Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

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A REAL Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 362Pooling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364Flattening. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366Dimensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367Typical Architecture. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367LeNet-5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 368A Multiclass Classification Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371Data Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371Data Preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 372Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375Logits. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375Softmax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375LogSoftmax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378Negative Log-Likelihood Loss. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378Cross-Entropy Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 382Classification Losses Showdown! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384Model Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384Model Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387Visualizing Filters and More! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 388Visualizing Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391Hooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394Visualizing Feature Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403Visualizing Classifier Layers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 406Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407Loader Apply. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409Putting It All Together. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 410Recap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 414Chapter 6: Rock, Paper, Scissors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 416Spoilers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 416Jupyter Notebook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 416Imports . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 416Rock, Paper, Scissors… . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417Rock Paper Scissors Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 418Data Preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419ImageFolder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419Standardization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 420
The Real Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424Three-Channel Convolutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425Fancier Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 428Dropout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431Two-Dimensional Dropout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437Model Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 438Optimizer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 438Learning Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439Model Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 440Regularizing Effect. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 440Visualizing Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 442Learning Rates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 444Finding LR. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 446Adaptive Learning Rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 454Moving Average (MA). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 454EWMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455EWMA Meets Gradients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 461Adam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 462Visualizing Adapted Gradients. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 463Stochastic Gradient Descent (SGD) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 470Momentum. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 471Nesterov . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 474Flavors of SGD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 475Learning Rate Schedulers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 478Epoch Schedulers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 479Validation Loss Scheduler. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 480Schedulers in StepByStep — Part I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 482Mini-Batch Schedulers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485Schedulers in StepByStep — Part II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 487Scheduler Paths . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 489Adaptive vs Cycling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 492Putting It All Together. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 492Recap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495Chapter 7: Transfer Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 498Spoilers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 498
Page 2 and 3: Deep Learning with PyTorchStep-by-S
Page 4 and 5: "What I cannot create, I do not und
Page 6 and 7: Train-Validation-Test Split. . . .
Page 8 and 9: Random Split. . . . . . . . . . . .
Page 10 and 11: The Precision Quirk . . . . . . . .
Page 14 and 15: Jupyter Notebook . . . . . . . . .
Page 16 and 17: Stacked RNN . . . . . . . . . . . .
Page 18 and 19: Attention Mechanism . . . . . . . .
Page 20 and 21: 4. Positional Encoding. . . . . . .
Page 22 and 23: Model Configuration & Training . .
Page 24 and 25: AcknowledgementsFirst and foremost,
Page 26 and 27: Frequently Asked Questions (FAQ)Why
Page 28 and 29: There is yet another advantage of f
Page 30 and 31: • Classes and methods are written
Page 32 and 33: What’s Next?It’s time to set up
Page 34 and 35: After choosing a repository, it wil
Page 36 and 37: 1. AnacondaIf you don’t have Anac
Page 38 and 39: 3. PyTorchPyTorch is the coolest de
Page 40 and 41: (pytorchbook) C:\> conda install py
Page 42 and 43: (pytorchbook)$ pip install torchviz
Page 44 and 45: 7. JupyterAfter cloning the reposit
Page 46 and 47: Part IFundamentals| 21
Page 48 and 49: notebook. If not, just click on Cha
Page 50 and 51: Also, let’s say that, on average,
Page 52 and 53: There is one exception to the "alwa
Page 54 and 55: Random Initialization1 # Step 0 - I
Page 56 and 57: Batch, Mini-batch, and Stochastic G
Page 58 and 59: Outputarray([[-2. , -1.94, -1.88, .
