TensorFlow

Open-source machine learning framework.

Overview

TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources.

Features

• Deep learning framework
• Tensor computations
• Neural network layers
• Model deployment
• GPU acceleration
• TensorFlow Extended (TFX)

Getting Started

pip install tensorflow

Basic Example

import tensorflow as tf
from tensorflow import keras
import numpy as np

# Create a simple neural network
model = keras.Sequential([
    keras.layers.Dense(128, activation='relu', input_shape=(784,)),
    keras.layers.Dropout(0.2),
    keras.layers.Dense(64, activation='relu'),
    keras.layers.Dropout(0.2),
    keras.layers.Dense(10, activation='softmax')
])

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

# Generate dummy data
(x_train, y_train), (_, _) = keras.datasets.mnist.load_data()
x_train = x_train.reshape(-1, 784).astype('float32') / 255.0

# Train the model
model.fit(x_train, y_train, epochs=5, batch_size=32)

# Make predictions
sample = np.random.rand(1, 784)
prediction = model.predict(sample)
predicted_class = np.argmax(prediction[0])

print(f"Predicted class: {predicted_class}")

PAPER-CODE Integration

PAPER-CODE provides:

• TensorFlow project templates
• Model architectures
• Training pipelines
• Deployment strategies

Resources

• TensorFlow Documentation
• TensorFlow GitHub