Deep Learning – Summer 2021/22
In recent years, deep neural networks have been used to solve complex machinelearning problems. They have achieved significant stateoftheart results in many areas.
The goal of the course is to introduce deep neural networks, from the basics to the latest advances. The course will focus both on theory as well as on practical aspects (students will implement and train several deep neural networks capable of achieving stateoftheart results, for example in image classification, object detection, lemmatization, speech recognition or 3D object recognition). No previous knowledge of artificial neural networks is required, but basic understanding of machine learning is advisable.
About
SIS code: NPFL114
Semester: summer
Ecredits: 7
Examination: 3/2 C+Ex
Guarantor: Milan Straka
Timespace Coordinates
 lectures: English lecture is held on Monday 9:00 in S9, Czech lecture on Monday 13:10 in S9; first lecture is on Feb 14
 practicals: there are two parallel practicals, a Czech one on Monday 17:20 in S9, and an English one on Tuesday 9:00 in S9; first practicals are on Feb 15
All lectures and practicals will be recorded and available on this website.
Lectures
1. Introduction to Deep Learning Slides PDF Slides CZ Lecture EN Lecture Questions numpy_entropy pca_first mnist_layers_activations
2. Training Neural Networks Slides PDF Slides CZ Lecture CZ Adam EN Lecture Questions sgd_backpropagation sgd_manual gym_cartpole
3. Training Neural Networks II Slides PDF Slides CZ Lecture EN Lecture Questions mnist_training mnist_regularization mnist_ensemble uppercase
4. Convolutional Neural Networks Slides PDF Slides CZ Lecture EN Lecture Questions mnist_cnn image_augmentation tf_dataset mnist_multiple cifar_competition
5. Convolutional Neural Networks II Slides PDF Slides CZ Lecture EN Lecture Questions cnn_manual cags_classification cags_segmentation
6. Object Detection Slides PDF Slides CZ Lecture EN Lecture Questions bboxes_utils svhn_competition
7. Recurrent Neural Networks Slides PDF Slides CZ Lecture EN Lecture Questions sequence_classification tagger_we tagger_cle tagger_competition
8. CRF, CTC, Word2Vec Slides PDF Slides CZ Lecture EN Lecture Questions tensorboard_projector tagger_crf tagger_crf_manual speech_recognition
9. Seq2seq, NMT, Transformer Slides PDF Slides CZ Lecture EN Lecture Questions lemmatizer_noattn lemmatizer_attn lemmatizer_competition
10. Easter Monday 3d_recognition homr_competition
11. Transformer, BERT Slides PDF Slides CZ Lecture EN Lecture Questions tagger_transformer sentiment_analysis reading_comprehension
12. Deep Generative Models Slides PDF Slides CZ Lecture EN Lecture Questions vae gan dcgan crac2022
13. Introduction to Deep Reinforcement Learning Slides PDF Slides CZ Lecture EN Lecture Questions monte_carlo reinforce reinforce_baseline reinforce_pixels
14. NASNet, Speech Synthesis, External Memory Networks Slides PDF Slides CZ Lecture EN Lecture Questions learning_to_learn
License
Unless otherwise stated, teaching materials for this course are available under CC BYSA 4.0.
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 14 Slides PDF Slides CZ Lecture EN Lecture Questions 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]
 Selfinformation, entropy, crossentropy, KLdivergence [Section 3.13 of DBL]
 Gaussian distribution [Section 3.9.3 of DLB]
 Machine Learning Basics [Section 5.15.1.3 of DLB]
 History of Deep Learning [Section 1.2 of DLB]
 Linear regression [Section 5.1.4 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]
 Universal approximation theorem
2. Training Neural Networks
Feb 21 Slides PDF Slides CZ Lecture CZ Adam EN Lecture Questions sgd_backpropagation sgd_manual 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
Feb 28 Slides PDF Slides CZ Lecture EN Lecture Questions mnist_training mnist_regularization mnist_ensemble uppercase
 Softmax with NLL (negative log likelihood) as a loss function [Section 6.2.2.3 of DLB, notably equation (6.30); plus slides 1012]
 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 1718]
 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 nonlinearities [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 07 Slides PDF Slides CZ Lecture EN Lecture Questions mnist_cnn image_augmentation tf_dataset mnist_multiple cifar_competition
 Introduction to convolutional networks [Chapter 9 and Sections 9.19.3 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 LargeScale 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 14 Slides PDF Slides CZ Lecture EN Lecture Questions cnn_manual 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
 SENet [SqueezeandExcitation Networks]
 EfficientNet [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks]
 EfficientNetV2 [EfficientNetV2: Smaller Models and Faster Training]
 Transposed convolution
 UNet [UNet: Convolutional Networks for Biomedical Image Segmentation]
6. Object Detection
Mar 21 Slides PDF Slides CZ Lecture EN Lecture Questions bboxes_utils svhn_competition
 Fast RCNN [Fast RCNN]
 Proposing RoIs using Faster RCNN [Faster RCNN: Towards RealTime Object Detection with Region Proposal Networks]
 Mask RCNN [Mask RCNN]
 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
Mar 28 Slides PDF Slides CZ Lecture EN Lecture Questions sequence_classification tagger_we tagger_cle tagger_competition
 Sequence modelling using Recurrent Neural Networks (RNN) [Chapter 10 until Section 10.2.1 (excluding) of DLB]
 The challenge of longterm dependencies [Section 10.7 of DLB]
 Long ShortTerm Memory (LSTM) [Section 10.10.1 of DLB, Sepp Hochreiter, Jürgen Schmidhuber (1997): Long shortterm 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 EncoderDecoder 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]
 Characterlevel embeddings using Recurrent neural networks [C2W model from Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation]
 Characterlevel embeddings using Convolutional neural networks [CharCNN from CharacterAware Neural Language Models]
8. CRF, CTC, Word2Vec
Apr 04 Slides PDF Slides CZ Lecture EN Lecture Questions tensorboard_projector tagger_crf tagger_crf_manual speech_recognition
 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]
Word2vec
word embeddings, notably the CBOW and Skipgram 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]
 Characterlevel embeddings using character ngrams [Described simultaneously in several papers as Charagram (Charagram: Embedding Words and Sentences via Character ngrams), Subword Information (Enriching Word Vectors with Subword Information or SubGram (SubGram: Extending SkipGram Word Representation with Substrings)]
9. Seq2seq, NMT, Transformer
Apr 11 Slides PDF Slides CZ Lecture EN Lecture Questions lemmatizer_noattn lemmatizer_attn lemmatizer_competition
 Neural Machine Translation using EncoderDecoder or SequencetoSequence 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 EncoderDecoder 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]
 Transformer architecture [Attention Is All You Need]
10. Easter Monday
Apr 18 3d_recognition homr_competition
11. Transformer, BERT
Apr 25 Slides PDF Slides CZ Lecture EN Lecture Questions tagger_transformer sentiment_analysis reading_comprehension
 Transformer architecture [Attention Is All You Need]
 BERT [BERT: Pretraining of Deep Bidirectional Transformers for Language Understanding]
 RoBERTa [RoBERTa: A Robustly Optimized BERT Pretraining Approach]
12. Deep Generative Models
May 02 Slides PDF Slides CZ Lecture EN Lecture Questions vae gan dcgan crac2022
 Autoencoders (undercomplete, sparse, denoising) [Chapter 14, Sections 1414.2.3 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, AutoEncoding 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]
13. Introduction to Deep Reinforcement Learning
May 09 Slides PDF Slides CZ Lecture EN Lecture Questions 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.
 Multiarmed bandits [Sections 22.4 of RLB]
 Markov Decision Process [Sections 33.3 of RLB]
 Policies and Value Functions [Sections 3.5 of RLB]
 Monte Carlo Methods [Sections 55.4 of RLB]
 Policy Gradient Methods [Sections 1313.1 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]
14. NASNet, Speech Synthesis, External Memory Networks
May 16 Slides PDF Slides CZ Lecture EN Lecture Questions learning_to_learn
 NasNet [Learning Transferable Architectures for Scalable Image Recognition]
 EfficientNet [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks]
 WaveNet [WaveNet: A Generative Model for Raw Audio]
 Parallel WaveNet [Parallel WaveNet: Fast HighFidelity 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 [Oneshot learning with MemoryAugmented Neural Networks]
Requirements
To pass the practicals, you need to obtain at least 80 points, excluding the bonus points. Note that all surplus points (both bonus and nonbonus) will be transfered to the exam. In total, assignments for at least 120 points (not including the bonus points) will be available, and if you solve all the assignments (any nonzero amount of points counts as solved), you automatically pass the exam with grade 1.
Environment
The tasks are evaluated automatically using the ReCodEx Code Examiner.
The evaluation is performed using Python 3.9, TensorFlow 2.8.0, TensorFlow Addons 0.16, TensorFlow Probability 0.12.1, TensorFlow Hub 0.11.0, and OpenAI Gym 0.20.0. You should install the exact version of these packages yourselves.