Page 60 and 61: one matrix for each data point, eac
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Sure, different values of b produce
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Output-3.044811379650508 -1.8337537
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each parameter using the chain rule
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What’s the impact of one update o
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gradients, we know we need to take
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Very High Learning RateWait, it may
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true_b = 1true_w = 2N = 100# Data G
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Let’s look at the cross-sections
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Zero Mean and Unit Standard Deviati
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Sure, in the real world, you’ll n
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computing the loss, as shown in the
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• visualizing the effects of usin
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If you’re using Jupyter’s defau
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Notebook Cell 1.1 - Splitting synth
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Step 2# Step 2 - Computing the loss
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Output[0.49671415] [-0.1382643][0.8
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Notebook Cell 1.2 - Implementing gr
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# Sanity Check: do we get the same
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Outputtensor(3.1416)tensor([1, 2, 3
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Outputtensor([[1., 2., 1.],[1., 1.,
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dummy_array = np.array([1, 2, 3])du
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n_cudas = torch.cuda.device_count()
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back_to_numpy = x_train_tensor.nump
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I am assuming you’d like to use y
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Outputtensor([0.1940], device='cuda
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print(error.requires_grad, yhat.req
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Output(tensor([0.], device='cuda:0'
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56 # need to tell it to let it go..
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computation.If you chose "Local Ins
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Figure 1.6 - Now parameter "b" does
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There are many optimizers: SGD is t
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41 optimizer.zero_grad() 34243 prin
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Notebook Cell 1.8 - PyTorch’s los
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Outputarray(0.00804466, dtype=float
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Let’s build a proper (yet simple)
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"What do we need this for?"It turns
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1 Instantiating a model2 What IS th
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In the __init__() method, we create
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LayersA Linear model can be seen as
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There are MANY different layers tha
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We use magic, just like that:%run -
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• Step 1: compute model’s predi
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RecapFirst of all, congratulations
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Chapter 2Rethinking the Training Lo
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Let’s take a look at the code onc
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Higher-Order FunctionsAlthough this
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def exponentiation_builder(exponent
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Apart from returning the loss value
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Our code should look like this; see
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There is no need to load the whole
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but if we want to get serious about
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How does this change our code so fa
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Run - Model Training V2%run -i mode
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piece of code that’s going to be
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for it. We could do the same for th
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EvaluationHow can we evaluate the m
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And then, we update our model confi
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Run - Model Training V4%run -i mode
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Loading Extension# Load the TensorB
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browser, you’ll likely see someth
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model’s graph (not quite the same
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Figure 2.5 - Scalars on TensorBoard
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Define - Model Training V51 %%write
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If, by any chance, you ended up wit
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The procedure is exactly the same,
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soon, so please bear with me for no
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After recovering our model’s stat
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Run - Model Configuration V31 # %lo
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This is the general structure you
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Chapter 2.1Going ClassySpoilersIn t
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# A completely empty (and useless)
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# These attributes are defined here
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# Creates the train_step function f
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# Builds function that performs a s
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setattrThe setattr function sets th
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See? We effectively modified the un
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the random seed as arguments.This s
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The current state of development of
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Lossesdef plot_losses(self):fig = p
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Run - Data Preparation V21 # %load
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Model TrainingWe start by instantia
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Making PredictionsLet’s make up s
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OutputOrderedDict([('0.weight', ten
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Run - Data Preparation V21 # %load
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• defining our StepByStep class
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import numpy as npimport torchimpor
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Next, we’ll standardize the featu
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Equation 3.1 - A linear regression
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The odds ratio is given by the rati
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As expected, probabilities that add
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Sigmoid Functiondef sigmoid(z):retu
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A picture is worth a thousand words
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OutputOrderedDict([('linear.weight'
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The first summation adds up the err
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IMPORTANT: Make sure to pass the pr
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To make it clear: In this chapter,
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argument of nn.BCEWithLogitsLoss().
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It is not that hard, to be honest.
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Figure 3.6 - Training and validatio
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Outputarray([[0.5504593 ],[0.949995
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decision boundary.Look at the expre
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Are my data points separable?That
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model = nn.Sequential()model.add_mo
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It looks like this:Figure 3.10 - Sp
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True and False Positives and Negati
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tpr_fpr(cm_thresh50)Output(0.909090
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The trade-off between precision and
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Figure 3.13 - Using a low threshold
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Figure 3.16 - Trade-offs for two di
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thresholds do not necessarily inclu
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actual data, it is as bad as it can
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If you want to learn more about bot
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Model Training1 n_epochs = 10023 sb
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step in your journey! What’s next
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Chapter 4Classifying ImagesSpoilers
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Data GenerationOur images are quite
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Images and ChannelsIn case you’re
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image_rgb = np.stack([image_r, imag
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That’s fairly straightforward; we
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• Transformations based on Tensor
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position of an object in a picture
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Outputtensor([[[0., 0., 0., 1., 0.]
