مدل‌های پیشرفته یادگیری عمیق در تنسورفلو: یادگیری ماشین مدرن در اکوسیستم گوگل کلاود

Use TensorFlow 2.x in the Google Colab ecosystem to create state-of-the-art deep learning models guided by hands-on examples. The Colab ecosystem provides a free cloud service with easy access to on-demand GPU (and TPU) hardware acceleration for fast execution of the models you learn to build. This book teaches you state-of-the-art deep learning models in an applied manner with the only requirement being an Internet connection. The Colab ecosystem provides everything else that you need, including Python, TensorFlow 2.x, GPU and TPU support, and Jupyter Notebooks. The book begins with an example-driven approach to building input pipelines that feed all machine learning models. You will learn how to provision a workspace on the Colab ecosystem to enable construction of effective input pipelines in a step-by-step manner. From there, you will progress into data augmentation techniques and TensorFlow datasets to gain a deeper understanding of how to work with complex datasets. You will find coverage of Tensor Processing Units (TPUs) and transfer learning followed by state-of-the-art deep learning models, including autoencoders, generative adversarial networks, fast style transfer, object detection, and reinforcement learning. Author Dr. Paper provides all the applied math, programming, and concepts you need to master the content. Examples range from relatively simple to very complex when necessary. Examples are carefully explained, concise, accurate, and complete. Care is taken to walk you through each topic through clear examples written in Python that you can try out and experiment with in the Google Colab ecosystem in the comfort of your own home or office. What You Will Learn Take advantage of the built-in support of the Google Colab ecosystem Work with TensorFlow data sets Create input pipelines to feed state-of-the-art deep learning models Create pipelined state-of-the-art deep learning models with clean and reliable Python code Leverage pre-trained deep learning models to solve complex machine learning tasks Create a simple environment to teach an intelligent agent to make automated decisions Who This Book Is For Readers who want to learn the highly popular TensorFlow deep learning platform, those who wish to master the basics of state-of-the-art deep learning models, and those looking to build competency with a modern cloud service tool such as Google Colab Table of Contents About the Author Introduction Chapter 1: Build TensorFlow Input Pipelines What Are Input Pipelines? Why Build Input Pipelines? Manual Workflow Automated Workflow Basic Input Pipeline Mechanics High-Performance Pipelines Google Developers Codelabs Create a New Notebook in Colab Import the TensorFlow Library GPU Hardware Accelerator Create a TensorFlow Dataset Consume the Dataset Dataset Structure Create a Dataset from Memory Load and Inspect Data Scale and Create the tf.data.Dataset Verify Scaling Check Tensor Shape Inspect Tensors Preserve the Shape Visualize Define Class Labels Convert a Numerical Label to a Class Label Create a Plot of Examples from the Dataset Build the Consumable Input Pipeline Configure the Dataset for Performance Build the Model Compile and Train the Model Load a TensorFlow Dataset as NumPy Inspect Shapes and Pixel Intensity Scale Prepare Data for TensorFlow Consumption Build the Consumable Input Pipeline Build the Model Compile and Train the Model Create a Dataset from Files Download and Inspect the Dataset Parse Data with the tf.keras.preprocessing Utility Set Parameters Create Training and Test Sets Inspect Tensors Get Class Names Display Examples Scale the Data Configure the Dataset for Performance Build the Input Pipeline Build the Model Compile and Train the Model Get Flowers from Google Cloud Storage Read Flowers as JPEG Files and Perform Simple Processing Read and Process Flowers as TFRecord Files Read TFRecord Files Set Parameters for Training Create Functions to Load and Process TFRecord Files Create Training and Test Sets Model Data Summary Chapter 2: Increase the Diversity of Your Dataset with Data Augmentation Data Augmentation Import the TensorFlow Library GPU Hardware Accelerator Augment with a Keras API Get Data Split Data Load Images Off Disk into Train and Test Sets Inspect the Training Set Get Number of Classes Create a Scaling Function Build the Input Pipeline Data Augmentation with Preprocessing Layers Display an Augmented Image Create the Model Compile and Train the Model Visualize Performance Apply Augmentation on Images Set an Index Variable Set an Image Show an Example Create Functions to Show Images Crop an Image Randomly Flip Image Left to Right Randomly Flip Image Up to Down Flip Image Up to Down Rotate Image 90 Degrees Adjust Gamma Adjust Contrast Adjust Brightness Adjust Saturation Hue Apply Transformations Directly on Images Create an Augmentation Function Display an Augmented Image Build the Input Pipeline Create the Model Compile and Train the