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Deep Learning with PyTorch

ELI STEVENS, LUCA ANTIGA, AND THOMAS VIEHMANN

FOREWORD BY SOUMITH CHINTALA

Copyright

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Publications was aware of a trademark claim, the designations have been printed in initial caps or all caps.

♾ Recognizing the importance of preserving what has been written, it is Manning’s policy to have the books we publish printed on acid-free paper, and we exert our best efforts to that end. Recognizing also our responsibility to conserve the resources of our planet, Manning books are printed on paper that is at least 15 percent recycled and processed without the use of elemental chlorine.

Manning Publications Co. 20 Baldwin Road Technical PO Box 761

Shelter Island, NY 11964

Development editor: Frances Lefkowitz

Technical development editor: Arthur Zubarev

Review editor: Ivan Martinović

Production editor: Deirdre Hiam

Copyeditor: Tiffany Taylor

Proofreader: Katie Tennant

Technical proofreader: Kostas Passadis

Typesetter: Gordan Salinović

Cover designer: Marija Tudor

ISBN: 9781617295263

dedication

To my wife (this book would not have happened without her invaluable support and partnership), my parents (I would not have happened without them), and my children (this book would have happened a lot sooner but for them).

Thank you for being my home, my foundation, and my joy.

--Eli Stevens

Same :-) But, really, this is for you, Alice and Luigi.

--Luca Antiga

To Eva, Rebekka, Jonathan, and David.

--Thomas Viehmann

contents

foreword preface acknowledgments aboutthisbook abouttheauthors aboutthecoverillustration

Part 1: Core PyTorch

1IntroducingdeeplearningandthePyTorchLibrary

1.1  The deep learning revolution

1.2  PyTorch for deep learning

1.3  Why PyTorch?

The deep learning competitive landscape

1.4  An overview of how PyTorch supports deep learning projects

1.5  Hardware and software requirements Using Jupyter Notebooks

1.6  Exercises

1.7  Summary

2Pretrainednetworks

2.1  A pretrained network that recognizes the subject of an image

AlexNet

Obtaining a pretrained network for image recognition 19

ResNet

Ready, set, almost run 22 Run! 25

2.2  A pretrained model that fakes it until it makes it

The GAN game

CycleGAN

A network that turns horses into zebras

2.3  A pretrained network that describes scenes

NeuralTalk2

2.4  Torch Hub

2.5  Conclusion

2.6  Exercises

2.7  Summary

3Itstartswithatensor

3.1  The world as floating-point numbers

3.2  Tensors: Multidimensional arrays

From Python lists to PyTorch tensors

Constructing our first tensors

The essence of tensors

3.3  Indexing tensors

3.4  Named tensors

3.5  Tensor element types

Specifying the numeric type with dtype

A dtype for every occasion

Managing a tensor’s dtype attribute

3.6  The tensor API

3.7  Tensors: Scenic views of storage

Indexing into storage

Modifying stored values: In-place operations

3.8  Tensor metadata: Size, offset, and stride

Views of another tensor’s storage

Transposing without copying

Transposing in higher dimensions 60 Contiguous tensors 60

3.9  Moving tensors to the GPU

Managing a tensor’s device attribute

3.10  NumPy interoperability

3.11  Generalized tensors are tensors, too

3.12  Serializing tensors

Serializing to HDF5 with h5py

3.13  Conclusion

3.14  Exercises

3.15  Summary

4Real-worlddatarepresentationusingtensors

4.1  Working with images

Adding color channels

Loading an image file 72 Changing the layout

Normalizing the data 74

4.2  3D images: Volumetric data

Loading a specialized format

4.3  Representing tabular data

Using a real-world dataset

Loading a wine data tensor 78 Representing scores

One-hot encoding

When to categorize

Finding thresholds 84

4.4  Working with time series

Adding a time dimension

Shaping the data by time period

Ready for training

4.5  Representing text

Converting text to numbers

One-hot-encoding characters 94 One-hot encoding whole words

Text embeddings 98 Text embeddings as a blueprint 100

4.6  Conclusion

4.7  Exercises

4.8  Summary

5Themechanicsoflearning

5.1  A timeless lesson in modeling

5.2  Learning is just parameter estimation

A hot problem

Gathering some data

Visualizing the data

Choosing a linear model as a first try

5.3  Less loss is what we want

From problem back to PyTorch

5.4  Down along the gradient

Decreasing loss

Getting analytical

Iterating to fit the model

Normalizing inputs

Visualizing (again)

5.5  PyTorch’s autograd: Backpropagating all things

Computing the gradient automatically

Optimizers a la carte

Training, validation, and overfitting 131 Autograd nits and switching it off 137

5.6  Conclusion

5.7  Exercise

5.8  Summary

6Usinganeuralnetworktofitthedata

6.1  Artificial neurons

Composing a multilayer network

Understanding the error function

All we need is activation

More activation functions

Choosing the best activation function 148 What learning means for a neural network 149

6.2  The PyTorch nn module

Using __ call __ rather than forward

Returning to the linear model

6.3  Finally a neural network

Replacing the linear model

Inspecting the parameters 159 Comparing to the linear model 161

6.4  Conclusion

6.5  Exercises

6.6  Summary

7Tellingbirdsfromairplanes:Learningfromimages

7.1  A dataset of tiny images

Downloading CIFAR-10

The Dataset class 166 Dataset transforms Normalizing data 170

7.2  Distinguishing birds from airplanes

Building the dataset

A fully connected model 174 Output of a classifier

Representing the output as probabilities

A loss for classifying

Training the classifier

The limits of going fully connected 189

7.3  Conclusion

7.4  Exercises

7.5  Summary

8.1  The case for convolutions

What convolutions do

8.2  Convolutions in action

Padding the boundary

Detecting features with convolutions

Looking further with depth and pooling 202 Putting it all together for our network 205

8.3  Subclassing nn.Module

Our network as an nn.Module

How PyTorch keeps track of parameters and submodules

The functional API

8.4  Training our convnet

Measuring accuracy

Saving and loading our model 214 Training on the GPU 215

8.5  Model design

Adding memory capacity: Width

Helping our model to converge and generalize: Regularization

Going deeper to learn more complex structures: Depth

Comparing the designs from this section

It’s already outdated

8.6  Conclusion

8.7  Exercises

8.8  Summary

9.1  Introduction to the use case

9.2  Preparing for a large-scale project

9.3  What is a CT scan, exactly?

9.4  The project: An end-to-end detector for lung cancer

Why can’t we just throw data at a neural network until it works?