Teamwork
Solving assignments in teams (of size at most 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.
No Cheating
Cheating is strictly prohibited and any student found cheating will be punished. The punishment can involve failing the whole course, or, in grave cases, being expelled from the faculty. While discussing assignments with any classmate is fine, each team must complete the assignments themselves, without using code they did not write (unless explicitly allowed). Of course, inside a team you are expected to share code and submit identical solutions.
numpy_entropy
Deadline: Feb 28, 7:59 a.m. 3 points
The goal of this exercise is to familiarize with Python, NumPy and ReCodEx submission system. Start with the numpy_entropy.py.
Load a file specified in args.data_path
, whose lines consist of data points of our
dataset, and load a file specified in args.model_path
, which describes a model probability distribution,
with each line being a tabseparated 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)
 crossentropy H(data distribution, model distribution)
 KLdivergence D_{KL}(data distribution, model distribution)
Use natural logarithms to compute the entropies and the divergence.
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 numpy_entropy.py data_path
numpy_entropy_data_1.txtmodel_path
numpy_entropy_model_1.txt
Entropy: 0.96 nats
Crossentropy: 1.07 nats
KL divergence: 0.11 nats
python3 numpy_entropy.py data_path
numpy_entropy_data_2.txtmodel_path
numpy_entropy_model_2.txt
Entropy: 0.96 nats
Crossentropy: inf nats
KL divergence: inf nats
python3 numpy_entropy.py data_path
numpy_entropy_data_3.txtmodel_path
numpy_entropy_model_3.txt
Entropy: 4.15 nats
Crossentropy: 4.23 nats
KL divergence: 0.08 nats
python3 numpy_entropy.py data_path
numpy_entropy_data_4.txtmodel_path
numpy_entropy_model_4.txt
Entropy: 4.99 nats
Crossentropy: 5.03 nats
KL divergence: 0.04 nats
python3 numpy_entropy.py data_path
numpy_entropy_data_1.txtmodel_path
numpy_entropy_model_1.txt
Entropy: 0.96 nats
Crossentropy: 1.07 nats
KL divergence: 0.11 nats
python3 numpy_entropy.py data_path
numpy_entropy_data_2.txtmodel_path
numpy_entropy_model_2.txt
Entropy: 0.96 nats
Crossentropy: inf nats
KL divergence: inf nats
 The last three tests use data available only in ReCodEx. They are quite similar to the numpy_entropy_data_3.txt numpy_entropy_model_3.txt and numpy_entropy_data_4.txt numpy_entropy_model_4.txt, but are generated with a different random seed.
pca_first
Deadline: Feb 28, 7:59 a.m. 2 points
The goal of this exercise is to familiarize with TensorFlow tf.Tensor
s,
shapes and basic tensor manipulation methods. Start with the
pca_first.py
(and you will also need the mnist.py
module).
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.
Finally, it is a good idea to read the
TensorFlow guide about tf.Tensor
s.
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 pca_first.py examples=1024 iterations=64
Total variance: 53.12
Explained variance: 9.64%
python3 pca_first.py examples=8192 iterations=128
Total variance: 53.05
Explained variance: 9.89%
python3 pca_first.py examples=55000 iterations=1024
Total variance: 52.74
Explained variance: 9.71%
mnist_layers_activations
Deadline: Mar 07, 7:59 a.m. 2 points
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
hidden_layers
.  Activation function of these hidden layers can be also specified as a command
line parameter
activation
, with supported values ofnone
,relu
,tanh
andsigmoid
.  Print the final accuracy on the test set.
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 mnist_layers_activations.py hidden_layers=0 activation=none
Epoch 1/10 loss: 0.5383  accuracy: 0.8613  val_loss: 0.2755  val_accuracy: 0.9308
Epoch 5/10 loss: 0.2783  accuracy: 0.9220  val_loss: 0.2202  val_accuracy: 0.9430
Epoch 10/10 loss: 0.2595  accuracy: 0.9273  val_loss: 0.2146  val_accuracy: 0.9434
loss: 0.2637  accuracy: 0.9259
python3 mnist_layers_activations.py hidden_layers=1 activation=none
Epoch 1/10 loss: 0.3828  accuracy: 0.8914  val_loss: 0.2438  val_accuracy: 0.9350
Epoch 5/10 loss: 0.2754  accuracy: 0.9222  val_loss: 0.2341  val_accuracy: 0.9370
Epoch 10/10 loss: 0.2640  accuracy: 0.9260  val_loss: 0.2318  val_accuracy: 0.9400
loss: 0.2795  accuracy: 0.9241
python3 mnist_layers_activations.py hidden_layers=1 activation=relu
Epoch 1/10 loss: 0.3195  accuracy: 0.9109  val_loss: 0.1459  val_accuracy: 0.9612
Epoch 5/10 loss: 0.0629  accuracy: 0.9811  val_loss: 0.0820  val_accuracy: 0.9776
Epoch 10/10 loss: 0.0237  accuracy: 0.9937  val_loss: 0.0801  val_accuracy: 0.9776
loss: 0.0829  accuracy: 0.9769
python3 mnist_layers_activations.py hidden_layers=1 activation=tanh
Epoch 1/10 loss: 0.3414  accuracy: 0.9039  val_loss: 0.1668  val_accuracy: 0.9570
Epoch 5/10 loss: 0.0750  accuracy: 0.9783  val_loss: 0.0813  val_accuracy: 0.9774
Epoch 10/10 loss: 0.0268  accuracy: 0.9937  val_loss: 0.0788  val_accuracy: 0.9744
loss: 0.0822  accuracy: 0.9751
python3 mnist_layers_activations.py hidden_layers=1 activation=sigmoid
Epoch 1/10 loss: 0.4969  accuracy: 0.8751  val_loss: 0.2150  val_accuracy: 0.9400
Epoch 5/10 loss: 0.1222  accuracy: 0.9649  val_loss: 0.1041  val_accuracy: 0.9718
Epoch 10/10 loss: 0.0594  accuracy: 0.9842  val_loss: 0.0805  val_accuracy: 0.9772
loss: 0.0862  accuracy: 0.9741
python3 mnist_layers_activations.py hidden_layers=3 activation=relu
Epoch 1/10 loss: 0.2753  accuracy: 0.9173  val_loss: 0.1128  val_accuracy: 0.9672
Epoch 5/10 loss: 0.0489  accuracy: 0.9843  val_loss: 0.0878  val_accuracy: 0.9778
Epoch 10/10 loss: 0.0226  accuracy: 0.9923  val_loss: 0.0892  val_accuracy: 0.9788
loss: 0.0770  accuracy: 0.9793
python3 mnist_layers_activations.py hidden_layers=10 activation=relu
Epoch 1/10 loss: 0.3598  accuracy: 0.8881  val_loss: 0.1457  val_accuracy: 0.9586
Epoch 5/10 loss: 0.0822  accuracy: 0.9775  val_loss: 0.1135  val_accuracy: 0.9766
Epoch 10/10 loss: 0.0525  accuracy: 0.9859  val_loss: 0.1108  val_accuracy: 0.9768
loss: 0.1342  accuracy: 0.9715
python3 mnist_layers_activations.py hidden_layers=10 activation=sigmoid
Epoch 1/10 loss: 2.2830  accuracy: 0.1088  val_loss: 1.9021  val_accuracy: 0.2120
Epoch 5/10 loss: 0.9505  accuracy: 0.6286  val_loss: 0.7622  val_accuracy: 0.7214
Epoch 10/10 loss: 0.4468  accuracy: 0.8919  val_loss: 0.3524  val_accuracy: 0.9212
loss: 0.4232  accuracy: 0.8993
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 mnist_layers_activations.py epochs=1 hidden_layers=0 activation=none
Epoch 1/1 loss: 0.5383  accuracy: 0.8613  val_loss: 0.2755  val_accuracy: 0.9308
loss: 0.3304  accuracy: 0.9110
python3 mnist_layers_activations.py epochs=1 hidden_layers=1 activation=none
Epoch 1/1 loss: 0.3828  accuracy: 0.8914  val_loss: 0.2438  val_accuracy: 0.9350
loss: 0.2956  accuracy: 0.9198
python3 mnist_layers_activations.py epochs=1 hidden_layers=1 activation=relu
Epoch 1/1 loss: 0.3195  accuracy: 0.9109  val_loss: 0.1459  val_accuracy: 0.9612
loss: 0.1738  accuracy: 0.9517
python3 mnist_layers_activations.py epochs=1 hidden_layers=1 activation=tanh
Epoch 1/1 loss: 0.3414  accuracy: 0.9039  val_loss: 0.1668  val_accuracy: 0.9570
loss: 0.2039  accuracy: 0.9422
python3 mnist_layers_activations.py epochs=1 hidden_layers=1 activation=sigmoid
Epoch 1/1 loss: 0.4969  accuracy: 0.8751  val_loss: 0.2150  val_accuracy: 0.9400
loss: 0.2627  accuracy: 0.9268
python3 mnist_layers_activations.py epochs=1 hidden_layers=3 activation=relu
Epoch 1/1 loss: 0.2753  accuracy: 0.9173  val_loss: 0.1128  val_accuracy: 0.9672
loss: 0.1309  accuracy: 0.9601
python3 mnist_layers_activations.py epochs=1 hidden_layers=10 activation=relu
Epoch 1/1 loss: 0.3598  accuracy: 0.8881  val_loss: 0.1457  val_accuracy: 0.9586
loss: 0.1806  accuracy: 0.9474
python3 mnist_layers_activations.py epochs=1 hidden_layers=10 activation=sigmoid
Epoch 1/1 loss: 2.2830  accuracy: 0.1088  val_loss: 1.9021  val_accuracy: 0.2120
loss: 1.9469  accuracy: 0.2065
sgd_backpropagation
Deadline: Mar 07, 7:59 a.m. 3 points
In this exercise you will learn how to compute gradients using the socalled 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.