Model Data Augmentation with ImageGenerator Process Flowers Data Create Datasets Create the Model Compile and Train the Model Augment Training Data Recompile and Train the Model Inspect the Data Visualize Summary Chapter 3: TensorFlow Datasets An Introduction to TensorFlow Datasets Import the TensorFlow Library GPU Hardware Accelerator Available Datasets Load a Dataset TFDS Metadata Iterate Over a Dataset As a Dictionary As Tuples As NumPy Arrays Visualization tfds.as_dataframe Take Examples tfds.show_examples Load Fashion-MNIST Metadata Display Split Information Visualize Slicing API Slicing Instructions Instructions as Strings Instructions with the ReadInstruction API Performance Tips Auto-caching Benchmark Datasets Reloading a TFDS Object Load Fashion-MNIST as a Single Tensor Ready Data for TensorFlow Consumption Build the Input Pipeline Build the Model Compile and Train the Model Load Beans as a tf.data.Dataset Metadata Visualize Check Shapes Reformat Images Configure Dataset for Performance Build the Model Compile and Train the Model Predict Summary Chapter 4: Deep Learning with TensorFlow Datasets An Experiment with cats_vs_dogs Import the TensorFlow Library GPU Hardware Accelerator Begin the Experiment Load the TFDS Object Metadata Split the Data Visualize Inspect Examples Reformat Images Build the Input Pipeline Visualize and Inspect Examples from a Batch Build the Model Compile and Train the Model Evaluate the Model for Generalizability Visualize Performance Augmentation with Preprocessing Layers Build the Model Compile and Train the Model Evaluate the Model for Generalizability Visualize Performance Apply Data Augmentation on Images Build the Input Pipeline Display an Augmented Image Build the Model Compile and Train the Model Evaluate the Model for Generalizability Visualize Performance Predictions Visualize Predictions An Experiment with rock_paper_scissors Configure TensorBoard Load Data Inspect the Data Preprocess the Data Visualize Processed Data Augment Training Data Create Data Augmentation Functions Augment Train Data Build the Input Pipeline Create the Model Compile and Train Visualize Performance Close TensorBoard Server Chapter 5: Introduction to Tensor Processing Units TPU Hardware Accelerator Advantages of Cloud TPU When to Use Cloud TPU CPUs GPUs TPUs Import the TensorFlow Library Enable TPU Runtime TPU Detection Configure the TPU for This Notebook Create a Distribution Strategy Manual Device Placement Run a Computation in All TPU Cores Eager Execution Experiments Digits Experiment Preprocess the Data Build the Input Pipeline Model Data Within TPU Scope MNIST Experiment Build the Input Pipeline Model Data Within TPU Scope Fashion-MNIST Experiment Transform Datasets into Image and Label Sets Model Data Within TPU Scope Save the Trained Model Make Inferences Flowers Experiment Read Flowers Data as TFRecord Files Set Parameters for Training Create Functions to Load and Process TFRecord Files Create Train and Test Sets Model Data Make Inferences Chapter 6: Simple Transfer Learning with TensorFlow Hub Pre-trained Models for Transfer Learning Import the TensorFlow Library GPU Hardware Accelerator TensorFlow Hub MobileNet-v2 Flowers MobileNet-v2 Experiment Load Flowers as a TFDS Object Explore Metadata Display Images and Shapes Build the Input Pipeline Create a Feature Vector Freeze the Pre-trained Model Create a Classification Head Compile and Train the Model Visualize Performance Make Predictions from Test Data Inspect the First Prediction Inspect the First Batch of Predictions Plot Predictions Flowers Inception-v3 Experiment Build the Input Pipeline Build the Model Compile and Train Visualize Performance Predictions Plot Model Predictions Summary Chapter 7: Advanced Transfer Learning Transfer Learning Import the TensorFlow Library GPU Hardware Accelerator Beans Experiment Load Beans Explore the Data Visualize Reformat Images Build the Input Pipeline Model with Xception Create a Model Model the Data Model Trained Data with Unfrozen Layers Model Beans with Inception Create a Model Model the Data Model Trained Data with Unfrozen Layers Generalize on Unseen Data Stanford Dogs Experiment Model Stanford Dogs with MobileNet Load Data Metadata Visualize Examples Check Image Shape Build the Input Pipeline Create the Model Compile and Train Model Trained Data with Unfrozen Layers Generalize Flowers Experiment Read Flowers as TFRecords Create Data Splits Create Functions to Load and Process TFRecord Files Create Training and Test Sets Create the Model Compile and Train Visualize Generalize Rock-Paper-Scissors Experiment Load the Data Visualize Build the Input Pipeline Create the Model Compile and Train Visualize Generalize Tips and Concepts Chapter 8: Stacked Autoencoders Import the TensorFlow Library GPU Hardware Accelerator Basic Stacked Encoder Experiment Load Data Scale Data Build the Stacked Encoder Model Compile and Train Visualize Performance Visualize the Reconstructions Breakdown Dimensionality Reduction Tying Weights Experiment Define a