The lecture content, including references to study materials. The main study material is the Deep Learning Book by Ian Goodfellow, Yoshua Bengio and Aaron Courville, (referred to as DLB).

References to study materials cover all theory required at the exam, and sometimes even more – the references in italics cover topics not required for the exam.

1. Introduction to Deep Learning

 Feb 24 Slides PDF Slides 2018 Video numpy_entropy pca_first mnist_layers_activations

• Random variables, probability distributions, expectation, variance, Bernoulli distribution, Categorical distribution [Sections 3.2, 3.3, 3.8, 3.9.1 and 3.9.2 of DLB]
• Self-information, entropy, cross-entropy, KL-divergence [Section 3.13 of DBL]
• Gaussian distribution [Section 3.9.3 of DLB]
• Machine Learning Basics [Section 5.1-5.1.3 of DLB]
• History of Deep Learning [Section 1.2 of DLB]
• Linear regression [Section 5.1.4 of DLB]
• Brief description of Logistic Regression, Maximum Entropy models and SVM [Sections 5.7.1 and 5.7.2 of DLB]
• Challenges Motivating Deep Learning [Section 5.11 of DLB]
• Neural network basics
  • Neural networks as graphs [Chapter 6 before Section 6.1 of DLB]
  • Output activation functions [Section 6.2.2 of DLB, excluding Section 6.2.2.4]
  • Hidden activation functions [Section 6.3 of DLB, excluding Section 6.3.3]
  • Basic network architectures [Section 6.4 of DLB, excluding Section 6.4.2]

2. Training Neural Networks

 Mar 2 Slides PDF Slides 2018 Video Questions sgd_backpropagation sgd_manual mnist_training gym_cartpole

• Capacity, overfitting, underfitting, regularization [Section 5.2 of DLB]
• Hyperparameters and validation sets [Section 5.3 of DLB]
• Maximum Likelihood Estimation [Section 5.5 of DLB]
• Neural network training
  • Gradient Descent and Stochastic Gradient Descent [Sections 4.3 and 5.9 of DLB]
  • Backpropagation algorithm [Section 6.5 to 6.5.3 of DLB, especially Algorithms 6.1 and 6.2; note that Algorithms 6.5 and 6.6 are used in practice]
  • SGD algorithm [Section 8.3.1 and Algorithm 8.1 of DLB]
  • SGD with Momentum algorithm [Section 8.3.2 and Algorithm 8.2 of DLB]
  • SGD with Nestorov Momentum algorithm [Section 8.3.3 and Algorithm 8.3 of DLB]
  • Optimization algorithms with adaptive gradients
    • AdaGrad algorithm [Section 8.5.1 and Algorithm 8.4 of DLB]
    • RMSProp algorithm [Section 8.5.2 and Algorithm 8.5 of DLB]
    • Adam algorithm [Section 8.5.3 and Algorithm 8.7 of DLB]

3. Training Neural Networks II

 Mar 9 Slides PDF Slides 2018 Video Questions explore_examples mnist_regularization mnist_ensemble uppercase

• Training neural network with a single hidden layer
• Softmax with NLL (negative log likelihood) as a loss function [Section 6.2.2.3 of DLB, notably equation (6.30); plus slides 10-12]
• Regularization [Chapter 7 until Section 7.1 of DLB]
  • Early stopping [Section 7.8 of DLB, without the How early stopping acts as a regularizer part]
  • L2 and L1 regularization [Sections 7.1 and 5.6.1 of DLB; plus slides 17-18]
  • Dataset augmentation [Section 7.4 of DLB]
  • Ensembling [Section 7.11 of DLB]
  • Dropout [Section 7.12 of DLB]
  • Label smoothing [Section 7.5.1 of DLB]
• Saturating non-linearities [Section 6.3.2 and second half of Section 6.2.2.2 of DLB]
• Parameter initialization strategies [Section 8.4 of DLB]
• Gradient clipping [Section 10.11.1 of DLB]

4. Convolutional Neural Networks

 Mar 23 Slides PDF Slides Video 2018 Video Questions mnist_cnn image_augmentation tf_dataset cifar_competition

• Introduction to convolutional networks [Chapter 9 and Sections 9.1-9.3 of DLB]
• Convolution as operation on 4D tensors [Section 9.5 of DLB, notably Equations (9.7) and (9.8)]
• Max pooling and average pooling [Section 9.3 of DLB]
• Stride and Padding schemes [Section 9.5 of DLB]
• AlexNet [ImageNet Classification with Deep Convolutional Neural Networks]
• VGG [Very Deep Convolutional Networks for Large-Scale Image Recognition]
• GoogLeNet (aka Inception) [Going Deeper with Convolutions]
• Batch normalization [Section 8.7.1 of DLB, optionally the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift]
• Inception v2 and v3 [Rethinking the Inception Architecture for Computer Vision]
• ResNet [Deep Residual Learning for Image Recognition]

5. Convolutional Neural Networks II

 Mar 30 Slides PDF Slides Video 2018 Video Questions mnist_web cags_classification cags_segmentation

• Residual CNN Networks
  • ResNet [Deep Residual Learning for Image Recognition]
  • WideNet [Wide Residual Network]
  • DenseNet [Densely Connected Convolutional Networks]
  • PyramidNet [Deep Pyramidal Residual Networks]
  • ResNeXt [Aggregated Residual Transformations for Deep Neural Networks]
• Regularizing CNN Networks
  • DropLayer [Deep Networks with Stochastic Depth]
  • CutOut [Improved Regularization of Convolutional Neural Networks with Cutout]
  • DropBlock [DropBlock: A regularization method for convolutional networks]
• SENet [Squeeze-and-Excitation Networks]
• EfficientNet [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks]

6. Object Detection

 Apr 06 Slides PDF Slides Video 2018 Video I 2018 Video II Questions cnn_manual bboxes_utils svhn_competition

• Fast R-CNN [Fast R-CNN]
• Proposing RoIs using Faster R-CNN [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks]
• Mask R-CNN [Mask R-CNN]
• Feature Pyramid Networks [Feature Pyramid Networks for Object Detection]
• Focal Loss, RetinaNet [Focal Loss for Dense Object Detection]
• EfficientDet [EfficientDet: Scalable and Efficient Object Detection]
• Group Normalization [Group Normalization]

7. Recurrent Neural Networks

 Apr 14 Slides PDF Slides Video Video – Practicals 2018 Video I 2018 Video II Questions 3d_recognition sequence_classification tagger_we

• Sequence modelling using Recurrent Neural Networks (RNN) [Chapter 10 until Section 10.2.1 (excluding) of DLB]
• The challenge of long-term dependencies [Section 10.7 of DLB]
• Long Short-Term Memory (LSTM) [Section 10.10.1 of DLB, Sepp Hochreiter, Jürgen Schmidhuber (1997): Long short-term memory, Felix A. Gers, Jürgen Schmidhuber, Fred Cummins (2000): Learning to Forget: Continual Prediction with LSTM]
• Gated Recurrent Unit (GRU) [Section 10.10.2 of DLB, Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, Yoshua Bengio: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation]
• Highway Networks [Training Very Deep Networks]
• RNN Regularization
  • Variational Dropout [A Theoretically Grounded Application of Dropout in Recurrent Neural Networks]
  • Layer Normalization [Layer Normalization]
• Bidirectional RNN [Section 10.3 of DLB]
• Word Embeddings [Section 14.2.4 of DLB]

8. Word Embeddings, CRF, CTC

 Apr 20 Slides PDF Slides Video Video – Practicals 2018 Video I 2018 Video II Questions mnist_multiple tagger_cle_rnn tagger_cle_cnn tagger_competition speech_recognition

• Character-level embeddings using Recurrent neural networks [C2W model from Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation]
• Character-level embeddings using Convolutional neural networks [CharCNN from Character-Aware Neural Language Models]
• Conditional Random Fields (CRF) loss [Sections 3.4.2 and A.7 of Natural Language Processing (Almost) from Scratch]
• Connectionist Temporal Classification (CTC) loss [Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks]

9. Word2Vec, Seq2seq, NMT

 Apr 27 Slides PDF Slides Video Video – Practicals 2018 Video I 2018 Video II Questions tensorboard_projector lemmatizer_noattn lemmatizer_attn lemmatizer_competition

