Welcome to the TensorFlow documentation page! 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.
Quick Start Guide
To get started with TensorFlow, you can follow these steps:
Install TensorFlow: First, make sure to install TensorFlow on your system. You can download it from the TensorFlow official website.
Understand the Basics: Familiarize yourself with the basic concepts of TensorFlow, such as tensors, graphs, and operations. For a comprehensive guide, check out the TensorFlow tutorials.
Explore Pre-trained Models: TensorFlow provides a variety of pre-trained models that you can use for your projects. Learn how to use these models in the TensorFlow models guide.
Experiment with Data: Practice with sample data to understand how to load, preprocess, and visualize data in TensorFlow. The TensorFlow datasets section provides a good starting point.
Join the Community: Engage with the TensorFlow community for support, resources, and ideas. You can find community forums on the TensorFlow GitHub page.
Example: MNIST Handwritten Digit Recognition
Here's a simple example to get you started with TensorFlow. This code will load the MNIST dataset, train a model, and make predictions on new data.
import tensorflow as tf
# Load the MNIST dataset
mnist = tf.keras.datasets.mnist
# Load the training data
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
# Preprocess the data
train_images = train_images / 255.0
test_images = test_images / 255.0
# Build the model
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(10, activation='softmax')
])
# Compile the model
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
# Train the model
model.fit(train_images, train_labels, epochs=5)
# Evaluate the model
test_loss, test_acc = model.evaluate(test_images, test_labels)
print(f'\nTest accuracy: {test_acc}')