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。
针对新手
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.
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
The Subclassing API provides a define-by-run interface for advanced research. Create a class for your model, then write the forward pass imperatively. Easily author custom layers, activations, and training loops. Run the “Hello World” example below, then visit the tutorials to learn more.
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。
了解开发者如何利用它们解决众多领域中的实际问题，并前往 hub.tensorflow.google.cn 探索 TensorFlow Hub 的预训练模型。