• Word2vec word embeddings, notably the CBOW and Skip-gram architectures [Efficient Estimation of Word Representations in Vector Space]
  • Hierarchical softmax [Section 12.4.3.2 of DLB or Distributed Representations of Words and Phrases and their Compositionality]
  • Negative sampling Distributed Representations of Words and Phrases and their Compositionality]
• Character-level embeddings using character n-grams [Described simultaneously in several papers as Charagram (Charagram: Embedding Words and Sentences via Character n-grams), Subword Information (Enriching Word Vectors with Subword Information or SubGram (SubGram: Extending Skip-Gram Word Representation with Substrings)]
• Neural Machine Translation using Encoder-Decoder or Sequence-to-Sequence architecture [Section 12.5.4 of DLB, Ilya Sutskever, Oriol Vinyals, Quoc V. Le: Sequence to Sequence Learning with Neural Networks and Kyunghyun Cho et al.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation]
• Using Attention mechanism in Neural Machine Translation [Section 12.4.5.1 of DLB, Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio: Neural Machine Translation by Jointly Learning to Align and Translate]
• Translating Subword Units [Rico Sennrich, Barry Haddow, Alexandra Birch: Neural Machine Translation of Rare Words with Subword Units]
• Google NMT [Yonghui Wu et al.: Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation]

10. Deep Generative Models

 May 04 Slides PDF Slides Video Video – Practicals 2018 Video Questions vae gan dcgan

• Autoencoders (undercomplete, sparse, denoising) [Chapter 14, Sections 14-14.2.3 of DLB]
• Deep Generative Models using Differentiable Generator Nets [Section 20.10.2 of DLB]
• Variational Autoencoders [Section 20.10.3 plus Reparametrization trick from Section 20.9 (but not Section 20.9.1) of DLB, Auto-Encoding Variational Bayes]
• Generative Adversarial Networks
  • GAN [Section 20.10.4 of DLB, Generative Adversarial Networks]
  • CGAN [Conditional Generative Adversarial Nets]
  • DCGAN [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks]
  • WGAN [Wasserstein GAN]
  • BigGAN [Large Scale Gan Training for High Fidelity Natural Image Synthesis]

11. Introduction to Deep Reinforcement Learning

 May 11 Slides PDF Slides Video Video – Practicals 2018 Video I 2018 Video II Questions omr_competition monte_carlo reinforce reinforce_baseline reinforce_pixels

Study material for Reinforcement Learning is the Reinforcement Learning: An Introduction; second edition by Richard S. Sutton and Andrew G. Barto (reffered to as RLB), available online.

• Multi-armed bandits [Sections 2-2.4 of RLB]
• Markov Decision Process [Sections 3-3.3 of RLB]
• Policies and Value Functions [Sections 3.5 of RLB]
• Monte Carlo Methods [Sections 5-5.4 of RLB]
• Policy Gradient Methods [Sections 13-13.1 of RLB]
• Policy Gradient Theorem [Section 13.2 of RLB]
• REINFORCE algorithm [Section 13.3 of RLB]
• REINFORCE with baseline algorithm [Section 13.4 of RLB]

12. Speech Synthesis, External Memory Networks

 May 18 Slides PDF Slides Video Video – Practicals 2018 Video Questions

• NasNet [Learning Transferable Architectures for Scalable Image Recognition]
• WaveNet [WaveNet: A Generative Model for Raw Audio]
• Parallel WaveNet [Parallel WaveNet: Fast High-Fidelity Speech Synthesis]
• Full speech synthesis pipeline Tacotron 2 [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions]
• Neural Turing Machine [Neural Turing Machines]
• Differenciable Neural Computer [Hybrid computing using a neural network with dynamic external memory]
• Memory Augmented Neural Networks [One-shot learning with Memory-Augmented Neural Networks]

13. Transformer, BERT

 May 25 Slides PDF Slides Video Questions sentiment_analysis

• Transformer architecture [Attention Is All You Need]
• BERT [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding]
• RoBERTa [RoBERTa: A Robustly Optimized BERT Pretraining Approach]
• ALBERT [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations]

Requirements

To pass the practicals, you need to obtain at least 80 points, excluding the bonus points. Note that up to 40 points above 80 (including the bonus points) will be transfered to the exam.

Environment

The tasks are evaluated automatically using the ReCodEx Code Examiner. The evaluation is performed using Python 3.6, TensorFlow 2.1.0, TensorFlow Addons 0.8.1, TensorFlow Hub 0.7.0, TensorFlow Probability 0.9.0, OpenAI Gym 0.15.4 and NumPy 1.18.1.

Installing to Central User Packages Repository

You can install all required packages to central user packages repository using pip3 install --user --upgrade pip setuptools followed by pip3 install --user tensorflow==2.1.0 tensorflow-addons==0.8.1 tensorflow-hub==0.7.0 tensorflow-probability==0.9.0 gym==0.15.4.

Installing to a Virtual Environment

Python supports virtual environments, which are directories containing independent sets of installed packages. You can create the virtual environment by running python3 -m venv VENV_DIR followed by VENV_DIR/bin/pip3 install --upgrade pip setuptools and VENV_DIR/bin/pip3 install tensorflow==2.1.0 tensorflow-addons==0.8.1 tensorflow-hub==0.7.0 tensorflow-probability==0.9.0 gym==0.15.4.

Problems With the Environment

Windows TensorFlow Fails with ImportError: DLL load failed

If your Windows TensorFlow fails with ImportError: DLL load failed, you are probably missing Visual C++ 2019 Redistributable.

Cannot Start tensorboard

If tensorboard cannot be found, make sure the directory with pip installed packages is in your PATH (that directory is either in your virtual environment if you use a virtual environment, or it should be ~/.local/bin on Linux and %UserProfile%\AppData\Roaming\Python\Python3[5-7] and %UserProfile%\AppData\Roaming\Python\Python3[5-7]\Scripts on Windows).

On Windows, tensorboard Shows a Blank Page

Some programs (even VS and VS code) erroneously change Windows system-wide MIME type of Javascript files to text/plain, which causes problems for tensorboard. If you encounter the issue, the easiest is to uninstall tensorboard (pip3 uninstall tensorboard) and then install a development version (pip3 install [--user] tb-nightly) which contains a fix. The development version is then started exactly as a stable one using a tensorboard command.

Warning About Missing libnvinfer, libnvinfer_plugin and TensorRT

TensorFlow 2.1 eagerly checks for availability of TensorRT during the first import tensorflow. In case you do not have it, a three-line warning is printed. You can safely ignore the warning, both the CPU and the GPU backends work without TensorRT.

Tunnelling Tensorboard in Deepnote

To access Tensorboard in DeepNote, first make sure you have labs/deepnote_ngrok – if you do not, run labs/deepnote_ngrok_get script to download it. Then start Tensorboard and finally run labs/deepnote_ngrok 6006 (or a different port on which you started tensorboard) to get a public URL you can open in another browser tab to access Tensorboard.

Teamwork

Solving assignments in teams of size 2 or 3 is encouraged, but everyone has to participate (it is forbidden not to work on an assignment and then submit a solution created by other team members). All members of the team must submit in ReCodEx individually, but can have exactly the same sources/models/results. Each such solution must explicitly list all members of the team to allow plagiarism detection using this template.

numpy_entropy

 Deadline: Mar 8, 23:59  3 points  Examples

The goal of this exercise is to familiarize with Python, NumPy and ReCodEx submission system. Start with the numpy_entropy.py.

Load a file numpy_entropy_data.txt, whose lines consist of data points of our dataset, and load numpy_entropy_model.txt, which describes a model probability distribution, with each line being a tab-separated pair of (data point, probability).

Then compute the following quantities using NumPy, and print them each on a separate line rounded on two decimal places (or inf for positive infinity, which happens when an element of data distribution has zero probability under the model distribution):

• entropy H(data distribution)
• cross-entropy H(data distribution, model distribution)
• KL-divergence D_KL(data distribution, model distribution)

Use natural logarithms to compute the entropies and the divergence.

A
BB
A
A
BB
A
CCC

A	0.5
BB	0.3
CCC	0.1
D	0.1

0.96
1.07
0.11

0.96
inf
inf

pca_first

 Deadline: Mar 8, 23:59  2 points  Examples

The goal of this exercise is to familiarize with TensorFlow tf.Tensors, shapes and basic tensor manipulation methods. Start with the pca_first.py.

In this assignment, you will compute the covariance matrix of several examples from the MNIST dataset, compute the first principal component and quantify the explained variance of it.

It is fine if you are not familiar with terms like covariance matrix or principal component – the template contains a detailed description of what you have to do.

mnist_layers_activations

 Deadline: Mar 8, 23:59  2 points  Examples

Before solving the assignment, start by playing with example_keras_tensorboard.py, in order to familiarize with TensorFlow and TensorBoard. Run it, and when it finishes, run TensorBoard using tensorboard --logdir logs. Then open http://localhost:6006 in a browser and explore the active tabs.

