TensorFlow 是一个端到端开源机器学习平台
借助 TensorFlow，初学者和专家可以轻松地创建机器学习模型。请参阅以下几部分，了解如何开始使用。
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)
针对专家
Subclassing API 提供了由运行定义的高级研究接口。为您的模型创建一个类，然后以命令方式编写前向传播。您可以轻松编写自定义层、激活函数和训练循环。请运行下面的“Hello World”示例，然后访问教程以了解详情。
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))
常见问题的解决方案
浏览分步教程以帮助您完成项目。
We're excited to introduce TensorFlow Recommenders (TFRS), an open-source TensorFlow package that makes building, evaluating, and serving sophisticated recommender models easy.
TF-Coder is a program synthesis tool that helps you write TensorFlow code. Instead of coding tricky tensor manipulations directly, demonstrate it through an illustrative example and TF-Coder provides the corresponding code automatically. Try it yourself in a Codelab!
Introducing a weight clustering API, proposed and contributed by Arm. Weight clustering helps reduce the storage and transfer size of your model by replacing many unique parameter values with a smaller number of unique values.
查看您与流行歌曲《Dance Monkey》的歌词同步程度。此浏览器体验使用 Facemesh 模型估算嘴唇周围的关键点，并对口型同步的准确性打分。