lstm_nn_room_occupancy_estimator.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "datalore": {
     "hide_input_from_viewers": true,
     "hide_output_from_viewers": true,
     "node_id": "Opm8zm3mAGy4F6o4Bh4gAw",
     "type": "MD"
    }
   },
   "source": [
    "**Exploratory data analysis to understand the distribution of sensor readings over time and their relationship with room occupancy**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "datalore": {
     "hide_input_from_viewers": true,
     "hide_output_from_viewers": true,
     "node_id": "sNj8p1QgvspjoE1wc1T6PT",
     "type": "CODE"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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
"
     },
     "metadata": {
      "image/png": {}
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_csv('/data/notebook_files/Occupancy_Estimation.csv')\n",
    "df['Timestamp'] = pd.to_datetime(df['Date'] + ' ' + df['Time'])\n",
    "\n",
    "df.set_index('Timestamp', inplace=True)\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.lineplot(x='Timestamp', y='S1_Temp', data=df, label='S1_Temp')\n",
    "sns.lineplot(x='Timestamp', y='Room_Occupancy_Count', data=df, label='Room Occupancy')\n",
    "plt.plot(df['S1_Temp'], label='Sensor Reading', color='blue')\n",
    "plt.title('Sensor Readings Over Time')\n",
    "plt.xlabel('Timestamp')\n",
    "plt.ylabel('Values')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "datalore": {
     "hide_input_from_viewers": true,
     "hide_output_from_viewers": true,
     "node_id": "TJM1RcZhaOWJlo9roE0Kvp",
     "type": "MD"
    }
   },
   "source": [
    "**Handling missing values and adding a meaningful train/test split**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "datalore": {
     "hide_input_from_viewers": true,
     "hide_output_from_viewers": true,
     "node_id": "UYykhon2zkFXjiNp7Im9TY",
     "type": "CODE"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Total Missing Data Before Imputation:\n",
      "Date                    0\n",
      "Time                    0\n",
      "S1_Temp                 0\n",
      "S2_Temp                 0\n",
      "S3_Temp                 0\n",
      "S4_Temp                 0\n",
      "S1_Light                0\n",
      "S2_Light                0\n",
      "S3_Light                0\n",
      "S4_Light                0\n",
      "S1_Sound                0\n",
      "S2_Sound                0\n",
      "S3_Sound                0\n",
      "S4_Sound                0\n",
      "S5_CO2                  0\n",
      "S5_CO2_Slope            0\n",
      "S6_PIR                  0\n",
      "S7_PIR                  0\n",
      "Room_Occupancy_Count    0\n",
      "dtype: int64\n",
      "\n",
      "Total Missing Data After Imputation:\n",
      "Date                    0\n",
      "Time                    0\n",
      "S1_Temp                 0\n",
      "S2_Temp                 0\n",
      "S3_Temp                 0\n",
      "S4_Temp                 0\n",
      "S1_Light                0\n",
      "S2_Light                0\n",
      "S3_Light                0\n",
      "S4_Light                0\n",
      "S1_Sound                0\n",
      "S2_Sound                0\n",
      "S3_Sound                0\n",
      "S4_Sound                0\n",
      "S5_CO2                  0\n",
      "S5_CO2_Slope            0\n",
      "S6_PIR                  0\n",
