Welcome to the TensorFlow API Reference! This document provides an overview of the TensorFlow API, including its features, usage, and examples. TensorFlow is an open-source software library for dataflow and differentiable programming across a range of tasks. It is widely used for machine learning and deep learning applications.
Installation
To get started with TensorFlow, you need to install it on your machine. You can find the installation instructions and dependencies here.
Core APIs
TensorFlow provides a set of core APIs that are used to build and train machine learning models.
- tf.data: An API for building input pipelines.
- tf.keras: High-level neural networks API.
- tf.linalg: Linear algebra operations.
- tf.math: Mathematical operations.
- tf.signal: Signal processing operations.
- tf.image: Image processing operations.
For more detailed information on these APIs, you can visit the TensorFlow API Guide.
Examples
Here's a simple example of a TensorFlow model:
import tensorflow as tf
# Define a model
model = tf.keras.Sequential([
tf.keras.layers.Dense(10, activation='relu', input_shape=(32,)),
tf.keras.layers.Dense(1)
])
# Compile the model
model.compile(optimizer='adam',
loss='mean_squared_error')
# Train the model
model.fit(tf.random.normal([1000, 32]), tf.random.normal([1000, 1]), epochs=10)
For more examples and tutorials, check out the TensorFlow Examples.
Community and Support
TensorFlow has a vibrant community of developers and researchers. You can join the TensorFlow community here.
If you have any questions or need support, you can ask on Stack Overflow.