What is a nodule?

Our data source: The LUNA Grand Challenge Downloading the LUNA data

9.5  Conclusion

9.6  Summary

10Combiningdatasourcesintoaunifieddataset

10.1  Raw CT data files

10.2  Parsing LUNA’s annotation data

Training and validation sets

Unifying our annotation and candidate data

10.3  Loading individual CT scans

Hounsfield Units

10.4  Locating a nodule using the patient coordinate system

The patient coordinate system

CT scan shape and voxel sizes

Converting between millimeters and voxel addresses

Extracting a nodule from a CT scan

10.5  A straightforward dataset implementation

Caching candidate arrays with the getCtRawCandidate function

Constructing our dataset in LunaDataset . __ init __

A training/validation split

Rendering the data

10.6  Conclusion

10.7  Exercises

10.8  Summary

11Trainingaclassificationmodeltodetectsuspected tumors

11.1  A foundational model and training loop

11.2  The main entry point for our application

11.3  Pretraining setup and initialization

Initializing the model and optimizer

Care and feeding of data loaders

11.4  Our first-pass neural network design

The core convolutions

The full model

11.5  Training and validating the model

The computeBatchLoss function

The validation loop is similar

11.6  Outputting performance metrics

The logMetrics function

11.7  Running the training script

Needed data for training

Interlude: The enumerateWithEstimate function

11.8  Evaluating the model: Getting 99.7% correct means we’re done, right?

11.9  Graphing training metrics with TensorBoard

Running TensorBoard

Adding TensorBoard support to the metrics logging function

11.10  Why isn’t the model learning to detect nodules? 11.11  Conclusion 11.12  Exercises

12.1  High-level plan for improvement

12.2  Good dogs vs. bad guys: False positives and false negatives

12.3  Graphing the positives and negatives

Recall is Roxie’s strength

Precision is Preston’s forte 326 Implementing precision and recall in logMetrics

Our ultimate performance metric: The F1 score

How does our model perform with our new metrics? 332

12.4  What does an ideal dataset look like?

Making the data look less like the actual and more like the “ideal” 336 Contrasting training with a balanced LunaDataset to previous runs

Recognizing the symptoms of overfitting 343

12.5  Revisiting the problem of overfitting

An overfit face-to-age prediction model

12.6  Preventing overfitting with data augmentation

Specific data augmentation techniques

Seeing the improvement from data augmentation

12.7  Conclusion

12.8  Exercises

12.9  Summary

13Usingsegmentationtofindsuspectednodules

13.1  Adding a second model to our project

13.2  Various types of segmentation

13.3  Semantic segmentation: Per-pixel classification

The U-Net architecture

13.4  Updating the model for segmentation

Adapting an off-the-shelf model to our project

13.5  Updating the dataset for segmentation

U-Net has very specific input size requirements

U-Net trade-offs for 3D vs. 2D data

Building the ground truth data

Implementing Luna2dSegmentationDataset

Designing our training and validation data

Implementing TrainingLuna2dSegmentationDataset

Augmenting on the GPU

13.6  Updating the training script for segmentation

Initializing our segmentation and augmentation models 387 Using the Adam optimizer

Dice loss

Getting images into TensorBoard

Updating our metrics logging 396 Saving our model 397

13.7  Results

13.8  Conclusion

13.9  Exercises

13.10  Summary

14.1  Towards the finish line

14.2  Independence of the validation set

14.3  Bridging CT segmentation and nodule candidate classification

Segmentation

Grouping voxels into nodule candidates 411 Did we find a nodule? Classification to reduce false positives 412

14.4  Quantitative validation

14.5  Predicting malignancy

Getting malignancy information

An area under the curve baseline: Classifying by diameter

Reusing preexisting weights: Fine-tuning

More output in TensorBoard

14.6  What we see when we diagnose

Training, validation, and test sets

14.7  What next? Additional sources of inspiration (and data)

Preventing overfitting: Better regularization

Refined training data

Competition results and research papers

14.8  Conclusion

Behind the curtain

14.9  Exercises 14.10  Summary

15.1  Serving PyTorch models

Our model behind a Flask server

What we want from deployment

Request batching

15.2  Exporting models

Interoperability beyond PyTorch with ONNX

PyTorch’s own export: Tracing

Our server with a traced model

15.3  Interacting with the PyTorch JIT

What to expect from moving beyond classic Python/PyTorch

458 The dual nature of PyTorch as interface and backend 460 TorchScript

Scripting the gaps of traceability 464

15.4  LibTorch: PyTorch in C++

Running JITed models from C++

C++ from the start: The C++ API

15.5  Going mobile

Improving efficiency: Model design and quantization

15.6  Emerging technology: Enterprise serving of PyTorch models

15.7  Conclusion

15.8  Exercises

15.9  Summary index

front matter foreword

When we started the PyTorch project in mid-2016, we were a band of open source hackers who met online and wanted to write better deep learning software. Two of the three authors of this book, Luca Antiga and Thomas Viehmann, were instrumental in developing PyTorch and making it the success that it is today.

Our goal with PyTorch was to build the most flexible framework possible to express deep learning algorithms. We executed with focus and had a relatively short development time to build a polished product for the developer market. This wouldn’t have been possible if we hadn’t been standing on the shoulders of giants. PyTorch derives a significant part of its codebase from the Torch7 project started in 2007 by Ronan Collobert and others, which has roots in the Lush programming language pioneered by Yann LeCun and Leon Bottou. This rich history helped us focus on what needed to change, rather than conceptually starting from scratch.

It is hard to attribute the success of PyTorch to a single factor. The project offers a good user experience and enhanced debuggability and flexibility, ultimately making users more productive. The huge adoption of PyTorch has resulted in a beautiful ecosystem of software and research

built on top of it, making PyTorch even richer in its experience.

