舉報 
會員
Keras Deep Learning Cookbook
Kerashasquicklyemergedasapopulardeeplearninglibrary.WritteninPython,itallowsyoutotrainconvolutionalaswellasrecurrentneuralnetworkswithspeedandaccuracy.TheKerasDeepLearningCookbookshowsyouhowtotackledifferentproblemsencounteredwhiletrainingefficientdeeplearningmodels,withthehelpofthepopularKeraslibrary.StartingwithinstallingandsettingupKeras,thebookdemonstrateshowyoucanperformdeeplearningwithKerasintheTensorFlow.Fromloadingdatatofittingandevaluatingyourmodelforoptimalperformance,youwillworkthroughastep-by-stepprocesstotackleeverypossibleproblemfacedwhiletrainingdeepmodels.Youwillimplementconvolutionalandrecurrentneuralnetworks,adversarialnetworks,andmorewiththehelpofthishandyguide.Inadditiontothis,youwilllearnhowtotrainthesemodelsforreal-worldimageandlanguageprocessingtasks.Bytheendofthisbook,youwillhaveapractical,hands-onunderstandingofhowyoucanleveragethepowerofPythonandKerastoperformeffectivedeeplearning
最新章節
- Leave a review - let other readers know what you think
- Other Books You May Enjoy
- Plotting the training and testing results
- How to do it...
- Sequential memory
- Adjustment during training
品牌：中圖公司
上架時間：2021-06-10 18:27:15
出版社：Packt Publishing
本書數字版權由中圖公司提供，并由其授權上海閱文信息技術有限公司制作發行
- Leave a review - let other readers know what you think 更新時間：2021-06-10 19:39:31
- Other Books You May Enjoy
- Plotting the training and testing results
- How to do it...
- Sequential memory
- Adjustment during training
- BoltzmannQPolicy
- Init code base
- Dueling policy
- Setting the last layer of the network
- init method
- DQN agent
- Getting ready
- Dueling DQN to play Cartpole
- Training the agent
- DQN agent class
- Hyperparameters for the DQN
- The act function
- The replay function
- The memory and remember
- Implementing the DQN agent
- How to do it...
- The CartPole game with Keras
- Introduction
- Reinforcement Learning
- See also
- Training
- Encoder-decoder architecture
- Data processing
- How to do it…
- Text summarization for reviews
- Introduction
- Text Summarization Using Keras Models
- Full code listing
- How to do it…
- Getting ready
- Sentiment analysis
- With embeddings
- Without embeddings
- How to do it...
- Getting ready
- Word embedding
- Introduction
- Natural Language Processing Using Keras Models
- Model fit and prediction
- Model creation
- Training data
- How to do it…
- Getting ready
- Sequence to sequence learning for the same length output with LSTM
- Observation
- Instantiate a sequential model
- How to do it…
- Load the dataset
- Getting ready
- Time series forecasting with LSTM
- Full code listing
- Train the model
- LSTM configuration and model
- Encoder
- How to do it...
- Getting ready
- LSTM memory example
- LSTM networks
- LSTM networks for time series data
- Instantiate a sequential model
- How to do it…
- Loading the dataset
- Getting ready
- Simple RNNs for time series data
- The need for RNNs
- Introduction
- Recurrent Neural Networks
- Average metrics of the model
- The output of the program
- Putting it all together
- Train the generator using feedback from a discriminator
- Combined model - generator and discriminator
- Compile the discriminator
- Summary of the discriminator
- Build the discriminator
- Discriminator
- Training the generator
- Summary of the generator
- Generator
- How to do it...
- Getting ready
- DCGAN
- Plotting the metrics
- Metrics of the BGAN model
- Iteration 10000
- Iteration 0
- Output the plots
- Train the BGAN
- Boundary seeking loss
- Initializing the BGAN class
- Discriminator
- Generator
- How to do it...
- Getting ready
- Boundary seeking GAN
- Average metrics of the GAN
- Output plots
- Training the GAN
- Initialize the GAN instance
- Building a discriminator
- Building a generator
- How to do it...
- Getting ready
- Basic GAN
- GAN overview
- Introduction
- Generative Adversarial Networks
- Modeling
- How to do it…
- Getting ready
- Digit recognition
- Predictions
- Modeling
- Data processing
- How to do it…
- Getting ready
- Cervical cancer classification
- Introduction
- Implementing Convolutional Neural Networks
- Full code listing
- Modeling
- Data processing
- How to do it...