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 sgd_backpropagation.py batch_size=64 hidden_layer=20 learning_rate=0.1
Dev accuracy after epoch 1 is 92.84
Dev accuracy after epoch 2 is 93.86
Dev accuracy after epoch 3 is 94.64
Dev accuracy after epoch 4 is 95.24
Dev accuracy after epoch 5 is 95.26
Dev accuracy after epoch 6 is 95.66
Dev accuracy after epoch 7 is 95.58
Dev accuracy after epoch 8 is 95.86
Dev accuracy after epoch 9 is 96.18
Dev accuracy after epoch 10 is 96.08
Test accuracy after epoch 10 is 95.53
python3 sgd_backpropagation.py batch_size=100 hidden_layer=32 learning_rate=0.2
Dev accuracy after epoch 1 is 93.66
Dev accuracy after epoch 2 is 95.00
Dev accuracy after epoch 3 is 95.72
Dev accuracy after epoch 4 is 95.80
Dev accuracy after epoch 5 is 96.34
Dev accuracy after epoch 6 is 96.16
Dev accuracy after epoch 7 is 96.42
Dev accuracy after epoch 8 is 96.36
Dev accuracy after epoch 9 is 96.60
Dev accuracy after epoch 10 is 96.58
Test accuracy after epoch 10 is 96.18
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 sgd_backpropagation.py epochs=2 batch_size=64 hidden_layer=20 learning_rate=0.1
Dev accuracy after epoch 1 is 92.84
Dev accuracy after epoch 2 is 93.86
Test accuracy after epoch 2 is 93.21
python3 sgd_backpropagation.py epochs=2 batch_size=100 hidden_layer=32 learning_rate=0.2
Dev accuracy after epoch 1 is 93.66
Dev accuracy after epoch 2 is 95.00
Test accuracy after epoch 2 is 93.93
sgd_manual
Deadline: Mar 07, 7:59 a.m. 2 points
The goal in this exercise is to extend your solution to the sgd_backpropagation assignment by manually computing the gradient.
While in this assignment we compute the gradient manually, we will nearly always use the automatic differentiation. Therefore, the assignment is more of a mathematical exercise than a realworld application. Furthermore, we will compute the derivatives together on the Feb 28 practicals.
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 you do not use tf.GradientTape
in your solution.
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 sgd_manual.py batch_size=64 hidden_layer=20 learning_rate=0.1
Dev accuracy after epoch 1 is 92.84
Dev accuracy after epoch 2 is 93.86
Dev accuracy after epoch 3 is 94.64
Dev accuracy after epoch 4 is 95.24
Dev accuracy after epoch 5 is 95.26
Dev accuracy after epoch 6 is 95.66
Dev accuracy after epoch 7 is 95.58
Dev accuracy after epoch 8 is 95.86
Dev accuracy after epoch 9 is 96.18
Dev accuracy after epoch 10 is 96.08
Test accuracy after epoch 10 is 95.53
python3 sgd_manual.py batch_size=100 hidden_layer=32 learning_rate=0.2
Dev accuracy after epoch 1 is 93.66
Dev accuracy after epoch 2 is 95.00
Dev accuracy after epoch 3 is 95.72
Dev accuracy after epoch 4 is 95.80
Dev accuracy after epoch 5 is 96.34
Dev accuracy after epoch 6 is 96.16
Dev accuracy after epoch 7 is 96.42
Dev accuracy after epoch 8 is 96.36
Dev accuracy after epoch 9 is 96.60
Dev accuracy after epoch 10 is 96.58
Test accuracy after epoch 10 is 96.18
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 sgd_manual.py epochs=2 batch_size=64 hidden_layer=20 learning_rate=0.1
Dev accuracy after epoch 1 is 92.84
Dev accuracy after epoch 2 is 93.86
Test accuracy after epoch 2 is 93.21
python3 sgd_manual.py epochs=2 batch_size=100 hidden_layer=32 learning_rate=0.2
Dev accuracy after epoch 1 is 93.66
Dev accuracy after epoch 2 is 95.00
Test accuracy after epoch 2 is 93.93
gym_cartpole
Deadline: Mar 07, 7:59 a.m. 3 points
Solve the CartPolev1 environment from the Gym library, 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 can be performed by running the
gym_cartpole.py
with evaluate
argument (optionally rendering if render
option is
provided), or directly calling the evaluate_model
method. 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).
When designing the model, you should consider that the size of the training data is very small and the data is quite noisy.
When submitting to ReCodEx, do not forget to also submit the trained model.
mnist_training
Deadline: Mar 14, 7:59 a.m. 2 points
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
orAdam
).  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
orlinear
(with degree 1, so linear 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.
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 mnist_training.py optimizer=SGD learning_rate=0.01
Epoch 1/10 loss: 0.7989  accuracy: 0.8098  val_loss: 0.3662  val_accuracy: 0.9146
Epoch 2/10 loss: 0.3991  accuracy: 0.8919  val_loss: 0.2848  val_accuracy: 0.9258
Epoch 3/10 loss: 0.3382  accuracy: 0.9054  val_loss: 0.2496  val_accuracy: 0.9350
Epoch 4/10 loss: 0.3049  accuracy: 0.9144  val_loss: 0.2292  val_accuracy: 0.9390
Epoch 5/10 loss: 0.2811  accuracy: 0.9216  val_loss: 0.2131  val_accuracy: 0.9426
Epoch 6/10 loss: 0.2623  accuracy: 0.9268  val_loss: 0.2003  val_accuracy: 0.9464
Epoch 7/10 loss: 0.2461  accuracy: 0.9315  val_loss: 0.1882  val_accuracy: 0.9500
Epoch 8/10 loss: 0.2323  accuracy: 0.9353  val_loss: 0.1821  val_accuracy: 0.9522
Epoch 9/10 loss: 0.2204  accuracy: 0.9386  val_loss: 0.1715  val_accuracy: 0.9560
Epoch 10/10 loss: 0.2094  accuracy: 0.9413  val_loss: 0.1650  val_accuracy: 0.9572  val_test_loss: 0.1978  val_test_accuracy: 0.9441
python3 mnist_training.py optimizer=SGD learning_rate=0.01 momentum=0.9
Epoch 1/10 loss: 0.3617  accuracy: 0.8973  val_loss: 0.1684  val_accuracy: 0.9560
Epoch 2/10 loss: 0.1803  accuracy: 0.9490  val_loss: 0.1274  val_accuracy: 0.9644
Epoch 3/10 loss: 0.1319  accuracy: 0.9625  val_loss: 0.1051  val_accuracy: 0.9706
Epoch 4/10 loss: 0.1048  accuracy: 0.9709  val_loss: 0.0922  val_accuracy: 0.9746
Epoch 5/10 loss: 0.0864  accuracy: 0.9756  val_loss: 0.0844  val_accuracy: 0.9782
Epoch 6/10 loss: 0.0731  accuracy: 0.9794  val_loss: 0.0791  val_accuracy: 0.9784
Epoch 7/10 loss: 0.0633  accuracy: 0.9825  val_loss: 0.0738  val_accuracy: 0.9818
Epoch 8/10 loss: 0.0550  accuracy: 0.9848  val_loss: 0.0746  val_accuracy: 0.9796
Epoch 9/10 loss: 0.0485  accuracy: 0.9866  val_loss: 0.0758  val_accuracy: 0.9796
Epoch 10/10 loss: 0.0429  accuracy: 0.9888  val_loss: 0.0704  val_accuracy: 0.9806  val_test_loss: 0.0677  val_test_accuracy: 0.9789
python3 mnist_training.py optimizer=SGD learning_rate=0.1
Epoch 1/10 loss: 0.3502  accuracy: 0.9021  val_loss: 0.1679  val_accuracy: 0.9576
Epoch 2/10 loss: 0.1784  accuracy: 0.9492  val_loss: 0.1265  val_accuracy: 0.9646
Epoch 3/10 loss: 0.1303  accuracy: 0.9629  val_loss: 0.0994  val_accuracy: 0.9724
Epoch 4/10 loss: 0.1033  accuracy: 0.9714  val_loss: 0.0891  val_accuracy: 0.9754