Several courses and university curricula, as well as a huge number of online blogs and tutorials, have been offered to make PyTorch easier to learn. However, we have seen very few books. In 2017, when someone asked me, “When is the PyTorch book going to be written?” I responded, “If it gets written now, I can guarantee that it will be outdated by the time it is completed.”

With the publication of Deep Learning with PyTorch, we finally have a definitive treatise on PyTorch. It covers the basics and abstractions in great detail, tearing apart the underpinnings of data structures like tensors and neural networks and making sure you understand their implementation. Additionally, it covers advanced subjects such as JIT and deployment to production (an aspect of PyTorch that no other book currently covers).

Additionally, the book covers applications, taking you through the steps of using neural networks to help solve a complex and important medical problem. With Luca’s deep expertise in bioengineering and medical imaging, Eli’s practical experience creating software for medical devices and detection, and Thomas’s background as a PyTorch core developer, this journey is treated carefully, as it should be.

All in all, I hope this book becomes your “extended” reference document and an important part of your library or workshop.

Cocreator of PyTorch

preface

As kids in the 1980s, taking our first steps on our Commodore VIC 20 (Eli), the Sinclair Spectrum 48K (Luca), and the Commodore C16 (Thomas), we saw the dawn of personal computers, learned to code and write algorithms on ever-faster machines, and often dreamed about where computers would take us. We also were painfully aware of the gap between what computers did in movies and what they could do in real life, collectively rolling our eyes when the main character in a spy movie said, “Computer, enhance.”

Later on, during our professional lives, two of us, Eli and Luca, independently challenged ourselves with medical image analysis, facing the same kind of struggle when writing algorithms that could handle the natural variability of the human body. There was a lot of heuristics involved when choosing the best mix of algorithms that could make things work and save the day. Thomas studied neural nets and pattern recognition at the turn of the century but went on to get a PhD in mathematics doing modeling.

When deep learning came about at the beginning of the 2010s, making its initial appearance in computer vision, it started being applied to medical image analysis tasks like

the identification of structures or lesions on medical images. It was at that time, in the first half of the decade, that deep learning appeared on our individual radars. It took a bit to realize that deep learning represented a whole new way of writing software: a new class of multipurpose algorithms that could learn how to solve complicated tasks through the observation of data.

To our kids-of-the-80s minds, the horizon of what computers could do expanded overnight, limited not by the brains of the best programmers, but by the data, the neural network architecture, and the training process. The next step was getting our hands dirty. Luca choose Torch 7 (http://torch.ch), a venerable precursor to PyTorch; it’s nimble, lightweight, and fast, with approachable source code written in Lua and plain C, a supportive community, and a long history behind it. For Luca, it was love at first sight. The only real drawback with Torch 7 was being detached from the ever-growing Python data science ecosystem that the other frameworks could draw from. Eli had been interested in AI since college,1 but his career pointed him in other directions, and he found other, earlier deep learning frameworks a bit too laborious to get enthusiastic about using them for a hobby project.

So we all got really excited when the first PyTorch release was made public on January 18, 2017. Luca started contributing to the core, and Eli was part of the community very early on, submitting the odd bug fix, feature, or documentation update. Thomas contributed a ton of features and bug fixes to PyTorch and eventually became

one of the independent core contributors. There was the feeling that something big was starting up, at the right level of complexity and with a minimal amount of cognitive overhead. The lean design lessons learned from the Torch 7 days were being carried over, but this time with a modern set of features like automatic differentiation, dynamic computation graphs, and NumPy integration.

Given our involvement and enthusiasm, and after organizing a couple of PyTorch workshops, writing a book felt like a natural next step. The goal was to write a book that would have been appealing to our former selves getting started just a few years back.

Predictably, we started with grandiose ideas: teach the basics, walk through end-to-end projects, and demonstrate the latest and greatest models in PyTorch. We soon realized that would take a lot more than a single book, so we decided to focus on our initial mission: devote time and depth to cover the key concepts underlying PyTorch, assuming little or no prior knowledge of deep learning, and get to the point where we could walk our readers through a complete project. For the latter, we went back to our roots and chose to demonstrate a medical image analysis challenge.

acknowledgments

We are deeply indebted to the PyTorch team. It is through their collective effort that PyTorch grew organically from a

summer internship project to a world-class deep learning tool. We would like to mention Soumith Chintala and Adam Paszke, who, in addition to their technical excellence, worked actively toward adopting a “community first” approach to managing the project. The level of health and inclusiveness in the PyTorch community is a testament to their actions.

Speaking of community, PyTorch would not be what it is if not for the relentless work of individuals helping early adopters and experts alike on the discussion forum. Of all the honorable contributors, Piotr Bialecki deserves our particular badge of gratitude. Speaking of the book, a particular shout-out goes to Joe Spisak, who believed in the value that this book could bring to the community, and also Jeff Smith, who did an incredible amount of work to bring that value to fruition. Bruce Lin’s work to excerpt part 1 of this text and provide it to the PyTorch community free of charge is also hugely appreciated.

We would like to thank the team at Manning for guiding us through this journey, always aware of the delicate balance between family, job, and writing in our respective lives. Thanks to Erin Twohey for reaching out and asking if we’d be interested in writing a book, and thanks to Michael Stephens for tricking us into saying yes. We told you we had no time! Brian Hanafee went above and beyond a reviewer’s duty. Arthur Zubarev and Kostas Passadis gave great feedback, and Jennifer Houle had to deal with our wacky art style. Our copyeditor, Tiffany Taylor, has an impressive eye for detail; any mistakes are ours and ours

alone. We would also like to thank our project editor, Deirdre Hiam, our proofreader, Katie Tennant, and our review editor, Ivan Martinovic´. There are also a host of people working behind the scenes, glimpsed only on the CC list of status update threads, and all necessary to bring this book to print. Thank you to every name we’ve left off this list! The anonymous reviewers who gave their honest feedback helped make this book what it is.

Frances Lefkowitz, our tireless editor, deserves a medal and a week on a tropical island after dragging this book over the finish line. Thank you for all you’ve done and for the grace with which you did it.