Your goal is to modify the mnist_layers_activations.py template and implement the following:

• A number of hidden layers (including zero) can be specified on the command line using parameter layers.
• Activation function of these hidden layers can be also specified as a command line parameter activation, with supported values of none, relu, tanh and sigmoid.
• Print the final accuracy on the test set.

In addition to submitting the task in ReCodEx, please also run the following variations and observe the results in TensorBoard:

• 0 layers, activation none
• 1 layer, activation none, relu, tanh, sigmoid
• 10 layers, activation sigmoid, relu

sgd_backpropagation

 Deadline: Mar 15 22, 23:59  3 points  Examples

In this exercise you will learn how to compute gradients using the so-called automatic differentiation, which is implemented by an automated backpropagation algorithm in TensorFlow. You will then perform training by running manually implemented minibatch stochastic gradient descent.

Starting with the sgd_backpropagation.py template, you should:

• implement a neural network with a single tanh hidden layer and categorical output layer;
• compute the crossentropy loss;
• use tf.GradientTape to automatically compute the gradient of the loss with respect to all variables;
• perform the SGD update.

sgd_manual

 Deadline: Mar 15 22, 23:59  2 points  Examples

The goal in this exercise is to extend your solution to the sgd_backpropagation assignment by manually computing the gradient.

Note that this assignment is the only one where we will compute the gradient manually, we will otherwise always use the automatic differentiation. Therefore, the assignment is more of a mathematical exercise and it is definitely not required to pass the course. Furthermore, we will compute the derivative of the output functions later on the Mar 9 lecture.

Start with the sgd_manual.py template, which is based on sgd_backpropagation.py one. Be aware that these templates generates each a different output file.

In order to check that you do not use automatic differentiation, ReCodEx checks that there is no GradientTape string in your source (except in the comments).

mnist_training

 Deadline: Mar 15 22, 23:59  3 points  Examples

This exercise should teach you using different optimizers, learning rates, and learning rate decays. Your goal is to modify the mnist_training.py template and implement the following:

• Using specified optimizer (either SGD or Adam).
• Optionally using momentum for the SGD optimizer.
• Using specified learning rate for the optimizer.
• Optionally use a given learning rate schedule. The schedule can be either exponential or polynomial (with degree 1, so inverse time decay). Additionally, the final learning rate is given and the decay should gradually decrease the learning rate to reach the final learning rate just after the training.

In addition to submitting the task in ReCodEx, please also run the following variations and observe the results in TensorBoard:

• SGD optimizer, learning_rate 0.01;
• SGD optimizer, learning_rate 0.01, momentum 0.9;
• SGD optimizer, learning_rate 0.1;
• Adam optimizer, learning_rate 0.001;
• Adam optimizer, learning_rate 0.01;
• Adam optimizer, exponential decay, learning_rate 0.01 and learning_rate_final 0.001;
• Adam optimizer, polynomial decay, learning_rate 0.01 and learning_rate_final 0.0001.

gym_cartpole

 Deadline: Mar 15 22, 23:59  3 points

Solve the CartPole-v1 environment from the OpenAI Gym, utilizing only provided supervised training data. The data is available in gym_cartpole-data.txt file, each line containing one observation (four space separated floats) and a corresponding action (the last space separated integer). Start with the gym_cartpole.py.

The solution to this task should be a model which passes evaluation on random inputs. This evaluation is performed by running the gym_cartpole_evaluate.py script, which loads a model and then evaluates it on 100 random episodes (optionally rendering if --render option is provided; note that the script can be also imported as a module and evaluate any given tf.keras.Model). In order to pass, you must achieve an average reward of at least 475 on 100 episodes. Your model should have either one or two outputs (i.e., using either sigmoid or softmax output function).

The size of the training data is very small and you should consider it when designing the model.

When submitting your model to ReCodEx, submit:

• one file with the model itself (with h5 suffix),
• the source code (or multiple sources) used to train the model (with py suffix), and possibly indicating teams.

explore_examples

Your goal in this zero-point assignment is to explore the prepared examples.

• The example_keras_models.py example demonstrates three different ways of constructing Keras models – sequential models, functional API and model subclassing.
• The example_keras_manual_batches.py shows how to train and evaluate Keras model when using custom batches.
• The example_manual.py illustrates how to implement a manual training loop without using Model.compile, with custom Optimizer, loss function and metric. However, this example is 2-3 times slower than the previous two ones.
• The example_manual_tf_function.py uses tf.function annotation to speed up execution of the previous example back to the level of Model.fit. See the official tf.function documentation for details.

mnist_regularization

 Deadline: Mar 22, 23:59  6 points  Examples

You will learn how to implement three regularization methods in this assignment. Start with the mnist_regularization.py template and implement the following:

• Allow using dropout with rate args.dropout. Add a dropout layer after the first Flatten and also after all Dense hidden layers (but not after the output layer).
• Allow using L2 regularization with weight args.l2. Use tf.keras.regularizers.L1L2 as a regularizer for all kernels (but not biases) of all Dense layers (including the last one).
• Allow using label smoothing with weight args.label_smoothing. Instead of SparseCategoricalCrossentropy, you will need to use CategoricalCrossentropy which offers label_smoothing argument.

In ReCodEx, there will be three tests (one for each regularization methods) and you will get 2 points for passing each one.

In addition to submitting the task in ReCodEx, also run the following variations and observe the results in TensorBoard (notably training, development and test set accuracy and loss):

• dropout rate 0, 0.3, 0.5, 0.6, 0.8;
• l2 regularization 0, 0.001, 0.0001, 0.00001;
• label smoothing 0, 0.1, 0.3, 0.5.

mnist_ensemble

 Deadline: Mar 22, 23:59  2 points  Examples

Your goal in this assignment is to implement model ensembling. The mnist_ensemble.py template trains args.models individual models, and your goal is to perform an ensemble of the first model, first two models, first three models, …, all models, and evaluate their accuracy on the development set.

uppercase

 Deadline: Mar 22, 23:59  4 points+5 bonus

This assignment introduces first NLP task. Your goal is to implement a model which is given Czech lowercased text and tries to uppercase appropriate letters. To load the dataset, use uppercase_data.py module which loads (and if required also downloads) the data. While the training and the development sets are in correct case, the test set is lowercased.

This is an open-data task, where you submit only the uppercased test set together with the training script (which will not be executed, it will be only used to understand the approach you took, and to indicate teams). Explicitly, submit exactly one .txt file and at least one .py file.

The task is also a competition. Everyone who submits a solution which achieves at least 97.0% accuracy will get 4 basic points; the 5 bonus points will be distributed depending on relative ordering of your solutions. The accuracy is computed per-character and can be evaluated by uppercase_eval.py script.

You may want to start with the uppercase.py template, which uses the uppercase_data.py to load the data, generate an alphabet of given size containing most frequent characters, and generate sliding window view on the data. The template also comments on possibilities of character representation.

Do not use RNNs, CNNs or Transformer in this task (if you have doubts, contact me).

mnist_cnn

 Deadline: Apr 05, 23:59  5 points  Examples

To pass this assignment, you will learn to construct basic convolutional neural network layers. Start with the mnist_cnn.py template and assume the requested architecture is described by the cnn argument, which contains comma-separated specifications of the following layers:

• C-filters-kernel_size-stride-padding: Add a convolutional layer with ReLU activation and specified number of filters, kernel size, stride and padding. Example: C-10-3-1-same
• CB-filters-kernel_size-stride-padding: Same as C-filters-kernel_size-stride-padding, but use batch normalization. In detail, start with a convolutional layer without bias and activation, then add batch normalization layer, and finally ReLU activation. Example: CB-10-3-1-same
• M-kernel_size-stride: Add max pooling with specified size and stride. Example: M-3-2
• R-[layers]: Add a residual connection. The layers contain a specification of at least one convolutional layer (but not a recursive residual connection R). The input to the specified layers is then added to their output (after the ReLU nonlinearity of the last one). Example: R-[C-16-3-1-same,C-16-3-1-same]
• F: Flatten inputs. Must appear exactly once in the architecture.
• H-hidden_layer_size: Add a dense layer with ReLU activation and specified size. Example: H-100
• D-dropout_rate: Apply dropout with the given dropout rate. Example: D-0.5

An example architecture might be --cnn=CB-16-5-2-same,M-3-2,F,H-100,D-0.5. You can assume the resulting network is valid; it is fine to crash if it is not.

