import tensorflow as tf mnist = tf.keras.datasets.mnist (x_train, y_train),(x_test, y_test) = mnist.load_data() x_train, x_test = x_train / 255.0, x_test / 255.0 model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(10, activation='softmax') ]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.fit(x_train, y_train, epochs=5) model.evaluate(x_test, y_test)
借助机器学习解决现实问题
查看相关示例，了解 TensorFlow 如何用于推进研究并构建 AI 赋能的应用。
GNNs can process complex relationships between objects, making them a powerful technique for traffic forecasting, medical discovery, and more.
Learn how LiteRT (formerly TensorFlow Lite) enables access to fetal ultrasound assessment, improving health outcomes for women and families around Kenya and the world.
TensorFlow 有哪些新变化
阅读 TensorFlow 团队和社区发布的最新公告。
探索生态系统
探索经过生产环境测试的工具，加快建模、部署和其他工作流。
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Library
TensorFlow.js
Train and run models directly in the browser using JavaScript or Node.js.
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Library
LiteRT
Deploy ML on mobile and edge devices such as Android, iOS, Raspberry Pi, and Edge TPU.
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API
tf.data
Preprocess data and create input pipelines for ML models.
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Library
TFX
Create production ML pipelines and implement MLOps best practices.
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API
tf.keras
Create ML models with TensorFlow's high-level API.
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Resource
Kaggle Models
Find pre-trained models ready for fine-tuning and deployment.
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Resource
TensorFlow Datasets
Browse the collection of standard datasets for initial training and validation.
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Tool
TensorBoard
Visualize and track development of ML models.