We would also like to thank our reviewers, who have helped to improve our book in many ways: Aleksandr Erofeev, Audrey Carstensen, Bachir Chihani, Carlos Andres Mariscal, Dale Neal, Daniel Berecz, Doniyor Ulmasov, Ezra Stevens, Godfred Asamoah, Helen Mary Labao Barrameda, Hilde Van Gysel, Jason Leonard, Jeff Coggshall, Kostas Passadis, Linnsey Nil, Mathieu Zhang, Michael Constant, Miguel Montalvo, Orlando Alejo Méndez Morales, Philippe Van Bergen, Reece Stevens, Srinivas K. Raman, and Yujan Shrestha.

To our friends and family, wondering what rock we’ve been hiding under these past two years: Hi! We missed you! Let’s have dinner sometime.

about this book

This book has the aim of providing the foundations of deep learning with PyTorch and showing them in action in a reallife project. We strive to provide the key concepts underlying deep learning and show how PyTorch puts them in the hands of practitioners. In the book, we try to provide intuition that will support further exploration, and in doing so we selectively delve into details to show what is going on behind the curtain.

Deep Learning with PyTorch doesn’t try to be a reference book; rather, it’s a conceptual companion that will allow you to independently explore more advanced material online. As such, we focus on a subset of the features offered by PyTorch. The most notable absence is recurrent neural networks, but the same is true for other parts of the PyTorch API.

Who should read this book

This book is meant for developers who are or aim to become deep learning practitioners and who want to get acquainted with PyTorch. We imagine our typical reader to be a computer scientist, data scientist, or software engineer, or an undergraduate-or-later student in a related program. Since we don’t assume prior knowledge of deep learning, some parts in the first half of the book may be a repetition of concepts that are already known to experienced practitioners. For those readers, we hope the

exposition will provide a slightly different angle to known topics.

We expect readers to have basic knowledge of imperative and object-oriented programming. Since the book uses Python, you should be familiar with the syntax and operating environment. Knowing how to install Python packages and run scripts on your platform of choice is a prerequisite. Readers coming from C++, Java, JavaScript, Ruby, or other such languages should have an easy time picking it up but will need to do some catch-up outside this book. Similarly, being familiar with NumPy will be useful, if not strictly required. We also expect familiarity with some basic linear algebra, such as knowing what matrices and vectors are and what a dot product is.

How this book is organized: A roadmap

Deep Learning with PyTorch is organized in three distinct parts. Part 1 covers the foundations, while part 2 walks you through an end-to-end project, building on the basic concepts introduced in part 1 and adding more advanced ones. The short part 3 rounds off the book with a tour of what PyTorch offers for deployment. You will likely notice different voices and graphical styles among the parts. Although the book is a result of endless hours of collaborative planning, discussion, and editing, the act of writing and authoring graphics was split among the parts: Luca was primarily in charge of part 1 and Eli of part 2.2 When Thomas came along, he tried to blend the style in part 3 and various sections here and there with the writing

Another random document with no related content on Scribd:

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P. Take good care ob him. (Imitates her voice, and tip-toes round the room.) How golly fine it am to be de cap’n’s mis’, a-sittin’ down har all fix’ up, and den walkin’ on deck wid de par-sol, totin’ de baby. Oh, Lor! (Sings softly.)

Min’ de pick’niny, Min’ de pick’niny, Take good care ob him.

Wot’s dem books? I dunno, caze I can’t read ’em all yit. But the cap’n’s mis’, she try larn me. Lemme see. (Takes up a book and reads.) “Meel-iss-see-felt-a-cold-han’-on-her-fore-head-an’-shescream-ded-scream-ded.” Wot’s dat? Golly! I can’t do dat. (Shuts up the book.) Sh! sh! de baby’s wokem up. He’ll holler ef he see me. I’ll make him tink I’m de cap’n’s mis’. (He takes the parasol and opens it, spreads the handkerchief over his face, and sits down by the cradle. Enter C M, ., leaning on M’ shoulder.)

M. Tell me, dear, just how you feel. (Sees P.) Oh, Phus! you’ll scare the baby.

P. Mis’, de baby was a gwine to wokem up, and I specks he’d tink ’twas you.

C. M. Phus, take off that rig, and go on deck, you lubber! (Exit P, .) Oh, I don’t know. I feel just as I did once when I was a boy, before I had the typhoid fever,—tired all over. (Sits.) My head is as light as a feather, and my feet are heavy as lead. I don’t feel as if I could step a step.

M. Lie down a little while, and perhaps you’ll feel better. How much farther do we go up river?

C. M. About two hundred miles. We shall reach the last station in a few days. (Takes off his jacket and shoes wearily, as he talks.)

Patsy is at the wheel, and you can bring me word if he wants anything.

M (aside). Oh, dear! I know he is going to be sick. (To him) Where is the chart of the river?

C. M. On deck, in the wheel-house.

M. And all the things you use?

C. M. Yes. Why?

M. Because I want to know, so that you can have a good long nap.

C. M. Our course is all marked out, and what to steer by; but I shall feel better, I hope, after I have had some sleep. You’d better go on deck, once in a while, see how things are going on, and let me know. (Exit ., holding by the doorway.)

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(Enter P, .)

P. Whar de cap’n? Pats say he want know which way ter go, and de cap’n must tell him.

M. Phus, do you remember how sick you were last year?

P. An’! wouldn’t ’a’ libed ef you hadn’t ’a’ nussed me.

M. Do you want to pay me for it?

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M. I don’t want any money, Phus. You can pay me in a better way.

P. An’ I sings in de cook-house w’en de pork’s a-fizzlin’, an’ Hank he likes it. (Sings mournfully.)

I’se poor Jo-Phus, ’Lijah cum down. Sick in de ’teamboat, ’Lijah cum down. Cap’n’s mis’ nuss me, ’Lijah cum down (Livelier ) An’ den I gits well, ’Lijah cum down Swing low de goolden charyot, Rock de baby, car’ long de cap’n’s mis’ ’Lijah cum down

(M does not listen.)

M. Phus, listen to me. The captain is very sick, and you can help me if you will; and more than pay me for anything I have done for you.

P. I’ll do ebryting. You so good to poor Phus—make me well, an’ larn me to read—see here. (Reads.) “Mee-liss-see-felt-a-screamded,” no, dat ain’t de place; “col’—col’—han’—” (cold hand.)

M Never mind reading now, Phus. I want you to stay here while I go on deck, and listen to the captain. If he wakes up and wants anything, you must go in and tell him I will come right down; then you come and call me. (Exit .)

