diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..a36c305a86384361ef3e749d52c34bd8db93207f --- /dev/null +++ b/.gitattributes @@ -0,0 +1,6 @@ +*.keras filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.png filter=lfs diff=lfs merge=lfs -text +*.jpg filter=lfs diff=lfs merge=lfs -text +*.jpeg filter=lfs diff=lfs merge=lfs -text +*.pdf filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..b486bd68cf176ee931483b4a2ff52fba6829daf2 --- /dev/null +++ b/.gitignore @@ -0,0 +1,53 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# Virtual environments +venv/ +env/ +ENV/ +env.bak/ +venv.bak/ + +# Jupyter Notebook +.ipynb_checkpoints + +# OS generated files +.DS_Store +.DS_Store? +._* +.Spotlight-V100 +.Trashes +ehthumbs.db +Thumbs.db + +# VS Code +.vscode/ + +# Models and large files (depending on your preference, uncomment if you don't want to push models) +# models/*.h5 +# models/*.keras diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..65596e0e9e326bc7a781144cddcbdb19359f0d44 --- /dev/null +++ b/README.md @@ -0,0 +1,86 @@ +![logo_ironhack_blue 7](https://user-images.githubusercontent.com/23629340/40541063-a07a0a8a-601a-11e8-91b5-2f13e4e6b441.png) + +# Project I | Deep Learning: Image Classification with CNN + +## Task Description + +Students will build a Convolutional Neural Network (CNN) model to classify images from a given dataset into predefined categories/classes. + +## Datasets (pick one!) + +1. The dataset for this task is the CIFAR-10 dataset, which consists of 60,000 32x32 color images in 10 classes, with 6,000 images per class. You can download the dataset from [here](https://www.cs.toronto.edu/~kriz/cifar.html). +2. The second dataset contains about 28,000 medium quality animal images belonging to 10 categories: dog, cat, horse, spyder, butterfly, chicken, sheep, cow, squirrel, elephant. The link is [here](https://www.kaggle.com/datasets/alessiocorrado99/animals10/data). + +## Assessment Components + +1. **Data Preprocessing** + - Data loading and preprocessing (e.g., normalization, resizing, augmentation). + - Create visualizations of some images, and labels. + +2. **Model Architecture** + - Design a CNN architecture suitable for image classification. + - Include convolutional layers, pooling layers, and fully connected layers. + +3. **Model Training** + - Train the CNN model using appropriate optimization techniques (e.g., stochastic gradient descent, Adam). + - Utilize techniques such as early stopping to prevent overfitting. + +4. **Model Evaluation** + - Evaluate the trained model on a separate validation set. + - Compute and report metrics such as accuracy, precision, recall, and F1-score. + - Visualize the confusion matrix to understand model performance across different classes. + +5. **Transfer Learning** + - Evaluate the accuracy of your model on a pre-trained models like ImagNet, VGG16, Inception... (pick one an justify your choice) + - You may find this [link](https://www.tensorflow.org/tutorials/images/transfer_learning_with_hub) helpful. + - [This](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html) is the Pytorch version. + - Perform transfer learning with your chosen pre-trained models i.e., you will probably try a few and choose the best one. + +5. **Code Quality** + - Well-structured and commented code. + - Proper documentation of functions and processes. + - Efficient use of libraries and resources. + +6. **Report** + - Write a concise report detailing the approach taken, including: + - Description of the chosen CNN architecture. + - Explanation of preprocessing steps. + - Details of the training process (e.g., learning rate, batch size, number of epochs). + - Results and analysis of models performance. + - What is your best model. Why? + - Insights gained from the experimentation process. + - Include visualizations and diagrams where necessary. + + 7. **Model deployment** + - Pick the best model + - Build an app using Flask - Can you host somewhere other than your laptop? **+5 Bonus points if you use [Tensorflow Serving](https://www.tensorflow.org/tfx/guide/serving)** + - User should be able to upload one or multiples images get predictions including probabilities for each prediction + + +## Evaluation Criteria + +- Accuracy of the trained models on the validation set. **30 points** +- Clarity and completeness of the report. **20 points** +- Quality of code implementation. **5 points** +- Proper handling of data preprocessing and models training. **30 points** +- Demonstration of understanding key concepts of deep learning. **5 points** +- Model deployment. **10 points** + + **Passing Score is 70 points**. + +## Submission Details + +- Deadline for submission: end of the week or as communicated by your teaching team. +- Submit the following: + 1. Python code files (`*.py`, `ipynb`) containing the model implementation and training process. + 2. A data folder with 5-10 images to test the deployed model/app if hosted somewhere else other than your laptop (strongly recommended! Not a must have) + 2. A PDF report documenting the approach, results, and analysis. + 3. Any additional files necessary for reproducing the results (e.g., requirements.txt, README.md). + 4. PPT presentation + +## Additional Notes + +- Students are encourage to experiment with different architectures, hyper-parameters, and optimization techniques. +- Provide guidance and resources for troubleshooting common issues during model training and evaluation. +- Students will discuss their approaches and findings in class during assessment evaluation sessions. + diff --git a/REPORT.md b/REPORT.md new file mode 100644 index 0000000000000000000000000000000000000000..54ffbafc9e5cd29b08d01e92c852f525ff56df9a --- /dev/null +++ b/REPORT.md @@ -0,0 +1,253 @@ +# ๐Ÿ“Š Project Report: CIFAR-10 Image Classification with CNN + +**Author:** Sebastian Lopez +**Date:** February 2026 +**Environment:** Python 3.10 | TensorFlow 2.18.1 | Keras 3.6.0 +**Dataset:** CIFAR-10 (60,000 images, 10 classes, 32ร—32 RGB) + +--- + +## 1. Introduction + +This project builds and evaluates two deep learning models for classifying images from the CIFAR-10 dataset into 10 categories: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck. + +**Models developed:** +1. **Custom CNN** โ€” A purpose-built convolutional neural network +2. **MobileNetV2 Transfer Learning** โ€” Leveraging pretrained ImageNet features + +--- + +## 2. Data Preprocessing + +### 2.1 Normalization +All pixel values were scaled from [0, 255] to [0.0, 1.0] by dividing by 255. This ensures consistent gradient magnitudes during backpropagation. + +### 2.2 Label Encoding +Integer labels (0โ€“9) were one-hot encoded into 10-dimensional binary vectors using `keras.utils.to_categorical()`. + +### 2.3 Data Augmentation (Custom CNN only) +Applied real-time augmentation during training to reduce overfitting: + +| Augmentation | Range | +|:-------------------|:------------| +| Rotation | ยฑ15ยฐ | +| Width/Height shift | ยฑ10% | +| Horizontal flip | Random | +| Zoom | ยฑ10% | + +### 2.4 Dataset Split +- **Training:** 45,000 images (90% of train set) +- **Validation:** 5,000 images (10% of train set) +- **Test:** 10,000 images (held-out) + +--- + +## 3. Model Architectures + +### 3.1 Custom CNN + +A 3-block convolutional network designed specifically for 32ร—32 CIFAR-10 images: + +``` +Block 1: Conv2D(32) ร— 2 โ†’ BatchNorm โ†’ MaxPool(2ร—2) โ†’ Dropout(0.25) +Block 2: Conv2D(64) ร— 2 โ†’ BatchNorm โ†’ MaxPool(2ร—2) โ†’ Dropout(0.25) +Block 3: Conv2D(128) ร— 2 โ†’ BatchNorm โ†’ MaxPool(2ร—2) โ†’ Dropout(0.25) +Head: Flatten โ†’ Dense(256) โ†’ BatchNorm โ†’ Dropout(0.5) โ†’ Dense(10, softmax) +``` + +**Design rationale:** +- Progressive filter increase captures increasingly complex features +- BatchNormalization stabilizes training +- Dropout at every block provides strong regularization +- `padding='same'` preserves spatial dimensions within blocks + +### 3.2 MobileNetV2 Transfer Learning + +``` +Input(32ร—32ร—3) โ†’ UpSampling2D(3ร—) โ†’ MobileNetV2(frozen, ImageNet weights) +โ†’ GlobalAveragePooling2D โ†’ Dense(256) โ†’ Dropout(0.5) โ†’ Dense(10, softmax) +``` + +**Why MobileNetV2?** +- **Efficient**: ~3.4M parameters โ€” lightweight enough for CPU training +- **Proven**: Excellent ImageNet accuracy despite small size +- **Practical**: Faster to train than VGG16 (~138M params) or ResNet50 (~25M params) +- **Upscaling**: 32ร—32 images are upscaled to 96ร—96 via UpSampling2D to meet MobileNetV2's minimum input requirements + +**Fine-tuning strategy:** +1. Phase 1: Train only the classification head (base frozen, lr=0.001) +2. Phase 2: Unfreeze top 20 layers of MobileNetV2, retrain with lr=0.0001 + +--- + +## 4. Training Configuration + +| Parameter | Custom CNN | Transfer Learning | +|:------------------|:-----------|:------------------| +| Optimizer | Adam | Adam | +| Initial LR | 0.001 | 0.001 โ†’ 0.0001 | +| Batch size | 64 | 64 | +| Max epochs | 100 | 50 + 30 | +| Early stopping | patience=10| patience=10/8 | +| Data augmentation | Yes | No (Phase 1) | +| LR reduction | ร—0.5 on plateau | ร—0.5 on plateau | + +--- + +## 5. Results + +### 5.1 Metrics Summary + +| Metric | Custom CNN | MobileNetV2 TL | +|:-----------|:-----------|:---------------| +| Accuracy | 0.8530 (85.30%) | 0.8340 (83.40%) | +| Precision | 0.8556 | 0.8353 | +| Recall | 0.8530 | 0.8340 | +| F1-Score | 0.8512 | 0.8339 | + +> **Note:** Run both training scripts to populate these results. The JSON outputs in `outputs/` contain the exact numbers. + +### 5.2 Training Curves +- `outputs/custom_cnn_history.png` โ€” Custom CNN accuracy and loss curves +- `outputs/transfer_learning_history.png` โ€” Transfer learning curves + +### 5.3 Confusion Matrices +- `outputs/custom_cnn_cm.png` โ€” Custom CNN confusion matrix +- `outputs/transfer_learning_cm.png` โ€” Transfer learning confusion matrix +- `outputs/model_comparison.png` โ€” Side-by-side metrics comparison + +### 5.4 Per-Class Confusion Analysis + +Both confusion matrices reveal systematic error patterns driven by visual similarity at 32ร—32 resolution: + +| Confusion Pair | Custom CNN Errors | MobileNetV2 Errors | Explanation | +|:---------------|:-----------------:|:-------------------:|:------------| +| **cat โ†” dog** | 198 | 256 | Both are furry, four-legged animals that share similar color palettes and body proportions at low resolution | +| **bird โ†” frog** | 87 | 32 | Small subjects against green/natural backgrounds; at 32ร—32, both reduce to small colored blobs | +| **automobile โ†” truck** | 82 | 107 | Both are wheeled vehicles with similar boxy shapes; the main discriminator (size) is lost at low resolution | +| **airplane โ†” ship** | 49 | 85 | Both frequently appear against blue backgrounds (sky vs. water), confusing color-based features | + +**Key observation:** The cat class is the hardest to classify for both models (~65% accuracy), while frog, truck, and ship consistently achieve >90% accuracy. This suggests the model relies heavily on distinctive color patterns (green for frog, blue/gray for ship) rather than fine-grained shape features. + +See `outputs/misclassified_examples.png` for a visual sample of misclassifications. + +--- + +## 6. Best Model Selection + +### ๐Ÿ† Winner: Custom CNN (85.3% accuracy) + +Lets see... + +## 7. Model Deployment + +The best model is deployed via a **Flask web application**: + +- **URL:** `http://localhost:5001` +- **Port note:** Port 5001 is used instead of Flask's default 5000 because macOS Monterey (12+) reserves port 5000 for the AirPlay Receiver service +- **Features:** + - Drag-and-drop or click-to-upload interface + - Supports single and multiple image uploads + - Displays top-10 predictions with probability bars + - API endpoint at `/api/predict` for programmatic access +- **Preprocessing:** Uploaded images are resized to 32ร—32, normalized to [0,1] + +--- + +## 8. Key Insights + +1. **Data augmentation significantly reduces overfitting** for the custom CNN, allowing it to train longer before early stopping triggers +2. **Transfer learning with frozen base layers** converges faster but doesn't always outperform custom architectures โ€” especially when the source domain (ImageNet, 224ร—224) differs significantly from the target (CIFAR-10, 32ร—32) +3. **Fine-tuning** the top layers of MobileNetV2 with a reduced learning rate provides an additional accuracy boost but cannot fully overcome the resolution mismatch +4. **Confusion matrix analysis** reveals that visually similar classes (cat/dog, automobile/truck) are the most commonly confused pairs โ€” see Section 5.4 +5. **Batch normalization** between convolutional layers stabilizes training and allows higher learning rates + +## 9. Bias & Limitations + +### 9.1 Dataset Representation Bias (Geographic/Cultural) + +CIFAR-10 was collected from internet sources, predominantly Western/English-language websites. This introduces geographic bias in how each class is represented: + +| Class | Bias Example | +|:------|:-------------| +| **Automobile** | Mostly American/European car designs โ€” the model may struggle with tuk-tuks, rickshaws, or vehicle types common in Asia and Africa | +| **Truck** | Predominantly modern pickup and delivery trucks โ€” would it recognize flatbed trucks from rural areas? | +| **Ship** | Mostly large vessels in open water โ€” canoes, kayaks, or fishing boats from other cultures are underrepresented | +| **Horse** | Photographed primarily in Western contexts (ranches, paddocks) โ€” horses in different cultural settings may confuse the model | +| **Bird** | Heavily weighted toward North American bird species โ€” tropical or exotic birds are underrepresented | + +### 9.2 Why This Matters + +1. **No geographic diversity audit**: The model learned to classify "an automobile as seen by English-speaking internet users in North America" โ€” not a universal definition +2. **Background/context bias**: Objects were photographed in typical contexts (planes in blue sky, ships in water). An airplane on a tarmac or a ship in dry dock would likely be harder to classify +3. **Color/lighting bias**: Most photos were taken in daylight. Night images or unusual lighting conditions may significantly degrade performance +4. **Resolution bias**: All images are compressed to 32ร—32, which means the model relies heavily on **color patterns and rough shapes** rather than fine details โ€” this is itself a form of information loss bias + +### 9.3 Other Limitations + +- **Artificial class balance**: CIFAR-10 is perfectly balanced (6,000 per class), which is unrealistic โ€” real-world data is almost never balanced +- **Open-set recognition**: The model has no concept of "none of the above" โ€” uploading an image of a banana will still produce a confident prediction for one of the 10 classes +- **Temporal bias**: CIFAR-10 images are from a specific time period. Modern cars, ships, and trucks look different than those from when the dataset was compiled +- **No cross-validation**: Results are based on a single train/validation/test split, which could introduce variance + +--- + +## 10. Folder Structure + +``` +project-1-deep-learning-image-classification-with-cnn/ +โ”œโ”€โ”€ README.md โ† Project brief +โ”œโ”€โ”€ requirements.txt โ† Dependencies (pinned versions) +โ”œโ”€โ”€ Dockerfile โ† Docker container config +โ”œโ”€โ”€ .gitignore โ† Git ignore rules +โ”œโ”€โ”€ Report/ โ† Documentation & presentation +โ”‚ โ”œโ”€โ”€ REPORT.md โ† This report +โ”‚ โ”œโ”€โ”€ CIFAR10_Image_Classification_CNN.ipynb โ† Full notebook +โ”‚ โ””โ”€โ”€ CIFAR10_Image_Classification_CNN_Presentation.ipynb โ† Slide deck +โ”œโ”€โ”€ src/ โ† Source modules +โ”‚ โ”œโ”€โ”€ data_loader.py +โ”‚ โ”œโ”€โ”€ model_builder.py +โ”‚ โ”œโ”€โ”€ train.py +โ”‚ โ””โ”€โ”€ evaluate.py +โ”œโ”€โ”€ notebooks/ โ† Executable pipeline scripts +โ”‚ โ”œโ”€โ”€ 01_data_exploration.py +โ”‚ โ”œโ”€โ”€ 02_custom_cnn.py +โ”‚ โ”œโ”€โ”€ 03_transfer_learning_cpu.py โ† CPU-optimized (pre-resize with cv2) +โ”‚ โ”œโ”€โ”€ 03_transfer_learning_gpu.py โ† GPU version (UpSampling2D in graph) +โ”‚ โ”œโ”€โ”€ 04_model_comparison.py โ† Compare both models +โ”‚ โ”œโ”€โ”€ 05_deploy.py โ† Launch Flask app (port 5001) +โ”‚ โ””โ”€โ”€ 06_misclassifications.py โ† Generate misclassification examples +โ”œโ”€โ”€ app/ โ† Flask web application +โ”‚ โ”œโ”€โ”€ app.py +โ”‚ โ”œโ”€โ”€ templates/index.html +โ”‚ โ””โ”€โ”€ static/style.css +โ”œโ”€โ”€ models/ โ† Saved trained models (.keras) +โ”œโ”€โ”€ outputs/ โ† Plots, metrics, reports +โ”œโ”€โ”€ test_images/ โ† Sample images for testing the app +โ””โ”€โ”€ Other documentation/ โ† Learning guides & extra docs +``` + +--- + +## 10. How to Run + +```bash +# 1. Install dependencies +conda activate ironhack.nn + +# 2. Explore the data +python notebooks/01_data_exploration.py + +# 3. Train the custom CNN +python notebooks/02_custom_cnn.py + +# 4. Train with transfer learning (CPU version โ€” pre-resizes images with cv2) +python notebooks/03_transfer_learning_cpu.py + +# 5. Compare both models +python notebooks/04_model_comparison.py + +# 6. Launch the Flask app (port 5001 โ€” macOS reserves 5000 for AirPlay) +python notebooks/05_deploy.py +# โ†’ Open http://localhost:5001 +``` diff --git a/app/app.py b/app/app.py new file mode 100644 index 0000000000000000000000000000000000000000..c99904b523581f0fac48a093268b342428cd5e71 --- /dev/null +++ b/app/app.py @@ -0,0 +1,218 @@ +""" +Flask Web Application for CIFAR-10 Image Classification. + +This app allows users to upload one or multiple images and receive +predictions with class probabilities from the best trained model. + +Usage: + conda run -n ironhack.nn python app/app.py + +Then navigate to http://localhost:5001 in your browser. +(Port 5001 is used because macOS Monterey+ reserves 5000 for AirPlay.) +""" + +import os +import sys +import json +import numpy as np +from flask import Flask, request, render_template, jsonify +from PIL import Image +import io + +# Add project root to path so we can import our modules +sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..')) + +from src.data_loader import CLASS_NAMES + +# TensorFlow / Keras imports +from keras.api.models import load_model +from keras.api.utils import img_to_array + +# โ”€โ”€ Configuration โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ +PROJECT_ROOT = os.path.join(os.path.dirname(__file__), '..') + +# Try to load the best model (transfer learning first, then custom CNN) +TRANSFER_MODEL_PATH = os.path.join(PROJECT_ROOT, 'models', 'transfer_learning.keras') +CUSTOM_MODEL_PATH = os.path.join(PROJECT_ROOT, 'models', 'custom_cnn.keras') + +app = Flask(__name__) +app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024 # 16 MB max upload + +# Global models dictionary +models = {} +default_model_name = None + + +def load_all_models(): + """Load both Custom CNN and MobileNetV2 models.""" + global models, default_model_name + + if os.path.exists(TRANSFER_MODEL_PATH): + models['transfer'] = { + 'model': load_model(TRANSFER_MODEL_PATH), + 'name': 'MobileNetV2 Transfer Learning' + } + print(f"โœ… Loaded model: {models['transfer']['name']}") + default_model_name = 'transfer' + + if os.path.exists(CUSTOM_MODEL_PATH): + models['custom'] = { + 'model': load_model(CUSTOM_MODEL_PATH), + 'name': 'Custom CNN' + } + print(f"โœ… Loaded model: {models['custom']['name']}") + default_model_name = 'custom' + + if not models: + print("โš ๏ธ No trained models found! Please run training scripts first.") + default_model_name = None + + +def preprocess_image(image_bytes): + """ + Preprocess a single uploaded image for prediction. + + Steps: + 1. Open and convert to RGB + 2. Resize to 32x32 (CIFAR-10 input size) + 3. Normalize pixel values to [0, 1] + 4. Add batch dimension + + Args: + image_bytes: Raw image bytes from upload. + + Returns: + numpy array of shape (1, 32, 32, 3), float32, normalized. + """ + img = Image.open(io.BytesIO(image_bytes)).convert('RGB') + img = img.resize((32, 32)) + img_array = img_to_array(img) / 255.0 + img_array = np.expand_dims(img_array, axis=0) + return img_array + + +def get_predictions(img_array, model_choice, top_n=5): + """ + Get top-N class predictions with probabilities. + + Args: + img_array: Preprocessed image array (1, 32, 32, 3). + model_choice: String key selecting which model to use ('custom' or 'transfer'). + top_n: Number of top predictions to return. + + Returns: + list of dicts: [{'class': str, 'probability': float}, ...] + """ + if not models or model_choice not in models: + return [{'class': 'No model loaded', 'probability': 0.0}] + + selected_model = models[model_choice]['model'] + predictions = selected_model.predict(img_array, verbose=0)[0] + top_indices = predictions.argsort()[-top_n:][::-1] + + results = [] + for idx in top_indices: + results.append({ + 'class': CLASS_NAMES[idx], + 'probability': float(predictions[idx]) + }) + return results + + +# โ”€โ”€ Routes โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + +@app.route('/') +def index(): + """Render the main upload page.""" + current_model = models.get(default_model_name, {}).get('name', 'Loading...') if default_model_name else 'No model loaded' + return render_template('index.html', model_name=current_model) + + +@app.route('/predict', methods=['POST']) +def predict(): + """ + Handle image upload(s) and return predictions. + + Accepts single or multiple files via the 'files' form field. + Returns JSON with predictions for each uploaded image. + """ + if 'files' not in request.files: + return jsonify({'error': 'No files uploaded'}), 400 + + files = request.files.getlist('files') + if not files or all(f.filename == '' for f in files): + return jsonify({'error': 'No files selected'}), 400 + + model_choice = request.form.get('model_choice', default_model_name) + all_results = [] + + for file in files: + if file.filename == '': + continue + + try: + image_bytes = file.read() + img_array = preprocess_image(image_bytes) + predictions = get_predictions(img_array, model_choice, top_n=10) + + all_results.append({ + 'filename': file.filename, + 'predictions': predictions + }) + except Exception as e: + all_results.append({ + 'filename': file.filename, + 'error': str(e) + }) + + current_model_name = models.get(model_choice, {}).get('name', 'Unknown Model') + + return render_template('index.html', + model_name=current_model_name, + selected_model=model_choice, + results=all_results) + + +@app.route('/api/predict', methods=['POST']) +def api_predict(): + """ + API endpoint: returns JSON predictions (for programmatic access). + """ + if 'files' not in request.files: + return jsonify({'error': 'No files uploaded'}), 400 + + files = request.files.getlist('files') + model_choice = request.form.get('model_choice', default_model_name) + current_model_name = models.get(model_choice, {}).get('name', 'Unknown Model') + + all_results = [] + + for file in files: + if file.filename == '': + continue + try: + image_bytes = file.read() + img_array = preprocess_image(image_bytes) + predictions = get_predictions(img_array, model_choice, top_n=10) + all_results.append({ + 'filename': file.filename, + 'predictions': predictions + }) + except Exception as e: + all_results.append({ + 'filename': file.filename, + 'error': str(e) + }) + + return jsonify({'model': current_model_name, 'results': all_results}) + + +# โ”€โ”€ Main โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + +if __name__ == '__main__': + load_all_models() + port = int(os.environ.get('PORT', 5001)) + print(f"\n ๐Ÿš€ Starting Flask app at http://localhost:{port}") + print(f" Available Models: {', '.join([m['name'] for m in models.values()])}") + print(f" Classes: {', '.join(CLASS_NAMES)}") + app.run(host='0.0.0.0', port=port, debug=True) diff --git a/app/static/style.css b/app/static/style.css new file mode 100644 index 0000000000000000000000000000000000000000..3b54af3f44d1c9b5fda3b8182377744af13ef187 --- /dev/null +++ b/app/static/style.css @@ -0,0 +1,397 @@ +/* โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• + CIFAR-10 Image Classifier โ€” Flask App Styles + Modern, premium design with glassmorphism and gradients + โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• */ + +:root { + --primary: #6C5CE7; + --primary-light: #A29BFE; + --secondary: #B388FF; + --accent: #CE93D8; + --grape: #7C4DFF; + --grape-light: #B47CFF; + --grape-glow: rgba(124, 77, 255, 0.25); + --bg-dark: #0c0c1d; + --bg-card: rgba(255, 255, 255, 0.05); + --bg-card-hover: rgba(255, 255, 255, 0.08); + --text: #E8E8F0; + --text-muted: #9B9BB0; + --border: rgba(255, 255, 255, 0.08); + --shadow: 0 8px 32px rgba(0, 0, 0, 0.3); + --radius: 16px; + --radius-sm: 8px; + --section-gap: 40px; +} + +* { + margin: 0; + padding: 0; + box-sizing: border-box; +} + +body { + font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif; + background: var(--bg-dark); + background-image: + radial-gradient(ellipse at 20% 50%, rgba(108, 92, 231, 0.15) 0%, transparent 50%), + radial-gradient(ellipse at 80% 20%, rgba(124, 77, 255, 0.10) 0%, transparent 50%), + radial-gradient(ellipse at 50% 80%, rgba(179, 136, 255, 0.08) 0%, transparent 50%); + color: var(--text); + min-height: 100vh; + line-height: 1.6; +} + +.container { + max-width: 800px; + margin: 0 auto; + padding: 40px 20px; +} + +/* โ”€โ”€โ”€ Header โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ */ + +.header { + text-align: center; + margin-bottom: var(--section-gap); +} + +.logo { + display: flex; + align-items: center; + justify-content: center; + margin-bottom: var(--section-gap); +} + +.header h1 { + font-size: 2rem; + font-weight: 700; + background: linear-gradient(135deg, var(--primary-light), var(--grape-light)); + -webkit-background-clip: text; + -webkit-text-fill-color: transparent; + background-clip: text; +} + +.subtitle { + color: var(--text-muted); + font-size: 0.95rem; + margin-bottom: 16px; +} + +.model-badge { + display: inline-block; + background: linear-gradient(135deg, var(--primary), var(--grape)); + color: white; + padding: 2px 12px; + border-radius: 20px; + font-size: 0.85rem; + font-weight: 600; +} + +.class-tags { + display: flex; + flex-direction: column; + align-items: center; + gap: 10px; + margin-top: 0; +} + +.tags-row { + display: flex; + justify-content: center; + gap: 8px; + flex-wrap: wrap; +} + +.tag { + background: rgba(124, 77, 255, 0.08); + border: 1px solid rgba(124, 77, 255, 0.2); + padding: 4px 12px; + border-radius: 20px; + font-size: 0.8rem; + color: var(--primary-light); + transition: all 0.2s ease; +} + +.tag:hover { + background: rgba(124, 77, 255, 0.15); + color: var(--text); + border-color: var(--grape); + transform: translateY(-1px); + box-shadow: 0 2px 12px var(--grape-glow); +} + +/* โ”€โ”€โ”€ Model Selection โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ */ + +.model-selection { + margin-bottom: var(--section-gap); + text-align: center; +} + +.model-label { + font-weight: 500; + font-size: 13px; + color: var(--text-muted); + margin-right: 8px; +} + +.model-select { + padding: 5px 10px; + border-radius: 6px; + border: 1px solid var(--grape); + background: rgba(124, 77, 255, 0.08); + backdrop-filter: blur(10px); + color: var(--text); + font-family: inherit; + font-size: 13px; + cursor: pointer; + outline: none; + transition: all 0.2s ease; +} + +.model-select:hover { + border-color: var(--grape-light); + box-shadow: 0 0 0 2px var(--grape-glow); +} + +.model-select:focus { + border-color: var(--grape-light); + box-shadow: 0 0 0 2px var(--grape-glow); +} + +/* โ”€โ”€โ”€ Upload Section โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ */ + +.upload-section { + margin-bottom: var(--section-gap); +} + +.dropzone { + background: var(--bg-card); + border: 2px dashed var(--border); + border-radius: var(--radius); + padding: 35px 20px; + text-align: center; + cursor: pointer; + transition: all 0.3s ease; + position: relative; +} + +.dropzone:hover, +.dropzone.dragover { + border-color: var(--grape); + background: rgba(124, 77, 255, 0.08); + transform: translateY(-2px); + box-shadow: 0 8px 24px var(--grape-glow); +} + +.dropzone-icon { + font-size: 3rem; + display: block; + margin-bottom: 12px; +} + +.dropzone-text { + font-size: 1.1rem; + font-weight: 500; + color: var(--text); +} + +.dropzone-subtext { + font-size: 0.85rem; + color: var(--text-muted); + margin-top: 4px; +} + +.file-input { + position: absolute; + top: 0; + left: 0; + width: 100%; + height: 100%; + opacity: 0; + cursor: pointer; +} + +.file-list { + margin-top: 12px; + display: flex; + flex-direction: column; + gap: 4px; +} + +.file-item { + background: var(--bg-card); + padding: 8px 16px; + border-radius: var(--radius-sm); + font-size: 0.85rem; + color: var(--text-muted); + border: 1px solid var(--border); +} + +.submit-btn { + display: block; + width: 100%; + margin-top: 20px; + padding: 16px; + font-size: 1.1rem; + font-weight: 600; + font-family: 'Inter', sans-serif; + color: white; + background: linear-gradient(135deg, var(--primary), var(--grape)); + border: none; + border-radius: var(--radius-sm); + cursor: pointer; + transition: all 0.3s ease; + box-shadow: 0 4px 16px var(--grape-glow); +} + +.submit-btn:hover:not(:disabled) { + transform: translateY(-2px); + box-shadow: 0 6px 24px rgba(124, 77, 255, 0.4); +} + +.submit-btn:disabled { + opacity: 0.4; + cursor: not-allowed; +} + +.btn-icon { + margin-right: 6px; +} + +/* โ”€โ”€โ”€ Results Section โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ */ + +.results-section { + margin-bottom: var(--section-gap); +} + +.results-title { + font-size: 1.4rem; + font-weight: 600; + margin-bottom: 24px; + text-align: center; +} + +.result-card { + background: var(--bg-card); + border: 1px solid var(--border); + border-radius: var(--radius); + padding: 24px; + margin-bottom: 20px; + backdrop-filter: blur(10px); + box-shadow: var(--shadow); + transition: transform 0.2s ease; +} + +.result-card:hover { + transform: translateY(-2px); +} + +.result-header { + display: flex; + justify-content: space-between; + align-items: center; + margin-bottom: 16px; + padding-bottom: 12px; + border-bottom: 1px solid var(--border); + flex-wrap: wrap; + gap: 8px; +} + +.filename { + font-weight: 500; + font-size: 0.95rem; +} + +.top-prediction { + background: linear-gradient(135deg, rgba(124, 77, 255, 0.15), rgba(179, 136, 255, 0.05)); + border: 1px solid rgba(124, 77, 255, 0.3); + color: var(--grape-light); + padding: 4px 14px; + border-radius: 20px; + font-size: 0.85rem; +} + +.predictions { + display: flex; + flex-direction: column; + gap: 8px; +} + +.prediction-row { + display: flex; + align-items: center; + gap: 12px; +} + +.pred-class { + width: 100px; + font-size: 0.9rem; + font-weight: 500; + text-align: right; + color: var(--text-muted); +} + +.prob-bar-container { + flex: 1; + height: 24px; + background: rgba(255, 255, 255, 0.04); + border-radius: 12px; + overflow: hidden; + position: relative; +} + +.prob-bar { + height: 100%; + background: linear-gradient(90deg, var(--primary), var(--grape)); + border-radius: 12px; + min-width: 2px; + transition: width 0.8s ease; +} + +.pred-prob { + width: 60px; + font-size: 0.85rem; + font-weight: 600; + color: var(--primary-light); + text-align: right; +} + +.error-msg { + color: #ef9a9a; + padding: 12px; + background: rgba(239, 154, 154, 0.1); + border-radius: var(--radius-sm); + border: 1px solid rgba(239, 154, 154, 0.2); +} + +/* โ”€โ”€โ”€ Footer โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ */ + +.footer { + text-align: center; + padding-top: var(--section-gap); + margin-top: var(--section-gap); + border-top: 1px solid rgba(124, 77, 255, 0.15); + color: var(--text-muted); + font-size: 0.85rem; +} + +/* โ”€โ”€โ”€ Responsive โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ */ + +@media (max-width: 600px) { + .header h1 { + font-size: 1.5rem; + } + + .result-header { + flex-direction: column; + align-items: flex-start; + } + + .pred-class { + width: 80px; + font-size: 0.8rem; + } + + .dropzone { + padding: 30px 16px; + } +} \ No newline at end of file diff --git a/app/templates/index.html b/app/templates/index.html new file mode 100644 index 0000000000000000000000000000000000000000..ff9022b79d38a8c8c07237c07d566699c9cd6d57 --- /dev/null +++ b/app/templates/index.html @@ -0,0 +1,163 @@ + + + + + + + + CIFAR-10 Image Classifier + + + + + +
+ +
+ + +
+
+ โœˆ๏ธ Airplane + ๐Ÿš— Automobile + ๐Ÿšข Ship + ๐Ÿšš Truck +
+
+ ๐Ÿฆ Bird + ๐Ÿฑ Cat + ๐ŸฆŒ Deer + ๐Ÿ• Dog + ๐Ÿธ Frog + ๐Ÿด Horse +
+
+
+ + +
+
+
+ + +
+ +
+
+ ๐Ÿ“ธ +

Drag & drop images here

+

or click to browse โ€” supports multiple files

+
+ +
+ +
+ +
+
+ + + {% if results %} +
+

๐ŸŽฏ Classification Results

+ + {% for result in results %} +
+
+ ๐Ÿ“„ {{ result.filename }} + {% if result.predictions %} + + Best: {{ result.predictions[0].class | capitalize }} + ({{ "%.1f" | format(result.predictions[0].probability * 100) }}%) + + {% endif %} +
+ + {% if result.error %} +
โš ๏ธ Error: {{ result.error }}
+ {% else %} +
+ {% for pred in result.predictions %} +
+ {{ pred.class | capitalize }} +
+
+
+ {{ "%.1f" | format(pred.probability * 100) }}% +
+ {% endfor %} +
+ {% endif %} +
+ {% endfor %} +
+ {% endif %} + + + +
+ + + + + \ No newline at end of file diff --git a/models/custom_cnn.keras b/models/custom_cnn.keras new file mode 100644 index 0000000000000000000000000000000000000000..87606ca6c78fec3260faf6ec2d0c5738dee6c9ee --- /dev/null +++ b/models/custom_cnn.keras @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6da05a207a55bbd1ba43269fc7094bb3bb1aa3199c4448bca009a85b78b8a3ad +size 9902170 diff --git a/models/transfer_learning.keras b/models/transfer_learning.keras new file mode 100644 index 0000000000000000000000000000000000000000..3b450075545806d393531aa70fef3a5938ba9193 --- /dev/null +++ b/models/transfer_learning.keras @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5516031beafea75d838dc61097ee0bd08eef101f2e9c661495f512ab15cac7e5 +size 19458191 diff --git a/notebooks_knowledge&presentation/.In_cloud_transferlearning_gpu_options.md b/notebooks_knowledge&presentation/.In_cloud_transferlearning_gpu_options.md new file mode 100644 index 0000000000000000000000000000000000000000..0856c9d0df25c313fc319b9e79ce947e61cbdffa --- /dev/null +++ b/notebooks_knowledge&presentation/.In_cloud_transferlearning_gpu_options.md @@ -0,0 +1,49 @@ +# Transfer Learning: GPU Cloud Options + +When performing Transfer Learning (like using MobileNetV2 or VGG16) on the CIFAR-10 dataset, training can be extremely slow on a local CPU (up to 20-25 hours). + +To speed up the process to just minutes, you have several excellent free cloud-based GPU options, as well as a strategy to optimize your local CPU training. + +--- + +## ๐Ÿฅ‡ Option 1: Google Colab (FREE, easiest, recommended) +Run your notebook on Google's free T4/V100 GPU in the cloud. + +1. Go to [colab.research.google.com](https://colab.research.google.com/) +2. Upload your `.ipynb` notebook or paste your code. +3. Set **Runtime โ†’ Change runtime type โ†’ T4 GPU** +4. *Result:* What takes 20h on your CPU takes ~8 minutes on a Colab GPU โœ… +5. *Cost:* Free (with limits), or ~$10/month for Colab Pro if you need more compute. + +--- + +## ๐Ÿฅˆ Option 2: Kaggle Notebooks (FREE, 30h GPU/week) +Run notebooks directly on Kaggle with free GPU access. + +1. Go to [kaggle.com/code](https://www.kaggle.com/code) +2. CIFAR-10 is even available as a built-in Kaggle dataset, making data loading instant. +3. Very similar interface to Colab, with zero setup required. +4. *Result:* Free T4 GPU, 30 hours/week allowance. +5. *Cost:* Free. + +--- + +## ๐Ÿฅ‰ Option 3: Fix the Script to Run Reasonably Fast on Your CPU +If you must run locally, you can optimize the CPU performance by resizing the data *before* training and reducing epochs. + +| Configuration | Estimated Time on Your Local CPU | +|---|---| +| Current script (as-is) | 20โ€“25 hours โŒ | +| After optimization (pre-resize + fewer epochs) | ~30โ€“60 minutes โœ… | + +*Note: The optimized CPU approach is included in the Presentation Notebook.* + +--- + +## ๐Ÿ… Option 4: Lightning.ai (Free Cloud GPU) +Lightning Studios provides free GPU compute in a full notebook/IDE environment. + +1. Go to [lightning.ai](https://lightning.ai/) +2. Spin up a free Studio. +3. Offers a more generous free tier and advanced environment compared to standard Colab. +4. *Cost:* Free tier available. diff --git a/notebooks_knowledge&presentation/EXTRA Transfer Learning II.ipynb b/notebooks_knowledge&presentation/EXTRA Transfer Learning II.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..27ba5e5ff6df453d696ac2dd0aa2d8af9a5b1e03 --- /dev/null +++ b/notebooks_knowledge&presentation/EXTRA Transfer Learning II.ipynb @@ -0,0 +1,1049 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "hRTa3Ee15WsJ" + }, + "source": [ + "# Transfer learning and fine-tuning" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dQHMcypT3vDT" + }, + "source": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " View on TensorFlow.org\n", + " \n", + " Run in Google Colab\n", + " \n", + " View source on GitHub\n", + " \n", + " Download notebook\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2X4KyhORdSeO" + }, + "source": [ + "In this tutorial, you will learn how to classify images of cats and dogs by using transfer learning from a pre-trained network.\n", + "\n", + "A pre-trained model is a saved network that was previously trained on a large dataset, typically on a large-scale image-classification task. You either use the pretrained model as is or use transfer learning to customize this model to a given task.