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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
目錄(255章)
倒序
- 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
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