P. Yaas, mis’! (Applies ear to keyhole of door, .)

C.

ACT III.

Forward deck of the Creole Bride. Wheel-house at . gangway and railing at ., table and two camp chairs at ., chairs . M at the wheel, with the chart and compass beside her.

M. I wonder if I am all right here! The course is not very clearly marked out. Willie is still so sick that he can’t tell me any more about steering, and Patsy don’t seem to know anything but his engine, or how to go when it is plain sailing. (Studies the chart.) Let me see! We must stop at three more stations before we reach the mouth of the Washita,—Munroe, Columbia, and Harrisonburg; and then we go down the Red and Yellow to Baton Rouge. Oh! yes, I see. We steer right here by Dead Man’s Bluff, and then by Run-away Swamp. How lucky I studied that book on navigation! It helps me so much to understand these marks on the chart. If Patsy would only behave well, I should be all right; but he don’t like the idea of being “bossed,” as he calls it, “by a woman.”

(Enter P, .)

M. Patsy, have you thrown out the line lately?

P Yes, mum.

M. Where are we?

P. Be-gorries! I dunno, mum.

M. How much water?

P Faix! the lid was varry well down, and the mud was yaller

M. That may mean something to you, I suppose. You can’t read. Bring me the line. (He bring it from )

P. It’s tin fut, mum. (Aside) Bedad, she thinks she’s cap’n.

M. That’ll do, Take the line forward, and mind your engine.

P (muttering). Mind the injun, is it? O’ coorse. Musha and faix, I wull! I’m the lasht lad not to be mindin’ me injun. (Drops the line and goes toward .)

M. Patsy!

P. Vart do yer want? I can’t be lavin’ my injun arl the time. True for yez!

M Patsy! I told you to take the line forward!

P. I’ll not do it, mum, for all of yez. Ye’re not the cap’n!

M (looking at him severely). Patsy! Take that line forrard, and be quick about it!

P (takes the line to ., and exit ., muttering). I’ll not be bossed by no woman!

M. I don’t know what I shall do with Patsy. He threatens to leave me at the next station, and I can’t find a decent engineer short of Baton Rouge; and I mustn’t trouble William with it, he is still so feeble.

(Enter P, .)

P. Mis’, de cap’n say he feel bet’ as did, an’ he wan’ ter see yer.

M. Very well, I’ll go down. You call Patsy to stand at the wheel; and then you go and stay with the baby.

P. Yes, mis’. (Calls, .) Pats! Har! you Pats, lave dat injyne an’ cum an’ stan’ by de wheel. Pay—ats! Pay—ats! Pay—a—ts! Cum, Pats, to de weel-house! Mis’ say so.

(Enter P . He takes the wheel.)

M (to P). Mind your helm now; keep her on her course. (Exit M, .)

P. Ugh! Bedad!

P (sits down at the wheel-house and takes his banjo). Bress de Lor’, de cap’n’s bet’ as was. He say he mean git well. (Sings and

rocks himself.)

Lor’ bress de cap’n, ’Lijah cum down.

Lor’ bress de cap’n’s mis’, ’Lijah cum down.

An’ let ’im git well, ’Lijah cum down.

As dis poor Jo-Phus did, “Lijah cum down.

Swing low de goolden charyot, Car’ long de baby, cap’n, an’ de cap’n’s mis’, ’Lijah cum down.

P (putting his head out of the wheel-house). Musha! Shtop yer hullabaloo, you black nayger.

P. Dere aint no sich man round here. My name’s Jo-see-phus, Herodytus Miller. (Exit .)

(Re-enter M, ., half supporting C M, who tries to walk; he sits down near the table wearily.)

C. M. (feebly). It’s no use, Mary, I can’t walk. I can’t use my legs a mite, and that’s a fact. The malaria has settled in them, and I don’t know as I shall ever walk again.

M (stands beside him, and keeps her eye on the vessel’s course). Yes, you will, dear. The doctor says so; and he says you must get away from the boat, go into the mountains and stay awhile, and then you will be as well as ever.

C. M. Oh, Mary! If I could only go to New England. I feel as if it would cure me. If I could only go to Maine, and see the White Hills, all covered with snow on top, from behind father’s house, see mother, and have some of their good victuals—(He breaks down.)

M. You shall go. It won’t cost any more to go there than it will to pay your board at some place near the mountains; and no matter if it does.

C. M. How can I leave the vessel? If I take the money to go East with, I shan’t be able to meet my payments, and shall lose my chance of buying into her.

M (to P). Ease her off a couple of points. (To W) Never mind that! Don’t worry. It’s better to lose everything else than to lose your health. But you will not lose the boat. I can run her while you’re gone. Only three months! The doctor says he thinks that will do.

C. M. I don’t know about your running the boat, Mary. Ours is a thousand-mile trip, you know, next time, and it’s easier to come down than it is to go up. The Yellow-red winds like a corkscrew.

M. I know that, William; but I think I can manage her. I have done it; and here we are safe so far, and no accident yet.

C. M. (considering). This cargo is secure, and the next one all promised. But I hate to leave you, Mary, and the baby.

M (to P). Keep her on her course, boy! (To W) I hate to have you go, William, only I know that it is for your good; and then, if I go, you’ll have to give up the boat, and we shan’t have anything to live on; and that will never do.

C. M. You’re right, Mary, as you always are.

(Enter H, the cook, with a waiter full of dishes.)

H. Here’s your lunch, sir.

C. M. Why, Hank! Have you come again? It isn’t more than half an hour since I ate my breakfast.

H (drawling). Yes, it is, sir. It’s an hour. And the doctor says you was to eat every hour.

C. M. (looks at the waiter). What have you got now?

M (to P, hurriedly). Hard a-port, there! Give that snag a wide berth! (She goes quickly towards the wheel-house.) Go below, Patsy, and fire up, or we shan’t get to Munroe till moonrise. (Exit P, ., muttering.)

H (to W). Waal, tha’s some fixings the Indians say is good for invaliges, and one on ’em showed me how to cook ’em.