After a successful ReCodEx submission, you can try obtaining the best accuracy on MNIST and then advance to cifar_competition.

image_augmentation

 Deadline: Apr 05, 23:59  1 points

The template image_augmentation.py creates a simple convolutional network for classifying CIFAR-10. Your goal is to perform image data augmentation operations using ImageDataGenerator and to utilize these data during training.

tf_dataset

 Deadline: Apr 05, 23:59  2 points

In this assignment you will familiarize yourselves with tf.data, which is TensorFlow high-level API for constructing input pipelines. If you want, you can read an official TensorFlow tf.data guide or reference API manual.

The goal of this assignment is to implement image augmentation preprocessing similar to image_augmentation, but with tf.data. Start with the tf_dataset.py template and implement the input pipelines employing the tf.data.Dataset.

cifar_competition

 Deadline: Apr 05, 23:59  5 points+5 bonus

The goal of this assignment is to devise the best possible model for CIFAR-10. You can load the data using the cifar10.py module. Note that the test set is different than that of official CIFAR-10.

This is an open-data task, where you submit only the test set labels together with the training script (which will not be executed, it will be only used to understand the approach you took, and to indicate teams). Explicitly, submit exactly one .txt file and at least one .py file.

The task is also a competition. Everyone who submits a solution which achieves at least 60% test set accuracy will get 5 points; the rest 5 points will be distributed depending on relative ordering of your solutions. Note that my solutions usually need to achieve at least ~73% on the development set to score 60% on the test set.

You may want to start with the cifar_competition.py template which generates the test set annotation in the required format.

mnist_web

You can try a Javascript-based demo of MNIST classification. This demo uses a neural network trained in TensorFlow using the mnist_web.py module, whose output was converted for Tensorflow.js with tensorflowjs_converter --input_format=keras command and is then utilized by mnist_web.html.

cags_classification

 Deadline: Apr 12, 23:59  6 points+5 bonus

The goal of this assignment is to use pretrained EfficientNet-B0 model to achieve best accuracy in CAGS classification.

The CAGS dataset consists of images of cats and dogs of size $224×224$, each classified in one of the 34 breeds and each containing a mask indicating the presence of the animal. To load the dataset, use the cags_dataset.py module. The dataset is stored in a TFRecord file and each element is encoded as a tf.train.Example. Therefore the dataset is loaded using tf.data API and each entry can be decoded using .map(CAGS.parse) call.

To load the EfficientNet-B0, use the the provided efficient_net.py module. Its method pretrained_efficientnet_b0(include_top):

• downloads the pretrained weights if they are not found;
• it returns a tf.keras.Model processing image of shape $(224, 224, 3)$ with float values in range $[0, 1]$ and producing a list of results:
  • the first value is the final network output:
    • if include_top == True, the network will include the final classification layer and produce a distribution on 1000 classes (whose names are in imagenet_classes.py);
    • if include_top == False, the network will return image features (the result of the last global average pooling);
  • the rest of outputs are the intermediate results of the network just before a convolution with $\textit{stride} > 1$ is performed (denoted $C_5, C_4, C_3, C_2, C_1$ in the Object Detection lecture).

An example performing classification of given images is available in image_classification.py.

A note on finetuning: each tf.keras.layers.Layer has a mutable trainable property indicating whether its variables should be updated – however, after changing it, you need to call .compile again (or otherwise make sure the list of trainable variables for the optimizer is updated). Furthermore, training argument passed to the invocation call decides whether the layer is executed in training regime (neurons gets dropped in dropout, batch normalization computes estimates on the batch) or in inference regime. There is one exception though – if trainable == False on a batch normalization layer, it runs in the inference regime even when training == True.

This is an open-data task, where you submit only the test set labels together with the training script (which will not be executed, it will be only used to understand the approach you took, and to indicate teams). Explicitly, submit exactly one .txt file and at least one .py file.

The task is also a competition. Everyone who submits a solution which achieves at least 90% test set accuracy will get 6 points; the rest 5 points will be distributed depending on relative ordering of your solutions.

You may want to start with the cags_classification.py template which generates the test set annotation in the required format.

cags_segmentation

 Deadline: Apr 19, 23:59  6 points+5 bonus

The goal of this assignment is to use pretrained EfficientNet-B0 model to achieve best image segmentation IoU score on the CAGS dataset. The dataset and the EfficientNet-B0 is described in the cags_classification assignment.

This is an open-data task, where you submit only the test set masks together with the training script (which will not be executed, it will be only used to understand the approach you took, and to indicate teams). Explicitly, submit exactly one .txt file and at least one .py file.

A mask is evaluated using intersection over union (IoU) metric, which is the intersection of the gold and predicted mask divided by their union, and the whole test set score is the average of its masks' IoU. A TensorFlow compatible metric is implemented by the class CAGSMaskIoU of the cags_segmentation_eval.py module, which can further be used to evaluate a file with predicted masks.

The task is also a competition. Everyone who submits a solution which achieves at least 85% test set IoU will get 6 points; the rest 5 points will be distributed depending on relative ordering of your solutions.

You may want to start with the cags_segmentation.py template, which generates the test set annotation in the required format – each mask should be encoded on a single line as a space separated sequence of integers indicating the length of alternating runs of zeros and ones.

cnn_manual

 Deadline: Apr 19, 23:59  3 points  Examples

To pass this assignment, you need to manually implement the forward and backward pass through a 2D convolutional layer. Start with the cnn_manual.py template, which construct a series of 2D convolutional layers with ReLU activation and valid padding, specified in the args.cnn option. The args.cnn contains comma separater layer specifications in the format filters-kernel_size-stride.

Of course, you cannot use any TensorFlow convolutional operation (instead, implement the forward and backward pass using matrix multiplication and other operations) nor the GradientTape for gradient computation.

bboxes_utils

 Deadline: Apr 19, 23:59  2 points

This is a preparatory assignment for svhn_competition. The goal is to implement several bounding box manipulation routines in the bboxes_utils.py module. Notably, you need to implement the following methods:

• bbox_to_fast_rcnn: convert a bounding box to a Fast R-CNN-like representation relative to a given anchor;
• bbox_from_fast_rcnn: convert a Fast R-CNN-like representation relative to an anchor back to a bounding box;
• bboxes_training: given a list of anchors and gold objects, assign gold objects to anchors and generate suitable training data (the exact algorithm is described in the template).

The bboxes_utils.py contains simple unit tests, which are evaluated when executing the module, which you can use to check the validity of your implementation.

When submitting to ReCodEx, you must submit exactly one Python source with methods bbox_to_fast_rcnn, bbox_to_fast_rcnn and bboxes_training. These methods are then executed and compared to the reference implementation.

svhn_competition

 Deadline: Apr 26, 23:59 May 03, 23:59  5 points+5 bonus

The goal of this assignment is to implement a system performing object recognition, optionally utilizing pretrained EfficientNet-B0 backbone.

The Street View House Numbers (SVHN) dataset annotates for every photo all digits appearing on it, including their bounding boxes. The dataset can be loaded using the svhn_dataset.py module. Similarly to the CAGS dataset, it is stored in a TFRecord file with tf.train.Example elements, which can be decoded using .map(SVHN.parse) call. Every element is a dictionary with the following keys:

• "image": a square 3-channel image,
• "classes": a 1D tensor with all digit labels appearing in the image,
• "bboxes": a [num_digits, 4] 2D tensor with bounding boxes of every digit in the image.

Given that the dataset elements are each of possibly different size and you want to preprocess them using a NumPy function bboxes_training, it might be more comfortable to convert the dataset to NumPy. Alternatively, you can call bboxes_training directly in tf.data.Dataset.map by using tf.numpy_function, see FAQ.

Similarly to the cags_classification, you can load the EfficientNet-B0 using the provided efficient_net.py module. Its method pretrained_efficientnet_b0(include_top, dynamic_shape=False) has gotten a new argument dynamic_shape, and with dynamic_shape=True it constructs a model capable of processing an input image of any size.

This is an open-data task, where you submit only the test set annotations together with the training script (which will not be executed, it will be only used to understand the approach you took, and to indicate teams). Explicitly, submit exactly one .txt file and at least one .py file.

Each test set image annotation consists of a sequence of space separated five-tuples label top left bottom right, and the annotation is considered correct, if exactly the gold digits are predicted, each with IoU at least 0.5. The whole test set score is then the prediction accuracy of individual images. An evaluation of a file with the predictions can be performed by the svhn_eval.py module.

The task is also a competition. Everyone submitting a solution with at least 20% test set accuracy will get 5 points; the rest 5 points will be distributed depending on relative ordering of your solutions. Note that I usually need at least 35% development set accuracy to achieve the required test set performance.

You should start with the svhn_competition.py template, which generates the test set annotation in the required format.