      "S7_PIR                  0\n",
      "Room_Occupancy_Count    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "occupancy_df = pd.read_csv('/data/notebook_files/Occupancy_Estimation.csv')\n",
    "\n",
    "print(\"\\nTotal Missing Data Before Imputation:\")\n",
    "print(occupancy_df.isnull().sum())\n",
    "\n",
    "numeric_columns = occupancy_df.select_dtypes(include=['number']).columns\n",
    "non_numeric_columns = occupancy_df.select_dtypes(exclude=['number']).columns\n",
    "numeric_imputer = SimpleImputer(strategy='mean')\n",
    "occupancy_df[numeric_columns] = numeric_imputer.fit_transform(occupancy_df[numeric_columns])\n",
    "non_numeric_imputer = SimpleImputer(strategy='most_frequent')\n",
    "occupancy_df[non_numeric_columns] = non_numeric_imputer.fit_transform(occupancy_df[non_numeric_columns])\n",
    "missing_values_after_imputation = occupancy_df.isnull().sum()\n",
    "\n",
    "print(\"\\nTotal Missing Data After Imputation:\")\n",
    "print(missing_values_after_imputation)\n",
    "\n",
    "X = occupancy_df.drop('Room_Occupancy_Count', axis=1)\n",
    "y = occupancy_df['Room_Occupancy_Count']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=42\n",
    ")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "datalore": {
     "hide_input_from_viewers": true,
     "hide_output_from_viewers": true,
     "node_id": "IXAVQLWcRn0uyWsEjtOUE3",
     "type": "MD"
    }
   },
   "source": [
    "**Neural network design utilising the temporal nature of the data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "datalore": {
     "hide_input_from_viewers": true,
     "hide_output_from_viewers": true,
     "node_id": "mdOfrlJn9cO7aTKoIBgKwj",
     "type": "CODE"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OccupancyLSTM(\n",
      "  (lstm): LSTM(16, 64, batch_first=True)\n",
      "  (fc): Linear(in_features=64, out_features=1, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "features = ['S1_Temp', 'S2_Temp', 'S3_Temp', 'S4_Temp', 'S1_Light', 'S2_Light', 'S3_Light', 'S4_Light',\n",
    "            'S1_Sound', 'S2_Sound', 'S3_Sound', 'S4_Sound', 'S5_CO2', 'S5_CO2_Slope', 'S6_PIR', 'S7_PIR']\n",
    "\n",
    "class OccupancyLSTM(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, num_layers, output_size):\n",
    "        super(OccupancyLSTM, self).__init__()\n",
    "        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)\n",
    "        self.fc = nn.Linear(hidden_size, output_size)\n",
    "\n",
    "    def forward(self, x):\n",
    "        lstm_out, _ = self.lstm(x)\n",
    "        lstm_out_last = lstm_out[:, -1, :]\n",
    "        output = self.fc(lstm_out_last)\n",
    "        return output\n",
    "\n",
    "input_size = len(features)\n",
    "hidden_size = 64 \n",
    "num_layers = 1\n",
    "output_size = 1\n",
    "\n",
    "model = OccupancyLSTM(input_size, hidden_size, num_layers, output_size)\n",
    "\n",
    "print(model)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "datalore": {
     "hide_input_from_viewers": true,
     "hide_output_from_viewers": true,
     "node_id": "owSqv2bySy5KRPGKxNORHw",
     "type": "MD"
    }
   },
   "source": [
    "**Training the neural network using training set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "datalore": {
     "hide_input_from_viewers": true,
     "hide_output_from_viewers": true,
     "node_id": "6apK9Tt7gyB2dIVCVEjFYW",