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Outputtensor([[[-1., -1., -1., 1.,
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We can convert the former into the
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composer = Compose([RandomHorizonta
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Output<torch.utils.data.dataset.Sub
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train_composer = Compose([RandomHor
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The minority class should have the
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train_loader = DataLoader(dataset=t
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implemented in Chapter 2.1? Let’s
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Let’s take one mini-batch of imag
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What does our model look like? Visu
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Model TrainingLet’s train our mod
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preceding hidden layer to compute i
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fig = sbs_nn.plot_losses()Figure 4.
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Equation 4.2 - Equivalence of deep
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w_nn_equiv = w_nn_output.mm(w_nn_hi
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Weights as PixelsDuring data prepar
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is only 0.25 (for z = 0) and that i
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nn.Tanh()(dummy_z)Outputtensor([-0.
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dummy_z = torch.tensor([-3., 0., 3.
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As you can see, in PyTorch the coef
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Figure 4.16 - Deep model (for real)
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Figure 4.18 - Losses (before and af
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Equation 4.3 - Activation functions
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Helper Function #41 def index_split
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Model Configuration1 # Sets learnin
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Bonus ChapterFeature SpaceThis chap
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Affine TransformationsAn affine tra
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Figure B.3 - Annotated model diagra
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Figure B.5 - In the beginning…But
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OK, now we can clearly see a differ
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In the model above, the sigmoid fun
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the more dimensions, the more separ
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import randomimport numpy as npfrom
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identity = np.array([[[[0, 0, 0],[0
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Figure 5.4 - Striding the image, on
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Output-----------------------------
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Outputtensor([[[[9., 5., 0., 7.],[0
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OutputParameter containing:tensor([
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Moreover, notice that if we were to
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In code, as usual, PyTorch gives us
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Outputtensor([[[[5., 5., 0., 8., 7.
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edge = np.array([[[[0, 1, 0],[1, -4
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A pooling kernel of two-by-two resu
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Outputtensor([[22., 23., 11., 24.,
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Figure 5.15 - LeNet-5 architectureS
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• second block: produces 16-chann
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Transformed Dataset1 class Transfor
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LossNew problem, new loss. Since we
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Outputtensor([4.0000, 1.0000, 0.500
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The loss only considers the predict
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Outputtensor([[-1.5229, -0.3146, -2
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IMPORTANT: I can’t stress this en
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figures at the beginning of this ch
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The three units in the output layer
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StepByStep Method@staticmethoddef _
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The meow() method is totally indepe
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StepByStep Methoddef visualize_filt
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dummy_model = nn.Linear(1, 1)dummy_
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dummy_listOutput[(Linear(in_feature
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Output{Conv2d(1, 1, kernel_size=(3,
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will be the externally defined vari
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Removing Hookssbs_cnn1.remove_hooks
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return figsetattr(StepByStep, 'visu
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Figure 5.22 - Feature maps (classif
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classification: The predicted class
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convolutional layers to our model a
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Capturing Outputsfeaturizer_layers
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the filters learned by the model pr
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given chapter are imported at its v
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Data PreparationThe data preparatio
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model anyway. We’ll use it to com
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StepByStep Method@staticmethoddef m
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"What’s wrong with the colors?"Th
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three_channel_filter = np.array([[[
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Fancier Model (Constructor)class CN
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Fancier Model (Classifier)def class
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torch.manual_seed(44)dropping_model
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Outputtensor([0.1000, 0.2000, 0.300
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Figure 6.8 - Output distribution fo
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Adaptive moment estimation (Adam) u
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torch.manual_seed(13)# Model Config
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Outputtorch.Size([5, 3, 3, 3])Its s
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Choosing a learning rate that works
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Higher-Order Learning Rate Function
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Perfect! Now let’s build the actu
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ax.set_xlabel('Learning Rate')ax.se
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LRFinderThe function we’ve implem
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value in our moving average has an
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Figure 6.15 - Distribution of weigh
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In code, the implementation of the
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As expected, the EWMA without corre
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optimizer = optim.Adam(model.parame
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IMPORTANT: The logging function mus
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Output{'state': {140601337662512: {
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different optimizer, set them to ca
