The document discusses updates and new features in TensorFlow 2.0 and related projects. It summarizes that TensorFlow 2.0 provides simpler APIs inspired by Keras, eager execution by default, distribution strategies for distributed training, and improved documentation. It also discusses TensorFlow Extended (TFX) for end-to-end machine learning pipelines, TensorFlow.js for machine learning in JavaScript, TensorFlow Lite for mobile and embedded devices, and new projects like TensorFlow Federated.
16. from keras.models import Sequential
from keras.layers import Dense
model = Sequential([
Dense(64, activation='relu')),
Dense(64, activation='relu')),
Dense(10, activation='softmax'))
])
Keras API
17. import tensorflow as tf
from tensorflow.keras.layers import Dense
model = tf.keras.Sequential([
Dense(64, activation='relu')),
Dense(64, activation='relu')),
Dense(10, activation='softmax'))
])
TensorFlow 💖 Keras
23. strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
model = tf.keras.Sequential(...)
model.compile(loss='mse', optimizer='sgd').
model.fit(dataset, epochs=2)
model.evaluate(dataset)
Multiple GPUs on One Machine
27. TF2
● Better documentation
● Broken up the monolith - take what you need
● Reduced complexity (deleted stuff)
● Better API (hello keras)
● Eager Execution by default
● … but lazily executed functions
● Strategy Scopes for distributing work
41. What is it?
● Complete JS implementation of TF
● No drivers
● Interactive
● Local Training & Inference (privacy)
● Uses WebGL to accelerate linear algebra (See TFJS-core)