     "type": "CODE"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/50], Loss: 0.0216\n",
      "Epoch [2/50], Loss: 0.0022\n",
      "Epoch [3/50], Loss: 0.0664\n",
      "Epoch [4/50], Loss: 0.0016\n",
      "Epoch [5/50], Loss: 0.0059\n",
      "Epoch [6/50], Loss: 0.0132\n",
      "Epoch [7/50], Loss: 0.0039\n",
      "Epoch [8/50], Loss: 0.0022\n",
      "Epoch [9/50], Loss: 0.0041\n",
      "Epoch [10/50], Loss: 0.0050\n",
      "Epoch [11/50], Loss: 0.0030\n",
      "Epoch [12/50], Loss: 0.0356\n",
      "Epoch [13/50], Loss: 0.0049\n",
      "Epoch [14/50], Loss: 0.0013\n",
      "Epoch [15/50], Loss: 0.0029\n",
      "Epoch [16/50], Loss: 0.0013\n",
      "Epoch [17/50], Loss: 0.0074\n",
      "Epoch [18/50], Loss: 0.0009\n",
      "Epoch [19/50], Loss: 0.0462\n",
      "Epoch [20/50], Loss: 0.0007\n",
      "Epoch [21/50], Loss: 0.0103\n",
      "Epoch [22/50], Loss: 0.0019\n",
      "Epoch [23/50], Loss: 0.0028\n",
      "Epoch [24/50], Loss: 0.0039\n",
      "Epoch [25/50], Loss: 0.0020\n",
      "Epoch [26/50], Loss: 0.0202\n",
      "Epoch [27/50], Loss: 0.0280\n",
      "Epoch [28/50], Loss: 0.0011\n",
      "Epoch [29/50], Loss: 0.0017\n",
      "Epoch [30/50], Loss: 0.0005\n",
      "Epoch [31/50], Loss: 0.0144\n",
      "Epoch [32/50], Loss: 0.0102\n",
      "Epoch [33/50], Loss: 0.0173\n",
      "Epoch [34/50], Loss: 0.0005\n",
      "Epoch [35/50], Loss: 0.0291\n",
      "Epoch [36/50], Loss: 0.0003\n",
      "Epoch [37/50], Loss: 0.0220\n",
      "Epoch [38/50], Loss: 0.0021\n",
      "Epoch [39/50], Loss: 0.0007\n",
      "Epoch [40/50], Loss: 0.0013\n",
      "Epoch [41/50], Loss: 0.0003\n",
      "Epoch [42/50], Loss: 0.0011\n",
      "Epoch [43/50], Loss: 0.0056\n",
      "Epoch [44/50], Loss: 0.0542\n",
      "Epoch [45/50], Loss: 0.0080\n",
      "Epoch [46/50], Loss: 0.0024\n",
      "Epoch [47/50], Loss: 0.0002\n",
      "Epoch [48/50], Loss: 0.0047\n",
      "Epoch [49/50], Loss: 0.0017\n",
      "Epoch [50/50], Loss: 0.0037\n",
      "Test Loss: 0.0076\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "features = ['S1_Temp','S2_Temp', 'S3_Temp', 'S4_Temp', 'S1_Light', 'S2_Light', 'S3_Light', 'S4_Light', 'S1_Sound', 'S2_Sound', 'S3_Sound', 'S4_Sound', 'S5_CO2', 'S5_CO2_Slope', 'S6_PIR', 'S7_PIR']\n",
    "\n",
    "scaler = StandardScaler()\n",
    "occupancy_df[features] = scaler.fit_transform(occupancy_df[features])\n",
    "\n",
    "X_tensor = torch.tensor(occupancy_df[features].values, dtype=torch.float32)\n",
    "y_tensor = torch.tensor(occupancy_df['Room_Occupancy_Count'].values, dtype=torch.float32).view(-1, 1)\n",
    "\n",
    "class EstimationModel(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size):\n",
    "        super(EstimationModel, self).__init__()\n",
    "        self.fc1 = nn.Linear(input_size, hidden_size)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.fc2 = nn.Linear(hidden_size, 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.fc1(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.fc2(x)\n",
    "        return x\n",
    "\n",
    "input_size = len(features)\n",
    "hidden_size = 64\n",
    "model = EstimationModel(input_size, hidden_size)\n",
    "\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "dataset = TensorDataset(X_tensor, y_tensor)\n",
    "dataloader = DataLoader(dataset, batch_size=32, shuffle=True)\n",
    "\n",
    "num_epochs = 50\n",