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• dampening: dampening factor for
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Figure 6.20 - Paths taken by SGD (w
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Equation 6.16 - Looking aheadOnce N
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Figure 6.22 - Path taken by each SG
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for epoch in range(4):# training lo
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course) up to a given number of epo
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Next, we create a protected method
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Mini-Batch SchedulersThese schedule
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Schedulers in StepByStep — Part I
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Scheduler PathsBefore trying out a
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After applying each scheduler to SG
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Data Preparation1 # Loads temporary
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Figure 6.31 - LossesEvaluationprint
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[96] http://www.samkass.com/theorie
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ImportsFor the sake of organization
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ILSVRC-2012The 2012 edition [111] o
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remained unchanged.ResNet (MSRA Tea
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Transfer Learning in PracticeIn Cha
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dropout. You’re already familiar
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OutputDownloading: "https://downloa
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Replacing the "Top" of the Model1 a
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Model Size Classifier Layer(s) Repl
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Model TrainingWe have everything se
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"Removing" the Top Layer1 alex.clas
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torch.save(train_preproc.tensors, '
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Outputtensor([[109, 124],[124, 124]
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Model Configuration1 optimizer_mode
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Figure 7.4 - 1x1 convolutionThe inp
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The weights used by PIL are 0.299 f
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• reduce the number of output cha
Page 556 and 557:
The constructor method defines the
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Does it sound familiar? That’s wh
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and w to represent these parameters
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A mini-batch of size 64 is small en
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normed1 = batch_normalizer(batch1[0
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OutputOrderedDict([('running_mean',
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OutputOrderedDict([('running_mean',
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batch_normalizer = nn.BatchNorm2d(n
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torch.manual_seed(23)dummy_points =
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np.concatenate([dummy_points[:5].nu
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Another advantage of these shortcut
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It should be pretty clear, except f
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Data Preparation1 # ImageNet statis
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Data Preparation — Preprocessing1
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• freezing the layers of the mode
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Extra ChapterVanishing and Explodin
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discussing it, let me illustrate it
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Model Configuration (2)1 loss_fn =
Page 592 and 593:
weights. If done properly, the init
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just did), or, if you are training
Page 596 and 597:
Figure E.3 - The effect of batch no
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Model Configuration1 torch.manual_s
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torch.manual_seed(42)parm = nn.Para
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(and only if) the norm exceeds the
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if callable(self.clipping): 1self.c
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Moreover, let’s use a ten times h
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Clipping with HooksFirst, we reset
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• visualizing the difference betw
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Chapter 8SequencesSpoilersIn this c
Page 614 and 615:
Before shuffling, the pixels were o
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And then let’s visualize the firs
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sequence so far, and a data point f
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Considering this, the not "unrolled
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linear_input = nn.Linear(n_features
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Outputtensor([[0.3924, 0.8146]], gr
Page 626 and 627:
Now we’re talking! The last hidde
Page 628 and 629:
Let’s take a look at the RNN’s
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◦ The initial hidden state, which
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batch_first argument to True so we
Page 634 and 635:
OutputOrderedDict([('weight_ih_l0',
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out, hidden = rnn_stacked(x)out, hi
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_l0_reverse).Once again, let’s cr
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For bidirectional RNNs, the last el
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Model Configuration1 class SquareMo
Page 644 and 645:
StepByStep.loader_apply(test_loader
Page 646 and 647:
Figure 8.14 - Final hidden states f
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Figure 8.16 - Transforming the hidd
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Since the RNN cell has both of them
Page 652 and 653:
Every gate worthy of its name will
Page 654 and 655:
• For r=0 and z=0, the cell becom
Page 656 and 657:
In code, we can use split() to get
Page 658 and 659:
Let’s pause for a moment here. Fi
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Square Model II — The QuickeningT
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Outputtensor([[53, 53],[75, 75]])Th
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Figure 8.22 - Transforming the hidd
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Equation 8.9 - LSTM—candidate hid
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Now, let’s visualize the internal
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OutputOrderedDict([('weight_ih', te
Page 672 and 673:
def forget_gate(h, x):thf = f_hidde
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Outputtensor([[-5.4936e-02, -8.3816
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1 First change: from RNN to LSTM2 S
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Like the GRU, the LSTM presents fou
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Output-----------------------------
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Before moving on to packed sequence
Page 684 and 685:
column-wise fashion, from top to bo
Page 686 and 687:
does match the last output.• No,
Page 688 and 689:
So, to actually get the last output
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Data Preparation1 class CustomDatas
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OutputPackedSequence(data=tensor([[
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Model Configuration & TrainingWe ca
Page 696 and 697:
size = 5weight = torch.ones(size) *
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torch.manual_seed(17)conv_seq = nn.
