TensorFlow is an end-to-end open source platform for machine learning
TensorFlow makes it easy for beginners and experts to create machine learning models. See the sections below to get started.
https://www.tensorflow.org/tutorials
Tutorials show you how to use TensorFlow with complete, end-to-end examples
https://www.tensorflow.org/guide
Guides explain the concepts and components of TensorFlow.
For beginners
The best place to start is with the user-friendly Sequential API. You can create models by plugging together building blocks. Run the “Hello World” example below, then visit the tutorials to learn more.
To learn ML, check out our education page. Begin with curated curriculums to improve your skills in foundational ML areas.
代码语言：javascript复制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)For experts
代码语言：javascript复制class MyModel(tf.keras.Model):
def __init__(self):
super(MyModel, self).__init__()
self.conv1 = Conv2D(32, 3, activation='relu')
self.flatten = Flatten()
self.d1 = Dense(128, activation='relu')
self.d2 = Dense(10, activation='softmax')
def call(self, x):
x = self.conv1(x)
x = self.flatten(x)
x = self.d1(x)
return self.d2(x)
model = MyModel()
with tf.GradientTape() as tape:
logits = model(images)
loss_value = loss(logits, labels)
grads = tape.gradient(loss_value, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))Learn about the relationship between TensorFlow and Keras
TensorFlow's high-level APIs are based on the Keras API standard for defining and training neural networks. Keras enables fast prototyping, state-of-the-art research, and production—all with user-friendly APIs.
Solutions to common problems
Explore step-by-step tutorials to help you with your projects.
https://www.tensorflow.org/tutorials/keras/classification
https://www.tensorflow.org/tutorials/generative/dcgan
https://www.tensorflow.org/tutorials/text/nmt_with_attention
News & announcements
Check out our blog for additional updates, and subscribe to our monthly TensorFlow newsletter to get the latest announcements sent directly to your inbox.