A baseline solution can use RetinaNet-like single stage detector, using only a single level of convolutional features (no FPN) with single-scale and single-aspect anchors. Focal loss is available as tfa.losses.SigmoidFocalCrossEntropy (using reduction=tf.losses.Reduction.SUM_OVER_BATCH_SIZE option is a good idea) and non-maximum suppression as tf.image.non_max_suppression or tf.image.combined_non_max_suppression.

3d_recognition

 Deadline: Apr 26, 23:59  5 points+5 bonus

Your goal in this assignment is to perform 3D object recognition. The input is voxelized representation of an object, stored as a 3D grid of either empty or occupied voxels, and your goal is to classify the object into one of 10 classes. The data is available in two resolutions, either as 20×20×20 data or 32×32×32 data. To load the dataset, use the modelnet.py module.

The official dataset offers only train and test sets, with the test set having a different distributions of labels. Our dataset contains also a development set, which has nearly the same label distribution as the test set.

The assignment is again an open-data task, where you submit only the test set labels together with the training script (which will not be executed, it will be only used to understand the approach you took, and to indicate teams). Explicitly, submit exactly one .txt file and at least one .py file.

The task is also a competition. Everyone submitting a solution with at least 85% test set accuracy will get 5 points; the rest 5 points will be distributed depending on relative ordering of your solutions.

You can start with the 3d_recognition.py template, which among others generates test set annotations in the required format.

sequence_classification

 Deadline: Apr 26, 23:59  6 points  Examples

The goal of this assignment is to introduce recurrent neural networks, manual TensorBoard log collection, and manual gradient clipping. Considering recurrent neural network, the assignment shows convergence speed and illustrates exploding gradient issue. The network should process sequences of 50 small integers and compute parity for each prefix of the sequence. The inputs are either 0/1, or vectors with one-hot representation of small integer.

Your goal is to modify the sequence_classification.py template and implement the following:

• Use specified RNN type (SimpleRNN, GRU and LSTM) and dimensionality.
• Process the sequence using the required RNN.
• Use additional hidden layer on the RNN outputs if requested.
• Implement gradient clipping if requested.

In addition to submitting the task in ReCodEx, please also run the following variations and observe the results in TensorBoard. Concentrate on the way how the RNNs converge, convergence speed, exploding gradient issues and how gradient clipping helps:

• --rnn_cell=SimpleRNN --sequence_dim=1, --rnn_cell=GRU --sequence_dim=1, --rnn_cell=LSTM --sequence_dim=1
• the same as above but with --sequence_dim=2
• the same as above but with --sequence_dim=10
• --rnn_cell=LSTM --hidden_layer=70 --rnn_cell_dim=30 --sequence_dim=30 and the same with --clip_gradient=1
• the same as above but with --rnn_cell=SimpleRNN
• the same as above but with --rnn_cell=GRU --hidden_layer=90

tagger_we

 Deadline: Apr 26, 23:59  3 points  Examples

In this assignment you will create a simple part-of-speech tagger. For training and evaluation, we will use Czech dataset containing tokenized sentences, each word annotated by gold lemma and part-of-speech tag. The morpho_dataset.py module (down)loads the dataset and can generate batches.

Your goal is to modify the tagger_we.py template and implement the following:

• Use specified RNN cell type (GRU and LSTM) and dimensionality.
• Create word embeddings for training vocabulary.
• Process the sentences using bidirectional RNN.
• Predict part-of-speech tags. Note that you need to properly handle sentences of different lengths in one batch using masking.

mnist_multiple

 Deadline: May 03, 23:59  2 points  Examples

In this assignment you will implement a model with multiple inputs. Start with the mnist_multiple.py template and:

• The goal is to create a model, which given two input MNIST images predicts, if the digit on the first one is larger than on the second one.
• The model has three outputs:
  • label prediction for the first image,
  • label prediction for the second image,
  • direct prediction whether the first digit is larger than the second one.
• In addition to direct prediction, you can use the predicted labels for both images and compare them – an indirect prediction.
• You need to implement:
  • the model, using multiple inputs, outputs, losses, and metrics;
  • construction of two-image batches using regular MNIST batches,
  • computation of direct and indirect prediction accuracy.

tagger_cle_rnn

 Deadline: May 03, 23:59  2 points  Examples

This assignment is a continuation of tagger_we. Using the tagger_cle_rnn.py template, implement character-level word embedding computation using a bidirectional character-level GRU.

Once submitted to ReCodEx, you should experiment with the effect of CLEs compared to a plain tagger_we, and the influence of their dimensionality. Note that tagger_we has by default word embeddings twice the size of word embeddings in tagger_cle_rnn.

tagger_cle_cnn

 Deadline: May 03, 23:59  2 points  Examples

This task is a continuation of tagger_cle_rnn assignment. Using the tagger_cle_cnn.py template, instead of using RNNs to generate character-level embeddings, process character sequences with 1D convolutional filters with varying kernel sizes and obtain fixed-size representations using global max-pooling. Compute the final embeddings by using a highway layer.

tagger_competition

 Deadline: May 03, 23:59  4 points+5 bonus

In this assignment, you should extend tagger_we/tagger_cle_rnn/tagger_cle_cnn into a real-world Czech part-of-speech tagger. We will use Czech PDT dataset loadable using the morpho_dataset.py module. Note that the dataset contains more than 1500 unique POS tags and that the POS tags have a fixed structure of 15 positions (so it is possible to generate the POS tag characters independently).

You can use the following additional data in this assignment:

• You can use outputs of a morphological analyzer loadable with morpho_analyzer.py. If a word form in train, dev or test PDT data is known to the analyzer, all its (lemma, POS tag) pairs are returned.
• You can use any unannotated text data (Wikipedia, Czech National Corpus, …), and also any pre-trained word embeddings (assuming they were trained on plain texts).

The assignment is again an open-data task, where you submit only the test set annotations together with the training script (which will not be executed, it will be only used to understand the approach you took, and to indicate teams). Explicitly, submit exactly one .txt file and at least one .py file. Note that all .zip files you submit will be extracted first.

The task is also a competition. Everyone submitting a solution with at least 92% label accuracy gets 4 points; the rest 5 points will be distributed depending on relative ordering of your solutions. Lastly, 3 bonus points will be given to anyone surpassing pre-neural-network state-of-the-art of 95.89% from Spoustová et al., 2009. You can evaluate a generated file using the morpho_evaluator.py module.

You can start with the tagger_competition.py template, which among others generates test set annotations in the required format.

speech_recognition

 Deadline: May 03, 23:59 May 10, 23:59  6 points+5 bonus

This assignment is a competition task in speech recognition area. Specifically, your goal is to predict a sequence of letters given a spoken utterance. We will be using TIMIT corpus, with input sound waves passed through the usual preprocessing – computing Mel-frequency cepstral coefficients (MFCCs). You can repeat exactly this preprocessing on a given audio using the timit_mfcc_preprocess.py script.

Because the data is not publicly available, you can download it only through ReCodEx. Please do not distribute it. To load the dataset using the timit_mfcc.py module.

This is an open-data task, where you submit only the test set annotations together with the training script (which will not be executed, it will be only used to understand the approach you took, and to indicate teams). Explicitly, submit exactly one .txt file and at least one .py file.

The task is also a competition. The evaluation is performed by computing edit distance to the gold letter sequence, normalized by its length (i.e., exactly as tf.edit_distance). Everyone submitting a solution with at most 50% test set edit distance will get 6 points; the rest 5 points will be distributed depending on relative ordering of your solutions. An evaluation (using for example development data) can be performed by speech_recognition_eval.py.

You should start with the speech_recognition.py template.

• To perform speech recognition, you should use CTC loss for training and CTC beam search decoder for prediction. Both the CTC loss and CTC decoder employ sparse tensor – therefore, start by studying them.
• A basic architecture:
  • converts target letters into sparse representation,
  • use a bidirectional RNN and an output linear layer without activation,
  • compute CTC loss (tf.nn.ctc_loss),
  • if required, perform decoding by a CTC decoder (tf.nn.ctc_beam_search_decoder) and possibly evaluate the results using normalized edit distance (tf.edit_distance).

tensorboard_projector

You can try exploring the TensorBoard Projector with pre-trained embeddings for 20k most frequent lemmas in Czech and English – after extracting the archive, start tensorboard --logdir dir_where_the_archive_is_extracted.

In order to use the Projector tab yourself, you can take inspiration from the projector_export.py script, which was used to export the above pre-trained embeddings.

lemmatizer_noattn

 Deadline: May 10, 23:59  4 points  Examples

The goal of this assignment is to create a simple lemmatizer. For training and evaluation, we use the same dataset as in tagger_we loadable by the morpho_dataset.py module.