- Classification for spam detection
- Full code listing
- Modeling
- Data processing
- How to do it...
- Classification for breast cancer
- Introduction
- Classification Using Different Keras Layers
- How to do it...
- Getting ready
- Optimization with RMSProp
- Adadelta optimizer
- How to do it...
- Getting ready
- Optimization with AdaDelta
- How to do it...
- Getting ready
- Optimization with Adam
- How to do it...
- Getting ready
- Optimization with stochastic gradient descent
- Common code for samples
- Optimization
- Code listing
- How to do it...
- Getting ready
- Model visualization
- Padding with a non-default value
- Padding with truncation
- Post-padding
- Pre-padding with default 0.0 padding
- How to do it...
- Getting ready
- Sequence padding
- Initializing ImageDataGenerator
- How to do it...
- Getting ready
- Feature standardization of image data
- Data Preprocessing Optimization and Visualization
- How to do it...
- Image classification using Keras functional APIs
- Model class
- How to do it...
- Keras functional APIs – linking the layers
- The output of the example
- How to do it...
- Keras functional APIs
- Concatenate function
- How to do it...
- Introduction – shared input layer
- Shared layer models
- Output of the sample
- Model training
- Initialize the loss
- Model compilation internals
- Model inspection internals
- Putting it all together
- Predict using the model
- Evaluate the model
- Train the model
- Compile the model
- Create a Sequential model
- How to do it...
- Sequential models
- Types of models
- Anatomy of a model
- Models in Keras – getting started
- How to do it...
- Load data from a CSV file
- How to do it...
- MNIST dataset
- Specifying the label mode
- How to do it...
- CIFAR-100 dataset
- How to do it...
- CIFAR-10 dataset
- Introduction
- Working with Keras Datasets and Models
- Installing Keras
- Installing the TensorFlow GPU version
- Installing NVIDIA CUDA profiler tools interface development files
- Installing cudnn
- Installing cuda
- How to do it...
- Getting ready
- Installing Keras on Ubuntu 16.04 with GPU enabled
- Installing the Docker container with the host volume mapped
- Installing the Docker container
- How to do it...
- Getting ready
- Installing Keras with Jupyter Notebook in a Docker image
- Using the Theano backend with Keras
- Installing Keras
- Installing TensorFlow
- Installing mkl
- Installing numpy and scipy
- Installing miniconda
- How to do it...
- Getting ready
- Installing Keras on Ubuntu 16.04
- Introduction
- Keras Installation
- Reviews
- Get in touch
- See also
- There's more…
- How it works…
- How to do it…
- Getting ready
- Sections
- Conventions used
- Download the color images
- Download the example code files
- To get the most out of this book
- What this book covers
- Who this book is for
- Preface
- Packt is searching for authors like you
- About the reviewer
- About the authors
- Contributors
- Packt.com
- Why subscribe?
- Packt Upsell
- Title Page
- coverpage
- coverpage
- Title Page
- Packt Upsell
- Why subscribe?
- Packt.com
- Contributors
- About the authors
- About the reviewer
- Packt is searching for authors like you
- Preface
- Who this book is for
- What this book covers
- To get the most out of this book
- Download the example code files
- Download the color images
- Conventions used
- Sections
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Get in touch
- Reviews
- Keras Installation
- Introduction
- Installing Keras on Ubuntu 16.04
- Getting ready
- How to do it...
- Installing miniconda
- Installing numpy and scipy
- Installing mkl
- Installing TensorFlow
- Installing Keras
- Using the Theano backend with Keras
- Installing Keras with Jupyter Notebook in a Docker image
- Getting ready
- How to do it...
- Installing the Docker container
- Installing the Docker container with the host volume mapped
- Installing Keras on Ubuntu 16.04 with GPU enabled
- Getting ready
- How to do it...
- Installing cuda
- Installing cudnn
- Installing NVIDIA CUDA profiler tools interface development files
- Installing the TensorFlow GPU version
- Installing Keras
- Working with Keras Datasets and Models
- Introduction
- CIFAR-10 dataset
- How to do it...
- CIFAR-100 dataset
- How to do it...
- Specifying the label mode
- MNIST dataset
- How to do it...
- Load data from a CSV file
- How to do it...