    "for epoch in range(num_epochs):\n",
    "    for batch_X, batch_y in dataloader:\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(batch_X)\n",
    "        loss = criterion(outputs, batch_y)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')\n",
    "\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    test_outputs = model(X_tensor)\n",
    "    test_loss = criterion(test_outputs, y_tensor)\n",
    "\n",
    "print(f'Test Loss: {test_loss.item():.4f}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "datalore": {
     "hide_input_from_viewers": true,
     "hide_output_from_viewers": true,
     "node_id": "oWqbjFd4ufFbXgEAtolIap",
     "type": "MD"
    }
   },
   "source": [
    "**Evaluating model's performance and reporting the training curves and metrics**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "datalore": {
     "hide_input_from_viewers": true,
     "hide_output_from_viewers": true,
     "node_id": "TIgtMX4cmcQlfs1ZjLt38A",
     "type": "CODE"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/50], Train Loss: 0.0031, Test Loss: 0.0049, MSE: 0.0049, R2: 0.9938\n",
      "Epoch [2/50], Train Loss: 0.0006, Test Loss: 0.0044, MSE: 0.0044, R2: 0.9944\n",
      "Epoch [3/50], Train Loss: 0.0087, Test Loss: 0.0042, MSE: 0.0042, R2: 0.9947\n",
      "Epoch [4/50], Train Loss: 0.0019, Test Loss: 0.0049, MSE: 0.0049, R2: 0.9939\n",
      "Epoch [5/50], Train Loss: 0.0014, Test Loss: 0.0040, MSE: 0.0040, R2: 0.9949\n",
      "Epoch [6/50], Train Loss: 0.0005, Test Loss: 0.0054, MSE: 0.0054, R2: 0.9932\n",
      "Epoch [7/50], Train Loss: 0.0018, Test Loss: 0.0044, MSE: 0.0044, R2: 0.9944\n",
      "Epoch [8/50], Train Loss: 0.0012, Test Loss: 0.0048, MSE: 0.0048, R2: 0.9940\n",
      "Epoch [9/50], Train Loss: 0.0055, Test Loss: 0.0040, MSE: 0.0040, R2: 0.9950\n",
      "Epoch [10/50], Train Loss: 0.0004, Test Loss: 0.0044, MSE: 0.0044, R2: 0.9944\n",
      "Epoch [11/50], Train Loss: 0.0018, Test Loss: 0.0043, MSE: 0.0043, R2: 0.9946\n",
      "Epoch [12/50], Train Loss: 0.0018, Test Loss: 0.0049, MSE: 0.0049, R2: 0.9938\n",
      "Epoch [13/50], Train Loss: 0.0011, Test Loss: 0.0036, MSE: 0.0036, R2: 0.9955\n",
      "Epoch [14/50], Train Loss: 0.0289, Test Loss: 0.0057, MSE: 0.0057, R2: 0.9928\n",
      "Epoch [15/50], Train Loss: 0.0040, Test Loss: 0.0040, MSE: 0.0040, R2: 0.9949\n",
      "Epoch [16/50], Train Loss: 0.0021, Test Loss: 0.0040, MSE: 0.0040, R2: 0.9949\n",
      "Epoch [17/50], Train Loss: 0.0026, Test Loss: 0.0041, MSE: 0.0041, R2: 0.9948\n",
      "Epoch [18/50], Train Loss: 0.0017, Test Loss: 0.0061, MSE: 0.0061, R2: 0.9924\n",
      "Epoch [19/50], Train Loss: 0.0003, Test Loss: 0.0044, MSE: 0.0044, R2: 0.9945\n",
      "Epoch [20/50], Train Loss: 0.0004, Test Loss: 0.0044, MSE: 0.0044, R2: 0.9945\n",
      "Epoch [21/50], Train Loss: 0.0058, Test Loss: 0.0036, MSE: 0.0036, R2: 0.9955\n",
      "Epoch [22/50], Train Loss: 0.0005, Test Loss: 0.0039, MSE: 0.0039, R2: 0.9952\n",
      "Epoch [23/50], Train Loss: 0.0114, Test Loss: 0.0045, MSE: 0.0045, R2: 0.9944\n",
      "Epoch [24/50], Train Loss: 0.0014, Test Loss: 0.0038, MSE: 0.0038, R2: 0.9952\n",
      "Epoch [25/50], Train Loss: 0.0028, Test Loss: 0.0045, MSE: 0.0045, R2: 0.9943\n",
      "Epoch [26/50], Train Loss: 0.0013, Test Loss: 0.0040, MSE: 0.0040, R2: 0.9949\n",
      "Epoch [27/50], Train Loss: 0.0006, Test Loss: 0.0038, MSE: 0.0038, R2: 0.9953\n",