Epoch 5/10 loss: 0.0848  accuracy: 0.9757  val_loss: 0.0847  val_accuracy: 0.9776
Epoch 6/10 loss: 0.0721  accuracy: 0.9794  val_loss: 0.0802  val_accuracy: 0.9778
Epoch 7/10 loss: 0.0620  accuracy: 0.9829  val_loss: 0.0724  val_accuracy: 0.9818
Epoch 8/10 loss: 0.0541  accuracy: 0.9853  val_loss: 0.0724  val_accuracy: 0.9808
Epoch 9/10 loss: 0.0480  accuracy: 0.9868  val_loss: 0.0745  val_accuracy: 0.9796
Epoch 10/10 loss: 0.0421  accuracy: 0.9890  val_loss: 0.0665  val_accuracy: 0.9824  val_test_loss: 0.0658  val_test_accuracy: 0.9800
python3 mnist_training.py optimizer=Adam learning_rate=0.001
Epoch 1/10 loss: 0.2699  accuracy: 0.9231  val_loss: 0.1166  val_accuracy: 0.9686
Epoch 2/10 loss: 0.1139  accuracy: 0.9665  val_loss: 0.0921  val_accuracy: 0.9748
Epoch 3/10 loss: 0.0769  accuracy: 0.9773  val_loss: 0.0831  val_accuracy: 0.9774
Epoch 4/10 loss: 0.0561  accuracy: 0.9833  val_loss: 0.0758  val_accuracy: 0.9780
Epoch 5/10 loss: 0.0425  accuracy: 0.9872  val_loss: 0.0732  val_accuracy: 0.9800
Epoch 6/10 loss: 0.0312  accuracy: 0.9910  val_loss: 0.0838  val_accuracy: 0.9804
Epoch 7/10 loss: 0.0268  accuracy: 0.9918  val_loss: 0.0776  val_accuracy: 0.9812
Epoch 8/10 loss: 0.0194  accuracy: 0.9941  val_loss: 0.0739  val_accuracy: 0.9818
Epoch 9/10 loss: 0.0154  accuracy: 0.9957  val_loss: 0.0796  val_accuracy: 0.9816
Epoch 10/10 loss: 0.0128  accuracy: 0.9962  val_loss: 0.0828  val_accuracy: 0.9778  val_test_loss: 0.0762  val_test_accuracy: 0.9786
python3 mnist_training.py optimizer=Adam learning_rate=0.01
Epoch 1/10 loss: 0.2354  accuracy: 0.9290  val_loss: 0.1425  val_accuracy: 0.9576
Epoch 2/10 loss: 0.1450  accuracy: 0.9590  val_loss: 0.1551  val_accuracy: 0.9584
Epoch 3/10 loss: 0.1240  accuracy: 0.9647  val_loss: 0.1432  val_accuracy: 0.9682
Epoch 4/10 loss: 0.1161  accuracy: 0.9697  val_loss: 0.1400  val_accuracy: 0.9626
Epoch 5/10 loss: 0.1081  accuracy: 0.9718  val_loss: 0.1329  val_accuracy: 0.9688
Epoch 6/10 loss: 0.0908  accuracy: 0.9771  val_loss: 0.1663  val_accuracy: 0.9688
Epoch 7/10 loss: 0.0936  accuracy: 0.9767  val_loss: 0.1644  val_accuracy: 0.9670
Epoch 8/10 loss: 0.0872  accuracy: 0.9784  val_loss: 0.1550  val_accuracy: 0.9686
Epoch 9/10 loss: 0.0817  accuracy: 0.9798  val_loss: 0.2147  val_accuracy: 0.9642
Epoch 10/10 loss: 0.0779  accuracy: 0.9807  val_loss: 0.1981  val_accuracy: 0.9718  val_test_loss: 0.1910  val_test_accuracy: 0.9726
python3 mnist_training.py optimizer=Adam learning_rate=0.01 decay=exponential learning_rate_final=0.001
Epoch 1/10 loss: 0.2235  accuracy: 0.9319  val_loss: 0.1255  val_accuracy: 0.9652
Epoch 2/10 loss: 0.1145  accuracy: 0.9659  val_loss: 0.1273  val_accuracy: 0.9666
Epoch 3/10 loss: 0.0761  accuracy: 0.9762  val_loss: 0.0905  val_accuracy: 0.9778
Epoch 4/10 loss: 0.0514  accuracy: 0.9842  val_loss: 0.1031  val_accuracy: 0.9736
Epoch 5/10 loss: 0.0323  accuracy: 0.9893  val_loss: 0.1046  val_accuracy: 0.9772
Epoch 6/10 loss: 0.0189  accuracy: 0.9938  val_loss: 0.1010  val_accuracy: 0.9794
Epoch 7/10 loss: 0.0127  accuracy: 0.9959  val_loss: 0.1019  val_accuracy: 0.9790
Epoch 8/10 loss: 0.0073  accuracy: 0.9977  val_loss: 0.1066  val_accuracy: 0.9792
Epoch 9/10 loss: 0.0039  accuracy: 0.9990  val_loss: 0.1049  val_accuracy: 0.9806
Epoch 10/10 loss: 0.0021  accuracy: 0.9997  val_loss: 0.1057  val_accuracy: 0.9798  val_test_loss: 0.0868  val_test_accuracy: 0.9809
python3 mnist_training.py optimizer=Adam learning_rate=0.01 decay=linear learning_rate_final=0.0001
Epoch 1/10 loss: 0.2292  accuracy: 0.9303  val_loss: 0.1176  val_accuracy: 0.9634
Epoch 2/10 loss: 0.1291  accuracy: 0.9621  val_loss: 0.1193  val_accuracy: 0.9658
Epoch 3/10 loss: 0.0973  accuracy: 0.9719  val_loss: 0.1094  val_accuracy: 0.9712
Epoch 4/10 loss: 0.0694  accuracy: 0.9796  val_loss: 0.1408  val_accuracy: 0.9656
Epoch 5/10 loss: 0.0523  accuracy: 0.9840  val_loss: 0.1234  val_accuracy: 0.9704
Epoch 6/10 loss: 0.0346  accuracy: 0.9889  val_loss: 0.1381  val_accuracy: 0.9740
Epoch 7/10 loss: 0.0249  accuracy: 0.9922  val_loss: 0.1105  val_accuracy: 0.9776
Epoch 8/10 loss: 0.0105  accuracy: 0.9968  val_loss: 0.1115  val_accuracy: 0.9780
Epoch 9/10 loss: 0.0050  accuracy: 0.9985  val_loss: 0.1144  val_accuracy: 0.9800
Epoch 10/10 loss: 0.0023  accuracy: 0.9995  val_loss: 0.1127  val_accuracy: 0.9788  val_test_loss: 0.0975  val_test_accuracy: 0.9812
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 mnist_training.py epochs=1 optimizer=SGD learning_rate=0.01
loss: 0.7989  accuracy: 0.8098  val_loss: 0.3662  val_accuracy: 0.9146  val_test_loss: 0.4247  val_test_accuracy: 0.8926
python3 mnist_training.py epochs=1 optimizer=SGD learning_rate=0.01 momentum=0.9
loss: 0.3617  accuracy: 0.8973  val_loss: 0.1684  val_accuracy: 0.9560  val_test_loss: 0.2011  val_test_accuracy: 0.9456
python3 mnist_training.py epochs=1 optimizer=SGD learning_rate=0.1
loss: 0.3502  accuracy: 0.9021  val_loss: 0.1679  val_accuracy: 0.9576  val_test_loss: 0.1996  val_test_accuracy: 0.9454
python3 mnist_training.py epochs=1 optimizer=Adam learning_rate=0.001
loss: 0.2699  accuracy: 0.9231  val_loss: 0.1166  val_accuracy: 0.9686  val_test_loss: 0.1385  val_test_accuracy: 0.9605
python3 mnist_training.py epochs=1 optimizer=Adam learning_rate=0.01
loss: 0.2354  accuracy: 0.9290  val_loss: 0.1425  val_accuracy: 0.9576  val_test_loss: 0.1692  val_test_accuracy: 0.9469
python3 mnist_training.py epochs=2 optimizer=Adam learning_rate=0.01 decay=exponential learning_rate_final=0.001
Epoch 1/2 loss: 0.1961  accuracy: 0.9400  val_loss: 0.0890  val_accuracy: 0.9728
Epoch 2/2 loss: 0.0663  accuracy: 0.9792  val_loss: 0.0675  val_accuracy: 0.9790  val_test_loss: 0.0721  val_test_accuracy: 0.9773
Final learning rate: 0.001
python3 mnist_training.py epochs=2 optimizer=Adam learning_rate=0.01 decay=linear learning_rate_final=0.0001
Epoch 1/2 loss: 0.2111  accuracy: 0.9356  val_loss: 0.1017  val_accuracy: 0.9690
Epoch 2/2 loss: 0.0701  accuracy: 0.9781  val_loss: 0.0708  val_accuracy: 0.9790  val_test_loss: 0.0693  val_test_accuracy: 0.9779
Final learning rate: 0.0001
mnist_regularization
Deadline: Mar 14, 7:59 a.m. 3 points
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 firstFlatten
and also after allDense
hidden layers (but not after the output layer).  Allow using L2 regularization with weight
args.l2
. Usetf.keras.regularizers.L2
as a regularizer for all kernels (but not biases) of allDense
layers (including the last one).  Allow using label smoothing with weight
args.label_smoothing
. Instead ofSparseCategoricalCrossentropy
, you will need to useCategoricalCrossentropy
which offerslabel_smoothing
argument.
