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HSaurabh0919 committed Jan 2, 2024
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"authorship_tag": "ABX9TyNDIhO1Yr+PmVAezfwSxCn9"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "YlOlFH1_1PDK"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.optim as optim\n",
"from torch.utils.data import DataLoader, TensorDataset"
]
},
{
"cell_type": "code",
"source": [
"# Set random seed for reproducibility\n",
"np.random.seed(0)\n",
"torch.manual_seed(0)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "tmqSTDd01gLR",
"outputId": "e8be9557-ef67-4758-8463-271f69d62caf"
},
"execution_count": 2,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<torch._C.Generator at 0x7d25b8b55130>"
]
},
"metadata": {},
"execution_count": 2
}
]
},
{
"cell_type": "code",
"source": [
"# Parameters\n",
"num_classes = 3\n",
"input_dim = 2 # Let's create 2D data for simplicity\n",
"num_data_per_class = 1000\n",
"hidden_dim = 100\n"
],
"metadata": {
"id": "EWhAZEH71iI4"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Generate random data\n",
"data = []\n",
"labels = []\n",
"for class_number in range(num_classes):\n",
" # Create data for each class\n",
" class_data = np.random.randn(num_data_per_class, input_dim) + (class_number * 3)\n",
" class_labels = np.full(num_data_per_class, class_number)\n",
" data.append(class_data)\n",
" labels.append(class_labels)"
],
"metadata": {
"id": "EbhBVSw71kdA"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Convert to single numpy arrays\n",
"data = np.vstack(data)\n",
"labels = np.hstack(labels)\n"
],
"metadata": {
"id": "0Hj_HZU22oDI"
},
"execution_count": 18,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Convert to PyTorch tensors\n",
"data_tensor = torch.tensor(data, dtype=torch.float32)\n",
"labels_tensor = torch.tensor(labels, dtype=torch.long)\n",
"\n",
"# Create a TensorDataset and DataLoader\n",
"dataset = TensorDataset(data_tensor, labels_tensor)\n",
"dataloader = DataLoader(dataset, batch_size=64, shuffle=True)\n"
],
"metadata": {
"id": "HXo20AgC2zfR"
},
"execution_count": 23,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Neural network\n",
"class SimpleNN(nn.Module):\n",
" def __init__(self):\n",
" super(SimpleNN, self).__init__()\n",
" self.fc1 = nn.Linear(input_dim, hidden_dim)\n",
" self.fc2 = nn.Linear(hidden_dim, num_classes)\n",
"\n",
" def forward(self, x):\n",
" x = torch.relu(self.fc1(x))\n",
" x = self.fc2(x)\n",
" return x\n",
"\n",
"model = SimpleNN()"
],
"metadata": {
"id": "-LPCukJs3Nxq"
},
"execution_count": 24,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Loss and optimizer\n",
"criterion = nn.CrossEntropyLoss()\n",
"optimizer = optim.Adam(model.parameters(), lr=0.001)\n"
],
"metadata": {
"id": "AphIpfTz3Tl6"
},
"execution_count": 25,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Training loop\n",
"num_epochs = 100\n",
"for epoch in range(num_epochs):\n",
" for inputs, targets in dataloader:\n",
" # Forward pass\n",
" outputs = model(inputs)\n",
" loss = criterion(outputs, targets)\n",
"\n",
" # Backward and optimize\n",
" optimizer.zero_grad()\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "vugQZjOY3YCw",
"outputId": "c9364454-2c45-4bd9-8d4b-6de46b787cc9"
},
"execution_count": 29,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch [1/100], Loss: 0.7061\n",
"Epoch [2/100], Loss: 0.5184\n",
"Epoch [3/100], Loss: 0.3409\n",
"Epoch [4/100], Loss: 0.3404\n",
"Epoch [5/100], Loss: 0.3013\n",
"Epoch [6/100], Loss: 0.2666\n",
"Epoch [7/100], Loss: 0.1944\n",
"Epoch [8/100], Loss: 0.1365\n",
"Epoch [9/100], Loss: 0.2394\n",
"Epoch [10/100], Loss: 0.1571\n",
"Epoch [11/100], Loss: 0.1054\n",
"Epoch [12/100], Loss: 0.1271\n",
"Epoch [13/100], Loss: 0.0823\n",
"Epoch [14/100], Loss: 0.0629\n",
"Epoch [15/100], Loss: 0.0954\n",
"Epoch [16/100], Loss: 0.1026\n",
"Epoch [17/100], Loss: 0.0657\n",
