Loss: 0.2242, Training Accuracy: 0.6329, Validation Loss: 0.2622, Validation Accuracy: 0.6329, Training Time: 130.69 seconds\n",
      "Epoch 21/200, Loss: 0.2252, Training Accuracy: 0.6338, Validation Loss: 0.2637, Validation Accuracy: 0.6338, Training Time: 130.53 seconds\n",
      "Epoch 00021: reducing learning rate of group 0 to 6.2500e-05.\n",
      "Epoch 22/200, Loss: 0.2195, Training Accuracy: 0.6320, Validation Loss: 0.2567, Validation Accuracy: 0.6320, Training Time: 131.12 seconds\n",
      "Epoch 23/200, Loss: 0.2194, Training Accuracy: 0.6355, Validation Loss: 0.2584, Validation Accuracy: 0.6355, Training Time: 130.96 seconds\n",
      "Epoch 24/200, Loss: 0.2180, Training Accuracy: 0.6273, Validation Loss: 0.2574, Validation Accuracy: 0.6273, Training Time: 131.12 seconds\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 25/200, Loss: 0.2171, Training Accuracy: 0.6294, Validation Loss: 0.2590, Validation Accuracy: 0.6294, Training Time: 131.20 seconds\n",
      "Epoch 00025: reducing learning rate of group 0 to 3.1250e-05.\n",
      "Epoch 26/200, Loss: 0.2155, Training Accuracy: 0.6358, Validation Loss: 0.2580, Validation Accuracy: 0.6358, Training Time: 130.79 seconds\n",
      "Epoch 27/200, Loss: 0.2149, Training Accuracy: 0.6373, Validation Loss: 0.2598, Validation Accuracy: 0.6373, Training Time: 130.84 seconds\n",
      "Epoch 28/200, Loss: 0.2151, Training Accuracy: 0.6352, Validation Loss: 0.2599, Validation Accuracy: 0.6352, Training Time: 131.37 seconds\n",
      "Epoch 00028: reducing learning rate of group 0 to 1.5625e-05.\n",
      "Epoch 29/200, Loss: 0.2136, Training Accuracy: 0.6350, Validation Loss: 0.2585, Validation Accuracy: 0.6350, Training Time: 130.82 seconds\n",
      "Epoch 30/200, Loss: 0.2124, Training Accuracy: 0.6382, Validation Loss: 0.2604, Validation Accuracy: 0.6382, Training Time: 130.79 seconds\n",
      "Epoch 31/200, Loss: 0.2102, Training Accuracy: 0.6320, Validation Loss: 0.2592, Validation Accuracy: 0.6320, Training Time: 131.06 seconds\n",
      "Epoch 00031: reducing learning rate of group 0 to 7.8125e-06.\n",
      "Epoch 32/200, Loss: 0.2122, Training Accuracy: 0.6347, Validation Loss: 0.2585, Validation Accuracy: 0.6347, Training Time: 130.94 seconds\n",
      "Epoch 33/200, Loss: 0.2113, Training Accuracy: 0.6323, Validation Loss: 0.2586, Validation Accuracy: 0.6323, Training Time: 130.99 seconds\n",
      "Epoch 34/200, Loss: 0.2116, Training Accuracy: 0.6329, Validation Loss: 0.2584, Validation Accuracy: 0.6329, Training Time: 131.00 seconds\n",
      "Epoch 00034: reducing learning rate of group 0 to 3.9063e-06.\n",
      "Epoch 35/200, Loss: 0.2120, Training Accuracy: 0.6352, Validation Loss: 0.2587, Validation Accuracy: 0.6352, Training Time: 131.17 seconds\n",
      "Epoch 36/200, Loss: 0.2127, Training Accuracy: 0.6358, Validation Loss: 0.2595, Validation Accuracy: 0.6358, Training Time: 131.26 seconds\n",
      "Early stopping at epoch 36 due to no improvement in validation loss.\n",
      "Fold 5 - Validation Loss: 0.2595, Validation Accuracy: 0.6179\n",
      "Model Size: 0.53 MB\n",
      "Average Validation Loss Across 5 Folds: 0.2616\n",
      "Average Validation Accuracy Across 5 Folds: 0.6222\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import numpy as np\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "from iterstrat.ml_stratifiers import MultilabelStratifiedKFold\n",
    "import time\n",
    "from sklearn.metrics import accuracy_score\n",
    "from torch.optim.lr_scheduler import ReduceLROnPlateau\n",
    "from torchsummary import summary\n",
    "import torch.nn.utils.prune as prune\n",
    "\n",
    "# Define the CustomCNN class\n",
    "class CustomCNN(nn.Module):\n",
    "    def __init__(self, num_classes):\n",
    "        super(CustomCNN, self).__init__()\n",
    "        self.conv_layers = nn.Sequential(\n",
    "            nn.Conv1d(12, 50, kernel_size=10),\n",
    "            nn.BatchNorm1d(50),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool1d(2),\n",
    "            nn.Conv1d(50, 150, kernel_size=10),\n",