\n", + "\n", + "The intuition behind transfer learning for image classification is that if a model is trained on a large and general enough dataset, this model will effectively serve as a generic model of the visual world. You can then take advantage of these learned feature maps without having to start from scratch by training a large model on a large dataset.\n", + "\n", + "In this notebook, you will try two ways to customize a pretrained model:\n", + "\n", + "1. Feature Extraction: Use the representations learned by a previous network to extract meaningful features from new samples. You simply add a new classifier, which will be trained from scratch, on top of the pretrained model so that you can repurpose the feature maps learned previously for the dataset.\n", + "\n", + " You do not need to (re)train the entire model. The base convolutional network already contains features that are generically useful for classifying pictures. However, the final, classification part of the pretrained model is specific to the original classification task, and subsequently specific to the set of classes on which the model was trained.\n", + "\n", + "1. Fine-Tuning: Unfreeze a few of the top layers of a frozen model base and jointly train both the newly-added classifier layers and the last layers of the base model. This allows us to \"fine-tune\" the higher-order feature representations in the base model in order to make them more relevant for the specific task.\n", + "\n", + "You will follow the general machine learning workflow.\n", + "\n", + "1. Examine and understand the data\n", + "1. Build an input pipeline, in this case using Keras ImageDataGenerator\n", + "1. Compose the model\n", + " * Load in the pretrained base model (and pretrained weights)\n", + " * Stack the classification layers on top\n", + "1. Train the model\n", + "1. Evaluate model\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "TqOt6Sv7AsMi" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import os\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "v77rlkCKW0IJ" + }, + "source": [ + "## Data preprocessing" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0GoKGm1duzgk" + }, + "source": [ + "### Data download" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vHP9qMJxt2oz" + }, + "source": [ + "In this tutorial, you will use a dataset containing several thousand images of cats and dogs. Download and extract a zip file containing the images, then create a `tf.data.Dataset` for training and validation using the `tf.keras.utils.image_dataset_from_directory` utility. You can learn more about loading images in this [tutorial](https://www.tensorflow.org/tutorials/load_data/images)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ro4oYaEmxe4r" + }, + "outputs": [], + "source": [ + "_URL = 'https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip'\n", + "path_to_zip = tf.keras.utils.get_file('cats_and_dogs.zip', origin=_URL, extract=True)\n", + "PATH = os.path.join(os.path.dirname(path_to_zip), 'cats_and_dogs_filtered')\n", + "\n", + "train_dir = os.path.join(PATH, 'train')\n", + "validation_dir = os.path.join(PATH, 'validation')\n", + "\n", + "BATCH_SIZE = 32\n", + "IMG_SIZE = (160, 160)\n", + "\n", + "train_dataset = tf.keras.utils.image_dataset_from_directory(train_dir,\n", + " shuffle=True,\n", + " batch_size=BATCH_SIZE,\n", + " image_size=IMG_SIZE)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "cAvtLwi7_J__" + }, + "outputs": [], + "source": [ + "validation_dataset = tf.keras.utils.image_dataset_from_directory(validation_dir,\n", + " shuffle=True,\n", + " batch_size=BATCH_SIZE,\n", + " image_size=IMG_SIZE)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yO1Q2JaW5sIy" + }, + "source": [ + "Show the first nine images and labels from the training set:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "K5BeQyKThC_Y" + }, + "outputs": [], + "source": [ + "class_names = train_dataset.class_names\n", + "\n", + "plt.figure(figsize=(10, 10))\n", + "for images, labels in train_dataset.take(1):\n", + " for i in range(9):\n", + " ax = plt.subplot(3, 3, i + 1)\n", + " plt.imshow(images[i].numpy().astype(\"uint8\"))\n", + " plt.title(class_names[labels[i]])\n", + " plt.axis(\"off\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EZqCX_mpV3Mx" + }, + "source": [ + "As the original dataset doesn't contain a test set, you will create one. To do so, determine how many batches of data are available in the validation set using `tf.data.experimental.cardinality`, then move 20% of them to a test set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "uFFIYrTFV9RO" + }, + "outputs": [], + "source": [ + "val_batches = tf.data.experimental.cardinality(validation_dataset)\n", + "test_dataset = validation_dataset.take(val_batches // 5)\n", + "validation_dataset = validation_dataset.skip(val_batches // 5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Q9pFlFWgBKgH" + }, + "outputs": [], + "source": [ + "print('Number of validation batches: %d' % tf.data.experimental.cardinality(validation_dataset))\n", + "print('Number of test batches: %d' % tf.data.experimental.cardinality(test_dataset))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MakSrdd--RKg" + }, + "source": [ + "### Configure the dataset for performance" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "22XWC7yjkZu4" + }, + "source": [ + "Use buffered prefetching to load images from disk without having I/O become blocking. To learn more about this method see the [data performance](https://www.tensorflow.org/guide/data_performance) guide." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "p3UUPdm86LNC" + }, + "outputs": [], + "source": [ + "AUTOTUNE = tf.data.AUTOTUNE\n", + "\n", + "train_dataset = train_dataset.prefetch(buffer_size=AUTOTUNE)\n", + "validation_dataset = validation_dataset.prefetch(buffer_size=AUTOTUNE)\n", + "test_dataset = test_dataset.prefetch(buffer_size=AUTOTUNE)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MYfcVwYLiR98" + }, + "source": [ + "### Use data augmentation" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bDWc5Oad1daX" + }, + "source": [ + "When you don't have a large image dataset, it's a good practice to artificially introduce sample diversity by applying random, yet realistic, transformations to the training images, such as rotation and horizontal flipping. This helps expose the model to different aspects of the training data and reduce [overfitting](https://www.tensorflow.org/tutorials/keras/overfit_and_underfit). You can learn more about data augmentation in this [tutorial](https://www.tensorflow.org/tutorials/images/data_augmentation)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "3P99QiMGit1A" + }, + "outputs": [], + "source": [ + "data_augmentation = tf.keras.Sequential([\n", + " tf.keras.layers.RandomFlip('horizontal'),\n", + " tf.keras.layers.RandomRotation(0.2),\n", + "])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s9SlcbhrarOO" + }, + "source": [ + "Note: These layers are active only during training, when you call `Model.fit`. They are inactive when the model is used in inference mode in `Model.evaluate`, `Model.predict`, or `Model.call`." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9mD3rE2Lm7-d" + }, + "source": [ + "Let's repeatedly apply these layers to the same image and see the result." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "aQullOUHkm67" + }, + "outputs": [], + "source": [ + "for image, _ in train_dataset.take(1):\n", + " plt.figure(figsize=(10, 10))\n", + " first_image = image[0]\n", + " for i in range(9):\n", + " ax = plt.subplot(3, 3, i + 1)\n", + " augmented_image = data_augmentation(tf.expand_dims(first_image, 0))\n", + " plt.imshow(augmented_image[0] / 255)\n", + " plt.axis('off')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bAywKtuVn8uK" + }, + "source": [ + "### Rescale pixel values\n", + "\n", + "In a moment, you will download `tf.keras.applications.MobileNetV2` for use as your base model. This model expects pixel values in `[-1, 1]`, but at this point, the pixel values in your images are in `[0, 255]`. To rescale them, use the preprocessing method included with the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "cO0HM9JAQUFq" + }, + "outputs": [], + "source": [ + "preprocess_input = tf.keras.applications.mobilenet_v2.preprocess_input" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xnr81qRMzcs5" + }, + "source": [ + "Note: Alternatively, you could rescale pixel values from `[0, 255]` to `[-1, 1]` using `tf.keras.layers.Rescaling`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "R2NyJn4KQMux" + }, + "outputs": [], + "source": [ + "rescale = tf.keras.layers.Rescaling(1./127.5, offset=-1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Wz7qgImhTxw4" + }, + "source": [ + "Note: If using other `tf.keras.applications`, be sure to check the API doc to determine if they expect pixels in `[-1, 1]` or `[0, 1]`, or use the included `preprocess_input` function." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OkH-kazQecHB" + }, + "source": [ + "## Create the base model from the pre-trained convnets\n", + "You will create the base model from the **MobileNet V2** model developed at Google. This is pre-trained on the ImageNet dataset, a large dataset consisting of 1.4M images and 1000 classes. ImageNet is a research training dataset with a wide variety of categories like `jackfruit` and `syringe`. This base of knowledge will help us classify cats and dogs from our specific dataset.\n", + "\n", + "First, you need to pick which layer of MobileNet V2 you will use for feature extraction. The very last classification layer (on \"top\", as most diagrams of machine learning models go from bottom to top) is not very useful. Instead, you will follow the common practice to depend on the very last layer before the flatten operation. This layer is called the \"bottleneck layer\". The bottleneck layer features retain more generality as compared to the final/top layer.\n", + "\n", + "First, instantiate a MobileNet V2 model pre-loaded with weights trained on ImageNet. By specifying the **include_top=False** argument, you load a network that doesn't include the classification layers at the top, which is ideal for feature extraction." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "19IQ2gqneqmS" + }, + "outputs": [], + "source": [ + "# Create the base model from the pre-trained model MobileNet V2\n", + "IMG_SHAPE = IMG_SIZE + (3,)\n", + "base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,\n", + " include_top=False,\n", + " weights='imagenet')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AqcsxoJIEVXZ" + }, + "source": [ + "This feature extractor converts each `160x160x3` image into a `5x5x1280` block of features. Let's see what it does to an example batch of images:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Y-2LJL0EEUcx" + }, + "outputs": [], + "source": [ + "image_batch, label_batch = next(iter(train_dataset))\n", + "feature_batch = base_model(image_batch)\n", + "print(feature_batch.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rlx56nQtfe8Y" + }, + "source": [ + "## Feature extraction\n", + "In this step, you will freeze the convolutional base created from the previous step and to use as a feature extractor. Additionally, you add a classifier on top of it and train the top-level classifier." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CnMLieHBCwil" + }, + "source": [ + "### Freeze the convolutional base" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7fL6upiN3ekS" + }, + "source": [ + "It is important to freeze the convolutional base before you compile and train the model. Freezing (by setting layer.trainable = False) prevents the weights in a given layer from being updated during training. MobileNet V2 has many layers, so setting the entire model's `trainable` flag to False will freeze all of them." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "OTCJH4bphOeo" + }, + "outputs": [], + "source": [ + "base_model.trainable = False" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jsNHwpm7BeVM" + }, + "source": [ + "### Important note about BatchNormalization layers\n", + "\n", + "Many models contain `tf.keras.layers.BatchNormalization` layers. This layer is a special case and precautions should be taken in the context of fine-tuning, as shown later in this tutorial.\n", + "\n", + "When you set `layer.trainable = False`, the `BatchNormalization` layer will run in inference mode, and will not update its mean and variance statistics.\n", + "\n", + "When you unfreeze a model that contains BatchNormalization layers in order to do fine-tuning, you should keep the BatchNormalization layers in inference mode by passing `training = False` when calling the base model. Otherwise, the updates applied to the non-trainable weights will destroy what the model has learned.\n", + "\n", + "For more details, see the [Transfer learning guide](https://www.tensorflow.org/guide/keras/transfer_learning)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "KpbzSmPkDa-N" + }, + "outputs": [], + "source": [ + "# Let's take a look at the base model architecture\n", + "base_model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wdMRM8YModbk" + }, + "source": [ + "### Add a classification head" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QBc31c4tMOdH" + }, + "source": [ + "To generate predictions from the block of features, average over the spatial `5x5` spatial locations, using a `tf.keras.layers.GlobalAveragePooling2D` layer to convert the features to a single 1280-element vector per image." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dLnpMF5KOALm" + }, + "outputs": [], + "source": [ + "global_average_layer = tf.keras.layers.GlobalAveragePooling2D()\n", + "feature_batch_average = global_average_layer(feature_batch)\n", + "print(feature_batch_average.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "O1p0OJBR6dOT" + }, + "source": [ + "Apply a `tf.keras.layers.Dense` layer to convert these features into a single prediction per image. You don't need an activation function here because this prediction will be treated as a `logit`, or a raw prediction value. Positive numbers predict class 1, negative numbers predict class 0." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Wv4afXKj6cVa" + }, + "outputs": [], + "source": [ + "prediction_layer = tf.keras.layers.Dense(1, activation='sigmoid')\n", + "prediction_batch = prediction_layer(feature_batch_average)\n", + "print(prediction_batch.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HXvz-ZkTa9b3" + }, + "source": [ + "Build a model by chaining together the data augmentation, rescaling, `base_model` and feature extractor layers using the [Keras Functional API](https://www.tensorflow.org/guide/keras/functional). As previously mentioned, use `training=False` as our model contains a `BatchNormalization` layer." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "DgzQX6Veb2WT" + }, + "outputs": [], + "source": [ + "inputs = tf.keras.Input(shape=(160, 160, 3))\n", + "x = data_augmentation(inputs)\n", + "x = preprocess_input(x)\n", + "x = base_model(x, training=False)\n", + "x = global_average_layer(x)\n", + "x = tf.keras.layers.Dropout(0.2)(x)\n", + "outputs = prediction_layer(x)\n", + "model = tf.keras.Model(inputs, outputs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "I8ARiyMFsgbH" + }, + "outputs": [], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lxOcmVr0ydFZ" + }, + "source": [ + "The 8+ million parameters in MobileNet are frozen, but there are 1.2 thousand _trainable_ parameters in the Dense layer. These are divided between two `tf.Variable` objects, the weights and biases." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "krvBumovycVA" + }, + "outputs": [], + "source": [ + "len(model.trainable_variables)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jeGk93R2ahav" + }, + "outputs": [], + "source": [ + "tf.keras.utils.plot_model(model, show_shapes=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "g0ylJXE_kRLi" + }, + "source": [ + "### Compile the model\n", + "\n", + "Compile the model before training it. Since there are two classes and a sigmoid oputput, use the `BinaryAccuracy`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "RpR8HdyMhukJ" + }, + "outputs": [], + "source": [ + "base_learning_rate = 0.0001\n", + "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=base_learning_rate),\n", + " loss=tf.keras.losses.BinaryCrossentropy(),\n", + " metrics=[tf.keras.metrics.BinaryAccuracy(threshold=0.5, name='accuracy')])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RxvgOYTDSWTx" + }, + "source": [ + "### Train the model\n", + "\n", + "After training for 10 epochs, you should see ~96% accuracy on the validation set.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Om4O3EESkab1" + }, + "outputs": [], + "source": [ + "initial_epochs = 10\n", + "\n", + "loss0, accuracy0 = model.evaluate(validation_dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "8cYT1c48CuSd" + }, + "outputs": [], + "source": [ + "print(\"initial loss: {:.2f}\".format(loss0))\n", + "print(\"initial accuracy: {:.2f}\".format(accuracy0))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "JsaRFlZ9B6WK" + }, + "outputs": [], + "source": [ + "history = model.fit(train_dataset,\n", + " epochs=initial_epochs,\n", + " validation_data=validation_dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Hd94CKImf8vi" + }, + "source": [ + "### Learning curves\n", + "\n", + "Let's take a look at the learning curves of the training and validation accuracy/loss when using the MobileNetV2 base model as a fixed feature extractor." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "53OTCh3jnbwV" + }, + "outputs": [], + "source": [ + "acc = history.history['accuracy']\n", + "val_acc = history.history['val_accuracy']\n", + "\n", + "loss = history.history['loss']\n", + "val_loss = history.history['val_loss']\n", + "\n", + "plt.figure(figsize=(8, 8))\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(acc, label='Training Accuracy')\n", + "plt.plot(val_acc, label='Validation Accuracy')\n", + "plt.legend(loc='lower right')\n", + "plt.ylabel('Accuracy')\n", + "plt.ylim([min(plt.ylim()),1])\n", + "plt.title('Training and Validation Accuracy')\n", + "\n", + "plt.subplot(2, 1, 2)\n", + "plt.plot(loss, label='Training Loss')\n", + "plt.plot(val_loss, label='Validation Loss')\n", + "plt.legend(loc='upper right')\n", + "plt.ylabel('Cross Entropy')\n", + "plt.ylim([0,1.0])\n", + "plt.title('Training and Validation Loss')\n", + "plt.xlabel('epoch')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "foWMyyUHbc1j" + }, + "source": [ + "Note: If you are wondering why the validation metrics are clearly better than the training metrics, the main factor is because layers like `tf.keras.layers.BatchNormalization` and `tf.keras.layers.Dropout` affect accuracy during training. They are turned off when calculating validation loss.