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Figure 8.32 - Applying dilated filt
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Model Configuration1 torch.manual_s
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We can actually find an expression
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Data Preparation1 def pack_collate(
Page 708 and 709:
and variable-length sequences.Model
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• generating variable-length sequ
Page 712 and 713:
import copyimport numpy as npimport
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Figure 9.3 - Sequence datasetThe co
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coordinates of a "perfect" square a
Page 718 and 719:
Let’s pretend for a moment that t
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to initialize the hidden state and
Page 722 and 723:
predictions in previous steps have
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the second set of predicted coordin
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Let’s create an instance of the m
Page 728 and 729:
Model Configuration & TrainingThe m
Page 730 and 731:
Sure, we can!AttentionHere is a (no
Page 732 and 733:
based on "the" and "zone," I’ve j
Page 734 and 735:
Figure 9.12 - Matching a query to t
Page 736 and 737:
Outputtensor([[[ 0.0832, -0.0356],[
Page 738 and 739:
utmost importance for the correct i
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Its formula is:Equation 9.3 - Cosin
Page 742 and 743:
second hidden state contributes to
Page 744 and 745:
Outputtensor([[[ 0.5475, 0.0875, -1
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alphas = F.softmax(scaled_products,
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Outputtensor([[[ 0.2138, -0.3175]]]
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Attention Mechanism1 class Attentio
Page 752 and 753:
"Why would I want to force it to do
Page 754 and 755:
1 Sets attention module and adjusts
Page 756 and 757:
encdec = EncoderDecoder(encoder, de
Page 758 and 759:
fig = sbs_seq_attn.plot_losses()Fig
Page 760 and 761:
Figure 9.20 - Attention scoresSee?
Page 762 and 763:
Wide vs Narrow AttentionThis mechan
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"What’s so special about it?"Even
Page 766 and 767:
Once again, the affine transformati
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Next, we shift our focus to the sel
Page 770 and 771:
Encoder + Self-Attention1 class Enc
Page 772 and 773:
Figure 9.27 - Encoder with self- an
Page 774 and 775:
The figure below depicts the self-a
Page 776 and 777:
shifted_seq = torch.cat([source_seq
Page 778 and 779:
Equation 9.17 - Decoder’s (masked
Page 780 and 781:
At evaluation / prediction time we
Page 782 and 783:
Outputtensor([[[0.4132, 0.3728],[0.