    "            nn.BatchNorm1d(150),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool1d(2),\n",
    "            nn.Conv1d(150, 300, kernel_size=10),\n",
    "            nn.BatchNorm1d(300),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool1d(2)\n",
    "        )\n",
    "        self.avg_pool = nn.AdaptiveAvgPool1d(1)\n",
    "        self.dropout = nn.Dropout(0.5)\n",
    "        self.fc = nn.Linear(300, num_classes)\n",
    "        self.sigmoid = nn.Sigmoid()\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv_layers(x)\n",
    "        x = self.avg_pool(x).squeeze()\n",
    "        x = self.dropout(x)\n",
    "        x = self.fc(x)\n",
    "        x = self.sigmoid(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "# Define a function to apply pruning to the model\n",
    "def apply_pruning(model, prune_rate=0.2):\n",
    "    for name, module in model.named_modules():\n",
    "        if isinstance(module, (nn.Conv1d, nn.Linear)):\n",
    "            prune.l1_unstructured(module, name=\"weight\", amount=prune_rate)\n",
    "    \n",
    "# Function to train the model with LR scheduler and model saving\n",
    "def train_model_with_scheduler(model, train_loader, val_loader, num_epochs, device, patience, fold_index):\n",
    "    optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "    criterion = nn.BCELoss()\n",
    "    scheduler = ReduceLROnPlateau(optimizer, mode='min', patience=patience // 2, factor=0.5, verbose=True)\n",
    "    best_val_loss = float('inf')\n",
    "    no_improvement_count = 0\n",
    "    val_accuracies = []  # Initialize a list to store validation accuracies\n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "        model.train()\n",
    "        total_loss = 0\n",
    "        start_time = time.time()\n",
    "        for batch_X, batch_Y in train_loader:\n",
    "            optimizer.zero_grad()\n",
    "            batch_X = batch_X.to(device, dtype=torch.float32)\n",
    "            batch_Y = batch_Y.to(device, dtype=torch.float32)\n",
    "            output = model(batch_X)\n",
    "            loss = criterion(output, batch_Y)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            total_loss += loss.item()\n",
    "\n",
    "        average_loss = total_loss / len(train_loader)\n",
    "        elapsed_time = time.time() - start_time\n",
    "\n",
    "        model.eval()\n",
    "        val_predictions = []\n",
    "        val_true_labels = []\n",
    "        with torch.no_grad():\n",
    "            for batch_X, batch_Y in val_loader:\n",
    "                batch_X = batch_X.to(device, dtype=torch.float32)\n",
    "                val_output = model(batch_X)\n",
    "                val_predictions.extend(val_output.cpu().numpy())\n",
    "                val_true_labels.extend(batch_Y.cpu().numpy())\n",
    "\n",
    "        val_predictions = np.array(val_predictions)\n",
    "        val_true_labels = np.array(val_true_labels)\n",
    "\n",
    "        val_accuracy = accuracy_score(val_true_labels, (val_predictions > 0.5).astype(int))\n",
    "        val_accuracies.append(val_accuracy)  # Append validation accuracy to the list\n",
    "\n",
    "        fold_loss = criterion(torch.Tensor(val_predictions), torch.Tensor(val_true_labels))\n",
    "\n",
    "        print(f\"Epoch {epoch + 1}/{num_epochs}, \"\n",
    "              f\"Loss: {average_loss:.4f}, \"\n",
    "              f\"Training Accuracy: {val_accuracy:.4f}, \"\n",
    "              f\"Validation Loss: {fold_loss.item():.4f}, \"\n",
    "              f\"Validation Accuracy: {val_accuracy:.4f}, \"\n",
    "              f\"Training Time: {elapsed_time:.2f} seconds\")\n",
    "\n",
    "        # Update learning rate based on validation loss\n",
    "        scheduler.step(fold_loss)\n",
    "\n",
    "        # Save the model if it's the best so far\n",
    "        if fold_loss < best_val_loss:\n",
    "            best_val_loss = fold_loss\n",
    "            no_improvement_count = 0\n",
    "            # Save the model state\n",