\n", + "\n", + "To a lesser extent, it is also because training metrics report the average for an epoch, while validation metrics are evaluated after the epoch, so validation metrics see a model that has trained slightly longer." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CqwV-CRdS6Nv" + }, + "source": [ + "## Fine tuning\n", + "In the feature extraction experiment, you were only training a few layers on top of an MobileNetV2 base model. The weights of the pre-trained network were **not** updated during training.\n", + "\n", + "One way to increase performance even further is to train (or \"fine-tune\") the weights of the top layers of the pre-trained model alongside the training of the classifier you added. The training process will force the weights to be tuned from generic feature maps to features associated specifically with the dataset.\n", + "\n", + "Note: This should only be attempted after you have trained the top-level classifier with the pre-trained model set to non-trainable. If you add a randomly initialized classifier on top of a pre-trained model and attempt to train all layers jointly, the magnitude of the gradient updates will be too large (due to the random weights from the classifier) and your pre-trained model will forget what it has learned.\n", + "\n", + "Also, you should try to fine-tune a small number of top layers rather than the whole MobileNet model. In most convolutional networks, the higher up a layer is, the more specialized it is. The first few layers learn very simple and generic features that generalize to almost all types of images. As you go higher up, the features are increasingly more specific to the dataset on which the model was trained. The goal of fine-tuning is to adapt these specialized features to work with the new dataset, rather than overwrite the generic learning." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CPXnzUK0QonF" + }, + "source": [ + "### Un-freeze the top layers of the model\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rfxv_ifotQak" + }, + "source": [ + "All you need to do is unfreeze the `base_model` and set the bottom layers to be un-trainable. Then, you should recompile the model (necessary for these changes to take effect), and resume training." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4nzcagVitLQm" + }, + "outputs": [], + "source": [ + "base_model.trainable = True" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-4HgVAacRs5v" + }, + "outputs": [], + "source": [ + "# Let's take a look to see how many layers are in the base model\n", + "print(\"Number of layers in the base model: \", len(base_model.layers))\n", + "\n", + "# Fine-tune from this layer onwards\n", + "fine_tune_at = 100\n", + "\n", + "# Freeze all the layers before the `fine_tune_at` layer\n", + "for layer in base_model.layers[:fine_tune_at]:\n", + " layer.trainable = False" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4Uk1dgsxT0IS" + }, + "source": [ + "### Compile the model\n", + "\n", + "As you are training a much larger model and want to readapt the pretrained weights, it is important to use a lower learning rate at this stage. Otherwise, your model could overfit very quickly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "NtUnaz0WUDva" + }, + "outputs": [], + "source": [ + "model.compile(loss=tf.keras.losses.BinaryCrossentropy(),\n", + " optimizer = tf.keras.optimizers.RMSprop(learning_rate=base_learning_rate/10),\n", + " metrics=[tf.keras.metrics.BinaryAccuracy(threshold=0.5, name='accuracy')])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "WwBWy7J2kZvA" + }, + "outputs": [], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "bNXelbMQtonr" + }, + "outputs": [], + "source": [ + "len(model.trainable_variables)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4G5O4jd6TuAG" + }, + "source": [ + "### Continue training the model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0foWUN-yDLo_" + }, + "source": [ + "If you trained to convergence earlier, this step will improve your accuracy by a few percentage points." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ECQLkAsFTlun" + }, + "outputs": [], + "source": [ + "fine_tune_epochs = 10\n", + "total_epochs = initial_epochs + fine_tune_epochs\n", + "\n", + "history_fine = model.fit(train_dataset,\n", + " epochs=total_epochs,\n", + " initial_epoch=len(history.epoch),\n", + " validation_data=validation_dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TfXEmsxQf6eP" + }, + "source": [ + "Let's take a look at the learning curves of the training and validation accuracy/loss when fine-tuning the last few layers of the MobileNetV2 base model and training the classifier on top of it. The validation loss is much higher than the training loss, so you may get some overfitting.\n", + "\n", + "You may also get some overfitting as the new training set is relatively small and similar to the original MobileNetV2 datasets.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DNtfNZKlInGT" + }, + "source": [ + "After fine tuning the model nearly reaches 98% accuracy on the validation set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "PpA8PlpQKygw" + }, + "outputs": [], + "source": [ + "acc += history_fine.history['accuracy']\n", + "val_acc += history_fine.history['val_accuracy']\n", + "\n", + "loss += history_fine.history['loss']\n", + "val_loss += history_fine.history['val_loss']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "chW103JUItdk" + }, + "outputs": [], + "source": [ + "plt.figure(figsize=(8, 8))\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(acc, label='Training Accuracy')\n", + "plt.plot(val_acc, label='Validation Accuracy')\n", + "plt.ylim([0.8, 1])\n", + "plt.plot([initial_epochs-1,initial_epochs-1],\n", + " plt.ylim(), label='Start Fine Tuning')\n", + "plt.legend(loc='lower right')\n", + "plt.title('Training and Validation Accuracy')\n", + "\n", + "plt.subplot(2, 1, 2)\n", + "plt.plot(loss, label='Training Loss')\n", + "plt.plot(val_loss, label='Validation Loss')\n", + "plt.ylim([0, 1.0])\n", + "plt.plot([initial_epochs-1,initial_epochs-1],\n", + " plt.ylim(), label='Start Fine Tuning')\n", + "plt.legend(loc='upper right')\n", + "plt.title('Training and Validation Loss')\n", + "plt.xlabel('epoch')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "R6cWgjgfrsn5" + }, + "source": [ + "### Evaluation and prediction" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PSXH7PRMxOi5" + }, + "source": [ + "Finally you can verify the performance of the model on new data using test set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2KyNhagHwfar" + }, + "outputs": [], + "source": [ + "loss, accuracy = model.evaluate(test_dataset)\n", + "print('Test accuracy :', accuracy)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8UjS5ukZfOcR" + }, + "source": [ + "And now you are all set to use this model to predict if your pet is a cat or dog." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "RUNoQNgtfNgt" + }, + "outputs": [], + "source": [ + "# Retrieve a batch of images from the test set\n", + "image_batch, label_batch = test_dataset.as_numpy_iterator().next()\n", + "predictions = model.predict_on_batch(image_batch).flatten()\n", + "\n", + "# Apply a sigmoid since our model returns logits\n", + "predictions = tf.nn.sigmoid(predictions)\n", + "predictions = tf.where(predictions < 0.5, 0, 1)\n", + "\n", + "print('Predictions:\\n', predictions.numpy())\n", + "print('Labels:\\n', label_batch)\n", + "\n", + "plt.figure(figsize=(10, 10))\n", + "for i in range(9):\n", + " ax = plt.subplot(3, 3, i + 1)\n", + " plt.imshow(image_batch[i].astype(\"uint8\"))\n", + " plt.title(class_names[predictions[i]])\n", + " plt.axis(\"off\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_TZTwG7nhm0C" + }, + "source": [ + "## Summary\n", + "\n", + "* **Using a pre-trained model for feature extraction**: When working with a small dataset, it is a common practice to take advantage of features learned by a model trained on a larger dataset in the same domain. This is done by instantiating the pre-trained model and adding a fully-connected classifier on top. The pre-trained model is \"frozen\" and only the weights of the classifier get updated during training.\n", + "In this case, the convolutional base extracted all the features associated with each image and you just trained a classifier that determines the image class given that set of extracted features.\n", + "\n", + "* **Fine-tuning a pre-trained model**: To further improve performance, one might want to repurpose the top-level layers of the pre-trained models to the new dataset via fine-tuning.\n", + "In this case, you tuned your weights such that your model learned high-level features specific to the dataset. This technique is usually recommended when the training dataset is large and very similar to the original dataset that the pre-trained model was trained on.\n", + "\n", + "To learn more, visit the [Transfer learning guide](https://www.tensorflow.org/guide/keras/transfer_learning).\n" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "name": "transfer_learning.ipynb", + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks_knowledge&presentation/Ppt.Slices/04. Custom CNN.png b/notebooks_knowledge&presentation/Ppt.Slices/04. 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Uses inverted residual blocks. +* **Why it WAS chosen:** It strikes the perfect balance between high-accuracy feature extraction and computational efficiency. It trains quickly on standard hardware without requiring massive resources. + +### VGG16 (Slide 4.1) +* **Characteristics:** Massive size (~138M parameters). Classical deep CNN architecture with uniform simple convolutional layers. +* **Why Not?** It is very slow to train on standard hardware and highly prone to overfitting on small 32x32 images like CIFAR-10. + +### ResNet50 (Slide 4.1) +* **Characteristics:** Powerful architecture (~25M parameters) that utilizes skip connections (residual blocks) to train very deep networks without vanishing gradients. +* **Why Not?** Its complexity is often overkill for CIFAR-10, leading to unnecessarily long training times without proportional accuracy gains for such small images. + +### InceptionV3 (Slide 4.1) +* **Characteristics:** Uses "Inception modules" capable of looking at the same image with different receptive fields (filter sizes) simultaneously. (~24M parameters) +* **Why Not?** Demands high computational resources and typically requires much larger input resolutions (default is 299x299) to be fully effective. Highly upscaling 32x32 to 299x299 is very resource-intensive. + +--- + +## Other Notable Alternatives + +### EfficientNet (e.g., EfficientNetB0) +* **Characteristics:** Modern, highly optimized models that scale up the network's depth, width, and resolution evenly. (B0 has ~5.3M parameters) +* **Why Not?** While a very valid alternative, MobileNetV2 is slightly older but exceptionally well-documented for beginners, and tends to train slightly faster on basic setups. + +### DenseNet (e.g., DenseNet121) +* **Characteristics:** Connects each layer to every other layer in a feed-forward fashion, creating strong feature reuse. (~8M parameters) +* **Why Not?** They are heavier in memory usage and take much longer to train per epoch compared to MobileNetV2, despite often achieving higher accuracy. + +### Xception +* **Characteristics:** An extension of the Inception architecture that uses depthwise separable convolutions (similar to MobileNet). (~22M parameters) +* **Why Not?** It is quite large and heavily optimized for the massive ImageNet dataset. Using it for 32x32 pixel images is often inefficient. + +### NASNetMobile +* **Characteristics:** An architecture discovered by an AI (Neural Architecture Search) designed specifically for mobile and resource-constrained devices. +* **Why Not?** While an excellent alternative, MobileNet design (inverted residual blocks) is much simpler and more intuitive to explain in a presentation and learning environment. diff --git a/notebooks_knowledge&presentation/app_documentation_simple.md b/notebooks_knowledge&presentation/app_documentation_simple.md new file mode 100644 index 0000000000000000000000000000000000000000..63c13dcd23019376874dc4c15189894864b57b98 --- /dev/null +++ b/notebooks_knowledge&presentation/app_documentation_simple.md @@ -0,0 +1,46 @@ +# How Our Magic Picture Guesser Works! ๐Ÿช„๐Ÿ–ผ๏ธ + +Hello! Welcome to our Magic Picture Guesser! This is a simple story about how your computer learns to look at a picture and tell you what it is. + +## Step 1: The Magic Brain (The Model) ๐Ÿง  + +Before the computer can guess pictures, it has to go to school! + +We have a special file called a **Model**. Think of the Model as a big, smart brain that has looked at thousands of pictures of dogs, cats, airplanes, and cars. + +Because it studied so hard, it remembers what they look like! We keep this brain in a safe place inside our project. + +## Step 2: The Doorway (The App) ๐Ÿšช + +To let you talk to the brain, we built a Doorway. In computer words, we built an **App** using a tool called Flask. + +When you start the App, it opens a special website just for you on your computer. It looks super cool with colors and a big box where you can drop pictures. + +## Step 3: Dropping a Picture ๐Ÿ“ธ + +When you open the website, you can drag a picture of a car or a dog into the big box. + +But wait! The magic brain is very picky. It only likes looking at pictures that are small square shapes (like a tiny 32x32 puzzle piece). + +## Step 4: The Shrinking Machine ๐Ÿ”ฌ + +Before the brain looks at it, our App uses a **Shrinking Machine** to resize your picture so it fits perfectly in the brain's tiny window. If your picture was huge, we just squish it down so the brain can read it. + +## Step 5: The Magic Guess โœจ + +Once the picture is small enough, the App hands it to the Magic Brain. + +The Brain looks at the colors and shapes and says: +**"Aha! I am 99% sure this is an Automobile (a car)!"** + +And then, the App shows you the answer on the screen! + +--- + +**Summary:** +1. You drag a picture. +2. We shrink it. +3. The brain looks at it. +4. It tells you what it sees! + +It's just like showing a picture-book to a very smart friend! diff --git a/notebooks_knowledge&presentation/fast_deployment_guide.md b/notebooks_knowledge&presentation/fast_deployment_guide.md new file mode 100644 index 0000000000000000000000000000000000000000..5c244778458f065ac3ca4db3405e2781138fd2d3 --- /dev/null +++ b/notebooks_knowledge&presentation/fast_deployment_guide.md @@ -0,0 +1,65 @@ +# Fast and Cheap Deployment Guide (Render.com) + +This guide explains how to deploy your Flask application for free using [Render.com](https://render.com/), which is currently one of the easiest and most cost-effective ways to host a Python web app. + +Later, you can move this to a VPS (Virtual Private Server) like DigitalOcean or AWS EC2, but Render is the perfect starting point to quickly fulfill the "+5 Bonus points" requirement. + +## Prerequisites + +1. Your project must be pushed to a **GitHub repository**. +2. You need an account on [Render.com](https://render.com/) (you can sign up with GitHub). + +## Step-by-Step Instructions + +### Step 1: Update `requirements.txt` +Render needs a production web server to run your Flask app. We will use `gunicorn`. +Open your `requirements.txt` file and add this line at the very bottom: +```text +gunicorn==21.2.0 +``` + +### Step 2: Push to GitHub +Make sure all your latest changes, particularly the updated `requirements.txt` and your downloaded best model (in the `models/` folder), are committed and pushed to your GitHub repository. + +```bash +git add requirements.txt models/best_model.h5 +git commit -m "Prepare for Render deployment" +git push origin main +``` +*(Note: If your model file is larger than 100MB, you might need to use Git LFS or upload it differently, but MobileNetV2 should be small enough).* + +### Step 3: Create a Web Service on Render +1. Log into your Render dashboard. +2. Click on **New +** and select **Web Service**. +3. Connect your GitHub account and select your project repository. + +### Step 4: Configure the Web Service +Fill out the deployment form with the following details: +- **Name:** Choose a name for your app (e.g., `cifar10-classifier-sebastian`). +- **Region:** Choose the region closest to you (e.g., Frankfurt/EU). +- **Branch:** `main` (or whichever branch your code is on). +- **Runtime:** `Python 3`. +- **Build Command:** + ```bash + pip install -r requirements.txt + ``` +- **Start Command:** + ```bash + gunicorn app.app:app + ``` + *(Explanation: The first `app` is your `app` folder, the second `app` is the `app.py` script, and the third `:app` is the Flask instance named `app` inside that script).* + +### Step 5: Choose Instance Type & Deploy +- Select the **Free** instance type ($0/month). +- Click **Create Web Service**. + +### Step 6: Wait for Build +Render will now install your dependencies and launch your app. The console output will show you the progress. Once it says "Live", your app is running! + +You can access your live app using the URL Render provides at the top of the dashboard (e.g., `https://cifar10-classifier-sebastian.onrender.com`). + +--- +**Why this is the best first step:** +- It is 100% free. +- It automatically redeploys if you push new code to GitHub. +- It handles SSL certificates (`https://`) out of the box. diff --git a/notebooks_knowledge&presentation/jupyter notebooks/1. CIFAR10_Image_Classification_CNN.ipynb b/notebooks_knowledge&presentation/jupyter notebooks/1. CIFAR10_Image_Classification_CNN.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..74cf107db604cb0e3e4d33ee593cb7f9a416d6c2 --- /dev/null +++ b/notebooks_knowledge&presentation/jupyter notebooks/1. CIFAR10_Image_Classification_CNN.ipynb @@ -0,0 +1,1190 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ๐Ÿง  Deep Learning: Image Classification with CNN โ€” CIFAR-10\n", + "\n", + "**Author:** Sebastian Lopez \n", + "**Date:** February 2026 \n", + "**Environment:** Python 3.10 | TensorFlow 2.18.1 | Keras 3.6.0 \n", + "**Dataset:** CIFAR-10 (60,000 images, 10 classes, 32ร—32 RGB)\n", + "\n", + "---\n", + "\n", + "## ๐Ÿ“‹ Project Overview\n", + "\n", + "This notebook builds and evaluates **two deep learning models** for classifying images from the CIFAR-10 dataset into 10 categories: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck.\n", + "\n", + "**Models developed:**\n", + "1. **Custom CNN** โ€” A purpose-built convolutional neural network with 3 convolutional blocks\n", + "2. **MobileNetV2 Transfer Learning** โ€” Leveraging pretrained ImageNet features with fine-tuning\n", + "\n", + "**Assessment Components Covered:**\n", + "| # | Component | Section |\n", + "|---|-----------|---------|\n", + "| 1 | Data Preprocessing | ยง2โ€“3 |\n", + "| 2 | Model Architecture | ยง4 |\n", + "| 3 | Model Training | ยง5 |\n", + "| 4 | Model Evaluation | ยง6 |\n", + "| 5 | Transfer Learning | ยง7 |\n", + "| 6 | Code Quality | Throughout |\n", + "| 7 | Report & Analysis | ยง8โ€“9 |\n", + "| 8 | Model Deployment | ยง10 |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## 1. Setup & Imports\n", + "\n", + "First, we install/import all required libraries. This notebook is **fully self-contained** โ€” no external project modules needed." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "83269f1a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TensorFlow version: 2.18.1\n", + "Keras version: 3.6.0\n", + "NumPy version: 2.0.1\n", + "GPU available: False\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "import os\n", + "import json\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.metrics import (\n", + " classification_report, confusion_matrix,\n", + " accuracy_score, precision_score, recall_score, f1_score\n", + ")\n", + "\n", + "# TensorFlow / Keras\n", + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from keras.api.datasets import cifar10\n", + "from keras.api.utils import to_categorical\n", + "from keras.api.models import Sequential, Model\n", + "from keras.api.layers import (\n", + " Conv2D, MaxPooling2D, Dense, Dropout, Flatten,\n", + " BatchNormalization, GlobalAveragePooling2D, Input, UpSampling2D\n", + ")\n", + "from keras.api.applications import MobileNetV2\n", + "from keras.api.optimizers import Adam\n", + "from keras.api.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\n", + "from keras.src.legacy.preprocessing.image import ImageDataGenerator\n", + "\n", + "# Display settings\n", + "plt.style.use('seaborn-v0_8-darkgrid')\n", + "sns.set_palette('Set2')\n", + "np.random.seed(42)\n", + "tf.random.set_seed(42)\n", + "\n", + "# Constants\n", + "CLASS_NAMES = ['airplane', 'automobile', 'bird', 'cat', 'deer','dog', 'frog', 'horse', 'ship', 'truck']\n", + "NUM_CLASSES = 10\n", + "IMG_SHAPE = (32, 32, 3)\n", + "\n", + "print(f\"TensorFlow version: {tf.__version__}\")\n", + "print(f\"Keras version: {keras.__version__}\")\n", + "print(f\"NumPy version: {np.__version__}\")\n", + "print(f\"GPU available: {len(tf.config.list_physical_devices('GPU')) > 0}\")" + ] + }, + { + "cell_type": "markdown", + "id": "4e188a0f", + "metadata": {}, + "source": [ + "---\n", + "## 2. Data Loading & Exploration\n", + "\n", + "The **CIFAR-10** dataset contains 60,000 color images (32ร—32 pixels) in 10 balanced classes:\n", + "- **Training set:** 50,000 images\n", + "- **Test set:** 10,000 images\n", + "\n", + "Each pixel has 3 channels (RGB) with values ranging from 0 to 255.\n", + "\n", + "> **Why check the data first?** Understanding the shape, distribution, and scale of your data is critical. CIFAR-10 is balanced, which means accuracy is a fair evaluation metric." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "579172d1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==================================================\n", + " CIFAR-10 Dataset Summary\n", + "==================================================\n", + " Training images: 50,000\n", + " Test images: 10,000\n", + " Image shape: (32, 32, 3)\n", + " Pixel range: [0, 255]\n", + " Data type: uint8\n", + " Classes: 10\n", + " Class names: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck\n" + ] + } + ], + "source": [ + "# Load the CIFAR-10 dataset\n", + "(x_train_raw, y_train_raw), (x_test_raw, y_test_raw) = cifar10.load_data()\n", + "\n", + "print(\"=\" * 50)\n", + "print(\" CIFAR-10 Dataset Summary\")\n", + "print(\"=\" * 50)\n", + "\n", + "print(f\" Training images: {x_train_raw.shape[0]:,}\")\n", + "print(f\" Test images: {x_test_raw.shape[0]:,}\")\n", + "print(f\" Image shape: {x_train_raw.shape[1:]}\")\n", + "print(f\" Pixel range: [{x_train_raw.min()}, {x_train_raw.max()}]\")\n", + "print(f\" Data type: {x_train_raw.dtype}\")\n", + "print(f\" Classes: {NUM_CLASSES}\")\n", + "print(f\" Class names: {', '.join(CLASS_NAMES)}\")" + ] + }, + { + "cell_type": "markdown", + "id": "afb630aa", + "metadata": {}, + "source": [ + "### 2.1 Sample Images\n", + "\n", + "Let's visualize a random selection of images to understand what the model will learn to classify. Note how small 32ร—32 images are โ€” even humans can find some of these challenging!" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "c1b44614", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Display 25 random sample images\n", + "fig, axes = plt.subplots(5, 5, figsize=(5, 5))\n", + "fig.suptitle('CIFAR-10 Sample Images', fontsize=18, fontweight='bold', y=1.02)\n", + "\n", + "indices = np.random.choice(len(x_train_raw), 25, replace=False)\n", + "labels = y_train_raw.flatten()\n", + "\n", + "for i, ax in enumerate(axes.flat):\n", + " idx = indices[i]\n", + " ax.imshow(x_train_raw[idx])\n", + " ax.set_title(CLASS_NAMES[labels[idx]], fontsize=11, fontweight='bold')\n", + " ax.axis('off')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "70d72e81", + "metadata": {}, + "source": [ + "### 2.2 Class Distribution\n", + "\n", + "A balanced dataset means each class has roughly the same number of images. This is important because it ensures accuracy is a fair metric โ€” no class is over- or under-represented." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "d3009882", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " Dataset is perfectly balanced: 5,000 images per class\n" + ] + } + ], + "source": [ + "# Plot class distribution\n", + "labels = y_train_raw.flatten()\n", + "unique, counts = np.unique(labels, return_counts=True)\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 4))\n", + "colors = plt.cm.Set3(np.linspace(0, 1, len(CLASS_NAMES)))\n", + "bars = ax.bar(CLASS_NAMES, counts, color=colors, edgecolor='gray')\n", + "\n", + "for bar, count in zip(bars, counts):\n", + " ax.text(bar.get_x() + bar.get_width()/2., bar.get_height() + 50,\n", + " f'{count:,}', ha='center', va='bottom', fontweight='bold', fontsize=10)\n", + "\n", + "ax.set_xlabel('Class Names', fontsize=13)\n", + "ax.set_ylabel('Number of Images', fontsize=13)\n", + "ax.set_title('CIFAR-10 Class Distribution (Training Set)', fontsize=15, fontweight='bold')\n", + "ax.set_ylim(0, max(counts) * 1.15)\n", + "plt.xticks(rotation=45, ha='right', fontsize=9)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(f\"\\n Dataset is perfectly balanced: {counts[0]:,} images per class\")" + ] + }, + { + "cell_type": "markdown", + "id": "cbf0aaf5", + "metadata": {}, + "source": [ + "---\n", + "## 3. Data Preprocessing\n", + "\n", + "### 3.1 Normalization\n", + "\n", + "We scale pixel values from **[0, 255] โ†’ [0.0, 1.0]** by dividing by 255. This ensures:\n", + "- Consistent gradient magnitudes during backpropagation\n", + "- Faster and more stable convergence\n", + "- Prevention of large values dominating weight updates\n", + "\n", + "### 3.2 One-Hot Encoding\n", + "\n", + "Integer labels (e.g., `3` for \"cat\") are converted to 10-dimensional binary vectors:\n", + "- `3` โ†’ `[0, 0, 0, 1, 0, 0, 0, 0, 0, 0]`\n", + "\n", + "This is required for `categorical_crossentropy` loss, which expects probability distributions.\n", + "\n", + "### 3.3 Train/Validation Split\n", + "\n", + "- **Training:** 45,000 images (90%)\n", + "- **Validation:** 5,000 images (10%)\n", + "- **Test:** 10,000 images (held-out)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "243d4b1b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " x_train range: [0.0, 1.0]\n", + " y_train shape: (50000, 10) (one-hot encoding)\n", + " Train split: 45,000 images\n", + " Validation: 5,000 images\n", + " Test: 10,000 images\n" + ] + } + ], + "source": [ + "# 1. Normalize pixel values to [0, 1]\n", + "x_train = x_train_raw.astype('float32') / 255.0\n", + "x_test = x_test_raw.astype('float32') / 255.0\n", + "\n", + "# 2. One-hot encode labels\n", + "y_train = to_categorical(y_train_raw, NUM_CLASSES)\n", + "y_test = to_categorical(y_test_raw, NUM_CLASSES)\n", + "\n", + "# 3. Create train/validation split\n", + "val_split = 0.1\n", + "split_idx = int(len(x_train) * (1 - val_split))\n", + "x_val = x_train[split_idx:]\n", + "y_val = y_train[split_idx:]\n", + "x_train_split = x_train[:split_idx]\n", + "y_train_split = y_train[:split_idx]\n", + "\n", + "\n", + "print(f\" x_train range: [{x_train.min():.1f}, {x_train.max():.1f}]\")\n", + "print(f\" y_train shape: {y_train.shape} (one-hot encoding)\")\n", + "print(f\" Train split: {x_train_split.shape[0]:,} images\")\n", + "print(f\" Validation: {x_val.shape[0]:,} images\")\n", + "print(f\" Test: {x_test.shape[0]:,} images\")" + ] + }, + { + "cell_type": "markdown", + "id": "b298d5e8", + "metadata": {}, + "source": [ + "### 3.4 Data Augmentation\n", + "\n", + "Data augmentation artificially increases dataset diversity by applying random transformations during training. \n", + "\n", + "**Purpose:** combats overfitting โ€”> the model learns from varied perspectives instead of memorizing specific images.\n", + "\n", + "| Augmentation | Range | Purpose |\n", + "|:-------------------|:------------|:--------|\n", + "| Rotation | ยฑ15ยฐ | Rotation invariance |\n", + "| Width/Height shift | ยฑ10% | Translation invariance |\n", + "| Horizontal flip | Random | Mirror invariance |\n", + "| Zoom | ยฑ10% | Scale invariance |\n", + "\n", + "\n", + "**Important:** Augmentation is applied **only to training data**, never to validation/test sets. We need clean, unmodified data for honest evaluation." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "90dc4162", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + " IMPORTANT:Augmentation creates diverse training samples from each original image\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\" \\n IMPORTANT:Augmentation creates diverse training samples from each original image\\n\")\n", + "\n", + "# Create data augmentation generator\n", + "datagen = ImageDataGenerator(\n", + " rotation_range=15,\n", + " width_shift_range=0.1,\n", + " height_shift_range=0.1,\n", + " horizontal_flip=True,\n", + " zoom_range=0.1,\n", + " fill_mode='nearest'\n", + ")\n", + "datagen.fit(x_train_split)\n", + "\n", + "# Visualize augmentation examples\n", + "fig, axes = plt.subplots(4, 5, figsize=(8, 6))\n", + "fig.suptitle('Data Augmentation Examples', fontsize=18, fontweight='bold', y=1.02)\n", + "\n", + "sample_indices = np.random.choice(len(x_train_split), 4, replace=False)\n", + "\n", + "for row, idx in enumerate(sample_indices):\n", + " original = x_train_split[idx]\n", + " label_idx = np.argmax(y_train_split[idx])\n", + "\n", + " # Show original\n", + " axes[row, 0].imshow(np.clip(original, 0, 1))\n", + " axes[row, 0].set_title(f'Original\\n({CLASS_NAMES[label_idx]})', fontsize=10, fontweight='bold')\n", + " axes[row, 0].axis('off')\n", + "\n", + " # Show augmented versions\n", + " img_batch = np.expand_dims(original, axis=0)\n", + " aug_iter = datagen.flow(img_batch, batch_size=1)\n", + " for col in range(1, 5):\n", + " aug_img = next(aug_iter)[0]\n", + " axes[row, col].imshow(np.clip(aug_img, 0, 1))\n", + " axes[row, col].set_title(f'Augmented {col}', fontsize=10)\n", + " axes[row, col].axis('off')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "82857e57", + "metadata": {}, + "source": [ + "---\n", + "## 4. Custom CNN Architecture\n", + "\n", + "### Architecture Design\n", + "\n", + "A **3-block convolutional network** designed for 32ร—32 CIFAR-10 images:\n", + "\n", + "```\n", + "Block 1: Conv2D(32) ร— 2 โ†’ BatchNorm โ†’ MaxPool(2ร—2) โ†’ Dropout(0.25)\n", + "Block 2: Conv2D(64) ร— 2 โ†’ BatchNorm โ†’ MaxPool(2ร—2) โ†’ Dropout(0.25)\n", + "Block 3: Conv2D(128) ร— 2 โ†’ BatchNorm โ†’ MaxPool(2ร—2) โ†’ Dropout(0.25)\n", + "Head: Flatten โ†’ Dense(256) โ†’ BatchNorm โ†’ Dropout(0.5) โ†’ Dense(10, softmax)\n", + "```\n", + "\n", + "**Design rationale:**\n", + "- **Progressive filter increase** (32โ†’64โ†’128): Early layers detect simple features (edges, textures); deeper layers capture complex patterns (eyes, wheels, wings)\n", + "- **BatchNormalization**: Stabilizes training by normalizing activations between layers\n", + "- **Dropout at every block**: Strong regularization to prevent overfitting\n", + "- **`padding='same'`**: Preserves spatial dimensions within each convolutional block" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "9ce11944", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/miniconda3/envs/ironhack.nn/lib/python3.10/site-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" + ] + }, + { + "data": { + "text/html": [ + "
Model: \"sequential_2\"\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential_2\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n",
+       "โ”ƒ Layer (type)                    โ”ƒ Output Shape           โ”ƒ       Param # โ”ƒ\n",
+       "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n",
+       "โ”‚ conv2d_12 (Conv2D)              โ”‚ (None, 32, 32, 32)     โ”‚           896 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ batch_normalization_14          โ”‚ (None, 32, 32, 32)     โ”‚           128 โ”‚\n",
+       "โ”‚ (BatchNormalization)            โ”‚                        โ”‚               โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ conv2d_13 (Conv2D)              โ”‚ (None, 32, 32, 32)     โ”‚         9,248 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ batch_normalization_15          โ”‚ (None, 32, 32, 32)     โ”‚           128 โ”‚\n",
+       "โ”‚ (BatchNormalization)            โ”‚                        โ”‚               โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ max_pooling2d_6 (MaxPooling2D)  โ”‚ (None, 16, 16, 32)     โ”‚             0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ dropout_9 (Dropout)             โ”‚ (None, 16, 16, 32)     โ”‚             0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ conv2d_14 (Conv2D)              โ”‚ (None, 16, 16, 64)     โ”‚        18,496 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ batch_normalization_16          โ”‚ (None, 16, 16, 64)     โ”‚           256 โ”‚\n",
+       "โ”‚ (BatchNormalization)            โ”‚                        โ”‚               โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ conv2d_15 (Conv2D)              โ”‚ (None, 16, 16, 64)     โ”‚        36,928 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ batch_normalization_17          โ”‚ (None, 16, 16, 64)     โ”‚           256 โ”‚\n",
+       "โ”‚ (BatchNormalization)            โ”‚                        โ”‚               โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ max_pooling2d_7 (MaxPooling2D)  โ”‚ (None, 8, 8, 64)       โ”‚             0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ dropout_10 (Dropout)            โ”‚ (None, 8, 8, 64)       โ”‚             0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ conv2d_16 (Conv2D)              โ”‚ (None, 8, 8, 128)      โ”‚        73,856 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ batch_normalization_18          โ”‚ (None, 8, 8, 128)      โ”‚           512 โ”‚\n",
+       "โ”‚ (BatchNormalization)            โ”‚                        โ”‚               โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ conv2d_17 (Conv2D)              โ”‚ (None, 8, 8, 128)      โ”‚       147,584 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ batch_normalization_19          โ”‚ (None, 8, 8, 128)      โ”‚           512 โ”‚\n",
+       "โ”‚ (BatchNormalization)            โ”‚                        โ”‚               โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ max_pooling2d_8 (MaxPooling2D)  โ”‚ (None, 4, 4, 128)      โ”‚             0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ dropout_11 (Dropout)            โ”‚ (None, 4, 4, 128)      โ”‚             0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ flatten_2 (Flatten)             โ”‚ (None, 2048)           โ”‚             0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ dense_6 (Dense)                 โ”‚ (None, 256)            โ”‚       524,544 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ batch_normalization_20          โ”‚ (None, 256)            โ”‚         1,024 โ”‚\n",
+       "โ”‚ (BatchNormalization)            โ”‚                        โ”‚               โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ dropout_12 (Dropout)            โ”‚ (None, 256)            โ”‚             0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ dense_7 (Dense)                 โ”‚ (None, 10)             โ”‚         2,570 โ”‚\n",
+       "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n",
+       "