Your goal is to modify the lemmatizer_noattn.py template and implement the following:

• Embed characters of source forms and run a bidirectional GRU encoder.
• Embed characters of target lemmas.
• Implement a training time decoder which uses gold target characters as inputs.
• Implement an inference time decoder which uses previous predictions as inputs.
• The initial state of both decoders is the output state of the corresponding GRU encoded form.

lemmatizer_attn

 Deadline: May 10, 23:59  3 points  Examples

This task is a continuation of the lemmatizer_noattn assignment. Using the lemmatizer_attn.py template, implement the following features in addition to lemmatizer_noattn:

• The bidirectional GRU encoder returns outputs for all input characters, not just the last.
• Implement attention in the decoders. Notably, project the encoder outputs and current state into same dimensionality vectors, apply non-linearity, and generate weights for every encoder output. Finally sum the encoder outputs using these weights and concatenate the computed attention to the decoder inputs.

Once submitted to ReCodEx, you should experiment with the effect of using the attention, and the influence of RNN dimensionality on network performance.

lemmatizer_competition

 Deadline: May 10, 23:59 May 17, 23:59  5 points+5 bonus

In this assignment, you should extend lemmatizer_noattn or lemmatizer_attn into a real-world Czech lemmatizer. As in tagger_competition, we will use Czech PDT dataset loadable using the morpho_dataset.py module.

You can also use the following additional data as in the tagger_competition assignment.

The assignment is again an open-data task, where you submit only the test set annotations together with the training script (which will not be executed, it will be only used to understand the approach you took, and to indicate teams). Explicitly, submit exactly one .txt file and at least one .py file. Note that all .zip files you submit will be extracted first.

The task is also a competition. Everyone submitting a solution with at least 92% accuracy will get 5 points; the rest 5 points will be distributed depending on relative ordering of your solutions. Lastly, 3 bonus points will be given to anyone surpassing pre-neural-network state-of-the-art of 97.86%. You can evaluate a generated file using the morpho_evaluator.py module.

You can start with the lemmatizer_competition.py template, which among others generates test set annotations in the required format.

vae

 Deadline: May 17, 23:59  3 points  Examples

In this assignment you will implement a simple Variational Autoencoder for three datasets in the MNIST format. Your goal is to modify the vae.py template and implement a VAE.

After submitting the assignment to ReCodEx, you can experiment with the three available datasets (mnist, mnist-fashion, and mnist-cifarcars) and different latent variable dimensionality (z_dim=2 and z_dim=100). The generated images are available in TensorBoard logs.

gan

 Deadline: May 17, 23:59  3 points  Examples

In this assignment you will implement a simple Generative Adversarion Network for three datasets in the MNIST format. Your goal is to modify the gan.py template and implement a GAN.

After submitting the assignment to ReCodEx, you can experiment with the three available datasets (mnist, mnist-fashion, and mnist-cifarcars) and maybe try different latent variable dimensionality. The generated images are available in TensorBoard logs.

You can also continue with dcgan assignment.

dcgan

 Deadline: May 17, 23:59  1 points  Examples

This task is a continuation of the gan assignment, which you will modify to implement the Deep Convolutional GAN (DCGAN).

Start with the dcgan.py template and implement a DCGAN. Note that most of the TODO notes are from the gan assignment.

After submitting the assignment to ReCodEx, you can experiment with the three available datasets (mnist, mnist-fashion, and mnist-cifarcars). However, note that you will need a lot of computational power (preferably a GPU) to generate the images.

omr_competition

 Deadline: May 24, 23:59  5 points+5 bonus

You should implement optical music recognition in your final competition assignment. The inputs are PNG images of monophonic scores starting with a clef, key signature, and a time signature, followed by several staves. The dataset is loadable using the omr_dataset.py module and is downloaded automatically if missing (note that is has 185MB). No other data or pretrained models are allowed for training.

The assignment is again an open-data task, where you submit only the annotated test set together with the training script (which will not be executed, it will be only used to understand the approach you took, and to indicate teams). Explicitly, submit exactly one .txt file and at least one .py file. Note that all .zip files you submit will be extracted first.

The task is also a competition. The evaluation is performed by computing edit distance to the gold letter sequence, normalized by its length. Everyone submitting a solution with at most 10% test set edit distance will get 5 points; the rest 5 points will be distributed depending on relative ordering of your solutions. Furthermore, 3 bonus points will be given to anyone surpassing current state-of-the-art of 0.80%. An evaluation (using for example development data) can be performed by omr_competition_eval.py.

You can start with the omr_competition.py template, which among others generates test set annotations in the required format.

monte_carlo

 Deadline: May 24, 23:59  2 points

Solve the discretized CartPole-v1 environment environment from the OpenAI Gym using the Monte Carlo reinforcement learning algorithm.

Use the supplied cart_pole_evaluator.py module (depending on gym_evaluator.py) to interact with the discretized environment. The environment has the following methods and properties:

• states: number of states of the environment
• actions: number of actions of the environment
• episode: number of the current episode (zero-based)
• reset(start_evaluate=False) → new_state: starts a new episode
• step(action) → new_state, reward, done, info: perform the chosen action in the environment, returning the new state, obtained reward, a boolean flag indicating an end of episode, and additional environment-specific information
• render(): render current environment state

Once you finish training (which you indicate by passing start_evaluate=True to reset), your goal is to reach an average return of 475 during 100 evaluation episodes. Note that the environment prints your 100-episode average return each 10 episodes even during training.

You can start with the monte_carlo.py template, which parses several useful parameters, creates the environment and illustrates the overall usage.

During evaluation in ReCodEx, two different random seeds will be employed, and you need to reach the required return on all of them. Time limit for each test is 5 minutes.

Note that gym_evaluator.py and cart_pole_evaluator.py must not be submitted to ReCodEx.

reinforce

 Deadline: May 24, 23:59  2 points

Solve the continuous CartPole-v1 environment environment from the OpenAI Gym using the REINFORCE algorithm.

The supplied cart_pole_evaluator.py module (depending on gym_evaluator.py) can create a continuous environment using environment(discrete=False). The continuous environment is very similar to the discrete environment, except that the states are vectors of real-valued observations with shape environment.state_shape.

Your goal is to reach an average return of 475 during 100 evaluation episodes. Start with the reinforce.py template.

During evaluation in ReCodEx, two different random seeds will be employed, and you need to reach the required return on all of them. Time limit for each test is 5 minutes.

Note that gym_evaluator.py and cart_pole_evaluator.py must not be submitted to ReCodEx.

reinforce_baseline

 Deadline: May 24, 23:59  2 points

This is a continuation of the reinforce assignment.

Using the reinforce_baseline.py template, solve the CartPole-v1 environment environment using the REINFORCE with baseline algorithm.

Using a baseline lowers the variance of the value function gradient estimator, which allows faster training and decreases sensitivity to hyperparameter values. To reflect this effect in ReCodEx, note that the evaluation phase will automatically start after 200 episodes. Using only 200 episodes for training in this setting is probably too little for the REINFORCE algorithm, but suffices for the variant with a baseline.

Your goal is to reach an average return of 475 during 100 evaluation episodes.

During evaluation in ReCodEx, two different random seeds will be employed, and you need to reach the required return on all of them. Time limit for each test is 5 minutes.

Note that gym_evaluator.py and cart_pole_evaluator.py must not be submitted to ReCodEx.

reinforce_pixels

 Deadline: May 24, 23:59  2 points

This is a continuation of the reinforce or reinforce_baseline assignments.

The supplied cart_pole_pixels_evaluator.py module (depending on gym_evaluator.py) generates a pixel representation of the CartPole environment as an $80×80$ image with three channels, with each channel representing one time step (i.e., the current observation and the two previous ones).

To pass the assignment, you need to reach an average return of 250 during 100 evaluation episodes. During evaluation in ReCodEx, two different random seeds will be employed, and you need to reach the required return on all of them. Time limit for each test is 10 minutes.

You can start with the reinforce_pixels.py template using the correct environment.

Note that gym_evaluator.py and cart_pole_pixels_evaluator.py must not be submitted to ReCodEx.

sentiment_analysis

 Deadline: Jun 7, 23:59  5 points

In this assignment you should try finetuning the mBERT model to perform sentiment analysis. We will use Czech dataset of Facebook comments, which can be loaded by the text_classification_dataset.py module.

Use the BERT implementation from the 🤗 Transformers library, which you can install by pip3 install [--user] transformers. Start by looking at the bert_example.py example demonstrating loading, tokenizing and calling a BERT model, and you can also read the documentation, specifically for the tokenizer and for TFBertModel.call.

The assignment is an open-data task, where you submit only the test set annotations together with the training script (which will not be executed, it will be only used to understand the approach you took, and to indicate teams). Explicitly, submit exactly one .txt file and at least one .py file. You pass if your test set accuracy is at least 75%.

