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333 changes: 333 additions & 0 deletions Assignment 3/QV_Assignment3_Archi (1).ipynb
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2cveR_jqy2Tm"
},
"outputs": [],
"source": [
"#Downloading dataset\n",
"import os\n",
"!mkdir -p ~/.kaggle\n",
"!cp kaggle.json ~/.kaggle/\n",
"!chmod 600 ~/.kaggle/kaggle.json\n",
"!kaggle datasets download -d raimiazeezbabatunde/candle-image-data\n",
"!unzip -q candle-image-data.zip -d ./candlestick_data\n",
"print(\"DONE\")"
]
},
{
"cell_type": "markdown",
"source": [
"STEPS:\n",
"1. Data collection\n",
"2. Preprocessing\n",
"3. Building the model\n",
"4. Training the model\n",
"5. Evaluating its performance\n",
"6. Visualizing the results\n"
],
"metadata": {
"id": "fa9SmoaX4bES"
}
},
{
"cell_type": "code",
"source": [
"# Importing Libraries\n",
"import torch\n",
"from torch import nn\n",
"from torch.utils.data import DataLoader, random_split\n",
"import torchvision.transforms as transforms\n",
"from torchvision.datasets import ImageFolder\n"
],
"metadata": {
"id": "_-n46hUU7QJb"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Resizing data and Data Augmentation\n",
"train_transform = transforms.Compose([\n",
" transforms.Resize((64,64)),\n",
" transforms.ColorJitter(brightness=0.2),\n",
" transforms.RandomCrop(64, padding=4),\n",
" transforms.ToTensor()\n",
"])\n",
"\n",
"test_transform = transforms.Compose([\n",
" transforms.Resize((64,64)),\n",
" transforms.ToTensor()\n",
"])\n",
"\n",
"# Loading dataset\n",
"train_dataset = ImageFolder(\n",
" root = '/content/candlestick_data',\n",
" transform=train_transform\n",
")\n",
"test_dataset = ImageFolder(\n",
" root = '/content/candlestick_data',\n",
" transform=test_transform\n",
")\n",
"\n",
"# Train-Test split\n",
"train_size = int(0.8*len(train_dataset))\n",
"test_size = len(train_dataset) - train_size\n",
"\n",
"generator = torch.Generator().manual_seed(11)\n",
"\n",
"train_data, _ = random_split(train_dataset, [train_size, test_size], generator=generator)\n",
"_, test_data = random_split(train_dataset, [train_size, test_size], generator=generator)\n",
"\n",
"\n",
"print(f\"Training Data: {len(train_data)} images\")\n",
"print(f\"Testing Data: {len(test_data)} images\")"
],
"metadata": {
"id": "ugQMZC8ahkXN"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"batch_size = 32\n",
"\n",
"train_dataloader = DataLoader(train_data, batch_size=batch_size, shuffle=True)\n",
"test_dataloader = DataLoader(test_data, batch_size=batch_size, shuffle=True)\n",
"\n",
"for X,y in train_dataloader:\n",
" print(f\"Shape of X: {X.shape}\") #[batchsize, channels(rbg), height, width]\n",
" print(f\"Shape of y: {y.shape}\")\n",
" break"
],
"metadata": {
"id": "KWvqtg-Ieiyb"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import torch\n",
"import torch.nn as nn"
],
"metadata": {
"id": "raHID3YHviqh"
},
"execution_count": 8,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"DEFINING THE CNN MODEL"
],
"metadata": {
"id": "HmnK9wLo0dVy"
}
},
{
"cell_type": "code",
"source": [
"# Defining CNN model\n",
"class CNN(nn.Module):\n",
" def __init__(self):\n",
" super().__init__()\n",
" # Feature Extractor\n",
" self.feature_extractor = nn.Sequential(\n",
" # Layer 1\n",
" nn.Conv2d(3, 32, kernel_size=3, padding=1),\n",
" nn.ReLU(),\n",
" nn.MaxPool2d(kernel_size=2),\n",
" # Layer 2\n",
" nn.Conv2d(32, 64, kernel_size=3, padding=1),\n",
" nn.ReLU(),\n",
" nn.MaxPool2d(kernel_size=2),\n",
" # Layer 3\n",