In ReCodEx, there will be six tests (two for each regularization methods) and you will get half a point for passing each one.
In addition to submitting the task in ReCodEx, also run the following variations and observe the results in TensorBoard (or online here), notably the 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
.
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 mnist_regularization.py epochs=1 dropout=0.3
loss: 0.7987  accuracy: 0.7616  val_loss: 0.3175  val_accuracy: 0.9110  val_test_loss: 0.3825  val_test_accuracy: 0.8882
python3 mnist_regularization.py epochs=1 dropout=0.5 hidden_layers 300 300
loss: 1.4363  accuracy: 0.5090  val_loss: 0.4447  val_accuracy: 0.8862  val_test_loss: 0.5256  val_test_accuracy: 0.8537
python3 mnist_regularization.py epochs=1 l2=0.001
loss: 0.9748  accuracy: 0.8374  val_loss: 0.5730  val_accuracy: 0.9188  val_test_loss: 0.6294  val_test_accuracy: 0.9049
python3 mnist_regularization.py epochs=1 l2=0.0001
loss: 0.6501  accuracy: 0.8396  val_loss: 0.3136  val_accuracy: 0.9210  val_test_loss: 0.3704  val_test_accuracy: 0.9075
python3 mnist_regularization.py epochs=1 label_smoothing=0.1
loss: 0.9918  accuracy: 0.8436  val_loss: 0.7645  val_accuracy: 0.9254  val_test_loss: 0.8047  val_test_accuracy: 0.9095
python3 mnist_regularization.py epochs=1 label_smoothing=0.3
loss: 1.5068  accuracy: 0.8428  val_loss: 1.3686  val_accuracy: 0.9332  val_test_loss: 1.3936  val_test_accuracy: 0.9125
mnist_ensemble
Deadline: Mar 14, 7:59 a.m. 2 points
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 test set.
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 mnist_ensemble.py models=5
Model 1, individual accuracy 97.69, ensemble accuracy 97.69
Model 2, individual accuracy 97.75, ensemble accuracy 98.03
Model 3, individual accuracy 97.90, ensemble accuracy 98.08
Model 4, individual accuracy 97.52, ensemble accuracy 98.05
Model 5, individual accuracy 97.59, ensemble accuracy 98.14
python3 mnist_ensemble.py models=5 hidden_layers=200
Model 1, individual accuracy 97.86, ensemble accuracy 97.86
Model 2, individual accuracy 98.09, ensemble accuracy 98.27
Model 3, individual accuracy 98.15, ensemble accuracy 98.41
Model 4, individual accuracy 98.13, ensemble accuracy 98.45
Model 5, individual accuracy 97.79, ensemble accuracy 98.39
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 mnist_ensemble.py epochs=1 models=5
Model 1, individual accuracy 95.17, ensemble accuracy 95.17
Model 2, individual accuracy 94.75, ensemble accuracy 95.10
Model 3, individual accuracy 95.19, ensemble accuracy 95.11
Model 4, individual accuracy 95.11, ensemble accuracy 95.13
Model 5, individual accuracy 95.20, ensemble accuracy 95.24
python3 mnist_ensemble.py epochs=1 models=5 hidden_layers=200
Model 1, individual accuracy 96.05, ensemble accuracy 96.05
Model 2, individual accuracy 96.11, ensemble accuracy 96.21
Model 3, individual accuracy 95.76, ensemble accuracy 96.16
Model 4, individual accuracy 95.85, ensemble accuracy 96.08
Model 5, individual accuracy 95.94, ensemble accuracy 96.10
uppercase
Deadline: Mar 14, 7:59 a.m. 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 opendata 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/ipynb file.
The task is also a competition. Everyone who submits
a solution which achieves at least 98.5% accuracy will get 4 basic points; the
5 bonus points will be distributed depending on relative ordering of your
solutions. The accuracy is computed percharacter and can be evaluated
by running uppercase_data.py
with evaluate
argument, or using its evaluate_file
method.
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: Mar 21, 7:59 a.m. 3 points
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 commaseparated specifications of the following layers:
Cfilterskernel_sizestridepadding
: Add a convolutional layer with ReLU activation and specified number of filters, kernel size, stride and padding. Example:C1031same
CBfilterskernel_sizestridepadding
: Same asCfilterskernel_sizestridepadding
, 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:CB1031same
Mpool_sizestride
: Add max pooling with specified size and stride, using the default"valid"
padding. Example:M32
R[layers]
: Add a residual connection. Thelayers
contain a specification of at least one convolutional layer (but not a recursive residual connectionR
). The input to theR
layer should be processed sequentially bylayers
, and the produced output (after the ReLU nonlinearty of the last layer) should be added to the input (of thisR
layer). Example:R[C1631same,C1631same]
F
: Flatten inputs. Must appear exactly once in the architecture.Hhidden_layer_size
: Add a dense layer with ReLU activation and specified size. Example:H100
Ddropout_rate
: Apply dropout with the given dropout rate. Example:D0.5
An example architecture might be cnn=CB1652same,M32,F,H100,D0.5
.
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
.
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 mnist_cnn.py epochs=1 cnn=F,H100
loss: 0.3195  accuracy: 0.9109  val_loss: 0.1459  val_accuracy: 0.9612
python3 mnist_cnn.py epochs=1 cnn=F,H100,D0.5
loss: 0.4794  accuracy: 0.8575  val_loss: 0.1617  val_accuracy: 0.9596
python3 mnist_cnn.py epochs=1 cnn=M52,F,H50
loss: 0.7223  accuracy: 0.7766  val_loss: 0.3934  val_accuracy: 0.8818
python3 mnist_cnn.py epochs=1 cnn=C835same,C832valid,F,H50
loss: 0.7543  accuracy: 0.7567  val_loss: 0.3445  val_accuracy: 0.9004
python3 mnist_cnn.py epochs=1 cnn=CB635valid,F,H32
loss: 0.5990  accuracy: 0.8108  val_loss: 0.2519  val_accuracy: 0.9230
python3 mnist_cnn.py epochs=1 cnn=CB835valid,R[CB831same,CB831same],F,H50
loss: 0.4530  accuracy: 0.8567  val_loss: 0.1787  val_accuracy: 0.9464
image_augmentation
Deadline: Mar 21, 7:59 a.m. 1 points
The template image_augmentation.py creates a simple convolutional network for classifying CIFAR10. Your goal is to perform image data augmentation operations using ImageDataGenerator and to utilize these data during training.
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 image_augmentation.py epochs=1 batch_size=50
loss: 2.1985  accuracy: 0.1670  val_loss: 1.9781  val_accuracy: 0.2620
python3 image_augmentation.py epochs=1 batch_size=100
loss: 2.1988  accuracy: 0.1678  val_loss: 1.9996  val_accuracy: 0.2680
tf_dataset
Deadline: Mar 21, 7:59 a.m. 2 points
In this assignment you will familiarize yourselves with tf.data
, which is
TensorFlow highlevel 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
.
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 tf_dataset.py epochs=1 batch_size=50
loss: 2.1262  accuracy: 0.1998  val_loss: 1.8775  val_accuracy: 0.3040
python3 tf_dataset.py epochs=1 batch_size=100
loss: 2.2113  accuracy: 0.1618  val_loss: 2.0246  val_accuracy: 0.2640
mnist_multiple
Deadline: Mar 21, 7:59 a.m. 3 points
In this assignment you will implement a model with multiple inputs and outputs. 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 four outputs:
 direct prediction whether the first digit is larger than the second one,
 digit classification for the first image,
 digit classification for the second image,
 indirect prediction comparing the digits predicted by the above two outputs.
