TensorFlow Models

 TensorFlow provides a comprehensive suite of tools for building and deploying machine learning (ML) models. Its models can be categorized into three main types:

1. Pre-trained Models

TensorFlow provides several pre-trained models that are ready to use for various tasks:

TensorFlow Hub

• A repository of pre-trained models for transfer learning.
• Models include image classification, object detection, text embeddings, and more.
• Example models:
  • Image: MobileNet, ResNet
  • Text: Universal Sentence Encoder
  • Audio: YAMNet

Model Garden

• Contains state-of-the-art (SOTA) models for research and production.
• Models are categorized by domains like vision, natural language processing (NLP), and reinforcement learning.
• Examples:
  • Vision: EfficientNet, Mask R-CNN
  • NLP: BERT, T5
  • Reinforcement Learning: Dopamine framework models

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2. Custom Models

TensorFlow allows users to build custom models from scratch, using:

• Keras Sequential API: For simple, stackable models.
• Keras Functional API: For complex architectures with branching or shared layers.
• Subclassing tf.keras.Model: For complete flexibility in designing model behavior.

Example:

import tensorflow as tf
from tensorflow.keras import layers

# Build a simple Sequential model
model = tf.keras.Sequential([
    layers.Dense(128, activation='relu'),
    layers.Dense(10, activation='softmax')
])

# Compile the model
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# Summary
model.summary()

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3. Saved Models

TensorFlow supports saving and loading models for deployment or further training. Models can be saved in two formats:

• HDF5 Format (.h5): Works well with Keras APIs.
• SavedModel Format: TensorFlow's native format, ideal for TensorFlow Serving.

Example:

# Save a model
model.save('my_model.h5')  # HDF5 format
model.save('my_model')     # SavedModel format

# Load a model
loaded_model = tf.keras.models.load_model('my_model')

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Tools for Training and Deployment

• TensorFlow Lite: Optimized for mobile and IoT devices.
• TensorFlow.js: For running models in a web browser.
• TensorFlow Serving: For deploying models in production.
• TFX (TensorFlow Extended): For end-to-end ML pipelines.

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Popular Model Use Cases

• Image Processing: Object detection, segmentation, classification.
• Natural Language Processing: Sentiment analysis, machine translation, question answering.
• Time-Series Analysis: Forecasting, anomaly detection.
• Generative Models: GANs, VAEs.

Would you like help building or using a specific TensorFlow model?