C. M. What are they, Hank? Name over your bill of fare.

H Waal, cap, this ere’s corn-pone, o’ coose; and a dodger or so; a slice o’ bacon; a helter-skelter; some succotash; two frog’s legs pealed and sizzled; a pigeon biled in milk; some baked punkin; eel’s tails soused; and some no-cake.

C. M. What! what! what! Are you going to stuff me to death, or poison me—which?

H. Oh, sir! you needn’t eat ’em all. The Injuns said if you eat just the right thing for you, you’d be sure to get well.

C. M. I dare say. They’d cure a dog with their charms and their notions.

H. Some of the vittals is good, and some pretty middlin’ poor, but it’s all good for suthin’,— or the pigs!

C. M. (laughing). I shouldn’t wonder. (Looking over the waiter.) What’s baked punkin for, Hank? It looks like raw, dried potatoparings.

H. The Indians said ’twas to chaw, and give you an appetite.

M (from the wheel-house). What in the world are the soused eel’s-tails for?

H. Oh, to make you feel lively, and cherk you up a little. They make brains.

C. M. What next? What’s the no-cake for, and where is it? Cake sounds kind o’ good. And hot biscuit. Mother’s hot biscuit! Oh! how I should like some of them.

H. Well, the no-cake is that aire white stuff piled up on that aire plate. It looks like something goodish; but when you chaw it, it feels like sand. The Injuns eat it, and they said ’twould make the cap’n sleep good.

C. M. I should think it would,—and dream of my grandmother. If it chews like sand, it will be heavy enough.

H. There ain’t no decent vittals for a sick man to eat in these diggings. ’Tain’t half so good as the Nantucket feed, such as my marm used to cook.

C. M. Oh, Hank! don’t speak of it! How I should like some fried perch,—some good fresh salt-water perch, with their heads on; and some steamed clams, fresh-dug Nantucket clams, with the shells all gaping at you. I feel as if I could eat a good four-quart tin pan full this minute, shells and all.

H. I’d like to make you a rippin’ good chowder, sir. Such as we have ter hum. What you want is real, good, hard, fresh cod-fish or haddock, head and all, some white potatoes (none o’ your flat yellow sweets), some onions, some Boston crackers, and a generous rasher of salt strip pork (none o’ your middlings). But I can’t do it. They never heerd of a Boston cracker, and there ain’t a decent piece o’ fresh salt-water fish between here and Nantucket. Only this darned canned stuff; and that’s enough to p’isen a feller.

M (to W, from the wheel-house). You’ll have some chowder when you get home, dear; and you’ll eat again of all the old New England food.

H. Oh, sir! you goin’ hum?

C. M. I think of it.

M (to H). Yes, he is going home; and pretty soon, too.

H. If you do, sir, I hope you’ll take a skip down to Nantucket, and see my folks. Marm ’ll be mighty glad to see you. I’ll write to her, and send her some money, and you can take the letter, sir, right along. And please, sir, fetch me word how the old place looks, and if marm seems comfortable.

C M. Yes, Hank, I’ll take your letter; and if I can’t go to see your mother, I will send it to her by express.

H. Thank you, sir, thank you; and if you should go to Annisport, and see Miss Leafy Jane, please tell her I hain’t forgot her, and if you can say I’ve been a good feller—and behaved tip-top—

C. M. Why, Hank! do you remember that little fly-away? You steady old boy, you. Of course you’ve been a good fellow, and I’ll tell her so,—if I see her,—but why don’t you write to her yourself?

H. Oh, sir! she might not like it.

C M. That’s so. Well, do as you like, Hank. You can leave the waiter. I will eat all I can of your concoctions. (Exit H, .)

C M. (turning towards M). I did not know that there was any love-making in that quarter.

M. Nor I, neither.

[Disposition of characters at end of act. C. M at table, ., eating M at the wheel, ]

C.

ACT IV.

The same as in Act II. Enter M, ., with her hands full of papers. She sits down at the table.

M. There! The bills of lading are signed, and all my accounts are straight, so we are ready to begin again. But here we are, still fast at New Orleans, when we ought to have got away three days ago. For some reason or other I can’t get the cargo that was promised, and so I have had to fill up with watermelons. Heavy, unprofitable things! (Writes.) I wish I could hear from William. Poor fellow! The doctor at home said he must take a sea-voyage; and he has gone off with his father to the Grand Banks, fishing. I wish I could see him!

(Enter P, ., bringing a large watermelon.)

P. Wattermillions is bos’; dey’s bos’ an’ cool.

M. Why, Phus, what do you want of that watermelon?

P. It’s such a golly big one; and den it’s marked so peart.

M. Why! there’s hundreds of them on board just as good.

P. O no! mis’, dere ain’t. Dis one hab de little Voudoo mark dat show dey’s sweet; an’ I wanted de baby to stick his little toof in it, an’ suck de juice. Oh, Lors! (Smacks his lips and sings.)

“Some are pa’shel to de appel, oddahs clamor fo’ de plum; Some fin’ ’joyment in de cherry, oddahs make de peaches hum; Some git fas’ned to de onion, oddahs lub de arti-choke; But my taste an’ wattahmillion er’ bound by a pleasant joke

“Hit er meller, hit er juicy, Hit er coolin’, hit er sweet! Hit er painless ter de stummick— Yo’ kin eat, an’ eat, an’ eat!”

I helped you bring ’em on board, didn’t I, mis’?

M. Yes, Phus; you’re always handy. I wish you could be the mate, in Patsy’s place, and help me steer the boat.

P. Lor’ bress you, mis’! I couldn’t do dat. I should steer for all de snags in de riber; an’ git twisted all up in de bay-yous, an’ run inter all de san’bars.

M Have you found anybody yet to take Patsy’s place, if he leaves?

P. No, mis’. All de boys dey say as dey won’t be de mate to no woman. Dey say you has no licens’, an’ can’t be de cap’n. An’ Mass’ Rumberg, he cum an’ take away de Keyhole’s Bride.

M. Oh, Phus! is that what they say? Then that is the reason that I could not get the cargo that was promised here; and when they knew, too, that I had been running the boat these three months all alone!

P. When de cap’n cum hum?

M. Not until December, Phus.

P. Whar’s he, mis’, now?

M. Away out to sea, on a ship; not a steamboat—a sailing vessel. The doctor said it would cure him if he took a sea-voyage.