 You need to implement:
 the model, using multiple inputs, outputs, losses and metrics;
 construction of twoimage dataset examples using regular MNIST data via the
tf.data
API.
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 mnist_multiple.py epochs=1 batch_size=50
loss: 0.9233  digit_1_loss: 0.3067  digit_2_loss: 0.3131  direct_prediction_loss: 0.3036  direct_prediction_accuracy: 0.8617  indirect_prediction_accuracy: 0.9424  val_loss: 0.3590  val_digit_1_loss: 0.1264  val_digit_2_loss: 0.0725  val_direct_prediction_loss: 0.1601  val_direct_prediction_accuracy: 0.9400  val_indirect_prediction_accuracy: 0.9796
python3 mnist_multiple.py epochs=1 batch_size=100
loss: 1.2151  digit_1_loss: 0.4227  digit_2_loss: 0.4280  direct_prediction_loss: 0.3645  direct_prediction_accuracy: 0.8301  indirect_prediction_accuracy: 0.9257  val_loss: 0.4846  val_digit_1_loss: 0.1704  val_digit_2_loss: 0.0990  val_direct_prediction_loss: 0.2153  val_direct_prediction_accuracy: 0.9164  val_indirect_prediction_accuracy: 0.9700
cifar_competition
Deadline: Mar 21, 7:59 a.m. 5 points+5 bonus
The goal of this assignment is to devise the best possible model for CIFAR10. You can load the data using the cifar10.py module. Note that the test set is different than that of official CIFAR10.
The task is a competition. Everyone who submits a solution which achieves at least 65% 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 around ~80% on the development set to score 65% 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.
cnn_manual
Deadline: Mar 28, 7:59 a.m. 3 points
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 constructs a series of 2D convolutional layers with ReLU
activation and valid
padding, specified in the args.cnn
option.
The args.cnn
contains commaseparated layer specifications in the format
filterskernel_sizestride
.
Of course, you cannot use any TensorFlow convolutional operation (instead,
implement the forward and backward pass using matrix multiplication and other
operations), nor the tf.GradientTape
for gradient computation.
To make debugging easier, the template supports a verify
option, which
allows comparing the forward pass and the three gradients you compute in the
backward pass to correct values.
Finally, it is a good idea to read the TensorFlow guide about tensor slicing.
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 cnn_manual.py epochs=1 cnn=511
Dev accuracy after epoch 1 is 91.06
Test accuracy after epoch 1 is 89.51
python3 cnn_manual.py epochs=1 cnn=531
Dev accuracy after epoch 1 is 94.08
Test accuracy after epoch 1 is 92.65
python3 cnn_manual.py epochs=1 cnn=532
Dev accuracy after epoch 1 is 91.82
Test accuracy after epoch 1 is 90.00
python3 cnn_manual.py epochs=1 cnn=532,1032
Dev accuracy after epoch 1 is 93.22
Test accuracy after epoch 1 is 91.31
cags_classification
Deadline: Mar 28, 7:59 a.m. 4 points+5 bonus
The goal of this assignment is to use pretrained EfficientNetB0 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,
which is decoded using the CAGS.parse
method.
To load the EfficientNetB0, use the provided
efficient_net.py
module. Its method pretrained_efficientnet_b0(include_top, dynamic_input_shape=False)
:
 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);
 if
 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).
 the first value is the final network output:
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
.
The task is a competition. Everyone who submits a solution which achieves at least 90% test set accuracy will get 4 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: Mar 28, 7:59 a.m. 4 points+5 bonus
The goal of this assignment is to use pretrained EfficientNetB0 model to
achieve best image segmentation IoU score on the CAGS dataset.
The dataset and the EfficientNetB0 is described in the cags_classification
assignment.
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 MaskIoUMetric
of the
cags_dataset.py
module, which can also evaluate your predictions (either by running with
task=segmentation evaluate=path
arguments, or using its
evaluate_segmentation_file
method).
The task is a competition. Everyone who submits a solution which achieves at least 87% test set IoU gets 4 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.
bboxes_utils
Deadline: Apr 4, 7:59 a.m. 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:
bboxes_to_fast_rcnn
: convert given bounding boxes to a Fast RCNNlike representation relative to the given anchors;bboxes_from_fast_rcnn
: convert Fast RCNNlike representations relative to given anchors back to bounding boxes;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. Note that the template does not contain type annotations because Python typing system is not flexible enough to describe the tensor shape changes.
When submitting to ReCodEx, the method main
is executed, returning the
implemented bboxes_to_fast_rcnn
, bboxes_from_fast_rcnn
and bboxes_training
methods. These methods are then executed and compared to the reference
implementation.
svhn_competition
Deadline: Apr 4, 7:59 a.m. 5 points+5 bonus
The goal of this assignment is to implement a system performing object recognition, optionally utilizing pretrained EfficientNetB0 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. Every element is a dictionary with the following keys:
"image"
: a square 3channel 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 bboxes_training
, it might be more comfortable to
convert the dataset to NumPy. Alternatively, you can implement bboxes_training
using TensorFlow operations or call Numpy implementation of bboxes_training
directly in tf.data.Dataset.map
by using tf.numpy_function
,
see FAQ.
Similarly to the cags_classification
, you can load the EfficientNetB0 using the provided
efficient_net.py
module. Note that the dynamic_input_shape=True
argument creates
a model capable of processing an input image of any size.
Each test set image annotation consists of a sequence of space separated
fivetuples 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.
You can again evaluate your predictions using the
svhn_dataset.py
module, either by running with evaluate=path
arguments, or using its
evaluate_file
method.
The task is a competition. Everyone who submits a solution which achieves at least 20% test set IoU gets 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 RetinaNetlike single stage detector, using only a single level of convolutional features (no FPN) with singlescale and singleaspect anchors. Focal loss is available as tf.losses.BinaryFocalCrossentropy and nonmaximum suppression as tf.image.non_max_suppression or tf.image.combined_non_max_suppression.
sequence_classification
Deadline: Apr 11, 7:59 a.m. 2 points
The goal of this assignment is to introduce recurrent neural networks. 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 onehot representation of small integer.
Your goal is to modify the sequence_classification.py template and implement the following:
 Use specified RNN type (
SimpleRNN
,GRU
andLSTM
) 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 (or online here). 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=SimpleRNN hidden_layer=70 rnn_cell_dim=30 sequence_dim=30
and the same withclip_gradient=1
 the same as above but with
rnn_cell=GRU hidden_layer=75
with and withoutclip_gradient=0.1
 the same as above but with
rnn_cell=LSTM hidden_layer=85
with and withoutclip_gradient=1