- Models in Keras – getting started
- Anatomy of a model
- Types of models
- Sequential models
- How to do it...
- Create a Sequential model
- Compile the model
- Train the model
- Evaluate the model
- Predict using the model
- Putting it all together
- Model inspection internals
- Model compilation internals
- Initialize the loss
- Model training
- Output of the sample
- Shared layer models
- Introduction – shared input layer
- How to do it...
- Concatenate function
- Keras functional APIs
- How to do it...
- The output of the example
- Keras functional APIs – linking the layers
- How to do it...
- Model class
- Image classification using Keras functional APIs
- How to do it...
- Data Preprocessing Optimization and Visualization
- Feature standardization of image data
- Getting ready
- How to do it...
- Initializing ImageDataGenerator
- Sequence padding
- Getting ready
- How to do it...
- Pre-padding with default 0.0 padding
- Post-padding
- Padding with truncation
- Padding with a non-default value
- Model visualization
- Getting ready
- How to do it...
- Code listing
- Optimization
- Common code for samples
- Optimization with stochastic gradient descent
- Getting ready
- How to do it...
- Optimization with Adam
- Getting ready
- How to do it...
- Optimization with AdaDelta
- Getting ready
- How to do it...
- Adadelta optimizer
- Optimization with RMSProp
- Getting ready
- How to do it...
- Classification Using Different Keras Layers
- Introduction
- Classification for breast cancer
- How to do it...
- Data processing
- Modeling
- Full code listing
- Classification for spam detection
- How to do it...
- Data processing
- Modeling
- Full code listing
- Implementing Convolutional Neural Networks
- Introduction
- Cervical cancer classification
- Getting ready
- How to do it…
- Data processing
- Modeling
- Predictions
- Digit recognition
- Getting ready
- How to do it…
- Modeling
- Generative Adversarial Networks
- Introduction
- GAN overview
- Basic GAN
- Getting ready
- How to do it...
- Building a generator
- Building a discriminator
- Initialize the GAN instance
- Training the GAN
- Output plots
- Average metrics of the GAN
- Boundary seeking GAN
- Getting ready
- How to do it...
- Generator
- Discriminator
- Initializing the BGAN class
- Boundary seeking loss
- Train the BGAN
- Output the plots
- Iteration 0
- Iteration 10000
- Metrics of the BGAN model
- Plotting the metrics
- DCGAN
- Getting ready
- How to do it...
- Generator
- Summary of the generator
- Training the generator
- Discriminator
- Build the discriminator
- Summary of the discriminator
- Compile the discriminator
- Combined model - generator and discriminator
- Train the generator using feedback from a discriminator
- Putting it all together
- The output of the program
- Average metrics of the model
- Recurrent Neural Networks
- Introduction
- The need for RNNs
- Simple RNNs for time series data
- Getting ready
- Loading the dataset
- How to do it…
- Instantiate a sequential model
- LSTM networks for time series data
- LSTM networks
- LSTM memory example
- Getting ready
- How to do it...
- Encoder
- LSTM configuration and model
- Train the model
- Full code listing
- Time series forecasting with LSTM
- Getting ready
- Load the dataset
- How to do it…
- Instantiate a sequential model
- Observation
- Sequence to sequence learning for the same length output with LSTM
- Getting ready
- How to do it…
- Training data
- Model creation
- Model fit and prediction
- Natural Language Processing Using Keras Models
- Introduction
- Word embedding
- Getting ready
- How to do it...
- Without embeddings
- With embeddings
- Sentiment analysis
- Getting ready
- How to do it…
- Full code listing
- Text Summarization Using Keras Models
- Introduction
- Text summarization for reviews
- How to do it…
- Data processing
- Encoder-decoder architecture
- Training
- See also
- Reinforcement Learning
- Introduction
- The CartPole game with Keras
- How to do it...
- Implementing the DQN agent
- The memory and remember
- The replay function
- The act function
- Hyperparameters for the DQN
- DQN agent class
- Training the agent
- Dueling DQN to play Cartpole
- Getting ready
- DQN agent
- init method
- Setting the last layer of the network
- Dueling policy
- Init code base
- BoltzmannQPolicy
- Adjustment during training
- Sequential memory
- How to do it...
- Plotting the training and testing results
- Other Books You May Enjoy
- Leave a review - let other readers know what you think 更新時間：2021-06-10 19:39:31