You can start with the sentiment_analysis.py template, which among others generates test set annotations in the required format.

In the competitions, your goal is to train a model and then predict target values on the given unannotated test set.

Submitting to ReCodEx

When submitting a competition solution to ReCodEx, you can include any number of files of any kind, and either submit them individuall or compess them in a .zip file. However, there should be exactly one text file with the test set annotation (.txt) and at least one Python source (.py) containing the model training and prediction. The Python sources are not executed, but must be included for inspection.

Evaluation in ReCodEx

• For every submission, ReCodEx checks the above conditions (exactly one .txt, at least one .py), whether the given annotations can be evaluated without error, and if the annotations surpass the required performance baseline. If all these checks pass, the assignment is marked as solved in ReCodEx and gets the regular points for the assignment.

• Just after the deadline, the newest submission of every user passing ReCodEx evaluation participates in a course competition. Additional bonus points are then awarded according to the ordering of the participating submissions.

• After the deadline, the exact performance becomes visible for all submissions.

What Is Allowed

• You can use only the given annotated data, either for training or evaluation.
• You can use any unannotated or manually created data for training or evaluation.
• The test set annotations must be the result of your system (so you cannot manually correct them; but your system can contain other parts than just neural networks, like hand-written rules).
• Do not use test set annotations in any way.
• Unless stated otherwise, you can use any algorithm to solve the competition task at hand. The implementation should be either created by you or you should understand it fully.

tf.data

• How to look what is in a tf.data.Dataset?

  The tf.data.Dataset is not just an array, but a description of a pipeline, which can produce data if requested. A simple way to run the pipeline is to iterate it using Python iterators:

  dataset = tf.data.Dataset.range(10)
  for entry in dataset:
      print(entry)
  

• How to use tf.data.Dataset with model.fit or model.evaluate?

  To use a tf.data.Dataset in Keras, the dataset elements should be pairs (input_data, gold_labels), where input_data and gold_labels must be already batched. For example, given CAGS dataset, you can preprocess training data for cags_classification as (for development data, you would remove the .shuffle):

  train = cags.train.map(CAGS.parse)
  train = train.map(lambda example: (example["image"], example["label"]))
  train = train.shuffle(10000, seed=args.seed)
  train = train.batch(args.batch_size)
  

• Is every iteration through a tf.data.Dataset the same?

  No. Because the dataset is only a pipeline generating data, it is called each time the dataset is iterated – therefore, every .shuffle is called in every iteration.

• How to generate different random numbers each epoch during tf.data.Dataset.map?

  When a global random seed is set, methods like tf.random.uniform generate the same sequence of numbers on each iteration.

  The easiest method I found is to create a Generator object and use it to produce random numbers.

  generator = tf.random.experimental.Generator.from_seed(42)
  data = tf.data.Dataset.from_tensor_slices(tf.zeros(10, tf.int32))
  data = data.map(lambda x: x + generator.uniform([], maxval=10, dtype=tf.int32))
  for _ in range(3):
      print(*[element.numpy() for element in data])
  

• How to call numpy methods or other non-tf functions in tf.data.Dataset.map?

  You can use tf.numpy_function to call a numpy function even in a computational graph. However, the results have no static shape information and you need to set it manually – ideally using tf.ensure_shape, which both sets the static shape and verifies during execution that the real shape mathes it.

  For example, to use the bboxes_training method from bboxes_utils, you could do something like:

  anchors = np.array(...)
  
  def prepare_data(example):
      anchor_classes, anchor_bboxes = tf.numpy_function(
          bboxes_utils.bboxes_training, [anchors, example["classes"], example["bboxes"], 0.5], (tf.int32, tf.float32))
      anchor_classes = tf.ensure_shape(anchor_classes, [len(anchors)])
      anchor_bboxes = tf.ensure_shape(anchor_bboxes, [len(anchors), 4])
      ...
  

• How to use ImageDataGenerator in tf.data.Dataset.map?

  The ImageDataGenerator offers a .random_transform method, so we can use tf.numpy_function from the previous answer:

  train_generator = tf.keras.preprocessing.image.ImageDataGenerator(...)
  
  def augment(image, label):
      return tf.ensure_shape(
          tf.numpy_function(train_generator.random_transform, [image], tf.float32),
          image.shape
      ), label
  
  dataset.map(augment)
  

Finetuning

• How to make a part of the network frozen, so that its weights are not updated?

  Each tf.keras.layers.Layer/tf.keras.Model has a mutable trainable property indicating whether its variables should be updated – however, after changing it, you need to call .compile again (or otherwise make sure the list of trainable variables for the optimizer is updated).

  Note that once trainable == False, the insides of a layer are no longer considered, even if some its sub-layers have trainable == True. Therefore, if you want to freeze only some sub-layers of a layer you use in your model, the layer itself must have trainable == True.

• How to choose whether dropout/batch normalization is executed in training or inference regime?

  When calling a tf.keras.layers.Layer/tf.keras.Model, a named option training can be specified, indicating whether training or inference regime should be used. For a model, this option is automatically passed to its layers which require it, and Keras automatically passes it during model.{fit,evaluate,predict}.

  However, you can manually pass for example training=False to a layer when using Functional API, meaning that layer is executed in the inference regime even when the whole model is training.

• How does trainable and training interact?

  The only layer, which is influenced by both these options, is batch normalization, for which:

  • if trainable == False, the layer is always executed in inference regime;
  • if trainable == True, the training/inference regime is chosen according to the training option.

Masking

• How can sequences of different length be processed by a RNN?

  Keras employs masking to indicate, which sequence elements are valid and which are just padding.

  Usually, a mask is created using a Embedding or Masking layer and is then automatically propagated. If model.compile is used, it is also automatically utilized in losses and metrics.

  However, in order for the mask propagation to work, you can use only tf.keras.layers to process the data, not raw TF operations like tf.concat or even the + operator (see tf.keras.layers.Concatenate/Add/...).

• How to compute masked losses and masked metrics manually?

  When you want to compute the losses and metrics manually, pass the mask as the third argument to their __call__ method (each individual component of loss/metric is then multiplied by the mask, zeroing out the ones for padding elements).

• How to print output masks of a tf.keras.Model?

  When you call the model directly, like model(input_batch), the mask of each output is available in a private ._keras_mask property, so for single-output models you can print it with print(model(input_batch)._keras_mask).

TensorBoard

• How to create TensorBoard logs manually?

  Start by creating a SummaryWriter using for example:

  writer = tf.summary.create_file_writer(args.logdir, flush_millis=10 * 1000)
  

  and then you can generate logs inside a with writer.as_default() block.

  You can either specify step manually in each call, or you can use tf.summary.experimental.set_step(step). Also, during training you usually want to log only some batches, so the logging block during training usually looks like:

  if optimizer.iterations % 100 == 0:
      tf.summary.experimental.set_step(optimizer.iterations)
      with self._writer.as_default():
          # logging
  

• What can be logged in TensorBoard?

  • scalar values:

    tf.summary.scalar(name like "train/loss", value, [step])
    

  • tensor values displayed as histograms or distributions:

    tf.summary.histogram(name like "train/output_layer", tensor value castable to `tf.float64`, [step])
    

  • images as tensors with shape [num_images, h, w, channels], where channels can be 1 (grayscale), 2 (grayscale + alpha), 3 (RGB), 4 (RGBA):

    tf.summary.image(name like "train/samples", images, [step], [max_outputs=at most this many images])
    

  • possibly large amount of text (e.g., all hyperparameter values, sample translations in MT, …) in Markdown format:

    tf.summary.text(name like "hyperparameters", markdown, [step])
    

  • audio as tensors with shape [num_clips, samples, channels] and values in $[-1,1]$ range:

    tf.summary.audio(name like "train/samples", clips, sample_rate, [step], [max_outputs=at most this many clips])
    

Requirements

To pass the practicals, you need to obtain at least 80 points, excluding the bonus points. Note that up to 40 points above 80 (including the bonus points) will be transfered to the exam.

To pass the exam, you need to obtain at least 60, 75 and 90 out of 100-point exam, to obtain grades 3, 2 and 1, respectively. (PhD students with binary grades require 75 points.) The exam consists of five 20-point questions, which are randomly generated, but always cover the whole course. In addition, you can get at most 40 surplus points from the practicals and at most 10 points for community work (i.e., fixing slides or reporting issues) – but only the points you already have at the time of the exam count.

Exam Questions

Lecture 2 Questions

• Training Neural Network
  Assume the artificial neural network on the right, with mean square error loss and gold output of 3. Compute the values of all weights $w_i$ after performing an SGD update with learning rate 0.1.

