Une plate-forme de bout en bout dédiée au machine learning

Lancez-vous avec TensorFlow

TensorFlow makes it easy to create ML models that can run in any environment. Learn how to use the intuitive APIs through interactive code samples.

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)

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What's new in TensorFlow

Read the latest announcements from the TensorFlow team and community.

Join the community

Collaborate, find support, and share your projects by joining interest groups or attending developer events.

Développer vos connaissances sur le ML

New to machine learning? Begin with TensorFlow's curated curriculums or browse the resource library of books, online courses, and videos.

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