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

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There we go, 50 dimensions! It’s time to try the famous "equation": KING - MAN +WOMAN = QUEEN. We’re calling the result a "synthetic queen":synthetic_queen = glove['king'] - glove['man'] + glove['woman']These are the corresponding embeddings:fig = plot_word_vectors(glove, ['king', 'man', 'woman', 'synthetic', 'queen'],other={'synthetic': synthetic_queen})Figure 11.17 - Synthetic queenHow similar is the "synthetic queen" to the actual "queen," you ask. It’s hard to tellby looking at the vectors above alone, but Gensim’s word vectors have asimilar_by_vector() method that computes cosine similarity between a givenvector and the whole vocabulary and returns the top N most similar words:glove.similar_by_vector(synthetic_queen, topn=5)Output[('king', 0.8859835863113403),('queen', 0.8609581589698792),('daughter', 0.7684512138366699),('prince', 0.7640699148178101),('throne', 0.7634971141815186)]"The most similar word to the 'synthetic queen' is … king?"Yes. It’s not always the case, but it’s fairly common to find out that, after performingWord Embeddings | 929
word embedding arithmetic, the word most similar to the result is the originalword itself. For this reason, it’s usual to exclude the original word from thesimilarity results. In this case, the most similar word to the "synthetic queen" is,indeed, the actual "queen.""OK, cool, but how does this arithmetic work?"The general idea is that the embeddings learned to encode abstract dimensions,like "gender," "royalty," "genealogy," or "profession." None of these abstractdimensions corresponds to a single numerical dimension, though.In its large 50-dimensional feature space, the model learned to place "man" as farapart from "woman" as "king" is from "queen" (roughly approximating the genderdifference between the two). Similarly, the model learned to place "king" as farapart from "man" as "queen" is from "woman" (roughly approximating the differenceof being a royal). The figure below depicts a hypothetical projection in twodimensions for easier visualization:Figure 11.18 - Projection of embeddingsFrom the figure above, it should be relatively clear that both arrows pointing up(blue) are approximately the same size, thus resulting in the equation below:930 | Chapter 11: Down the Yellow Brick Rabbit Hole
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Deep Learning with PyTorchStep-by-S
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"What I cannot create, I do not und
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Train-Validation-Test Split. . . .
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Random Split. . . . . . . . . . . .
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The Precision Quirk . . . . . . . .
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A REAL Filter . . . . . . . . . . .
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Jupyter Notebook . . . . . . . . .
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Stacked RNN . . . . . . . . . . . .
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Attention Mechanism . . . . . . . .
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4. Positional Encoding. . . . . . .
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Model Configuration & Training . .
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AcknowledgementsFirst and foremost,
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Frequently Asked Questions (FAQ)Why
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There is yet another advantage of f
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• Classes and methods are written
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What’s Next?It’s time to set up
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After choosing a repository, it wil
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1. AnacondaIf you don’t have Anac
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3. PyTorchPyTorch is the coolest de
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(pytorchbook) C:\> conda install py
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(pytorchbook)$ pip install torchviz
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7. JupyterAfter cloning the reposit
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Part IFundamentals| 21
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notebook. If not, just click on Cha
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Also, let’s say that, on average,
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There is one exception to the "alwa
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Random Initialization1 # Step 0 - I
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Batch, Mini-batch, and Stochastic G
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Outputarray([[-2. , -1.94, -1.88, .
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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
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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 =
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weights. If done properly, the init
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just did), or, if you are training
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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
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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
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Now we’re talking! The last hidde
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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
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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
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StepByStep.loader_apply(test_loader
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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
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Every gate worthy of its name will
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• For r=0 and z=0, the cell becom
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In code, we can use split() to get
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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
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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
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column-wise fashion, from top to bo
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does match the last output.• No,
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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
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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(
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and variable-length sequences.Model
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• generating variable-length sequ
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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
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Let’s pretend for a moment that t
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to initialize the hidden state and
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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
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Model Configuration & TrainingThe m
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Sure, we can!AttentionHere is a (no
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based on "the" and "zone," I’ve j
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Figure 9.12 - Matching a query to t
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Outputtensor([[[ 0.0832, -0.0356],[
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utmost importance for the correct i
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Its formula is:Equation 9.3 - Cosin
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second hidden state contributes to
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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
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"Why would I want to force it to do
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1 Sets attention module and adjusts
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encdec = EncoderDecoder(encoder, de
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fig = sbs_seq_attn.plot_losses()Fig
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Figure 9.20 - Attention scoresSee?
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Wide vs Narrow AttentionThis mechan
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"What’s so special about it?"Even
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Once again, the affine transformati
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Next, we shift our focus to the sel
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Encoder + Self-Attention1 class Enc
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Figure 9.27 - Encoder with self- an
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The figure below depicts the self-a
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shifted_seq = torch.cat([source_seq
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Equation 9.17 - Decoder’s (masked
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At evaluation / prediction time we
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Outputtensor([[[0.4132, 0.3728],[0.
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Figure 9.33 - Encoder + decoder + a
Page 786 and 787:
64 return outputsThe encoder-decode
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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
Page 800 and 801:
Let’s put it all together into a
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Outputtensor([[[-1.0000, 0.0000],[-
Page 804 and 805:
Decoder with Positional Encoding1 c
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Visualizing PredictionsLet’s plot
Page 808 and 809:
Next, we’re moving on to the thre
Page 810 and 811:
Data Generation & Preparation1 # Tr
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59 self.trg_masks)60 else:61 # Deco
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Model Configuration1 class EncoderS
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1617 @property18 def alphas(self):1
Page 818 and 819:
Output(0.016193246061448008, 0.0341
Page 820 and 821:
sequential order of the data• fig
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following imports:import copyimport
Page 824 and 825:
Figure 10.2 - Chunking: the wrong a
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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
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layer_norm = nn.LayerNorm(d_model)n
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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
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46 for i in range(self.target_len):
Page 870 and 871:
Figure 10.18 - Losses - PyTorch’s
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Figure 10.20 - Sample image—label
Page 874 and 875:
4041 # Builds a weighted random sam
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Figure 10.23 - Sample image—split
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Einops"There is more than one way t
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Figure 10.26 - Two patch embeddings
Page 882 and 883:
Now each sequence has ten elements,
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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
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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"
Page 910 and 911: 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 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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