\n" + ], + "text/plain": [ + "โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n", + "โ”ƒ\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0mโ”ƒ\n", + "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n", + "โ”‚ conv2d_12 (\u001b[38;5;33mConv2D\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m) โ”‚ \u001b[38;5;34m896\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ batch_normalization_14 โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m) โ”‚ \u001b[38;5;34m128\u001b[0m โ”‚\n", + "โ”‚ (\u001b[38;5;33mBatchNormalization\u001b[0m) โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ conv2d_13 (\u001b[38;5;33mConv2D\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m) โ”‚ \u001b[38;5;34m9,248\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ batch_normalization_15 โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m) โ”‚ \u001b[38;5;34m128\u001b[0m โ”‚\n", + "โ”‚ (\u001b[38;5;33mBatchNormalization\u001b[0m) โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ max_pooling2d_6 (\u001b[38;5;33mMaxPooling2D\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ dropout_9 (\u001b[38;5;33mDropout\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ conv2d_14 (\u001b[38;5;33mConv2D\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) โ”‚ \u001b[38;5;34m18,496\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ batch_normalization_16 โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) โ”‚ \u001b[38;5;34m256\u001b[0m โ”‚\n", + "โ”‚ (\u001b[38;5;33mBatchNormalization\u001b[0m) โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ conv2d_15 (\u001b[38;5;33mConv2D\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) โ”‚ \u001b[38;5;34m36,928\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ batch_normalization_17 โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) โ”‚ \u001b[38;5;34m256\u001b[0m โ”‚\n", + "โ”‚ (\u001b[38;5;33mBatchNormalization\u001b[0m) โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ max_pooling2d_7 (\u001b[38;5;33mMaxPooling2D\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ dropout_10 (\u001b[38;5;33mDropout\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ conv2d_16 (\u001b[38;5;33mConv2D\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) โ”‚ \u001b[38;5;34m73,856\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ batch_normalization_18 โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) โ”‚ \u001b[38;5;34m512\u001b[0m โ”‚\n", + "โ”‚ (\u001b[38;5;33mBatchNormalization\u001b[0m) โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ conv2d_17 (\u001b[38;5;33mConv2D\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) โ”‚ \u001b[38;5;34m147,584\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ batch_normalization_19 โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) โ”‚ \u001b[38;5;34m512\u001b[0m โ”‚\n", + "โ”‚ (\u001b[38;5;33mBatchNormalization\u001b[0m) โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ max_pooling2d_8 (\u001b[38;5;33mMaxPooling2D\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ dropout_11 (\u001b[38;5;33mDropout\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ flatten_2 (\u001b[38;5;33mFlatten\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ dense_6 (\u001b[38;5;33mDense\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) โ”‚ \u001b[38;5;34m524,544\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ batch_normalization_20 โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) โ”‚ \u001b[38;5;34m1,024\u001b[0m โ”‚\n", + "โ”‚ (\u001b[38;5;33mBatchNormalization\u001b[0m) โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ dropout_12 (\u001b[38;5;33mDropout\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ dense_7 (\u001b[38;5;33mDense\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) โ”‚ \u001b[38;5;34m2,570\u001b[0m โ”‚\n", + "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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Training the Custom CNN\n", + "\n", + "### Training Configuration\n", + "\n", + "| Parameter | Value | Rationale |\n", + "|:----------|:------|:----------|\n", + "| Optimizer | Adam | Adaptive learning rates, good default |\n", + "| Learning rate | 0.001 | Standard starting point for Adam |\n", + "| Batch size | 64 | Balance between speed and gradient quality |\n", + "| Max epochs | 100 | Early stopping will intervene before this |\n", + "| Data augmentation | Yes | Reduces overfitting on training set |\n", + "\n", + "### Callbacks\n", + "\n", + "- **EarlyStopping** (`patience=10`): Stops training when validation loss stops improving for 10 epochs. Restores the best weights automatically.\n", + "- **ReduceLROnPlateau** (`factor=0.5, patience=5`): Halves the learning rate when validation loss plateaus, helping escape local minima.\n", + "- **ModelCheckpoint**: Saves the best model (lowest validation loss) to disk.\n", + "\n", + "> ๐ŸซŸ **Training took ~17h** in my hardware. i skiiped because a pre-trained model exists in models ๐Ÿ—‚๏ธ folder" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "2028163a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Callbacks configured:\n", + " EarlyStopping (patience=10, restore_best_weights=True)\n", + " ModelCheckpoint (save_best_only=True)\n", + " ReduceLROnPlateau (factor=0.5, patience=5)\n" + ] + } + ], + "source": [ + "# Training configuration\n", + "BATCH_SIZE = 64\n", + "EPOCHS = 100\n", + "MODEL_SAVE_DIR = 'models'\n", + "os.makedirs(MODEL_SAVE_DIR, exist_ok=True)\n", + "CUSTOM_CNN_PATH = os.path.join(MODEL_SAVE_DIR, 'custom_cnn.keras')\n", + "\n", + "# Define callbacks\n", + "callbacks_custom = [\n", + " EarlyStopping(\n", + " monitor='val_loss',\n", + " patience=10,\n", + " restore_best_weights=True,\n", + " verbose=1\n", + " ),\n", + " ModelCheckpoint(\n", + " filepath=CUSTOM_CNN_PATH,\n", + " monitor='val_loss',\n", + " save_best_only=True,\n", + " verbose=1\n", + " ),\n", + " ReduceLROnPlateau(\n", + " monitor='val_loss',\n", + " factor=0.5,\n", + " patience=5,\n", + " min_lr=1e-6,\n", + " verbose=1\n", + " )\n", + "]\n", + "\n", + "print(\"Callbacks configured:\")\n", + "print(\" EarlyStopping (patience=10, restore_best_weights=True)\")\n", + "print(\" ModelCheckpoint (save_best_only=True)\")\n", + "print(\" ReduceLROnPlateau (factor=0.5, patience=5)\")" + ] + }, + { + "cell_type": "markdown", + "id": "3c9f9424", + "metadata": {}, + "source": [ + "### ๐ŸซŸ **Training took ~17h** in my hardware. i skiiped because a pre-trained model exists in models ๐Ÿ—‚๏ธ folder" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "6b75e41d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "' \\n%%time\\n# Train the custom CNN with data augmentation\\n\\nhistory_custom = custom_cnn.fit(\\n datagen.flow(x_train_split, y_train_split, batch_size=BATCH_SIZE),\\n epochs=EPOCHS,\\n validation_data=(x_val, y_val),\\n callbacks=callbacks_custom,\\n steps_per_epoch=len(x_train_split) // BATCH_SIZE,\\n verbose=1\\n)\\n'" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\" \n", + "%%time\n", + "# Train the custom CNN with data augmentation\n", + "\n", + "history_custom = custom_cnn.fit(\n", + " datagen.flow(x_train_split, y_train_split, batch_size=BATCH_SIZE),\n", + " epochs=EPOCHS,\n", + " validation_data=(x_val, y_val),\n", + " callbacks=callbacks_custom,\n", + " steps_per_epoch=len(x_train_split) // BATCH_SIZE,\n", + " verbose=1\n", + ")\n", + "\"\"\" " + ] + }, + { + "cell_type": "markdown", + "id": "161c84b2", + "metadata": {}, + "source": [ + "## Pre-trained model in models ๐Ÿ—‚๏ธ Folder" + ] + }, + { + "cell_type": "markdown", + "id": "4f50e15f", + "metadata": {}, + "source": [ + "#### OPCION 1 - With verification" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "96465348", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "โœ… Loaded pre-trained Custom CNN model from /Users/sebastianlopez/Desktop/it-studies/ironhack/week_7/day_2/models/custom_cnn.keras\n" + ] + } + ], + "source": [ + "# Load pre-trained models\n", + "\"\"\" import os\n", + " from tensorflow import keras--- IGNORE ---\"\"\"\n", + "\n", + "MODEL_DIR = '/Users/sebastianlopez/Desktop/it-studies/ironhack/week_7/day_2/models'\n", + "CUSTOM_CNN_PATH = os.path.join(MODEL_DIR, 'custom_cnn.keras')\n", + "TRANSFER_LEARNING_PATH = os.path.join(MODEL_DIR, 'transfer_learning.keras')\n", + "\n", + "# Load the Custom CNN model\n", + "if os.path.exists(CUSTOM_CNN_PATH):\n", + " custom_cnn = keras.models.load_model(CUSTOM_CNN_PATH)\n", + " print(f\"โœ… Loaded pre-trained Custom CNN model from {CUSTOM_CNN_PATH}\")\n", + "else:\n", + " print(f\"โŒ Custom CNN model not found at {CUSTOM_CNN_PATH}. Please train the model or check the path.\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "94a24864", + "metadata": {}, + "source": [ + "#### OPCION 2 - Faster " + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "2a041968", + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow import keras\n", + "custom_cnn = keras.models.load_model('/Users/sebastianlopez/Desktop/it-studies/ironhack/week_7/day_2/models/custom_cnn.keras')" + ] + }, + { + "cell_type": "markdown", + "id": "27650eb1", + "metadata": {}, + "source": [ + "---\n", + "## 6. Model Evaluation โ€” Custom CNN\n", + "\n", + "### Evaluation Metrics\n", + "\n", + "- **Accuracy**: Overall percentage of correct predictions\n", + "- **Precision**: Of all predictions for class X, how many were correct? (avoids false positives)\n", + "- **Recall**: Of all actual class X samples, how many were found? (avoids false negatives)\n", + "- **F1-Score**: Harmonic mean of precision and recall โ€” a balanced single metric\n", + "- **Confusion Matrix**: Grid showing true vs. predicted labels for every class pair" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3095716c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Custom CNN โ€” Evaluation Results\n", + " Test Loss: 0.4460\n", + " Test Accuracy: 0.8530 (85.30%)\n", + " Precision: 0.8556\n", + " Recall: 0.8530\n", + " F1-Score: 0.8512\n", + "============================================================\n", + "\n", + "Classification Report:\n", + "\n", + " precision recall f1-score support\n", + "\n", + " airplane 0.89 0.84 0.86 1000\n", + " automobile 0.89 0.96 0.92 1000\n", + " bird 0.86 0.75 0.80 1000\n", + " cat 0.81 0.65 0.72 1000\n", + " deer 0.82 0.86 0.84 1000\n", + " dog 0.82 0.77 0.80 1000\n", + " frog 0.74 0.97 0.84 1000\n", + " horse 0.90 0.90 0.90 1000\n", + " ship 0.94 0.91 0.92 1000\n", + " truck 0.88 0.92 0.90 1000\n", + "\n", + " accuracy 0.85 10000\n", + " macro avg 0.86 0.85 0.85 10000\n", + "weighted avg 0.86 0.85 0.85 10000\n", + "\n" + ] + } + ], + "source": [ + "def evaluate_and_report(model, x_test, y_test, model_name='Model'):\n", + " \"\"\"Evaluate model and return metrics dictionary with full report.\"\"\"\n", + " # Get test loss and accuracy\n", + " loss, accuracy = model.evaluate(x_test, y_test, verbose=0)\n", + " \n", + " # Get predictions\n", + " y_pred_proba = model.predict(x_test, verbose=0)\n", + " y_pred = np.argmax(y_pred_proba, axis=1)\n", + " y_true = np.argmax(y_test, axis=1)\n", + " \n", + " # Compute metrics\n", + " prec = precision_score(y_true, y_pred, average='weighted')\n", + " rec = recall_score(y_true, y_pred, average='weighted')\n", + " f1 = f1_score(y_true, y_pred, average='weighted')\n", + " report = classification_report(y_true, y_pred, target_names=CLASS_NAMES)\n", + " \n", + " # Print summary\n", + " \n", + " print(f\" {model_name} โ€” Evaluation Results\")\n", + " \n", + " print(f\" Test Loss: {loss:.4f}\")\n", + " print(f\" Test Accuracy: {accuracy:.4f} ({accuracy*100:.2f}%)\")\n", + " print(f\" Precision: {prec:.4f}\")\n", + " print(f\" Recall: {rec:.4f}\")\n", + " print(f\" F1-Score: {f1:.4f}\")\n", + " print(f\"{'='*60}\")\n", + " print(f\"\\nClassification Report:\\n\")\n", + " print(report)\n", + " \n", + " return {\n", + " 'loss': loss, 'accuracy': accuracy, 'precision': prec,\n", + " 'recall': rec, 'f1_score': f1, 'y_true': y_true, 'y_pred': y_pred\n", + " }\n", + "\n", + "# Evaluate Custom CNN\n", + "metrics_custom = evaluate_and_report(custom_cnn, x_test, y_test, 'Custom CNN')" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "50281ab2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\" \\nfig, axes = plt.subplots(1, 2, figsize=(14, 5))\\n# --- Grรกfico de Exactitud (Accuracy) ---\\naxes[0].plot(history_custom.history['accuracy'], label='Train Accuracy', linewidth=2)\\naxes[0].plot(history_custom.history['val_accuracy'], label='Validation Accuracy', linewidth=2)\\naxes[0].set_title('Custom CNN Accuracy', fontsize=14)\\naxes[0].set_xlabel('Epoch')\\naxes[0].set_ylabel('Accuracy')\\naxes[0].legend()\\naxes[0].grid(True, alpha=0.3)\\n# --- Grรกfico de Pรฉrdida (Loss) ---\\naxes[1].plot(history_custom.history['loss'], label='Train Loss', linewidth=2)\\naxes[1].plot(history_custom.history['val_loss'], label='Validation Loss', linewidth=2)\\naxes[1].set_title('Custom CNN Loss', fontsize=14)\\naxes[1].set_xlabel('Epoch')\\naxes[1].set_ylabel('Loss')\\naxes[1].legend()\\naxes[1].grid(True, alpha=0.3)\\nplt.tight_layout()\\n\\nplt.show()\\n\"" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# NameError: name 'history_custom' is not defined \n", + "# Run just if the model was trained inthis jupyter \n", + "\n", + "\"\"\" \n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + "# --- Grรกfico de Exactitud (Accuracy) ---\n", + "axes[0].plot(history_custom.history['accuracy'], label='Train Accuracy', linewidth=2)\n", + "axes[0].plot(history_custom.history['val_accuracy'], label='Validation Accuracy', linewidth=2)\n", + "axes[0].set_title('Custom CNN Accuracy', fontsize=14)\n", + "axes[0].set_xlabel('Epoch')\n", + "axes[0].set_ylabel('Accuracy')\n", + "axes[0].legend()\n", + "axes[0].grid(True, alpha=0.3)\n", + "# --- Grรกfico de Pรฉrdida (Loss) ---\n", + "axes[1].plot(history_custom.history['loss'], label='Train Loss', linewidth=2)\n", + "axes[1].plot(history_custom.history['val_loss'], label='Validation Loss', linewidth=2)\n", + "axes[1].set_title('Custom CNN Loss', fontsize=14)\n", + "axes[1].set_xlabel('Epoch')\n", + "axes[1].set_ylabel('Loss')\n", + "axes[1].legend()\n", + "axes[1].grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "\n", + "plt.show()\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "id": "f234b4f2", + "metadata": {}, + "source": [ + "#### CUSTON CNN TRAINING\n", + "![Custom CNN Training](/Users/sebastianlopez/Desktop/it-studies/ironhack/week_7/day_2/outputs/custom_cnn_history.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "fe33efbf", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "๐Ÿ” Most confused pair: cat โ†” frog (108 not match)\n" + ] + } + ], + "source": [ + "# Confusion Matrix\n", + "cm = confusion_matrix(metrics_custom['y_true'], metrics_custom['y_pred'])\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 6))\n", + "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n", + " xticklabels=CLASS_NAMES, yticklabels=CLASS_NAMES, ax=ax)\n", + "ax.set_xlabel('Predicted Label', fontsize=13)\n", + "ax.set_ylabel('True Label', fontsize=13)\n", + "ax.set_title('Custom CNN โ€” Confusion Matrix', fontsize=15, fontweight='bold')\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Identify most confused pairs\n", + "np.fill_diagonal(cm, 0)\n", + "max_idx = np.unravel_index(cm.argmax(), cm.shape)\n", + "print(f\"\\n๐Ÿ” Most confused pair: {CLASS_NAMES[max_idx[0]]} โ†” {CLASS_NAMES[max_idx[1]]} ({cm[max_idx]} not match)\")" + ] + }, + { + "cell_type": "markdown", + "id": "92062b36", + "metadata": {}, + "source": [ + "# 7. Training model Transfer learning ResNet50" + ] + }, + { + "cell_type": "markdown", + "id": "c7bf43d5", + "metadata": {}, + "source": [ + "#### 1: Load the Base Model and Freeze It" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7b276e82", + "metadata": {}, + "outputs": [], + "source": [ + "from keras.applications import ResNet50\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense, GlobalAveragePooling2D\n", + "\n", + "# 1. Load the pre-trained ResNet50 model\n", + "# 'weights=imagenet' downloads the pre-trained knowledge.\n", + "# 'include_top=False' chops off the original 1000-class output layer.\n", + "resnet_base = ResNet50(weights='imagenet', include_top=False, input_shape=(32, 32, 3))\n", + "\n", + "# 2. FREEZE the base model \n", + "# This is the most crucial step of Transfer Learning!\n", + "# We want to use its existing knowledge, not overwrite it.\n", + "resnet_base.trainable = False \n", + "print(\"Base model loaded and frozen!\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bcc6afd8", + "metadata": {}, + "outputs": [], + "source": [ + "# 3. Build our customized Transfer Learning Model\n", + "transfer_model = Sequential([\n", + " resnet_base, # The frozen ResNet50 layers\n", + " GlobalAveragePooling2D(), # Safely flattens the output of ResNet50\n", + " Dense(256, activation='relu'), # Our new \"thinking\" layer\n", + " Dense(10, activation='softmax') # Our 10 outputs for CIFAR-10 classification\n", + "])\n", + "# 4. Compile the model\n", + "# NOTE: If your Y labels are NOT one-hot encoded, change 'categorical_crossentropy' to 'sparse_categorical_crossentropy'\n", + "transfer_model.compile(\n", + " optimizer='adam',\n", + " loss='categorical_crossentropy', \n", + " metrics=['accuracy']\n", + ")\n", + "transfer_model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c79d3046", + "metadata": {}, + "outputs": [], + "source": [ + "# 5. Train the model!\n", + "# Documentation says we can use fewer epochs because the base model already knows how to \"see\" images.\n", + "\n", + "history_tl = transfer_model.fit(\n", + " X_train, y_train, # Use whatever variables you used for the custom CNN training\n", + " validation_split=0.2, \n", + " epochs=10, \n", + " batch_size=64\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ironhack.nn", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_knowledge&presentation/jupyter notebooks/2. CIFAR10_Image_Classification_CNN_Presentation 09.52.53.ipynb b/notebooks_knowledge&presentation/jupyter notebooks/2. CIFAR10_Image_Classification_CNN_Presentation 09.52.53.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..53b46bada290b834825d64b54b4ca4d94ffc14b2 --- /dev/null +++ b/notebooks_knowledge&presentation/jupyter notebooks/2. CIFAR10_Image_Classification_CNN_Presentation 09.52.53.ipynb @@ -0,0 +1,690 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Guided Lab: Image Classification using CNN (CIFAR-10)\n", + "\n", + "At the end of this tutorial, you will get familiarized with:\n", + "\n", + "* Creating deep networks using Keras\n", + "* Steps necessary in training a neural network\n", + "* Prediction and performance analysis using neural networks\n", + "* Using Transfer Learning for complex images" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# **Environment set up**\n", + "**Keras** is a Python library that provides, in a simple way, the creation of a wide range of Deep Learning models using as backend other libraries such as TensorFlow. \n", + "\n", + "Keras is used since its learning curve is very smooth compared to others, and at the moment it is one of the popular middleware to implement neural networks." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-02-28 16:43:51.698653: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.6.0\n" + ] + } + ], + "source": [ + "from tensorflow import keras\n", + "import tensorflow as tf\n", + "print(keras.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# **Understanding Data: CIFAR-10 Dataset**\n", + "The CIFAR-10 dataset consists of 60,000 32x32 color images in 10 classes, with 6,000 images per class. \n", + "\n", + "In Keras, the CIFAR-10 dataset is preloaded in the form of four Numpy arrays. `x_train` and `y_train` contain the training set, while `x_test` and `y_test` contain the test data. The images are encoded as Numpy arrays and their corresponding labels ranging from 0 to 9." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training data shape: (50000, 32, 32, 3)\n", + "Testing data shape: (10000, 32, 32, 3)\n" + ] + } + ], + "source": [ + "from keras.datasets import cifar10\n", + "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n", + "\n", + "print('Training data shape:', x_train.shape)\n", + "print('Testing data shape:', x_test.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualizing the Data\n", + "\n", + "A plot of the first nine images in the dataset is created showing the natural image nature of the images to be classified.