P. Is de sea bigger dan de Missip’ or de Gulf Mex’?

M. Oh, yes, Phus! a good deal bigger, and wider, too. You can’t see across.

P. O, sho!

M (rising and walking about). And the waves are so high! and white on the top! and they come booming in on the rocks! and the breeze! Oh! the breeze is so sweet, so salt, so fresh! It is enough to do your soul good to smell it.

P. Golly! mis’. It mus’ be hunky, if it’s sweet, and salt, and fresh, an’ comes in boomin’ at ye, on de rocks, all at once.

M (smiling). Better go out again, Phus, and look among the boys for a mate.

P Yes, mis’. (Exit )

M. I think I’ll write to mother, and tell her my troubles. If she can’t help me any, it will do me good to write; and I can get Phus to carry it to the Post Office before we start. (She writes.)

(Enter M. R.)

M. R. (slowly and deliberately). Mrs. Miller, I came to see what you were going to do about the boat. Your husband has been gone a long time; and it seems there is no prospect of his immediate return. So we might as well talk the matter over now as at any other time.

M (rises and offers him a seat). Mr. Romberg? I don’t know as I have seen you before. You are the largest owner in the Creole Bride, I believe? Why do you wish to know what I am going to do? (Sitting.)

M. R. (sitting). I (and the other owners) don’t want the boat to be eating her head off here at the wharf.

M. We shall not stay here longer than this afternoon. As soon as I come to terms with my mate, I shall be ready to steam her up.

M. R. I don’t see how you can run this boat.

M (rising). Why not, sir? I have run her for the last three or four months. I carried her ’way up the Red and Yellow, and down again to Baton Rouge, through the most crooked part of our whole thousandmile route; and I steered most of the time myself. The mate don’t know much about handling the wheel.

M. R. The merchants, I find, are not willing to trust you with a cargo; so I don’t see but you will have to give it up. You won’t be able to meet your payments; and I must look out for my own property, as well as that of the rest of the owners, for it is all in my care.

M. Is not Mr. Miller’s contract as captain of the boat all right? It does not expire till next year. He is all paid up to the first of the

month; and I hope to be able to pay the next quarter,—that is, if I can go on running the boat.

M R. Yes, madam; but you must understand that the contract is with Captain Miller, and not with his wife; that is where the trouble is. Husband and wife are not one in this business. Captain Miller’s contract is all right, and he is paid up; but if he dies, the whole thing will have to be settled.

M (alarmed). But my husband is not dead. He is not going to die! Why can’t I run the boat up to Cairo? I have a full cargo, and another is promised there. I know the route for the next three months. I have been over it all.

M. R. (rising). Mrs. Miller, you cannot be a captain in name.

M. But, Mr. Romberg, I am the captain.

M. R. No, Mrs. Miller. You may run the boat, but you cannot act as captain,—you have no license. The fact is, the law does not allow it. That is what the owners say; and we consulted a lawyer, and he gave it as his opinion, after careful consideration, that a woman cannot be master of a vessel legally.

M. Then we must lose our chance of owning the boat; and I cannot raise the money needed for the support of my poor sick husband and my little baby,—just because I am a woman! Oh! Mr. Romberg! this is hard indeed!

M. R. I suppose it is rather hard; but that is the way of the law, in Louisiana, at least, and I think all over the United States. When our fathers framed the constitution, they thought it was better that woman should be confined to the domestic sphere. The home, the home is their place,—not the decks of vessels. They wanted to protect women in their proper sphere.

M. Protect them! Hinder them, I should think!

M. R. (approaching M). If Captain Miller, now, were not living, you might find some likely river-man to marry you, and be captain of the boat, in name; and then you could keep on acting as master,— your mate, perhaps,—then you’d be all right.

M Marry! The mate! Patsy! Oh, Mr Romberg! Oh, sir! what do you mean?

M R. (aside). Gad! the women are all alike. How they stick to one man! (To her) I don’t see what else you can do.

M. There was Captain Tucker’s wife; after he died she took the boat.

M. R. Yes, but she did not run it long; all of us owners objected to a petticoat captain, and we discharged her.

M (severely). Then what has become of her and all her six children?

M. R. Oh, she tends in a lager-beer saloon in Natchez.

M (indignantly). Yes, and I suppose her children are given away or put out to service—all because she is a woman! She has to do this degrading work to get an honest living, and all because you wouldn’t allow her to do the only work she always had done and was best fitted to do. She run the boat three years before her husband died.

M. R. Well, she might have married and had some one to be her captain. The merchants sent one of their best river-men to marry her, but she ordered him off the boat.

M. I don’t blame her!

M. R. There ain’t much a woman can do round here but get married. There’s many a likely man that is not a river-man who would like to get a good smart Yankee woman like you.

M (sharply). Mr. Romberg! what do you mean?

M. R. I mean, of course, if your husband does not come back, which seems most likely—

Mary (turning away). Oh! What shall I do?

M. R. My dear Mrs. Miller! you must be as wise as a serpent as well as harmless as a dove.

M Oh, sir! how can I be wise without money, without friends, with my hands tied by a little child, and my means of earning a living taken away?

M. R. Well, there is a month or two yet before I shall be obliged to ask you to give up your husband’s papers. Meanwhile, you can go on to Cairo, and come back; go along the Red and Yellow, and leave your cargo. You needn’t take on any more. I’ll see you again when you come down to New Orleans; and then, if your husband has not returned, we must close up our accounts. That is what the rest of the owners say, and I agree.

M. Oh, Mr. Romberg! is there nothing I can do to keep the boat? Can I not get a license? Did a woman never have a captain’s license?

M. R. I never heard of one. And I don’t think there ever was one. It would be absurd! But I must bid you good-morning.

M. Good-morning, sir. (Exit M. R, .) Indeed! what kind of a woman does he take me to be! Telling me about marrying another man so as to have a captain! I will show him that I can be master of my own boat. I go into a lager-beer saloon! As Mary Gandy I would not have done it; and as Mary Miller I certainly shall not. I give up the boat! My William’s boat? Never! Unless they put me on shore by force. Why cannot I get a license? I’ll try! and then, if worst comes to worst, I must make my way somehow back home again. If I could only hear from mother! (Sits down at the table— arranges papers.)