Custom Layer Build the Tied Weights Model Compile and Train Visualize Training Performance Visualize Reconstructions Denoising Experiment Build the Denoising Model Compile and Train Visualize Training Performance Visualize Reconstructions Dropout Experiment Build the Dropout Model Compile and Train Visualize Training Performance Visualize Reconstructions Summary Chapter 9: Convolutional and Variational Autoencoders Import the TensorFlow Library GPU Hardware Accelerator Convolutional Encoder Experiment Load Data Inspect Data Display Examples Get Training Data Inspect Shapes Preprocess Image Data Create a Convolutional Autoencoder Compile and Train Visualize Training Performance Visualize Reconstructions Variational Autoencoder Experiment Load Data Inspect Data Scale Create a Custom Layer to Sample Codings Create the VAE Model Compile and Train Add Latent Loss and Reconstruction Loss to the Model Visualize Reconstructions Generate New Images TFP Experiment Create a TFP VAE Model TFP VAE Encoder TFP VAE Decoder Build the TFP VAE Model Compile and Train Efficacy Test Summary Chapter 10: Generative Adversarial Networks Import the TensorFlow Library GPU Hardware Accelerator GAN Experiment Load Data Scale Build the GAN Compile the Discriminator Model Build the Input Pipeline Create a Custom Loop for Training Train the GAN DCGAN Experiment with Small Images Create the Generator Create the Discriminator Create the DCGAN Compile the Discriminator Model Reshape Build the Input Pipeline Train Generate Images with the Trained Generator DCGAN Experiment with Large Images Inspect Metadata Load Data for Training Massage the Data Build the Input Pipeline Build the Model Compile the Discriminator Model Rescale Train Model and Generate Images Summary Chapter 11: Progressive Growing Generative Adversarial Networks Latent Space Learning Import the TensorFlow Library GPU Hardware Accelerator Create Environment for Experiments Install Packages for Creating Animations Install Libraries Create Functions for Image Display Create Latent Space Dimensions Set Verbosity for Error Logging Image Generation Experiment Create a Function to Interpolate a Hypersphere Load the Pre-trained Model Generate and Display an Image Create a Function to Generate Multiple Images Create an Animation Display Interpolated Image Vectors Display Multiple Images from a Single Vector Create a Target Latent Vector from an Uploaded Image Custom Loop Learning Experiment Create the Feature Vector Display an Image from the Feature Vector Create the Target Image Create a Function to Find the Closest Latent Vector Create the Loss Function Train the Model Training Loss Animate Compare Learned Images to the Target Try a Different Loss Function Algorithm Create a Target from an Uploaded Image Create a Target from a Google Drive Image Create a Target from a Wikimedia Commons Image Latent Vectors and Image Arrays Generate a New Image from a Latent Vector Generate a New Image from an Image Vector Summary Chapter 12: Fast Style Transfer Why Is Style Transfer Important? Arbitrary Neural Artistic Stylization Import the TensorFlow Library GPU Hardware Accelerator ANAS Experiment Import Requisite Libraries Get Images from Google Drive Preprocess Images Display Processed Images Prepare Image Batches Load the Model Feed the Model Explore the Pastiche Visualize Image Stylization with Multiple Images Get Images Process Images Visualize Processed Images Create Reference Dictionaries Create a Pastiche TensorFlow Lite Experiment Architecture for a Pre-trained TensorFlow Lite Model Crop Images Display Cropped Images Stylize Images Create the Style Prediction Model Create the Style Transform Model Create the Pastiche Style Blending Prepare the Content Image Blend the Style Bottleneck Vectors Save the Pastiche Summary Chapter 13: Object Detection Object Detection in a Natural Scene Detection vs. Classification Bounding Boxes Basic Structure Import the TensorFlow Library GPU Hardware Accelerator Object Detection Experiment Import Requisite Libraries Create Functions for the Experiment Load a Pre-trained Object Detection Model Load an Image from Google Drive Prepare the Image Run Object Detection on the Image Detect Images from Complex Scenes Create a Download Function Load an Image Scene Detect Detect on More Scenes Find the Source Find the Source of a Wikipedia Commons Image Summary Chapter 14: An Introduction to Reinforcement Learning Challenges of Reinforcement Learning Import the TensorFlow Library GPU Hardware Accelerator Reinforcement Learning Experiment Install and Configure OpenAI Gym on Colab Import Libraries Create an Environment Display the Rendering from the Environment Display Actions Simple Neural Network Reward Policy Model Predictions Animate Implement a Basic Reward Policy Reinforce Policy Gradient Algorithm How Do Policy Gradients Work? Train the Model with Policy Gradients Discount and Normalize the Rewards Train the Learner Render the Frames from the Reinforce Policy Gradient Algorithm Animate the Policy Summary Index