    "            torch.save(model.state_dict(), f'best_custom_cnn_model_fold{fold_index}.pth')\n",
    "\n",
    "        if average_loss < best_val_loss:\n",
    "            best_val_loss = average_loss\n",
    "            no_improvement_count = 0\n",
    "        else:\n",
    "            no_improvement_count += 1\n",
    "\n",
    "        if no_improvement_count >= patience:\n",
    "            print(f\"Early stopping at epoch {epoch + 1} due to no improvement in validation loss.\")\n",
    "            break\n",
    "\n",
    "    # Calculate the average validation accuracy\n",
    "    average_val_accuracy = np.mean(val_accuracies)\n",
    "    return average_val_accuracy, fold_loss.item()\n",
    "\n",
    "# Constants\n",
    "num_folds = 5\n",
    "batch_size = 64\n",
    "num_epochs = 200\n",
    "patience = 5\n",
    "\n",
    "# Data preparation\n",
    "multilabel_stratified_kfold = MultilabelStratifiedKFold(n_splits=num_folds, shuffle=True, random_state=42)\n",
    "\n",
    "fold_losses = []\n",
    "fold_accuracies = []\n",
    "\n",
    "device = torch.device(\"cpu\")\n",
    "    \n",
    "# Training loop with LR scheduler and quantized_model saving\n",
    "for fold_index, (train_idx, val_idx) in enumerate(multilabel_stratified_kfold.split(X_train_transposed_tensor, Y_train_tensor)):\n",
    "    num_train_samples = len(train_idx)\n",
    "    num_val_samples = len(val_idx)\n",
    "    print(f\"Fold {fold_index + 1}/{num_folds} - Train samples: {num_train_samples}, Validation samples: {num_val_samples}\")\n",
    "\n",
    "    X_train_fold_transposed_tensor, X_val_fold_transposed_tensor = X_train_transposed_tensor[train_idx], X_train_transposed_tensor[val_idx]\n",
    "    Y_train_fold_tensor, Y_val_fold_tensor = Y_train_tensor[train_idx], Y_train_tensor[val_idx]\n",
    "\n",
    "    X_train_fold_transposed_tensor = X_train_fold_transposed_tensor.to(device)\n",
    "    Y_train_fold_tensor = Y_train_fold_tensor.to(device)\n",
    "    X_val_fold_transposed_tensor = X_val_fold_transposed_tensor.to(device)\n",
    "    Y_val_fold_tensor = Y_val_fold_tensor.to(device)\n",
    "    \n",
    "    train_fold_dataset = TensorDataset(X_train_fold_transposed_tensor, Y_train_fold_tensor)\n",
    "    val_fold_dataset = TensorDataset(X_val_fold_transposed_tensor, Y_val_fold_tensor)\n",
    "\n",
    "    train_fold_loader = DataLoader(train_fold_dataset, batch_size=batch_size, shuffle=True)\n",
    "    val_fold_loader = DataLoader(val_fold_dataset, batch_size=batch_size)\n",
    "\n",
    "    num_classes = Y_data_encoded.shape[1]\n",
    "    \n",
    "    # Create a new instance of CustomCNN\n",
    "    quantized_model = CustomCNN(num_classes)\n",
    "\n",
    "    # Load only matching keys from the model's state_dict\n",
    "    state_dict = model.state_dict()\n",
    "    new_state_dict = quantized_model.state_dict()\n",
    "    for name, param in state_dict.items():\n",
    "        if name in new_state_dict:\n",
    "            new_state_dict[name].copy_(param)\n",
    "\n",
    "    # Quantize the new instance\n",
    "    quantized_model = torch.quantization.quantize_dynamic(\n",
    "        quantized_model,\n",
    "        {nn.Conv1d, nn.BatchNorm1d, nn.ReLU, nn.MaxPool1d},\n",
    "        dtype=torch.qint8\n",
    "    )\n",
    "\n",
    "    quantized_model = quantized_model.to(device)\n",
    "\n",
    "    # Apply pruning to the quantized model\n",
    "    apply_pruning(quantized_model, prune_rate=0.1)\n",
    "\n",
    "    val_accuracy, fold_loss = train_model_with_scheduler(quantized_model, train_fold_loader, val_fold_loader, num_epochs, device, patience, fold_index)\n",
    "\n",
    "    # Append fold_loss and val_accuracy to their respective lists\n",
    "    fold_losses.append(fold_loss)\n",
    "    fold_accuracies.append(val_accuracy)\n",
    "    \n",
    "    print(f\"Fold {fold_index + 1} - Validation Loss: {fold_loss:.4f}, Validation Accuracy: {val_accuracy:.4f}\")\n",
    "\n",
    "    # Calculate and print quantized_model size\n",
    "    model_size = sum(p.numel() for p in quantized_model.parameters() if p.requires_grad) / 1e6\n",