\n", + "\n", + "**Let us create a 3*3 subplot to visualize the first 9 images of the dataset.**" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', \n", + " 'dog', 'frog', 'horse', 'ship', 'truck']\n", + "\n", + "plt.figure(figsize=(5, 5))\n", + "for i in range(9):\n", + " plt.subplot(3, 3, i + 1)\n", + " plt.xticks([])\n", + " plt.yticks([])\n", + " plt.grid(False)\n", + " plt.imshow(x_train[i])\n", + " plt.title(class_names[y_train[i][0]])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data normalization\n", + "\n", + "The pixel values for each image in the dataset are unsigned integers in the range between 0 and 255.\n", + "\n", + "**Let us normalize the pixel values of the RGB images, e.g. rescale them to the range [0, 1].**" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "x_train = x_train.astype('float32') / 255.0\n", + "x_test = x_test.astype('float32') / 255.0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Preparing labels\n", + "\n", + "The labels for each input data indicate which object represents the image.\n", + "\n", + "In this case, we will represent this label with a vector of 10 positions. This is known as **one-hot encoding.**\n", + "\n", + "**Let us convert the labels into the equivalent one-hot encoded form.**" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training labels shape: (50000, 10)\n" + ] + } + ], + "source": [ + "from keras.utils import to_categorical\n", + "\n", + "y_train = to_categorical(y_train, num_classes=10)\n", + "y_test = to_categorical(y_test, num_classes=10)\n", + "\n", + "print('Training labels shape:', y_train.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# **Defining Model (Custom CNN)**\n", + "\n", + "The Sequential class of the Keras library is a wrapper for the sequential neural network model.\n", + "\n", + "**Define the following model:**\n", + "\n", + "**For the convolutional front-end, start with convolutional layers with a small filter size (3,3) followed by max pooling layers.**" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/miniconda3/envs/ironhack.nn/lib/python3.10/site-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" + ] + } + ], + "source": [ + "from keras.models import Sequential\n", + "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n", + "\n", + "model = Sequential()\n", + "model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=(32, 32, 3)))\n", + "model.add(MaxPooling2D((2, 2)))\n", + "\n", + "model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))\n", + "model.add(MaxPooling2D((2, 2)))\n", + "\n", + "model.add(Flatten())\n", + "model.add(Dense(128, activation='relu'))\n", + "model.add(Dropout(0.5))\n", + "model.add(Dense(10, activation='softmax'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Let us check the architecture using summary().**" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Model: \"sequential\"\n",
+       "
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โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n",
+       "โ”ƒ Layer (type)                    โ”ƒ Output Shape           โ”ƒ       Param # โ”ƒ\n",
+       "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n",
+       "โ”‚ conv2d (Conv2D)                 โ”‚ (None, 32, 32, 32)     โ”‚           896 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ max_pooling2d (MaxPooling2D)    โ”‚ (None, 16, 16, 32)     โ”‚             0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ conv2d_1 (Conv2D)               โ”‚ (None, 16, 16, 64)     โ”‚        18,496 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ max_pooling2d_1 (MaxPooling2D)  โ”‚ (None, 8, 8, 64)       โ”‚             0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ flatten (Flatten)               โ”‚ (None, 4096)           โ”‚             0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ dense (Dense)                   โ”‚ (None, 128)            โ”‚       524,416 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ dropout (Dropout)               โ”‚ (None, 128)            โ”‚             0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ dense_1 (Dense)                 โ”‚ (None, 10)             โ”‚         1,290 โ”‚\n",
+       "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n",
+       "
\n" + ], + "text/plain": [ + "โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n", + "โ”ƒ\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0mโ”ƒ\n", + "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n", + "โ”‚ conv2d (\u001b[38;5;33mConv2D\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m) โ”‚ \u001b[38;5;34m896\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) โ”‚ \u001b[38;5;34m18,496\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ flatten (\u001b[38;5;33mFlatten\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ dense (\u001b[38;5;33mDense\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) โ”‚ \u001b[38;5;34m524,416\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ dropout (\u001b[38;5;33mDropout\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ dense_1 (\u001b[38;5;33mDense\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) โ”‚ \u001b[38;5;34m1,290\u001b[0m โ”‚\n", + "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Total params: 545,098 (2.08 MB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m545,098\u001b[0m (2.08 MB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Trainable params: 545,098 (2.08 MB)\n",
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This can be achieved by using the `evaluate()` method.\n", + "\n", + "**Let us print the test accuracy of the network.**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "test_loss, test_acc = model.evaluate(x_test, y_test)\n", + "print('Test loss:', test_loss)\n", + "print('Test accuracy:', test_acc)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "# **Defining Model (Transfer Learning)**\n", + "\n", + "We can use a pre-trained model like MobileNetV2 to extract features. However, running this on a local CPU can be very slow. \n", + "\n", + "To solve this for local CPUs, we **resize the images once before training**, instead of resizing them on-the-fly inside the model during every epoch.\n", + "\n", + "## CPU Optimization: Pre-resizing the data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.applications.mobilenet_v2 import preprocess_input\n", + "\n", + "print('Resizing data for MobileNetV2... This takes a minute but saves HOURS later!')\n", + "\n", + "# MobileNetV2 expects values in [-1, 1], not [0, 1]. \n", + "# We also need to resize to at least 96x96 for MobileNetV2.\n", + "\n", + "# Note: We only use a subset of the training data (e.g., 20% = 10k images) \n", + "# to keep RAM usage and computation time reasonable on a standard CPU.\n", + "SUBSET_SIZE = 10000\n", + "\n", + "x_subset = x_train[:SUBSET_SIZE] * 255.0 # Scale back to 0-255 first\n", + "y_subset = y_train[:SUBSET_SIZE]\n", + "\n", + "x_train_resized = tf.image.resize(x_subset, (96, 96))\n", + "x_train_preprocessed = preprocess_input(x_train_resized)\n", + "\n", + "x_test_resized = tf.image.resize(x_test[:2000] * 255.0, (96, 96))\n", + "x_test_preprocessed = preprocess_input(x_test_resized)\n", + "\n", + "print('Resized training data shape:', x_train_preprocessed.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Building the Transfer Learning Model\n", + "We load MobileNetV2 without its top layer, and add our own dense layer for CIFAR-10 classification." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.applications import MobileNetV2\n", + "from tensorflow.keras.layers import GlobalAveragePooling2D\n", + "\n", + "base_model = MobileNetV2(input_shape=(96, 96, 3), include_top=False, weights='imagenet')\n", + "base_model.trainable = False # Freeze the base model!\n", + "\n", + "tl_model = Sequential([\n", + " base_model,\n", + " GlobalAveragePooling2D(),\n", + " Dense(10, activation='softmax')\n", + "])\n", + "\n", + "tl_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", + "tl_model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training the Transfer Learning Model (CPU Optimized)\n", + "Because we pre-resized the images and froze the base model, this should run reasonably fast (1-2 minutes per epoch on CPU)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('Training Transfer Learning model on CPU...')\n", + "tl_history = tl_model.fit(\n", + " x_train_preprocessed, \n", + " y_subset, \n", + " epochs=5, \n", + " batch_size=64, \n", + " validation_split=0.2\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## \"\"\" Optional \"\"\" - Cloud GPU Pipeline Code\n", + "If you decide to run this in **Google Colab** (Option 1), you can use the much simpler, fully-integrated code below, since the GPU is fast enough to handle the resizing on-the-fly during training." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "# OPTIONAL: Run this ONLY if you are using Google Colab or Kaggle (GPU enabled)\n", + "from tensorflow.keras.layers import Resizing, Input\n", + "from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2, preprocess_input\n", + "from tensorflow.keras.models import Model\n", + "\n", + "# Reload original raw data (0-255)\n", + "(x_train_raw, y_train_raw), (x_test_raw, y_test_raw) = cifar10.load_data()\n", + "y_train_cat = to_categorical(y_train_raw, 10)\n", + "y_test_cat = to_categorical(y_test_raw, 10)\n", + "\n", + "base_model = MobileNetV2(input_shape=(96, 96, 3), include_top=False, weights='imagenet')\n", + "base_model.trainable = False\n", + "\n", + "# Build pipeline that resizes ON THE FLY (fast on GPU, slow on CPU)\n", + "inputs = Input(shape=(32, 32, 3))\n", + "x = Resizing(96, 96)(inputs)\n", + "x = preprocess_input(x)\n", + "x = base_model(x)\n", + "x = GlobalAveragePooling2D()(x)\n", + "outputs = Dense(10, activation='softmax')(x)\n", + "\n", + "gpu_model = Model(inputs, outputs)\n", + "gpu_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "# Train on ALL data (50k images) - this will be very fast on a GPU!\n", + "gpu_history = gpu_model.fit(x_train_raw, y_train_cat, epochs=5, batch_size=128, validation_split=0.2)\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "# **Misclassification Analysis**\n", + "\n", + "Even our best model (Custom CNN, 85.3% accuracy) makes confident mistakes. Here are 5 examples where the model was **highly confident but wrong**:\n", + "\n", + "| True Label | Predicted | Confidence | Why the model failed |\n", + "|:-----------|:----------|:-----------|:---------------------|\n", + "| **Cat** | Dog | 99.5% | Both are furry, four-legged animals with similar color palettes at 32ร—32 |\n", + "| **Dog** | Cat | 95.2% | Same confusion pair in reverse โ€” shows this is a systematic weakness |\n", + "| **Bird** | Frog | 100.0% | Small subject against green/natural background at low resolution |\n", + "| **Automobile** | Truck | 99.7% | Both are wheeled vehicles with boxy shapes โ€” size (the key discriminator) is lost at 32ร—32 |\n", + "| **Airplane** | Ship | 99.8% | Both appear against blue backgrounds (sky vs. water) |\n", + "\n", + "**Key insight:** The model relies heavily on **color patterns and rough shapes** rather than fine-grained details, because 32ร—32 resolution simply does not preserve enough information." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from PIL import Image\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Display the pre-generated misclassification examples\n", + "img = Image.open(\"../outputs/misclassified_examples.png\")\n", + "plt.figure(figsize=(20, 5))\n", + "plt.imshow(img)\n", + "plt.axis(\"off\")\n", + "plt.title(\"Misclassification Examples - Custom CNN\", fontsize=16, fontweight=\"bold\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "# **Dataset Bias: What The Model Does Not Know**\n", + "\n", + "CIFAR-10 was collected from internet images, predominantly from **Western/English-language sources**. This means our model learned a geographically and culturally *narrow* view of each class:\n", + "\n", + "| Class | What the model learned | What it probably has not seen |\n", + "|:------|:----------------------|:----------------------------|\n", + "| **Automobile** | American/European car designs | Tuk-tuks, rickshaws, vehicles common in Asia/Africa |\n", + "| **Truck** | Modern pickup & delivery trucks | Flatbed trucks from rural areas, different regions |\n", + "| **Ship** | Large vessels in open water | Canoes, kayaks, fishing boats from other cultures |\n", + "| **Horse** | Western contexts (ranches, paddocks) | Horses in different cultural settings or landscapes |\n", + "| **Bird** | North American bird species | Tropical birds, exotic species from other continents |\n", + "\n", + "> **The model classifies an automobile as seen by English-speaking internet users, not a universal automobile.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "# **Why This Matters**\n", + "\n", + "### 4 Types of Bias in Our Model\n", + "\n", + "1. **Geographic diversity was never audited**\n", + " - The training data was never checked for regional representation\n", + " - The model has a Western-centric worldview in every class\n", + "\n", + "2. **Background/context bias**\n", + " - Objects appear in typical contexts (planes in blue sky, ships in water)\n", + " - An airplane on a tarmac or a ship in dry dock would likely be harder to classify\n", + "\n", + "3. **Color/lighting bias**\n", + " - Nearly all training images are in daylight\n", + " - Night images or unusual lighting would degrade performance\n", + "\n", + "4. **Resolution bias**\n", + " - Everything is 32x32 pixels: the model uses color patterns and rough shapes, not details\n", + " - This is itself a form of **information loss bias**\n", + "\n", + "### Other Limitations\n", + "- **No none of the above**: Upload a banana and the model will still confidently say it is a frog\n", + "- **Perfect class balance** (6,000/class) is unrealistic, real-world data is imbalanced\n", + "- **Temporal bias**: Cars, trucks, and ships from the dataset era look different from today" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ironhack.nn", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks_knowledge&presentation/terminal_commands.md b/notebooks_knowledge&presentation/terminal_commands.md new file mode 100644 index 0000000000000000000000000000000000000000..ae0ad31ed04c814b68ade29c5754b568846647a9 --- /dev/null +++ b/notebooks_knowledge&presentation/terminal_commands.md @@ -0,0 +1,76 @@ +# Important Terminal Commands + +This document contains a summary of the most important terminal commands for the CIFAR-10 Image Classification project, organized by workflow. + +## 1. Environment Setup + +```bash +# Create and activate conda environment +conda create -n ironhack.nn python=3.10 +conda activate ironhack.nn + +# Install dependencies +pip install -r requirements.txt +``` + +## 2. Training the Model + +```bash +# Run the training notebook (or script) +jupyter notebook notebooks/ + +# Or run training scripts directly +python src/train.py # if you have a training script +``` + +## 3. Running the Flask Web App + +```bash +# Activate environment first +conda activate ironhack.nn + +# Start the Flask server +python app/app.py +# or with a custom port: +python app/app.py (or flask run --port 5001) +``` + +## 4. Docker (Containerized Deployment) + +```bash +# Build the Docker image +docker build -t cifar10-classifier . + +# Run the container +docker run -p 5001:5001 cifar10-classifier +``` + +## 5. Git (Version Control) + +```bash +# Check status of changes +git status + +# Stage and commit changes +git add . +git commit -m "your message here" + +# Push to remote +git push origin main +``` + +## 6. Useful Utility Commands + +```bash +# Check installed packages +pip list + +# Check Python version +python --version + +# Check GPU availability (if using TensorFlow) +python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))" + +# Check model file sizes +ls -lh models/ +``` diff --git a/outputs/augmentation_examples.png b/outputs/augmentation_examples.png new file mode 100644 index 0000000000000000000000000000000000000000..9d48b1f246274e8dbd55711779df886e0479af05 --- /dev/null +++ b/outputs/augmentation_examples.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:05d0c390839e85bd2ec2cb806f0fc65c8b0214ec5a26ac859404968ee00672d7 +size 146298 diff --git a/outputs/class_distribution.png b/outputs/class_distribution.png new file mode 100644 index 0000000000000000000000000000000000000000..4f3de6a7dc390fb85208bc66f41689eaf1951cf0 --- /dev/null +++ b/outputs/class_distribution.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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new file mode 100644 index 0000000000000000000000000000000000000000..788e9c87a98ed9480456308a7266ccff5eaefd64 --- /dev/null +++ b/outputs/transfer_learning_history.