HybridNet: Adaptive CNN-LSTM Fusion

A hybrid deep learning architecture combining CNNs and Bidirectional LSTMs with an adaptive gating mechanism for state-of-the-art CIFAR-10 classification.

AI/MLComputer VisionDeep Learning

# features

Key Features

Core technologies and system features.

Hybrid Architecture

Dual-stream CNN and Bi-LSTM network capturing both local spatial features and global dependencies.

Adaptive Fusion

Learned gating mechanism that dynamically weights CNN and LSTM feature maps based on input context.

Optimized Training

Advanced data augmentation and callback strategies (EarlyStopping, Checkpointing) for robust convergence.

Performance Tracking

Real-time accuracy/loss visualization and comprehensive confusion matrix analysis reaching 72%+.

# source

Project Source Code

Explore the primary logical modules.

EXPLORER

Cell 3 — Configuration

srcCell 3 — Configuration

1# Configuration - tweak these for your experiment
2BATCH_SIZE = 128
3IMG_SIZE = 32
4NUM_CLASSES = 10
5EPOCHS = 25
6SEED = 42
7AUTOTUNE = tf.data.AUTOTUNE
8ROOT_DIR = '/content'  # change if mounting Drive

# simulation

Live Simulation Output

Simulated console execution.

simulation

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# repositories

Source Code

GitHub repositories for this project.

HybridNet: Adaptive CNN-LSTM Fusion Repository

Access the complete source code on GitHub.