Page 784 and 785:
Figure 9.33 - Encoder + decoder + a
Page 786 and 787:
64 return outputsThe encoder-decode
Page 788 and 789:
Figure 9.34 - Losses—encoder + de
Page 790 and 791:
curse. On the one hand, it makes co
Page 792 and 793:
"Are we done now? Is this good enou
Page 794 and 795:
Figure 9.46 - Consistent distancesA
Page 796 and 797:
Let’s recap what we’ve already
Page 798 and 799:
Let’s see it in code:max_len = 10
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Let’s put it all together into a
Page 802 and 803:
Outputtensor([[[-1.0000, 0.0000],[-
Page 804 and 805:
Decoder with Positional Encoding1 c
Page 806 and 807:
Visualizing PredictionsLet’s plot
Page 808 and 809:
Next, we’re moving on to the thre
Page 810 and 811:
Data Generation & Preparation1 # Tr
Page 812 and 813:
59 self.trg_masks)60 else:61 # Deco
Page 814 and 815:
Model Configuration1 class EncoderS
Page 816 and 817:
1617 @property18 def alphas(self):1
Page 818 and 819:
Output(0.016193246061448008, 0.0341
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sequential order of the data• fig
Page 822 and 823:
following imports:import copyimport
Page 824 and 825:
Figure 10.2 - Chunking: the wrong a
Page 826 and 827:
chunks to compute the other half of
Page 828 and 829:
67 # N, L, n_heads, d_k68 context =
Page 830 and 831:
dummy_points = torch.randn(16, 2, 4
Page 832 and 833:
Stacking Encoders and DecodersLet
Page 834 and 835:
"… with great depth comes great c
Page 836 and 837:
Transformer EncoderWe’ll be repre
Page 838 and 839:
Let’s see it in code, starting wi
Page 840 and 841:
Transformer Encoder1 class EncoderT
Page 842 and 843:
of the encoder-decoder (or Transfor
Page 844 and 845:
In PyTorch, the decoder "layer" is
Page 846 and 847:
In PyTorch, the decoder is implemen
Page 848 and 849:
Equation 10.7 - Data points' means
Page 850 and 851:
layer_norm = nn.LayerNorm(d_model)n
Page 852 and 853:
Figure 10.10 - Layer norm vs batch
Page 854 and 855:
Outputtensor([[[ 1.4636, 2.3663],[
Page 856 and 857:
The TransformerLet’s start with t
Page 858 and 859:
"values") in the decoder.• decode
Page 860 and 861:
Data Preparation1 # Generating trai
Page 862 and 863:
Figure 10.15 - Losses—Transformer
Page 864 and 865:
• First, and most important, PyTo
Page 866 and 867:
decode(), with a single one, encode
Page 868 and 869:
46 for i in range(self.target_len):
Page 870 and 871:
Figure 10.18 - Losses - PyTorch’s
Page 872 and 873:
Figure 10.20 - Sample image—label
Page 874 and 875:
4041 # Builds a weighted random sam
Page 876 and 877:
Figure 10.23 - Sample image—split
Page 878 and 879:
Einops"There is more than one way t
Page 880 and 881:
Figure 10.26 - Two patch embeddings
Page 882 and 883:
Now each sequence has ten elements,
Page 884 and 885:
It takes an instance of a Transform
Page 886 and 887:
Putting It All TogetherIn this chap
Page 888 and 889:
1. Encoder-DecoderThe encoder-decod
Page 890 and 891:
This is the actual encoder-decoder
Page 892 and 893:
3. DecoderThe Transformer decoder h
Page 894 and 895:
5. Encoder "Layer"The encoder "laye
Page 896 and 897:
7. "Sub-Layer" WrapperThe "sub-laye
Page 898 and 899:
8. Multi-Headed AttentionThe multi-
Page 900 and 901:
Model Configuration & TrainingModel
Page 902 and 903:
• training the Transformer to tac
Page 904 and 905:
Part IVNatural Language Processing|
Page 906 and 907:
Additional SetupThis is a special c
Page 908 and 909:
"Down the Yellow Brick Rabbit Hole"
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The actual texts of the books are c
Page 912 and 913:
"What is this punkt?"That’s the P
Page 914 and 915:
14 # If there is a configuration fi
Page 916 and 917:
Sentence Tokenization in spaCyBy th
Page 918 and 919:
AttributesThe Dataset has many attr
Page 920 and 921:
Output{'labels': 1,'sentence': 'The
Page 922 and 923:
elements from the text. But preproc
Page 924 and 925:
Data AugmentationLet’s briefly ad
Page 926 and 927:
The corpora’s dictionary is not a
Page 928 and 929:
Finally, if we want to convert a li
Page 930 and 931:
Once we’re happy with the size an
Page 932 and 933:
from transformers import BertTokeni
Page 934 and 935:
"What about the separation token?"T
Page 936 and 937:
The last output, attention_mask, wo
Page 938 and 939:
Outputtensor([[ 3, 27, 1, ..., 0, 0
Page 940 and 941:
vector, right? And our vocabulary i
Page 942 and 943:
Maybe you filled this blank in with
Page 944 and 945:
Continuous Bag-of-Words (CBoW)In th
Page 946 and 947:
That’s a fairly simple model, rig
Page 948 and 949:
Figure 11.13 - Continuous bag-of-wo
Page 950 and 951:
Figure 11.15 - Reviewing restaurant
Page 952 and 953:
You got that right—arithmetic—r
Page 954 and 955:
There we go, 50 dimensions! It’s
Page 956 and 957:
Equation 11.1 - Embedding arithmeti
Page 958 and 959:
Only 82 out of 50,802 words in the
Page 960 and 961:
Now we can use its encode() method
Page 962 and 963:
Model I — GloVE + ClassifierData
Page 964 and 965:
Pre-trained PyTorch EmbeddingsThe e
Page 966 and 967:
Model Configuration & TrainingLet
Page 968 and 969:
6 self.encoder = encoder7 self.mlp
Page 970 and 971:
Figure 11.20 - Losses—Transformer
Page 972 and 973:
Outputtensor([[[2.6334e-01, 6.9912e
Page 974 and 975:
I want to introduce you to…ELMoBo
Page 976 and 977:
OutputToken: 32 watchThe get_token(
Page 978 and 979:
Helper Function to Retrieve Embeddi
Page 980 and 981:
Output(tensor(-0.5047, device='cuda
Page 982 and 983:
torch.all(new_flair_sentences[0].to
Page 984 and 985:
Outputtensor(0.3504, device='cuda:0
Page 986 and 987:
We can leverage this fact to slight
Page 988 and 989:
We can easily get the embeddings fo
Page 990 and 991:
Figure 11.24 - Losses—simple clas
Page 992 and 993:
We can inspect the pre-trained mode
Page 994 and 995:
Every word piece is prefixed with #
Page 996 and 997:
far, our models used these embeddin
Page 998 and 999:
position_ids = torch.arange(512).ex
Page 1000 and 1001:
Pre-training TasksMasked Language M
Page 1002 and 1003:
Then, let’s create an instance of
Page 1004 and 1005:
If these two sentences were the inp
Page 1006 and 1007:
The BERT model may take many other
Page 1008 and 1009:
The contextual word embeddings are
Page 1010 and 1011:
Model Configuration1 class BERTClas
Page 1012 and 1013:
"Which BERT is that? DistilBERT?!"D
Page 1014 and 1015:
Well, you probably don’t want to
Page 1016 and 1017:
set num_labels=1 as argument.If you
Page 1018 and 1019:
Output{'attention_mask': [1, 1, 1,
Page 1020 and 1021:
OutputTrainingArguments(output_dir=
Page 1022 and 1023:
Method for Computing Accuracy1 def
Page 1024 and 1025:
loaded_model = (AutoModelForSequenc
Page 1026 and 1027:
logits.logits.argmax(dim=1)Outputte
Page 1028 and 1029:
For a complete list of available ta
Page 1030 and 1031:
[215]. For a demo of GPT-2’s capa
Page 1032 and 1033:
in Chapter 9, and I reproduce it be
Page 1034 and 1035:
Data Preparation1 auto_tokenizer =
Page 1036 and 1037:
Data Preparation1 lm_train_dataset
Page 1038 and 1039:
The training arguments are roughly
Page 1040 and 1041:
device_index = (model.device.indexi
Page 1042 and 1043:
• learning that a language model
Page 1044 and 1045:
[167] https://huggingface.co/docs/d
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