" nn.Conv2d(64, 32, kernel_size=3, padding=1),\n",
" nn.ReLU(),\n",
" nn.MaxPool2d(kernel_size=2),\n",
" # Flattening\n",
" nn.Flatten()\n",
" )\n",
" # Classifier\n",
" self.classifier = nn.Sequential(\n",
" nn.Dropout(p=0.2),\n",
" nn.Linear(32*8*8, 2)\n",
" )\n",
" def forward(self, x):\n",
" x = self.feature_extractor(x)\n",
" x = self.classifier(x)\n",
" return x\n",
"\n"
],
"metadata": {
"id": "h5N7SeVFj34n"
},
"execution_count": 16,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"TRAINING THE MODEL"
],
"metadata": {
"id": "sgOfeqp-0hR-"
}
},
{
"cell_type": "code",
"source": [
"import torch.optim as optim"
],
"metadata": {
"id": "Elu59fnQ1EeT"
},
"execution_count": 10,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Deciding values of Hyperparamters\n",
"lr = 0.001\n",
"num_epochs = 50"
],
"metadata": {
"id": "fGqQwdfA2qwT"
},
"execution_count": 11,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Device configuration\n",
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
],
"metadata": {
"id": "QWTok6-puSqf"
},
"execution_count": 12,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Training and Evaluation of model\n",
"train_losses = []\n",
"val_accuracies = []\n",
"\n",
"model = CNN().to(device)\n",
"class_weights = torch.tensor([0.83, 0.17]).to(device)\n",
"# Choosing loss function and optimizer\n",
"criterion = nn.CrossEntropyLoss(weight = class_weights)\n",
"optimizer = optim.Adam(model.parameters(), lr)\n",
"\n",
"for epoch in range(num_epochs):\n",
" model.train()\n",
" running_loss = 0.0\n",
" for X,y in train_dataloader:\n",
" X = X.to(device)\n",
" y = y.to(device)\n",
" # Forward pass\n",
" pred = model(X)\n",
" # Evaluate loss\n",
" loss = criterion(pred, y)\n",
" # Backpropagation\n",
" loss.backward()\n",
" optimizer.step()\n",
" optimizer.zero_grad()\n",
"\n",
" running_loss += loss.item()\n",
"\n",
" avg_loss = running_loss / len(train_dataloader)\n",
" train_losses.append(avg_loss)\n",
"\n",
" model.eval()\n",
" correct = 0\n",
" total = 0\n",
"\n",
" with torch.no_grad():\n",
" for X,y in test_dataloader:\n",
" X = X.to(device)\n",
" y = y.to(device)\n",
" output = model(X)\n",
" _, pred = torch.max(output.data, 1)\n",
" total += y.size(0)\n",
" correct += (pred == y).sum().item()\n",
"\n",
" accuracy = 100*correct/total\n",
" val_accuracies.append(accuracy)\n",
" print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {avg_loss:.4f}, Val Accuracy: {accuracy:.2f}%')"
],
"metadata": {
"id": "sjVYD2yS0jX_",
"collapsed": true
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Plotting results\n",
"import matplotlib.pyplot as plt\n",
"\n",
"def plot_results(train_losses, val_accuracies):\n",
" plt.figure(figsize=(12, 5))\n",
"\n",
" # Plotting Training loss curve\n",
" plt.subplot(1, 2, 1)\n",
" plt.plot(train_losses, label='Training Loss', color='tab:blue')\n",
" plt.title('Training Loss per Epoch')\n",
" plt.xlabel('Epoch')\n",
" plt.ylabel('Loss')\n",
" plt.grid()\n",
" plt.legend()\n",
"\n",
" # Plotting accuracy curve\n",
" plt.subplot(1, 2, 2)\n",
" plt.plot(val_accuracies, label='Validation Accuracy', color='tab:green')\n",
" plt.title('Validation Accuracy per Epoch')\n",
" plt.xlabel('Epoch')\n",
" plt.ylabel('Accuracy (%)')\n",
" plt.grid()\n",
" plt.legend()\n",
"\n",
" plt.tight_layout()\n",
" plt.show()\n",
"plot_results(train_losses, val_accuracies)\n",
"\n"
],
"metadata": {
"id": "sW-ml_Hur_e6"
},
"execution_count": null,
"outputs": []
}
]
}
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