"Epoch [18/100], Loss: 0.0571\n",
"Epoch [19/100], Loss: 0.0781\n",
"Epoch [20/100], Loss: 0.1290\n",
"Epoch [21/100], Loss: 0.1111\n",
"Epoch [22/100], Loss: 0.0277\n",
"Epoch [23/100], Loss: 0.0666\n",
"Epoch [24/100], Loss: 0.0677\n",
"Epoch [25/100], Loss: 0.0634\n",
"Epoch [26/100], Loss: 0.1145\n",
"Epoch [27/100], Loss: 0.0605\n",
"Epoch [28/100], Loss: 0.0545\n",
"Epoch [29/100], Loss: 0.0649\n",
"Epoch [30/100], Loss: 0.0454\n",
"Epoch [31/100], Loss: 0.0644\n",
"Epoch [32/100], Loss: 0.0344\n",
"Epoch [33/100], Loss: 0.0289\n",
"Epoch [34/100], Loss: 0.1207\n",
"Epoch [35/100], Loss: 0.0529\n",
"Epoch [36/100], Loss: 0.0273\n",
"Epoch [37/100], Loss: 0.0446\n",
"Epoch [38/100], Loss: 0.1538\n",
"Epoch [39/100], Loss: 0.0500\n",
"Epoch [40/100], Loss: 0.0764\n",
"Epoch [41/100], Loss: 0.0608\n",
"Epoch [42/100], Loss: 0.0551\n",
"Epoch [43/100], Loss: 0.0282\n",
"Epoch [44/100], Loss: 0.0652\n",
"Epoch [45/100], Loss: 0.0698\n",
"Epoch [46/100], Loss: 0.1298\n",
"Epoch [47/100], Loss: 0.1606\n",
"Epoch [48/100], Loss: 0.0512\n",
"Epoch [49/100], Loss: 0.0657\n",
"Epoch [50/100], Loss: 0.0276\n",
"Epoch [51/100], Loss: 0.0652\n",
"Epoch [52/100], Loss: 0.0865\n",
"Epoch [53/100], Loss: 0.0498\n",
"Epoch [54/100], Loss: 0.0831\n",
"Epoch [55/100], Loss: 0.0339\n",
"Epoch [56/100], Loss: 0.0284\n",
"Epoch [57/100], Loss: 0.0929\n",
"Epoch [58/100], Loss: 0.0434\n",
"Epoch [59/100], Loss: 0.0197\n",
"Epoch [60/100], Loss: 0.0400\n",
"Epoch [61/100], Loss: 0.0241\n",
"Epoch [62/100], Loss: 0.0732\n",
"Epoch [63/100], Loss: 0.0176\n",
"Epoch [64/100], Loss: 0.0970\n",
"Epoch [65/100], Loss: 0.0605\n",
"Epoch [66/100], Loss: 0.0340\n",
"Epoch [67/100], Loss: 0.0252\n",
"Epoch [68/100], Loss: 0.0528\n",
"Epoch [69/100], Loss: 0.0499\n",
"Epoch [70/100], Loss: 0.0312\n",
"Epoch [71/100], Loss: 0.0168\n",
"Epoch [72/100], Loss: 0.0136\n",
"Epoch [73/100], Loss: 0.0375\n",
"Epoch [74/100], Loss: 0.0872\n",
"Epoch [75/100], Loss: 0.0491\n",
"Epoch [76/100], Loss: 0.0209\n",
"Epoch [77/100], Loss: 0.1018\n",
"Epoch [78/100], Loss: 0.0953\n",
"Epoch [79/100], Loss: 0.0132\n",
"Epoch [80/100], Loss: 0.0776\n",
"Epoch [81/100], Loss: 0.0260\n",
"Epoch [82/100], Loss: 0.0133\n",
"Epoch [83/100], Loss: 0.0261\n",
"Epoch [84/100], Loss: 0.1178\n",
"Epoch [85/100], Loss: 0.0251\n",
"Epoch [86/100], Loss: 0.0174\n",
"Epoch [87/100], Loss: 0.0227\n",
"Epoch [88/100], Loss: 0.0441\n",
"Epoch [89/100], Loss: 0.1605\n",
"Epoch [90/100], Loss: 0.0686\n",
"Epoch [91/100], Loss: 0.0928\n",
"Epoch [92/100], Loss: 0.0176\n",
"Epoch [93/100], Loss: 0.0114\n",
"Epoch [94/100], Loss: 0.0748\n",
"Epoch [95/100], Loss: 0.0560\n",
"Epoch [96/100], Loss: 0.0120\n",
"Epoch [97/100], Loss: 0.0267\n",
"Epoch [98/100], Loss: 0.0381\n",
"Epoch [99/100], Loss: 0.0307\n",
"Epoch [100/100], Loss: 0.0050\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Test the model\n",
"model.eval()\n",
"with torch.no_grad():\n",
" correct = 0\n",
" total = 0\n",
" for inputs, targets in dataloader:\n",
" outputs = model(inputs)\n",
" _, predicted = torch.max(outputs.data, 1)\n",
" total += targets.size(0)\n",
" correct += (predicted == targets).sum().item()\n",
"\n",
"print(f'Accuracy: {100 * correct / total} %')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xmMrr4sq3xsJ",
"outputId": "40449874-ee32-4791-fe23-f3c0c5c92396"
},
"execution_count": 30,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Accuracy: 98.06666666666666 %\n"
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "hXvHLhcT4L5r",
"outputId": "cd23a7bb-3710-48b3-d499-06fb8e2a0793"
},
"execution_count": 34,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"1"
]
},
"metadata": {},
"execution_count": 34
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "iFzQWI8J4N5n"
},
"execution_count": null,
"outputs": []
}
]
}

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