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 sequence_classification.py rnn_cell=SimpleRNN epochs=5
Epoch 1/5 loss: 0.6951  accuracy: 0.5095  val_loss: 0.6926  val_accuracy: 0.5176
Epoch 2/5 loss: 0.6924  accuracy: 0.5158  val_loss: 0.6921  val_accuracy: 0.5217
Epoch 3/5 loss: 0.6918  accuracy: 0.5165  val_loss: 0.6913  val_accuracy: 0.5114
Epoch 4/5 loss: 0.6901  accuracy: 0.5196  val_loss: 0.6881  val_accuracy: 0.5157
Epoch 5/5 loss: 0.6842  accuracy: 0.5220  val_loss: 0.6793  val_accuracy: 0.5231
python3 sequence_classification.py rnn_cell=GRU epochs=5
Epoch 1/5 loss: 0.6926  accuracy: 0.5126  val_loss: 0.6917  val_accuracy: 0.5157
Epoch 2/5 loss: 0.6885  accuracy: 0.5170  val_loss: 0.6823  val_accuracy: 0.5143
Epoch 3/5 loss: 0.4987  accuracy: 0.7328  val_loss: 0.1574  val_accuracy: 0.9795
Epoch 4/5 loss: 0.0684  accuracy: 0.9935  val_loss: 0.0305  val_accuracy: 0.9975
Epoch 5/5 loss: 0.0219  accuracy: 0.9991  val_loss: 0.0121  val_accuracy: 0.9998
python3 sequence_classification.py rnn_cell=LSTM epochs=5
Epoch 1/5 loss: 0.6929  accuracy: 0.5130  val_loss: 0.6927  val_accuracy: 0.5153
Epoch 2/5 loss: 0.6919  accuracy: 0.5155  val_loss: 0.6902  val_accuracy: 0.5156
Epoch 3/5 loss: 0.6837  accuracy: 0.5192  val_loss: 0.6748  val_accuracy: 0.5285
Epoch 4/5 loss: 0.3839  accuracy: 0.7918  val_loss: 0.0695  val_accuracy: 1.0000
Epoch 5/5 loss: 0.0351  accuracy: 1.0000  val_loss: 0.0183  val_accuracy: 1.0000
python3 sequence_classification.py rnn_cell=LSTM epochs=5 hidden_layer=50
Epoch 1/5 loss: 0.6807  accuracy: 0.5193  val_loss: 0.6615  val_accuracy: 0.5233
Epoch 2/5 loss: 0.6485  accuracy: 0.5373  val_loss: 0.6378  val_accuracy: 0.5309
Epoch 3/5 loss: 0.6204  accuracy: 0.5641  val_loss: 0.5772  val_accuracy: 0.6306
Epoch 4/5 loss: 0.0874  accuracy: 0.9566  val_loss: 0.0015  val_accuracy: 1.0000
Epoch 5/5 loss: 8.0165e04  accuracy: 1.0000  val_loss: 3.8375e04  val_accuracy: 1.0000
python3 sequence_classification.py rnn_cell=LSTM epochs=5 hidden_layer=50 clip_gradient=0.01
Epoch 1/5 loss: 0.6818  accuracy: 0.5173  val_loss: 0.6676  val_accuracy: 0.5241
Epoch 2/5 loss: 0.6509  accuracy: 0.5374  val_loss: 0.6393  val_accuracy: 0.5448
Epoch 3/5 loss: 0.6301  accuracy: 0.5458  val_loss: 0.6148  val_accuracy: 0.5622
Epoch 4/5 loss: 0.5852  accuracy: 0.6121  val_loss: 0.4589  val_accuracy: 0.7884
Epoch 5/5 loss: 0.0372  accuracy: 0.9881  val_loss: 0.0060  val_accuracy: 0.9993
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 sequence_classification.py train_sequences=1000 sequence_length=20 rnn_cell=SimpleRNN epochs=5
Epoch 1/5 loss: 0.7125  accuracy: 0.4996  val_loss: 0.6997  val_accuracy: 0.4929
Epoch 2/5 loss: 0.6962  accuracy: 0.4948  val_loss: 0.6935  val_accuracy: 0.4985
Epoch 3/5 loss: 0.6931  accuracy: 0.5155  val_loss: 0.6922  val_accuracy: 0.5264
Epoch 4/5 loss: 0.6923  accuracy: 0.5286  val_loss: 0.6917  val_accuracy: 0.5362
Epoch 5/5 loss: 0.6917  accuracy: 0.5343  val_loss: 0.6913  val_accuracy: 0.5323
python3 sequence_classification.py train_sequences=1000 sequence_length=20 rnn_cell=GRU epochs=5
Epoch 1/5 loss: 0.6926  accuracy: 0.5243  val_loss: 0.6922  val_accuracy: 0.5217
Epoch 2/5 loss: 0.6922  accuracy: 0.5210  val_loss: 0.6920  val_accuracy: 0.5217
Epoch 3/5 loss: 0.6919  accuracy: 0.5247  val_loss: 0.6916  val_accuracy: 0.5217
Epoch 4/5 loss: 0.6917  accuracy: 0.5301  val_loss: 0.6913  val_accuracy: 0.5217
Epoch 5/5 loss: 0.6912  accuracy: 0.5276  val_loss: 0.6908  val_accuracy: 0.5220
python3 sequence_classification.py train_sequences=1000 sequence_length=20 rnn_cell=LSTM epochs=5
Epoch 1/5 loss: 0.6928  accuracy: 0.5358  val_loss: 0.6925  val_accuracy: 0.5339
Epoch 2/5 loss: 0.6926  accuracy: 0.5319  val_loss: 0.6924  val_accuracy: 0.5279
Epoch 3/5 loss: 0.6925  accuracy: 0.5298  val_loss: 0.6923  val_accuracy: 0.5343
Epoch 4/5 loss: 0.6924  accuracy: 0.5332  val_loss: 0.6922  val_accuracy: 0.5297
Epoch 5/5 loss: 0.6922  accuracy: 0.5358  val_loss: 0.6920  val_accuracy: 0.5293
python3 sequence_classification.py train_sequences=1000 sequence_length=20 rnn_cell=LSTM epochs=5 hidden_layer=50
Epoch 1/5 loss: 0.6917  accuracy: 0.5434  val_loss: 0.6903  val_accuracy: 0.5306
Epoch 2/5 loss: 0.6876  accuracy: 0.5395  val_loss: 0.6843  val_accuracy: 0.5350
Epoch 3/5 loss: 0.6784  accuracy: 0.5550  val_loss: 0.6732  val_accuracy: 0.5350
Epoch 4/5 loss: 0.6667  accuracy: 0.5549  val_loss: 0.6620  val_accuracy: 0.5299
Epoch 5/5 loss: 0.6547  accuracy: 0.5597  val_loss: 0.6508  val_accuracy: 0.5278
python3 sequence_classification.py train_sequences=1000 sequence_length=20 rnn_cell=LSTM epochs=5 hidden_layer=50 clip_gradient=0.01
Epoch 1/5 loss: 0.6916  accuracy: 0.5417  val_loss: 0.6903  val_accuracy: 0.5308
Epoch 2/5 loss: 0.6876  accuracy: 0.5390  val_loss: 0.6844  val_accuracy: 0.5305
Epoch 3/5 loss: 0.6789  accuracy: 0.5533  val_loss: 0.6742  val_accuracy: 0.5333
Epoch 4/5 loss: 0.6675  accuracy: 0.5512  val_loss: 0.6629  val_accuracy: 0.5411
Epoch 5/5 loss: 0.6563  accuracy: 0.5532  val_loss: 0.6536  val_accuracy: 0.5332
tagger_we
Deadline: Apr 11, 7:59 a.m. 3 points
In this assignment you will create a simple partofspeech tagger. For training and evaluation, we will use Czech dataset containing tokenized sentences, each word annotated by gold lemma and partofspeech tag. The morpho_dataset.py module (down)loads the dataset and provides mappings between strings and integers.
Your goal is to modify the tagger_we.py template and implement the following:
 Use specified RNN cell type (
GRU
andLSTM
) and dimensionality.  Create word embeddings for training vocabulary.
 Process the sentences using bidirectional RNN.
 Predict partofspeech tags. Note that you need to properly handle sentences of different lengths in one batch using tf.RaggedTensors.
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 tagger_we.py max_sentences=5000 rnn_cell=LSTM rnn_cell_dim=16
Epoch 1/5 loss: 1.3015  accuracy: 0.6197  val_loss: 0.5595  val_accuracy: 0.8438
Epoch 2/5 loss: 0.2059  accuracy: 0.9561  val_loss: 0.3388  val_accuracy: 0.8954
Epoch 3/5 loss: 0.0510  accuracy: 0.9888  val_loss: 0.3189  val_accuracy: 0.8941
Epoch 4/5 loss: 0.0306  accuracy: 0.9920  val_loss: 0.3265  val_accuracy: 0.8916
Epoch 5/5 loss: 0.0213  accuracy: 0.9947  val_loss: 0.3260  val_accuracy: 0.8926
python3 tagger_we.py max_sentences=5000 rnn_cell=GRU rnn_cell_dim=16
Epoch 1/5 loss: 0.9769  accuracy: 0.7228  val_loss: 0.4172  val_accuracy: 0.8750
Epoch 2/5 loss: 0.1204  accuracy: 0.9740  val_loss: 0.3330  val_accuracy: 0.8852
Epoch 3/5 loss: 0.0365  accuracy: 0.9900  val_loss: 0.3138  val_accuracy: 0.8903
Epoch 4/5 loss: 0.0261  accuracy: 0.9919  val_loss: 0.3234  val_accuracy: 0.8840
Epoch 5/5 loss: 0.0203  accuracy: 0.9935  val_loss: 0.3246  val_accuracy: 0.8837
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 tagger_we.py epochs=1 max_sentences=1000 rnn_cell=LSTM rnn_cell_dim=16
loss: 2.3174  accuracy: 0.3603  val_loss: 1.9011  val_accuracy: 0.4222
python3 tagger_we.py epochs=1 max_sentences=1000 rnn_cell=GRU rnn_cell_dim=16
loss: 2.1435  accuracy: 0.4186  val_loss: 1.5338  val_accuracy: 0.5498
tagger_cle
Deadline: Apr 11, 7:59 a.m. 3 points
This assignment is a continuation of tagger_we
. Using the
tagger_cle.py
template, implement characterlevel word embedding computation using
a bidirectional characterlevel 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_cle
has by default smaller word embeddings so that the size
of word representation (64 + 32 + 32) is the same as in the tagger_we
assignment.
