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Accelerate development with access to a wide range of optimized models for vision, NLP, and more.
Deploy models on mobile, web, edge devices, or cloud infrastructure with TensorFlow Serving and Lite.
Run TensorFlow on CPUs, GPUs, TPUs, and across operating systems including Linux, Windows, macOS.
Join a thriving ecosystem with thousands of contributors, tutorials, and real-world use cases.
Use pip or conda to install TensorFlow and set up your development environment.
Load TensorFlow and supporting libraries like NumPy, Pandas, and Matplotlib.
Use Keras or low-level APIs to build neural networks tailored to your task.
Feed data into your model, optimize parameters, and validate performance using metrics.
Export models and deploy them using TensorFlow Serving, Lite, or JS. Monitor with TensorBoard.
import tensorflow as tf
print(tf.__version__)
from tensorflow import keras
from tensorflow.keras import layers
# Define a simple Sequential model
model = keras.Sequential([
layers.Dense(64, activation='relu', input_shape=(10,)),
layers.Dense(1)
])
# Compile the model
model.compile(optimizer='adam', loss='mse')
# Dummy data
import numpy as np
x_train = np.random.rand(100, 10)
y_train = np.random.rand(100, 1)
# Train the model
model.fit(x_train, y_train, epochs=5)
# Print model summary
model.summary()Build models to detect and classify objects, faces, and scenes in images and videos.
Create chatbots, sentiment analyzers, and translation systems using text data.
Deliver personalized content and product suggestions based on user behavior.
Predict trends like stock prices, energy usage, or demand using historical data.
Explore TensorFlow’s ecosystem and find the tools, platforms, and docs to accelerate your workflow.
Common questions about TensorFlow’s capabilities, usage, and ecosystem.