更新时间：2021-06-25 22:51:43
封面
版权信息
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Foreword
Contributors
About the author
About the reviewer
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Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Conventions used
Get in touch
Reviews
TensorFlow 101
What is TensorFlow?
TensorFlow core
Code warm-up - Hello TensorFlow
Tensors
Constants
Operations
Placeholders
Creating tensors from Python objects
Variables
Tensors generated from library functions
Populating tensor elements with the same values
Populating tensor elements with sequences
Populating tensor elements with a random distribution
Getting Variables with tf.get_variable()
Data flow graph or computation graph
Order of execution and lazy loading
Executing graphs across compute devices - CPU and GPGPU
Placing graph nodes on specific compute devices
Simple placement
Dynamic placement
Soft placement
GPU memory handling
Multiple graphs
TensorBoard
A TensorBoard minimal example
TensorBoard details
Summary
High-Level Libraries for TensorFlow
TF Estimator - previously TF Learn
TF Slim
TFLearn
Creating the TFLearn Layers
TFLearn core layers
TFLearn convolutional layers
TFLearn recurrent layers
TFLearn normalization layers
TFLearn embedding layers
TFLearn merge layers
TFLearn estimator layers
Creating the TFLearn Model
Types of TFLearn models
Training the TFLearn Model
Using the TFLearn Model
PrettyTensor
Sonnet
Keras 101
Installing Keras
Neural Network Models in Keras
Workflow for building models in Keras
Creating the Keras model
Sequential API for creating the Keras model
Functional API for creating the Keras model
Keras Layers
Keras core layers
Keras convolutional layers
Keras pooling layers
Keras locally-connected layers
Keras recurrent layers
Keras embedding layers
Keras merge layers
Keras advanced activation layers
Keras normalization layers
Keras noise layers
Adding Layers to the Keras Model
Sequential API to add layers to the Keras model
Functional API to add layers to the Keras Model
Compiling the Keras model
Training the Keras model
Predicting with the Keras model
Additional modules in Keras
Keras sequential model example for MNIST dataset
Classical Machine Learning with TensorFlow
Simple linear regression
Data preparation
Building a simple regression model
Defining the inputs parameters and other variables
Defining the model
Defining the loss function
Defining the optimizer function