(Enter P, .)

P. O, Lor’! Mis’ Miller! Here’s suthin’ I forgits. I met de pos’man out here, an’ he holl’d at me (She does not look up.)—“Har, you nig!” I looks round, and sez: “Whar? whar? I dun’ see no nig.” He laf, an’ sez, “You know who dat is?” “Whar?” sez I. “On dis let’,” sez he. “No,” sez I; “who is it?” “It’s Mrs. Mary Miller,” sez he. “Lor’,” sez I, “dat’s my cap’n’s mis’; gib it yere.” “Well, fotch it, then,” sez he, “an’ be darn quick ’bout it.” “I will,” sez I. (M looks up.)

M A letter? Oh, give it to me! How long have you had it?

P Jes dis minit, mis’.

M (tearing the envelope). From home, and written by dear brother John. Dear little fellow! (Reads.)

D M,

Mother wants me to write She says: Tell Mary that I talked it all over with your father, and he asked old Pete Rosson, and then I wrote to the lecture woman up to Boston, and she says you must have a captain’s license so’s you can keep the boat And she says you must apply to the Local Inspectors (here is a blank for you to fill out), and that if you pass your examination they will see that it is sent to Washington to the Solicitor of the Treasury. You must write to Mr. Le Brun or Mr. Cholmly, Local Inspectors, New Orleans, La. Do it right off before Mr. Romberg gets a chance to take away the boat. And oh! mother says you must sign your own name to the application Mary Miller, or Mary Gandy Miller (’cause it isn’t legal to sign your husband’s name, and Mrs. is nothing but a title). She’s found out that a woman has no more right, legally, to use her husband’s first name and title than he has to use hers. She says Martha Washington had more sense than to call herself Mrs. George, or Mrs. General, or Mrs. President Washington. Plain Martha Washington was good enough for her And oh! the folks round here are real proud of you, to think you can manage a steamboat, and old Pete Rosson says “it’s a darned shame you have such a hard time, and he hopes you won’t give up the ship ” He expects to go to the Legislature this winter, and he says “if the men at Washington don’t let you have the captain’s license, he’ll vote agin every mother’s son on ’em ”

Yours, as usual, J Q A G.

M (folding the letter). Dear, dear folks at home! How good they are to tell me just what to do! I must write my application at once. (Sits down at the table.)

P. Is de folks well, mis’, an’ de cap’n?

M (writing). Yes, Phus, the folks are well; but the letter is not from the captain. I do not expect to hear from him at present.

P. O, Lor’! mis, is dat so?

M. Yes, Phus. You wait round till I get this letter done, then you carry it to the post-office. I want an answer from it, right off, as soon

as I can get it.

P. Yes, mis’. (He goes out, ., keeps popping his head in and tiptoeing round.)

M (folding up the letter, and putting it in a long envelope). There! my blank is all filled out, and my letter written; both signed plain Mary Miller, which means to me (sighing) that I must hereafter stand alone,—legally, at any rate, and take the responsibility of all my actions. No more hiding behind a husband’s or a father’s name.

Plain Mary Miller! A good name, and I must show that I am worthy of it. (To P) There, be as quick as you can; and then come back here and take care of the baby while I go on deck. (She goes to the cradle.)

P. Yes, mis’! I’m skippin’. (Exit .)

C.

ACT V.

Same as in Act III., with the addition of a hammock slung near the wheel-house, containing the baby. Enter M from the wheel-house with a small sailor hat and reefer on. She takes them off, and lays them on a chair as she talks.

M. Here we are at last, safe at New Orleans. I wish I could hear from Washington; and why don’t I hear from William? I sent home the last money I had saved up, and I shall have no more if they take the boat away. I can’t give her up! And I can’t do anything else to earn a living. This is my business—my life.

(Enter P, .)

P. Oh, mis’! Pats he say he won’t help unload de boat; an’ I can’t get nobody to help, as you tole me. Dey all say dey won’t be bos’ by no woman.

M (sighs). Well, Phus, you’re willing to work for me, ain’t you? You won’t leave your mistress, will you?

P. Neber! No, mis’! I allus work for you an’ de cap’n an’ de baby. Hank, too, he stay. He ben hawlin out de cargo like sixty. He say wimmin good ’nough for him. He ruther be cook to wimmin bos’; cos dey knows more ’bout de fixin’s, an’ dey neber sez, “darn dat stuff.”

M. Phus, you run and tell Patsy he can go. He’s all paid up; and I don’t want him any more. And, here! take my reefer and hat down into the cabin. I shan’t want them at present.

P. Yes, mis’. (He goes out, .)

M (swinging the hammock gently). Must I leave my happy home, where I came a bride? (Leans over the baby) My baby’s birthplace? Why! I love every timber in this tight little steamboat. She

is as dear to me as one of the biggest houses on the river is to the fine lady who lives in it.

P (re-entering). Oh, mis’! Pats he say he will go wid you up riber a piece, to where he woman lib, an’ get off dar.

M. Very well. I’ll see him by and by; but I don’t know as I shall want him. Oh! if my license would only come!

P You licens’, mis; wot’s you licens’?

M (sadly). Why, Phus, I have asked the big men at Washington to give me a license; same as the other river-captains have.

P (whimpering). Oh, Lor’, mis, bress de Lor’! I hope it’ll cum. (Sits on floor at ., and sings softly.) Bring ’long de licens’,—’Lijah cum down

(Takes a book from his pocket, sits on floor at , and reads with a great deal of action.)

M (looking at him). Poor Phus! If the big men at Washington could only see me as he sees me, and know, as he knows, how well I can handle a boat, they would very soon say yes to my application.

(Enter M. R, .)

M. R. Good-day, Mrs. Miller. I am sorry to be obliged to proceed against you, and ask you to deliver up your husband’s papers. I might be willing to wait a little longer; but the other owners are not satisfied. They say that as you cannot get a captain’s license, some man must take the boat.

M. Cannot get a captain’s license? How do you know that? I have applied for one; and am expecting every minute to hear from Washington.

M. R. I know that. Here is the Delta with a long account of your case, and the decision of the Solicitor of the Treasury

M (coming forward). Let me see it! I have heard nothing about it. We have had no mail since we got in.

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