      "Epoch [28/50], Train Loss: 0.0019, Test Loss: 0.0041, MSE: 0.0041, R2: 0.9949\n",
      "Epoch [29/50], Train Loss: 0.1036, Test Loss: 0.0047, MSE: 0.0047, R2: 0.9941\n",
      "Epoch [30/50], Train Loss: 0.0021, Test Loss: 0.0041, MSE: 0.0041, R2: 0.9948\n",
      "Epoch [31/50], Train Loss: 0.0014, Test Loss: 0.0066, MSE: 0.0066, R2: 0.9917\n",
      "Epoch [32/50], Train Loss: 0.0014, Test Loss: 0.0041, MSE: 0.0041, R2: 0.9949\n",
      "Epoch [33/50], Train Loss: 0.0156, Test Loss: 0.0038, MSE: 0.0038, R2: 0.9953\n",
      "Epoch [34/50], Train Loss: 0.0093, Test Loss: 0.0036, MSE: 0.0036, R2: 0.9955\n",
      "Epoch [35/50], Train Loss: 0.0009, Test Loss: 0.0036, MSE: 0.0036, R2: 0.9955\n",
      "Epoch [36/50], Train Loss: 0.0008, Test Loss: 0.0034, MSE: 0.0034, R2: 0.9957\n",
      "Epoch [37/50], Train Loss: 0.0015, Test Loss: 0.0042, MSE: 0.0042, R2: 0.9947\n",
      "Epoch [38/50], Train Loss: 0.0008, Test Loss: 0.0039, MSE: 0.0039, R2: 0.9951\n",
      "Epoch [39/50], Train Loss: 0.0290, Test Loss: 0.0035, MSE: 0.0035, R2: 0.9956\n",
      "Epoch [40/50], Train Loss: 0.0004, Test Loss: 0.0038, MSE: 0.0038, R2: 0.9952\n",
      "Epoch [41/50], Train Loss: 0.0039, Test Loss: 0.0039, MSE: 0.0039, R2: 0.9951\n",
      "Epoch [42/50], Train Loss: 0.0027, Test Loss: 0.0036, MSE: 0.0036, R2: 0.9954\n",
      "Epoch [43/50], Train Loss: 0.0075, Test Loss: 0.0034, MSE: 0.0034, R2: 0.9957\n",
      "Epoch [44/50], Train Loss: 0.0090, Test Loss: 0.0039, MSE: 0.0039, R2: 0.9952\n",
      "Epoch [45/50], Train Loss: 0.0118, Test Loss: 0.0036, MSE: 0.0036, R2: 0.9955\n",
      "Epoch [46/50], Train Loss: 0.0023, Test Loss: 0.0031, MSE: 0.0031, R2: 0.9961\n",
      "Epoch [47/50], Train Loss: 0.0007, Test Loss: 0.0034, MSE: 0.0034, R2: 0.9958\n",
      "Epoch [48/50], Train Loss: 0.0006, Test Loss: 0.0033, MSE: 0.0033, R2: 0.9959\n",
      "Epoch [49/50], Train Loss: 0.0027, Test Loss: 0.0033, MSE: 0.0033, R2: 0.9959\n",
      "Epoch [50/50], Train Loss: 0.0010, Test Loss: 0.0034, MSE: 0.0034, R2: 0.9958\n"
     ]
    },
    {
     "data": {
      "image/png": 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
"
     },
     "metadata": {
      "image/png": {}
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "train_losses = []\n",
    "test_losses = []\n",
    "mse_scores = []\n",
    "r2_scores = []\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    for batch_X, batch_y in dataloader:\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(batch_X)\n",
    "        loss = criterion(outputs, batch_y)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "    train_losses.append(loss.item())\n",
    "\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        test_outputs = model(X_tensor)\n",
    "        test_loss = criterion(test_outputs, y_tensor)\n",
    "\n",
    "    test_losses.append(test_loss.item())\n",
    "\n",
    "    test_predictions = test_outputs.numpy()\n",
    "    true_values = y_tensor.numpy()\n",
    "\n",
    "    mse = mean_squared_error(true_values, test_predictions)\n",
    "    r2 = r2_score(true_values, test_predictions)\n",
    "\n",
    "    mse_scores.append(mse)\n",
    "    r2_scores.append(r2)\n",
    "\n",
    "    print(f'Epoch [{epoch+1}/{num_epochs}], Train Loss: {loss.item():.4f}, Test Loss: {test_loss.item():.4f}, MSE: {mse:.4f}, R2: {r2:.4f}')\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(range(1, num_epochs + 1), train_losses, label='Training Loss')\n",