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dc1facdc75cec201242c687df7c9879880e0f4d74ea941c70530a1777107995e +size 107591 diff --git a/outputs/transfer_learning_metrics.json b/outputs/transfer_learning_metrics.json new file mode 100644 index 0000000000000000000000000000000000000000..e56f5972a7297d85d2378da01efaab2307109617 --- /dev/null +++ b/outputs/transfer_learning_metrics.json @@ -0,0 +1,8 @@ +{ + "model": "MobileNetV2 Transfer Learning", + "loss": 0.5526939630508423, + "accuracy": 0.8339999914169312, + "precision": 0.8353133287095834, + "recall": 0.834, + "f1_score": 0.8339057892820989 +} \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..b73cfe04b36139d385bd9b8801aeee775b3a41be --- /dev/null +++ b/requirements.txt @@ -0,0 +1,10 @@ +tensorflow==2.18.1 +keras==3.6.0 +numpy==2.0.1 +pandas==2.3.1 +matplotlib==3.10.0 +seaborn==0.13.2 +scikit-learn==1.7.1 +Pillow==11.3.0 +flask==3.1.3 +opencv-python-headless==4.13.0.92 diff --git a/src/__init__.py b/src/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b792f47d07fc90a94150a4dad263c574176481e9 --- /dev/null +++ b/src/__init__.py @@ -0,0 +1 @@ +# CNN Image Classification Project - Source Package diff --git a/src/data_loader.py b/src/data_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..ef693793dd7c861339f1c1edde542f626ef5a265 --- /dev/null +++ b/src/data_loader.py @@ -0,0 +1,113 @@ +""" +Data Loading and Preprocessing Module for CIFAR-10 Image Classification. + +This module handles: +- Loading the CIFAR-10 dataset from Keras +- Normalizing pixel values to [0, 1] +- One-hot encoding labels +- Data augmentation via ImageDataGenerator +""" + +import numpy as np +from tensorflow import keras +from keras.api.datasets import cifar10 +from keras.api.utils import to_categorical +from keras.src.legacy.preprocessing.image import ImageDataGenerator + + +# CIFAR-10 class names (in order of label index 0-9) +CLASS_NAMES = [ + 'airplane', 'automobile', 'bird', 'cat', 'deer', + 'dog', 'frog', 'horse', 'ship', 'truck' +] + +NUM_CLASSES = 10 +IMG_SHAPE = (32, 32, 3) + + +def load_cifar10_data(): + """ + Load the CIFAR-10 dataset and return raw train/test splits. + + Returns: + tuple: (x_train, y_train), (x_test, y_test) + - x: uint8 images of shape (N, 32, 32, 3) + - y: integer labels of shape (N, 1) + """ + (x_train, y_train), (x_test, y_test) = cifar10.load_data() + return (x_train, y_train), (x_test, y_test) + + +def preprocess_data(x_train, y_train, x_test, y_test): + """ + Normalize images and one-hot encode labels. + + Args: + x_train: Training images (uint8). + y_train: Training labels (int). + x_test: Test images (uint8). + y_test: Test labels (int). + + Returns: + tuple: (x_train, y_train, x_test, y_test) + - x: float32 images normalized to [0, 1] + - y: one-hot encoded labels of shape (N, 10) + """ + # Normalize pixel values to [0, 1] + x_train = x_train.astype('float32') / 255.0 + x_test = x_test.astype('float32') / 255.0 + + # One-hot encode labels + y_train = to_categorical(y_train, NUM_CLASSES) + y_test = to_categorical(y_test, NUM_CLASSES) + + return x_train, y_train, x_test, y_test + + +def create_data_augmentation_generator(): + """ + Create an ImageDataGenerator with augmentation transformations. + + Augmentations applied: + - Random rotation up to 15 degrees + - Random width/height shift up to 10% + - Random horizontal flip + - Random zoom up to 10% + + Returns: + ImageDataGenerator: configured generator for training data augmentation. + """ + datagen = ImageDataGenerator( + rotation_range=15, + width_shift_range=0.1, + height_shift_range=0.1, + horizontal_flip=True, + zoom_range=0.1, + fill_mode='nearest' + ) + return datagen + + +def get_prepared_data(): + """ + Full pipeline: load CIFAR-10, preprocess, and return ready-to-use data. + + Returns: + dict with keys: + 'x_train', 'y_train', 'x_test', 'y_test': processed arrays + 'datagen': ImageDataGenerator for augmented training + """ + (x_train, y_train), (x_test, y_test) = load_cifar10_data() + x_train, y_train, x_test, y_test = preprocess_data( + x_train, y_train, x_test, y_test + ) + datagen = create_data_augmentation_generator() + datagen.fit(x_train) + + return { + 'x_train': x_train, + 'y_train': y_train, + 'x_test': x_test, + 'y_test': y_test, + 'datagen': datagen + } diff --git a/src/evaluate.py b/src/evaluate.py new file mode 100644 index 0000000000000000000000000000000000000000..6f60c054e44c73684b77cda4032bc48be6d694a7 --- /dev/null +++ b/src/evaluate.py @@ -0,0 +1,156 @@ +""" +Evaluation Module for CIFAR-10 Image Classification. + +Provides functions to: +- Evaluate model accuracy on test data +- Generate classification reports (precision, recall, F1) +- Plot and save confusion matrices +- Plot training history curves +""" + +import os +import numpy as np +import matplotlib +matplotlib.use('Agg') # Non-interactive backend for saving plots +import matplotlib.pyplot as plt +import seaborn as sns +from sklearn.metrics import ( + classification_report, confusion_matrix, + accuracy_score, precision_score, recall_score, f1_score +) + +from .data_loader import CLASS_NAMES + + +def evaluate_model(model, x_test, y_test): + """ + Evaluate a trained model on the test set. + + Args: + model: Trained Keras model. + x_test: Test images (normalized). + y_test: Test labels (one-hot encoded). + + Returns: + dict: Dictionary with loss, accuracy, precision, recall, f1, + y_true (int labels), y_pred (int predictions), + and the full classification_report string. + """ + # Get test loss and accuracy + loss, accuracy = model.evaluate(x_test, y_test, verbose=0) + + # Get predictions + y_pred_proba = model.predict(x_test, verbose=0) + y_pred = np.argmax(y_pred_proba, axis=1) + y_true = np.argmax(y_test, axis=1) + + # Compute metrics + precision = precision_score(y_true, y_pred, average='weighted') + recall = recall_score(y_true, y_pred, average='weighted') + f1 = f1_score(y_true, y_pred, average='weighted') + + report = classification_report(y_true, y_pred, target_names=CLASS_NAMES) + + return { + 'loss': loss, + 'accuracy': accuracy, + 'precision': precision, + 'recall': recall, + 'f1_score': f1, + 'y_true': y_true, + 'y_pred': y_pred, + 'classification_report': report + } + + +def plot_confusion_matrix(y_true, y_pred, class_names=CLASS_NAMES, + save_path=None, title='Confusion Matrix'): + """ + Plot and optionally save a confusion matrix heatmap. + + Args: + y_true: True integer labels. + y_pred: Predicted integer labels. + class_names: List of class name strings. + save_path: Optional file path to save the plot. + title: Plot title. + """ + cm = confusion_matrix(y_true, y_pred) + + fig, ax = plt.subplots(figsize=(10, 8)) + sns.heatmap( + cm, annot=True, fmt='d', cmap='Blues', + xticklabels=class_names, yticklabels=class_names, + ax=ax + ) + ax.set_xlabel('Predicted Label', fontsize=12) + ax.set_ylabel('True Label', fontsize=12) + ax.set_title(title, fontsize=14) + plt.tight_layout() + + if save_path: + os.makedirs(os.path.dirname(save_path), exist_ok=True) + plt.savefig(save_path, dpi=150, bbox_inches='tight') + print(f"Confusion matrix saved to: {save_path}") + + plt.close(fig) + + +def plot_training_history(history, save_path=None, title_prefix=''): + """ + Plot training and validation accuracy/loss curves. + + Args: + history: Keras History object from model.fit(). + save_path: Optional file path to save the plot. + title_prefix: Optional prefix for plot titles (e.g., 'Custom CNN'). + """ + fig, axes = plt.subplots(1, 2, figsize=(14, 5)) + + # Accuracy plot + axes[0].plot(history.history['accuracy'], label='Train Accuracy', linewidth=2) + axes[0].plot(history.history['val_accuracy'], label='Validation Accuracy', linewidth=2) + axes[0].set_title(f'{title_prefix} Accuracy', fontsize=14) + axes[0].set_xlabel('Epoch') + axes[0].set_ylabel('Accuracy') + axes[0].legend() + axes[0].grid(True, alpha=0.3) + + # Loss plot + axes[1].plot(history.history['loss'], label='Train Loss', linewidth=2) + axes[1].plot(history.history['val_loss'], label='Validation Loss', linewidth=2) + axes[1].set_title(f'{title_prefix} Loss', fontsize=14) + axes[1].set_xlabel('Epoch') + axes[1].set_ylabel('Loss') + axes[1].legend() + axes[1].grid(True, alpha=0.3) + + plt.tight_layout() + + if save_path: + os.makedirs(os.path.dirname(save_path), exist_ok=True) + plt.savefig(save_path, dpi=150, bbox_inches='tight') + print(f"Training history saved to: {save_path}") + + plt.close(fig) + + +def print_evaluation_summary(metrics, model_name='Model'): + """ + Print a formatted evaluation summary. + + Args: + metrics: Dictionary returned by evaluate_model(). + model_name: Name of the model for display. + """ + print(f"\n{'='*60}") + print(f" {model_name} โ€” Evaluation Results") + print(f"{'='*60}") + print(f" Test Loss: {metrics['loss']:.4f}") + print(f" Test Accuracy: {metrics['accuracy']:.4f} ({metrics['accuracy']*100:.2f}%)") + print(f" Precision: {metrics['precision']:.4f}") + print(f" Recall: {metrics['recall']:.4f}") + print(f" F1-Score: {metrics['f1_score']:.4f}") + print(f"{'='*60}") + print(f"\nClassification Report:\n") + print(metrics['classification_report']) diff --git a/src/model_builder.py b/src/model_builder.py new file mode 100644 index 0000000000000000000000000000000000000000..85b9dfda0ed0ca80e70d0d565b6c839439a650ac --- /dev/null +++ b/src/model_builder.py @@ -0,0 +1,153 @@ +""" +Model Architecture Builder for CIFAR-10 Image Classification. + +This module defines: +- A custom CNN architecture designed for 32x32 CIFAR-10 images +- A transfer learning model using MobileNetV2 pretrained on ImageNet +""" + +from keras.api.models import Sequential, Model +from keras.api.layers import ( + Conv2D, MaxPooling2D, Dense, Dropout, Flatten, + BatchNormalization, GlobalAveragePooling2D, Input, + UpSampling2D +) +from keras.api.applications import MobileNetV2 + +from .data_loader import NUM_CLASSES, IMG_SHAPE + + +def build_custom_cnn(input_shape=IMG_SHAPE, num_classes=NUM_CLASSES): + """ + Build a custom CNN architecture for CIFAR-10 classification. + + Architecture: + Block 1: Conv2D(32) -> BatchNorm -> Conv2D(32) -> BatchNorm -> MaxPool -> Dropout(0.25) + Block 2: Conv2D(64) -> BatchNorm -> Conv2D(64) -> BatchNorm -> MaxPool -> Dropout(0.25) + Block 3: Conv2D(128) -> BatchNorm -> Conv2D(128) -> BatchNorm -> MaxPool -> Dropout(0.25) + Dense: Flatten -> Dense(256) -> BatchNorm -> Dropout(0.5) -> Dense(10, softmax) + + Args: + input_shape (tuple): Shape of input images (H, W, C). Default: (32, 32, 3) + num_classes (int): Number of output classes. Default: 10 + + Returns: + keras.Model: Compiled CNN model + """ + model = Sequential([ + # --- Block 1 --- + Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=input_shape), + BatchNormalization(), + Conv2D(32, (3, 3), activation='relu', padding='same'), + BatchNormalization(), + MaxPooling2D(pool_size=(2, 2)), + Dropout(0.25), + + # --- Block 2 --- + Conv2D(64, (3, 3), activation='relu', padding='same'), + BatchNormalization(), + Conv2D(64, (3, 3), activation='relu', padding='same'), + BatchNormalization(), + MaxPooling2D(pool_size=(2, 2)), + Dropout(0.25), + + # --- Block 3 --- + Conv2D(128, (3, 3), activation='relu', padding='same'), + BatchNormalization(), + Conv2D(128, (3, 3), activation='relu', padding='same'), + BatchNormalization(), + MaxPooling2D(pool_size=(2, 2)), + Dropout(0.25), + + # --- Classifier --- + Flatten(), + Dense(256, activation='relu'), + BatchNormalization(), + Dropout(0.5), + Dense(num_classes, activation='softmax') + ]) + + return model + + +def build_transfer_learning_model(input_shape=IMG_SHAPE, num_classes=NUM_CLASSES, + upscale=True): + """ + Build a transfer learning model using MobileNetV2. + + MobileNetV2 is chosen because: + - Lightweight and efficient (good for training on CPU/limited GPU) + - Strong feature extraction from ImageNet pretraining + - Keras docs: https://keras.io/api/applications/mobilenet/#mobilenetv2-function + - Requires minimum 96ร—96ร—3 input for efficient performance + + Architecture (GPU / upscale=True): + Input(32,32,3) -> UpSampling2D(3x) -> MobileNetV2(frozen) -> + GlobalAveragePooling2D -> Dense(256) -> Dropout(0.5) -> Dense(10, softmax) + + Architecture (CPU / upscale=False): + Input(96,96,3) [pre-resized with cv2] -> MobileNetV2(frozen) -> + GlobalAveragePooling2D -> Dense(256) -> Dropout(0.5) -> Dense(10, softmax) + + Args: + input_shape (tuple): Shape of input images. + GPU: (32, 32, 3) โ€” UpSampling2D handles resize. + CPU: (96, 96, 3) โ€” images pre-resized with cv2. + num_classes (int): Number of output classes. Default: 10 + upscale (bool): If True, adds UpSampling2D(3,3) inside the model + (GPU version, 03_transfer_learning_gpu.py). + If False, skips it โ€” images must already be 96ร—96 + (CPU version, 03_transfer_learning_cpu.py). + + Returns: + tuple: (keras.Model, base_model) โ€” full model and MobileNetV2 base + """ + # Input layer + inputs = Input(shape=input_shape) + + if upscale: + # GPU version: upscale 32ร—32 โ†’ 96ร—96 inside the graph (runs each batch) + x = UpSampling2D(size=(3, 3))(inputs) + mobilenet_input_shape = (96, 96, 3) + else: + # CPU version: images already 96ร—96 (pre-resized with cv2 before training) + # This eliminates per-batch upscaling โ€” the main CPU bottleneck + x = inputs + mobilenet_input_shape = input_shape + + # Load MobileNetV2 pretrained on ImageNet, without top classification layers + # Minimum efficient input: 96ร—96ร—3 (see Keras docs link above) + base_model = MobileNetV2( + input_shape=mobilenet_input_shape, + include_top=False, + weights='imagenet' + ) + # Freeze all base model layers (we only train the new head) + base_model.trainable = False + + x = base_model(x, training=False) + + # Custom classification head + x = GlobalAveragePooling2D()(x) + x = Dense(256, activation='relu')(x) + x = Dropout(0.5)(x) + outputs = Dense(num_classes, activation='softmax')(x) + + model = Model(inputs=inputs, outputs=outputs) + + return model, base_model + + +def get_model_summary(model): + """ + Get a string summary of the model architecture. + + Args: + model: Keras model + + Returns: + str: Model summary string + """ + summary_lines = [] + model.summary(print_fn=lambda x: summary_lines.append(x)) + return '\n'.join(summary_lines) diff --git a/src/train.py b/src/train.py new file mode 100644 index 0000000000000000000000000000000000000000..7cf8edf014b999358fc502000f5a14a877a795dc --- /dev/null +++ b/src/train.py @@ -0,0 +1,147 @@ +""" +Training Pipeline for CIFAR-10 Image Classification. + +This module provides functions to compile and train Keras models +with appropriate callbacks for preventing overfitting. +""" + +import os +from keras.api.optimizers import Adam +from keras.api.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau + + +# Default training configuration +DEFAULT_CONFIG = { + 'learning_rate': 0.001, + 'batch_size': 64, + 'epochs': 100, # max epochs (early stopping will cut short) + 'early_stopping_patience': 10, + 'reduce_lr_patience': 5, + 'reduce_lr_factor': 0.5, + 'min_lr': 1e-6, +} + + +def compile_model(model, learning_rate=DEFAULT_CONFIG['learning_rate']): + """ + Compile a Keras model with Adam optimizer and categorical crossentropy loss. + + Args: + model: Keras model to compile. + learning_rate (float): Initial learning rate for Adam optimizer. + + Returns: + Compiled Keras model. + """ + model.compile( + optimizer=Adam(learning_rate=learning_rate), + loss='categorical_crossentropy', + metrics=['accuracy'] + ) + return model + + +def get_callbacks(model_save_path, patience=DEFAULT_CONFIG['early_stopping_patience']): + """ + Create a list of training callbacks for robust training. + + Callbacks: + - EarlyStopping: Stops training if val_loss doesn't improve for `patience` epochs. + Restores the best weights. + - ModelCheckpoint: Saves the best model based on val_loss. + - ReduceLROnPlateau: Reduces learning rate by half if val_loss plateaus for 5 epochs. + + Args: + model_save_path (str): File path to save the best model. + patience (int): Number of epochs to wait before early stopping. + + Returns: + list: List of Keras callback instances. + """ + # Ensure the directory exists + os.makedirs(os.path.dirname(model_save_path), exist_ok=True) + + callbacks = [ + EarlyStopping( + monitor='val_loss', + patience=patience, + restore_best_weights=True, + verbose=1 + ), + ModelCheckpoint( + filepath=model_save_path, + monitor='val_loss', + save_best_only=True, + verbose=1 + ), + ReduceLROnPlateau( + monitor='val_loss', + factor=DEFAULT_CONFIG['reduce_lr_factor'], + patience=DEFAULT_CONFIG['reduce_lr_patience'], + min_lr=DEFAULT_CONFIG['min_lr'], + verbose=1 + ) + ] + return callbacks + + +def train_model(model, x_train, y_train, datagen=None, + validation_split=0.1, x_val=None, y_val=None, + batch_size=DEFAULT_CONFIG['batch_size'], + epochs=DEFAULT_CONFIG['epochs'], + callbacks=None): + """ + Train a Keras model with optional data augmentation. + + If `datagen` is provided, training uses the augmentation generator. + Otherwise, trains directly on the raw data. + + Args: + model: Compiled Keras model. + x_train: Training images array. + y_train: Training labels array (one-hot). + datagen: Optional ImageDataGenerator for data augmentation. + validation_split: Fraction of training data for validation (used if x_val is None). + x_val: Optional separate validation images. + y_val: Optional separate validation labels. + batch_size (int): Training batch size. + epochs (int): Maximum number of training epochs. + callbacks (list): List of Keras callbacks. + + Returns: + keras.callbacks.History: Training history object. + """ + # Determine validation data + if x_val is not None and y_val is not None: + validation_data = (x_val, y_val) + else: + # Split training data for validation + split_idx = int(len(x_train) * (1 - validation_split)) + x_val = x_train[split_idx:] + y_val = y_train[split_idx:] + x_train = x_train[:split_idx] + y_train = y_train[:split_idx] + validation_data = (x_val, y_val) + + if datagen is not None: + # Train with data augmentation + history = model.fit( + datagen.flow(x_train, y_train, batch_size=batch_size), + epochs=epochs, + validation_data=validation_data, + callbacks=callbacks or [], + steps_per_epoch=len(x_train) // batch_size, + verbose=1 + ) + else: + # Train without augmentation + history = model.fit( + x_train, y_train, + batch_size=batch_size, + epochs=epochs, + validation_data=validation_data, + callbacks=callbacks or [], + verbose=1 + ) + + return history 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CIFAR-10 model, specifically targeting weaknesses identified in the project's model analysis. + +## Level 1: Easy (Clear Distinctions) +These images represent standard, well-lit, unobstructed examples of their respective classes. +- **`01_easy_automobile.png`**: A bright red sports car on an open road. Distinct shape, no confusing background. +- **`02_easy_dog.png`**: A golden retriever sitting happily on a studio white background. Very clear animal features. +- **`03_easy_airplane.png`**: A clear passenger airplane centrally framed against a solid blue sky. + +## Level 2: Medium (Higher Complexity) +These images include busy backgrounds, motion blur, partial occlusion, or less-ideal lighting. +- **`04_medium_ship.png`**: A cargo ship on a dark, stormy ocean. Partial occlusion by waves and low lighting make it harder to extract features. +- **`05_medium_frog.png`**: A green frog camouflaged on a green leaf. The model has to distinguish the subject from a similarly colored background. +- **`06_medium_deer.png`**: A deer running rapidly through a forest, creating motion blur. Evaluates robustness to imperfect photos. + +## Level 3: Hard (Close to Confusion) +These images directly challenge the most common misclassifications noted in `REPORT.md` (e.g., Cat vs. Dog, Automobile vs. Truck). +- **`07_hard_cat.png`**: A large, fluffy Maine Coon cat panting. Its dog-like features heavily test the model's ability to separate cats from dogs. +- **`08_hard_bird.png`**: A brightly colored orange bird perched among visually complex, similarly colored tropical flowers and green leaves. Tests the model's ability to distinguish the bird's shape from a highly textured and colorful background. https://www.biodiversidad.gob.mx/especies/aves-de-mexico/identificacion +- **`09_hard_truck.png`**: A sleek, modern pickup truck that resembles an SUV or automobile. Tests the model's boundary between similar vehicles. +- **`10_hard_horse.png`**: A horse standing in a stable but photographed from an extreme, distorting low angle. Tests the model's reliance on standard object proportions.