  Different networks architectures, activation functions (tanh, sigmoid, softmax) and losses (MSE, NLL) may appear in the exam.

• Maximum Likelihood Estimation
  Formulate maximum likelihood estimator for neural network parameters and derive the following two losses:

  • NLL (negative log likelihood) loss for networks returning a probability distribution
  • MSE (mean square error) loss for networks returning a real number with a normal distribution with a fixed variance

• Backpropagation Algorithm, SGD with Momentum
  Write down the backpropagation algorithm. Then, write down the SGD algorithm with momentum. Finally, formulate SGD with Nestorov momentum and explain the difference to SGD with regular momentum.

• Adagrad and RMSProp
  Write down the AdaGrad algorithm and show that it tends to internally decay learning rate by a factor of $1/\sqrt{t}$ in step $t$. Furthermore, write down RMSProp algorithm and compare it to Adagrad.

• Adam
  Write down the Adam algorithm and explain the bias-correction terms $(1-\beta^t)$.

Lecture 3 Questions

• Regularization
  Define overfitting and sketch what a regularization is. Then describe basic regularization methods like early stopping, L2 and L1 regularization, dataset augmentation, ensembling and label smoothing.

• Dropout
  Describe the dropout method and write down exactly how is it used during training and during inference. Then explain why it cannot be used on RNN state, describe the variational dropout variant, and also describe layer normalization.

• Network Convergence
  Describe factors influencing network convergence, namely:

  • Parameter initialization strategies (explain also why batch normalization helps with the initialization range).
  • Problems with saturating non-linearities (and again, why batch normalization helps; also discuss why NLL (compared to MSE) helps with saturating non-linearities on the output layer).
  • Gradient clipping (and the difference between clipping individual gradient elements or the gradient as a whole).

Lecture 4 Questions

• Convolution
  Write down equations of how convolution of a given image is computed. Assume the input is an image $I$ of size $H \times W$ with $C$ channels, the kernel $K$ has size $N \times M$, the stride is $T \times S$, the operation performed is in fact cross-correlation (as usual in convolutional neural networks) and that $O$ output channels are computed. Explain both $\textit{SAME}$ and $\textit{VALID}$ padding schemes and write down output size of the operation for both these padding schemes.

• Batch Normalization
  Describe the batch normalization method and explain how it is used during training and during inference. Explicitly write over what is being normalized in case of fully connected layers, and in case of convolutional layers. Compare batch normalization to layer normalization.

Lecture 5 Questions

• VGG and ResNet
  Describe overall architecture of VGG and ResNet (you do not need to remember exact number of layers/filters, but you should know when a BatchNorm is executed, when ReLU, and how residual connections work when the number of channels increases). Then describe two ResNet extensions (WideNet, DenseNet, PyramidNet, ResNeXt).

• CNN Regularization, SE, MBConv
  Describe CNN regularization methods (networks with stochastic depth, Cutout, DropBlock). Then show a Squeeze and excitation block for a ResNet and finally sketch mobile inverted bottleneck with separable convolutions.

• Transposed Convolution
  Write down equations of how convolution of a given image is computed. Assume the input is an image $I$ of size $H \times W$ with $C$ channels, the kernel $K$ has size $N \times M$, the stride is $S$, the operation performed is in fact cross-correlation (as usual in convolutional neural networks) and that $O$ output channels are computed. Then write down the equation of transposed convolution (or equivalently backpropagation through a convolution to its inputs).

Lecture 6 Questions

• Two-stage Object Detection
  Define object detection task and describe Fast-RCNN and Faster-RCNN architectures. Notably, show what the overall architectures of the networks are, explain the RoI-pooling, show how the network parametrizes bounding boxes, how do the losses looks like, how are RoI chosen during training, how the objects are predicted, and what region proposal network does.

• Image Segmentation
  Define object detection and image segmentation tasks, and sketch a Faster-RCNN and Mask-RCNN architectures. Notably, show what the overall architecture of the networks is, explain the RoI-pooling and RoI-align layer, show how the network parametrizes bounding boxes, how do the losses looks like, how are RoI chosen during training and how the objects are predicted.

• Single-stage Object Detection
  Define object detection task and describe single-stage detector architecture. Namely, show feature pyramid network, define focal loss and sketch RetinaNet – the overall architecture including the convolutional classification and bounding box prediction heads, overall loss, how the gold labels are generated, and how the objects are predicted.

Lecture 7 Questions

• LSTM
  Write down how the Long Short-Term Memory cell operates.

• GRU and Highway Networks
  Show a basic RNN cell (using just one hidden layer) and then write down how it is extended using gating into the Gated Recurrent Unit. Finally, describe highway networks and compare them to RNN.

Lecture 8 Questions

• Character-level word embeddings
  Describe why are character-level word embeddings useful. Then describe the two following methods:

  • RNN: using bidirectional recurrent neural networks
  • CNN: describe how convolutional networks (CNNs) can be used to compute character-level word embeddings. Write down the exact equation computing the embedding, assuming that the input word consists of characters $\{x_1, \ldots, x_N\}$ represented by embeddings $\{e_1, \ldots, e_N\}$ for $e_i \in \mathbb R^D$, and we use $F$ filters of widths $w_1, \ldots, w_F$. Also explicitly count the number of parameters.

• Sequence classification and CRF
  Describe how RNNs, bidirectional RNNs and multi-layer RNNs can be used to classify every element of a given sequence (i.e., what the architecture of a tagger might be; include also residual connections and suitable places for dropout layers). Then, explain how a CRF layer works, define score computation for a given sequence of inputs and sequence of labels, describe the loss computation during training, and sketch the inference algorithm.

• CTC Loss
  Describe CTC loss and the whole settings which can be solved utilizing CTC loss. Then show how CTC loss can be computed. Finally, describe greedy and beam search CTC decoding.

Lecture 9 Questions

• Word2vec and Hierarchical and Negative Sampling
  Explain how can word embeddings be precomputed using the CBOW and Skip-gram models. First start with the variants where full softmax is performed, and then describe how hierarchical softmax and negative sampling is used to speedup training of word embeddings.

• Neural Machine Translation and Attention
  Draw/write how an encoder-decoder architecture is used for machine translation, both during training and during inference. Then describe the architecture of an attention module.

• Neural Machine Translation and Subwords
  Draw/write how an encoder-decoder architecture is used for machine translation, both during training and during inference (without attention). Furthermore, elaborate on how subword units are used to reduce out-of-vocabulary problem and sketch BPE algorithm and WordPieces algorithm for constructing fixed number of subword units.

Lecture 10 Questions

• Variational Autoencoders
  Describe deep generative modelling using variational autoencoders – show VAE architecture, devise training algorithm, write training loss, and propose sampling procedure.

• Generative Adversarial Networks
  Describe deep generative modelling using generative adversarial networks -- show GAN architecture and describe training procedure and training loss. Mention also CGAN (conditional GAN) and sketch generator and discriminator architecture in a DCGAN.

Lecture 11 Questions

• Reinforcement learning
  Describe the general reinforcement learning settings and formulate the Monte Carlo algorithm. Then, formulate and prove the policy gradient theorem and write down the REINFORCE algorithm.

• REINFORCE with baseline
  Describe the general reinforcement learning settings, formulate the policy gradient theorem and write down the REINFORCE algorithm. Then explain what is the baseline, show policy gradient theorem with the baseline (including the proof of why the baseline can be included), and write down the REINFORCE with baseline algorithm.

Lecture 12 Questions

• Speech Synthesis
  Describe the WaveNet network (what a dilated convolution and gated activations are, how the residual block looks like, what the overall architecture is, and how global and local conditioning work). Discuss parallelizability of training and inference, show how Parallel WaveNet can speedup inference, and sketch how it is trained.

• Neural Turing Machines
  Sketch an overall architecture of a Neural Turing Machine with an LSTM controller, assuming $R$ reading heads and one write head. Describe the addressing mechanism (content addressing and its combination with previous weights, shifts, and sharpening) and reading and writing operations. Finally, describe the inputs and the outputs of the controller.

Lecture 13 Questions

• Transformer
  Describe Transformer architecture, namely the self-attention layer, multi-head self-attention layer, masked self-attention and overall architecture of an encoder and a decoder. Describe positional embeddings, learning rate schedule during training and parallelizability of training and inference.

• BERT
  Describe the BERT model architecture (including multi-head self-attention layer) and its pre-training – format of input and output data, masked language model and next sentence prediction. Define GELU and describe how the BERT model can be finetuned to perform POS tagging, sentiment analysis and paraphrase detection (detect if two sentences have the same meaning).

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