    "    print(f\"Model Size: {model_size:.2f} MB\")\n",
    "\n",
    "# Calculate and print the average validation loss\n",
    "average_fold_loss = np.mean(fold_losses)\n",
    "average_accuracy = np.mean(fold_accuracies)\n",
    "print(f\"Average Validation Loss Across {num_folds} Folds: {average_fold_loss:.4f}\")\n",
    "print(f\"Average Validation Accuracy Across {num_folds} Folds: {average_accuracy:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "498749d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Size: 4.28 MB\n",
      "Inference Time per Sample: 3.0239 ms\n",
      "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv1d'>.\n",
      "[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm1d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool1d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
      "[INFO] Register count_adap_avgpool() for <class 'torch.nn.modules.pooling.AdaptiveAvgPool1d'>.\n",
      "[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.\n",
      "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
      "FLOPs: 636.49 GFLOPs\n"
     ]
    }
   ],
   "source": [
    "# Define a DataLoader for the test dataset\n",
    "batch_size = 64  # You can adjust this batch size as needed\n",
    "test_dataset = TensorDataset(X_test_transposed_tensor, Y_test_tensor)\n",
    "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)\n",
    "\n",
    "import numpy as np\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 모델을 평가 모드로 설정\n",
    "quantized_model.eval()\n",
    "\n",
    "# 테스트 데이터를 모델에 전달하고 예측을 얻음\n",
    "y_scores = []  # 모델의 예측 확률을 저장할 리스트\n",
    "y_true = []    # 실제 레이블을 저장할 리스트\n",
    "\n",
    "with torch.no_grad():\n",
    "    for batch_data, batch_labels in test_loader:  # 이전에 정의한 test_loader를 사용\n",
    "        batch_data = batch_data.to(device)\n",
    "        quantized_model(batch_data)\n",
    "        outputs = model(batch_data)\n",
    "\n",
    "        # 예측 확률과 실제 레이블을 저장\n",
    "        y_scores.extend(outputs.cpu().numpy())\n",
    "        y_true.extend(batch_labels.cpu().numpy())\n",
    "\n",
    "# y_true와 y_scores를 넘파이 배열로 변환\n",
    "y_true = np.array(y_true)\n",
    "y_scores = np.array(y_scores)\n",
    "\n",
    "# ROC 곡선 계산\n",
    "fpr = dict()\n",
    "tpr = dict()\n",
    "roc_auc = dict()\n",
    "for i in range(num_classes):\n",
    "    fpr[i], tpr[i], _ = roc_curve(y_true[:, i], y_scores[:, i])\n",
    "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
    "\n",
    "# ROC 그래프 그리기\n",
    "plt.figure(figsize=(10, 6))\n",
    "for i in range(num_classes):\n",
    "    plt.plot(fpr[i], tpr[i], lw=2, label=f'Class {class_names[i]} (AUC = {roc_auc[i]:.2f})')\n",
    "\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic (ROC)')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "import os\n",
    "\n",
    "# Define a function to measure model size\n",
    "def model_size(model):\n",
    "    torch.save(model.state_dict(), \"temp.pth\")\n",
    "    size_mb = os.path.getsize(\"temp.pth\") / 1e6\n",
    "    os.remove('temp.pth')\n",
    "    return size_mb\n",
    "# 모델 크기 측정\n",
    "model_size_mb = model_size(quantized_model)\n",
    "print(f'Model Size: {model_size_mb:.2f} MB')\n",
    "\n",
    "# 추론 시간 측정\n",
    "import time\n",
    "num_samples = len(Y_test_tensor)\n",
    "input_data = X_test_transposed_tensor.to(device)\n",
    "\n",
    "start_time = time.time()\n",
    "with torch.no_grad():\n",
    "    quantized_model(input_data)\n",
    "\n",
    "inference_time = (time.time() - start_time) / num_samples * 1000  # 밀리초 단위로 변환\n",
    "print(f'Inference Time per Sample: {inference_time:.4f} ms')\n",
    "\n",
    "# FLOPs 계산\n",
    "import thop\n",
    "input_data = input_data.to(device)\n",
    "flops, params = thop.profile(quantized_model, inputs=(input_data,))\n",
    "print(f'FLOPs: {flops / 1e9:.2f} GFLOPs')  # GFLOPs: 기가 플로팅 포인트 연산 수 (10^9 FLOPs)\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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