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Assignment3_Harsh #175

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harsh10bajaj Jan 4, 2026
5a7740f
Created using kaggle
harsh10bajaj Jan 4, 2026
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Merge branch 'Smira555:main' into main
harsh10bajaj Jan 12, 2026
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harsh10bajaj Jan 12, 2026
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Assignment 4
harsh10bajaj Jan 12, 2026
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Delete Assignment 4/Harsh_Assignment4/txt
harsh10bajaj Jan 12, 2026
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362 changes: 362 additions & 0 deletions Assignment 3/Assignment3_Harsh/Assignment3_Harsh.ipynb
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{
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.12.12",
"mimetype": "text/x-python",
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"pygments_lexer": "ipython3",
"nbconvert_exporter": "python",
"file_extension": ".py"
},
"kaggle": {
"accelerator": "none",
"dataSources": [
{
"sourceId": 8077192,
"sourceType": "datasetVersion",
"datasetId": 4766889
}
],
"dockerImageVersionId": 31234,
"isInternetEnabled": false,
"language": "python",
"sourceType": "notebook",
"isGpuEnabled": false
},
"colab": {
"name": "notebook08af728a3c",
"provenance": [],
"gpuType": "T4"
},
"accelerator": "GPU"
},
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"source": [
"# IMPORTANT: RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES,\n",
"# THEN FEEL FREE TO DELETE THIS CELL.\n",
"# NOTE: THIS NOTEBOOK ENVIRONMENT DIFFERS FROM KAGGLE'S PYTHON\n",
"# ENVIRONMENT SO THERE MAY BE MISSING LIBRARIES USED BY YOUR\n",
"# NOTEBOOK.\n",
"import kagglehub\n",
"raimiazeezbabatunde_candle_image_data_path = kagglehub.dataset_download('raimiazeezbabatunde/candle-image-data')\n",
"\n",
"print('Data source import complete.')\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "h96wK5A00qQn",
"outputId": "88fc15c0-8008-433f-eb0a-cd073a4d556b"
},
"cell_type": "code",
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Using Colab cache for faster access to the 'candle-image-data' dataset.\n",
"Data source import complete.\n"
]
}
],
"execution_count": 15
},
{
"cell_type": "code",
"source": [
"import tensorflow as tf\n",
"tf.config.list_physical_devices('GPU')\n"
],
"metadata": {
"trusted": true,
"execution": {
"iopub.status.busy": "2026-01-03T12:24:45.663619Z",
"iopub.execute_input": "2026-01-03T12:24:45.664888Z",
"iopub.status.idle": "2026-01-03T12:24:45.672517Z",
"shell.execute_reply.started": "2026-01-03T12:24:45.664846Z",
"shell.execute_reply": "2026-01-03T12:24:45.671289Z"
},
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "SnlKA7DT0qQp",
"outputId": "e30c5da7-c5a0-448a-c41d-a17e21afa8c6"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
]
},
"metadata": {},
"execution_count": 16
}
],
"execution_count": 16
},
{
"cell_type": "code",
"source": [
"import os\n",
"os.listdir(\"/kaggle/input\")\n",
"os.listdir(\"/kaggle/input/candle-image-data\")\n"
],
"metadata": {
"trusted": true,
"execution": {
"iopub.status.busy": "2026-01-03T12:24:45.6748Z",
"iopub.execute_input": "2026-01-03T12:24:45.675235Z",
"iopub.status.idle": "2026-01-03T12:24:45.706538Z",
"shell.execute_reply.started": "2026-01-03T12:24:45.67519Z",
"shell.execute_reply": "2026-01-03T12:24:45.704819Z"
},
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "jXTJdCNQ0qQq",
"outputId": "9368f55f-ff21-40a9-d34a-81f745a72e98"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['Test', 'Train']"
]
},
"metadata": {},
"execution_count": 17
}
],
"execution_count": 17
},
{
"cell_type": "code",
"source": [
"import tensorflow as tf\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout, BatchNormalization\n",
"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n"
],
"metadata": {
"trusted": true,
"execution": {
"iopub.status.busy": "2026-01-03T12:24:45.70797Z",
"iopub.execute_input": "2026-01-03T12:24:45.708305Z",
"iopub.status.idle": "2026-01-03T12:24:45.728187Z",
"shell.execute_reply.started": "2026-01-03T12:24:45.708277Z",
"shell.execute_reply": "2026-01-03T12:24:45.726603Z"
},
"id": "LQ56anym0qQr"
},
"outputs": [],
"execution_count": 18
},
{
"cell_type": "code",
"source": [
"IMG_SIZE = (128, 128)\n",
"BATCH_SIZE = 32\n",
"EPOCHS = 5\n"
],
"metadata": {
"trusted": true,
"execution": {
"iopub.status.busy": "2026-01-03T12:24:45.731034Z",
"iopub.execute_input": "2026-01-03T12:24:45.731772Z",
"iopub.status.idle": "2026-01-03T12:24:45.759003Z",
"shell.execute_reply.started": "2026-01-03T12:24:45.731687Z",