    "plt.plot(range(1, num_epochs + 1), test_losses, label='Test Loss')\n",
    "plt.title('Training and Testing Loss Over Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "datalore": {
     "hide_input_from_viewers": true,
     "hide_output_from_viewers": true,
     "node_id": "oRRG9OcFHxKmm0a3vjPsa8",
     "type": "MD"
    }
   },
   "source": [
    "**Question 4.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "datalore": {
     "hide_input_from_viewers": true,
     "hide_output_from_viewers": true,
     "node_id": "2TP0UQnNW9UojnJocLJE4F",
     "type": "CODE"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output size: 125x125x64\n",
      "Number of weights: 9408\n",
      "Number of biases: 64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Layer 2: Conv-1: 64 kernels of size 7x7, stride of 2 and padding of 0\n",
    "\n",
    "def calculate_conv_output_size(input_size, kernel_size, stride, padding):\n",
    "    return ((input_size - kernel_size + 2 * padding) // stride) + 1\n",
    "\n",
    "input_size = 256\n",
    "kernel_size = 7\n",
    "stride = 2\n",
    "padding = 0\n",
    "num_kernels=64\n",
    "kernel_height = 7\n",
    "kernel_width = 7\n",
    "num_input_channels = 3\n",
    "\n",
    "output_size = calculate_conv_output_size(input_size, kernel_size, stride, padding)\n",
    "num_weights = num_kernels * kernel_height * kernel_width * num_input_channels\n",
    "num_biases = num_kernels\n",
    "\n",
    "print(f\"Output size: {output_size}x{output_size}x{num_kernels}\")\n",
    "\n",
    "print(f\"Number of weights: {num_weights}\")\n",
    "print(f\"Number of biases: {num_biases}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "datalore": {
     "hide_input_from_viewers": true,
     "hide_output_from_viewers": true,
     "node_id": "4XuLP9hpA1PjY4bgnRuJMJ",
     "type": "MD"
    }
   },
   "source": [
    "**Question 7**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "datalore": {
     "hide_input_from_viewers": true,
     "hide_output_from_viewers": true,
     "node_id": "RFEg1BUZvYHVN3YpP2xlkH",
     "type": "CODE"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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
"
     },
     "metadata": {
      "image/png": {}
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def loss_function(epsilon):\n",
    "    return np.sum(np.square(2 * np.arctan(np.square(epsilon) + 0.5463) - 1))/5\n",
    "\n",
    "epsilon_values = np.linspace(0.01, 2.0, 100)\n",
    "\n",
    "loss_values = np.array([loss_function(epsilon) for epsilon in epsilon_values])\n",
    "\n",
    "plt.plot(epsilon_values, loss_values, label='Loss Function')\n",
    "plt.title('Loss Function Visualization')\n",
    "plt.xlabel('Epsilon (ϵ)')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "datalore": {
     "hide_input_from_viewers": true,
     "hide_output_from_viewers": true,
     "node_id": "I3htW2O7FQKlBXZg6qlrE4",
     "type": "CODE"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "datalore": {
   "base_environment": "default",
   "computation_mode": "JUPYTER",
   "package_manager": "pip",
   "packages": [],
   "report_row_ids": [],
   "version": 3
  },
  "kernelspec": {
   "display_name": "Python",
   "language": "python",
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}