"shell.execute_reply": "2026-01-03T12:24:45.757596Z"
},
"id": "rv2Gp1IE0qQr"
},
"outputs": [],
"execution_count": 19
},
{
"cell_type": "code",
"source": [
"train_datagen = ImageDataGenerator(\n",
" rescale=1./255,\n",
" rotation_range=5,\n",
" width_shift_range=0.05,\n",
" height_shift_range=0.05,\n",
" zoom_range=0.1,\n",
" brightness_range=[0.8, 1.2],\n",
" horizontal_flip=True\n",
")\n",
"\n",
"val_test_datagen = ImageDataGenerator(rescale=1./255)\n"
],
"metadata": {
"trusted": true,
"execution": {
"iopub.status.busy": "2026-01-03T12:24:45.760332Z",
"iopub.execute_input": "2026-01-03T12:24:45.760607Z",
"iopub.status.idle": "2026-01-03T12:24:45.794219Z",
"shell.execute_reply.started": "2026-01-03T12:24:45.760583Z",
"shell.execute_reply": "2026-01-03T12:24:45.792562Z"
},
"id": "F0Sa3vpm0qQs"
},
"outputs": [],
"execution_count": 20
},
{
"cell_type": "code",
"source": [
"DATASET_PATH = \"/kaggle/input/candle-image-data\"\n",
"\n",
"train_data = train_datagen.flow_from_directory(\n",
" DATASET_PATH + \"/Train\",\n",
" target_size=IMG_SIZE,\n",
" batch_size=BATCH_SIZE,\n",
" class_mode=\"binary\"\n",
")\n",
"\n",
"\n",
"\n",
"test_data = val_test_datagen.flow_from_directory(\n",
" DATASET_PATH + \"/Test\",\n",
" target_size=IMG_SIZE,\n",
" batch_size=BATCH_SIZE,\n",
" class_mode=\"binary\",\n",
" shuffle=False\n",
")\n",
"print(train_data.class_indices)\n",
"# {'DOWN': 0, 'UP': 1}\n"
],
"metadata": {
"trusted": true,
"execution": {
"iopub.status.busy": "2026-01-03T12:24:45.795754Z",
"iopub.execute_input": "2026-01-03T12:24:45.796462Z",
"iopub.status.idle": "2026-01-03T12:24:46.11179Z",
"shell.execute_reply.started": "2026-01-03T12:24:45.796412Z",
"shell.execute_reply": "2026-01-03T12:24:46.110248Z"
},
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_spHZSmF0qQt",
"outputId": "0e367a75-13b8-41ae-b141-8259c18cb6e8"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Found 1433 images belonging to 2 classes.\n",
"Found 351 images belonging to 2 classes.\n",
"{'Down': 0, 'Up': 1}\n"
]
}
],
"execution_count": 21
},
{
"cell_type": "code",
"source": [
"model = Sequential([\n",
" Conv2D(32, (3,3), activation='relu', input_shape=(128,128,3)),\n",
" BatchNormalization(),\n",
" MaxPooling2D(2,2),\n",
"\n",
" Conv2D(64, (3,3), activation='relu'),\n",
" BatchNormalization(),\n",
" MaxPooling2D(2,2),\n",
"\n",
" Conv2D(128, (3,3), activation='relu'),\n",
" BatchNormalization(),\n",
" MaxPooling2D(2,2),\n",
"\n",
" Flatten(),\n",
" Dense(256, activation='relu'),\n",
" Dropout(0.5),\n",
"\n",
" Dense(1, activation='sigmoid') # UP / DOWN\n",
"])\n"
],
"metadata": {
"trusted": true,
"execution": {
"iopub.status.busy": "2026-01-03T12:24:46.113508Z",
"iopub.execute_input": "2026-01-03T12:24:46.113866Z",
"iopub.status.idle": "2026-01-03T12:24:46.263228Z",
"shell.execute_reply.started": "2026-01-03T12:24:46.113835Z",
"shell.execute_reply": "2026-01-03T12:24:46.262068Z"
},
"id": "xr7wQn7x0qQu"
},
"outputs": [],
"execution_count": 22
},
{
"cell_type": "code",
"source": [
"model.compile(\n",
" optimizer='adam',\n",
" loss='binary_crossentropy',\n",
" metrics=['accuracy']\n",
")\n",
"history = model.fit(\n",
" train_data,\n",
" epochs=EPOCHS,\n",
"\n",
")\n",
"test_loss, test_acc = model.evaluate(test_data)\n",
"print(\"Test Accuracy:\", test_acc)\n"
],
"metadata": {
"trusted": true,
"execution": {
"iopub.status.busy": "2026-01-03T12:24:46.264637Z",
"iopub.execute_input": "2026-01-03T12:24:46.264976Z",
"iopub.status.idle": "2026-01-03T12:25:38.447409Z",
"shell.execute_reply.started": "2026-01-03T12:24:46.264951Z",
"shell.execute_reply": "2026-01-03T12:25:38.446449Z"
},
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "tNSajhCu0qQv",
"outputId": "2fe38300-ba44-475e-9980-af14087815a6"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/5\n",
"\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 357ms/step - accuracy: 0.5084 - loss: 3.5710\n",
"Epoch 2/5\n",
"\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 304ms/step - accuracy: 0.5315 - loss: 1.1040\n",
"Epoch 3/5\n",
"\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 307ms/step - accuracy: 0.5681 - loss: 0.7230\n",
"Epoch 4/5\n",
"\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 309ms/step - accuracy: 0.5705 - loss: 0.6868\n",
"Epoch 5/5\n",
"\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 305ms/step - accuracy: 0.5454 - loss: 0.6975\n",
"\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 313ms/step - accuracy: 0.7536 - loss: 9.8348\n",
"Test Accuracy: 0.4472934603691101\n"
]
}
],
"execution_count": 23
}
]
}
1 change: 1 addition & 0 deletions Assignment 3/Assignment3_Harsh/txt
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