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 tagger_cle.py max_sentences=5000 rnn_cell=LSTM rnn_cell_dim=16 cle_dim=16
Epoch 1/5 loss: 1.2229  accuracy: 0.6372  val_loss: 0.4645  val_accuracy: 0.8702
Epoch 2/5 loss: 0.1907  accuracy: 0.9598  val_loss: 0.2491  val_accuracy: 0.9249
Epoch 3/5 loss: 0.0557  accuracy: 0.9883  val_loss: 0.2151  val_accuracy: 0.9267
Epoch 4/5 loss: 0.0344  accuracy: 0.9910  val_loss: 0.2125  val_accuracy: 0.9277
Epoch 5/5 loss: 0.0262  accuracy: 0.9925  val_loss: 0.2069  val_accuracy: 0.9295
python3 tagger_cle.py max_sentences=5000 rnn_cell=LSTM rnn_cell_dim=16 cle_dim=16 word_masking=0.1
Epoch 1/5 loss: 1.3114  accuracy: 0.6076  val_loss: 0.5267  val_accuracy: 0.8527
Epoch 2/5 loss: 0.3150  accuracy: 0.9197  val_loss: 0.2760  val_accuracy: 0.9161
Epoch 3/5 loss: 0.1540  accuracy: 0.9588  val_loss: 0.2244  val_accuracy: 0.9294
Epoch 4/5 loss: 0.1123  accuracy: 0.9676  val_loss: 0.2145  val_accuracy: 0.9309
Epoch 5/5 loss: 0.0961  accuracy: 0.9700  val_loss: 0.2049  val_accuracy: 0.9344
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 tagger_cle.py epochs=1 max_sentences=1000 rnn_cell=LSTM rnn_cell_dim=16 cle_dim=16
loss: 2.2428  accuracy: 0.3493  val_loss: 1.8235  val_accuracy: 0.4233
python3 tagger_cle.py epochs=1 max_sentences=1000 rnn_cell=LSTM rnn_cell_dim=16 cle_dim=16 word_masking=0.1
loss: 2.2494  accuracy: 0.3465  val_loss: 1.8439  val_accuracy: 0.4232
tagger_competition
Deadline: Apr 11, 7:59 a.m. 4 points+5 bonus
In this assignment, you should extend tagger_cle
into a realworld Czech partofspeech 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 pretrained word embeddings (assuming they were trained on plain texts).
The task is a competition. Everyone who submits a solution with at least 92.5% 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 preneuralnetwork stateoftheart of 96.35%.
You can start with the
tagger_competition.py
template, which among others generates test set annotations in the required format. Note that
you can evaluate the predictions as usual using the morpho_dataset.py
module, either by running with task=tagger evaluate=path
arguments, or using its
evaluate_file
method.
tensorboard_projector
You can try exploring the TensorBoard Projector with pretrained 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 pretrained embeddings from the Word2vec format.
tagger_crf
Deadline: Apr 19, 7:59 a.m. 2 points
This assignment is an extension of tagger_we
task. Using the
tagger_crf.py
template, implement named entity recognition using CRF loss and CRF decoding
from the tensorflow_addons
package.
The evaluation is performed using the provided metric computing F1 score of the span prediction (i.e., a recognized possiblymultiword named entity is true positive if both the entity type and the span exactly match).
In practice, characterlevel embeddings (and also pretrained word embeddings) would be used to obtain superior results.
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 tagger_crf.py max_sentences=5000 rnn_cell=LSTM rnn_cell_dim=24
Epoch 1/5 loss: 18.5371  val_loss: 14.0865  val_f1: 0.0317
Epoch 2/5 loss: 9.7936  val_loss: 11.5969  val_f1: 0.2428
Epoch 3/5 loss: 5.9049  val_loss: 9.8079  val_f1: 0.3645
Epoch 4/5 loss: 3.1811  val_loss: 9.5350  val_f1: 0.4276
Epoch 5/5 loss: 1.7330  val_loss: 9.2801  val_f1: 0.4398
python3 tagger_crf.py max_sentences=5000 rnn_cell=GRU rnn_cell_dim=24
Epoch 1/5 loss: 17.6696  val_loss: 13.5141  val_f1: 0.1700
Epoch 2/5 loss: 8.1954  val_loss: 10.2339  val_f1: 0.4070
Epoch 3/5 loss: 3.7555  val_loss: 9.4217  val_f1: 0.4528
Epoch 4/5 loss: 1.6607  val_loss: 10.1525  val_f1: 0.4546
Epoch 5/5 loss: 0.8472  val_loss: 10.6141  val_f1: 0.4744
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 tagger_crf.py epochs=2 max_sentences=1000 rnn_cell=LSTM rnn_cell_dim=24
Epoch 1/2 loss: 29.9874  val_loss: 20.2837  val_f1: 0.0000e+00
Epoch 2/2 loss: 17.2559  val_loss: 18.0548  val_f1: 0.0030
python3 tagger_crf.py epochs=2 max_sentences=1000 rnn_cell=GRU rnn_cell_dim=24
Epoch 1/2 loss: 29.1122  val_loss: 19.1089  val_f1: 0.0000e+00
Epoch 2/2 loss: 15.7085  val_loss: 17.1493  val_f1: 0.0172
tagger_crf_manual
Deadline: Apr 19, 7:59 a.m. 2 points
This assignment is an extension of tagger_crf
, where we will perform the CRF
loss computation (but not CRF decoding) manually.
The tagger_crf_manual.py
template is nearly identical to tagger_crf
, the only difference is the
crf_loss
method, where you should manually implement the CRF loss.
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 tagger_crf_manual.py max_sentences=5000 rnn_cell=LSTM rnn_cell_dim=24
Epoch 1/5 loss: 18.5371  val_loss: 14.0865  val_f1: 0.0317
Epoch 2/5 loss: 9.7936  val_loss: 11.5969  val_f1: 0.2428
Epoch 3/5 loss: 5.9049  val_loss: 9.8079  val_f1: 0.3645
Epoch 4/5 loss: 3.1811  val_loss: 9.5350  val_f1: 0.4276
Epoch 5/5 loss: 1.7330  val_loss: 9.2801  val_f1: 0.4398
python3 tagger_crf_manual.py max_sentences=5000 rnn_cell=GRU rnn_cell_dim=24
Epoch 1/5 loss: 17.6696  val_loss: 13.5141  val_f1: 0.1700
Epoch 2/5 loss: 8.1954  val_loss: 10.2339  val_f1: 0.4070
Epoch 3/5 loss: 3.7555  val_loss: 9.4217  val_f1: 0.4528
Epoch 4/5 loss: 1.6607  val_loss: 10.1525  val_f1: 0.4546
Epoch 5/5 loss: 0.8472  val_loss: 10.6141  val_f1: 0.4744
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 tagger_crf_manual.py epochs=2 max_sentences=1000 rnn_cell=LSTM rnn_cell_dim=24
Epoch 1/2 loss: 29.9874  val_loss: 20.2837  val_f1: 0.0000e+00
Epoch 2/2 loss: 17.2559  val_loss: 18.0548  val_f1: 0.0030
python3 tagger_crf_manual.py epochs=2 max_sentences=1000 rnn_cell=GRU rnn_cell_dim=24
Epoch 1/2 loss: 29.1122  val_loss: 19.1089  val_f1: 0.0000e+00
Epoch 2/2 loss: 15.7085  val_loss: 17.1493  val_f1: 0.0172
speech_recognition
Deadline: Apr 19, 7:59 a.m. 5 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 Czech recordings from the Common Voice,
with input sound waves passed through the usual preprocessing – computing
Melfrequency cepstral coefficients (MFCCs).
You can repeat this preprocessing on a given audio using the wav_decode
and
mfcc_extract
methods from the
common_voice_cs.py module.
This module can also load the dataset, downloading it when necessary (note that
it has 200MB, so it might take a while). Furthermore, you can listen to the
development portion of the dataset.
Additional following data can be utilized in this assignment:
 You can use any unannotated text data (Wikipedia, Czech National Corpus, …), and also any pretrained word embeddings or language models (assuming they were trained on plain texts).
 You can use any unannotated speech data.
The task is a competition.
The evaluation is performed by computing the edit distance to the gold letter
sequence, normalized by its length (a corresponding Keras metric
EditDistanceMetric
is provided by the common_voice_cs.py).
Everyone who submits a solution with at most 50% test set edit distance
gets 5 points; the rest 5 points will be distributed
depending on relative ordering of your solutions. Note that
you can evaluate the predictions as usual using the common_voice_cs.py
module, either by running with evaluate=path
arguments, or using its
evaluate_file
method.
Start with the speech_recognition.py template which contains instructions for using the CTC loss and generates the test set annotation in the required format.
lemmatizer_noattn
Deadline: Apr 25, 7:59 a.m. 3 points
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
updated 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.
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 lemmatizer_noattn.py epochs=1 max_sentences=1000 batch_size=2 cle_dim=24 rnn_dim=24
500/500  23s  loss: 2.9663  val_accuracy: 0.1311  23s/epoch  46ms/step
python3 lemmatizer_noattn.py epochs=1 max_sentences=500 batch_size=2 cle_dim=32 rnn_dim=32
250/250  19s  loss: 3.0615  val_accuracy: 0.0043  19s/epoch  77ms/step
lemmatizer_attn
Deadline: Apr 25, 7:59 a.m. 3 points
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 nonlinearity, 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.
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 lemmatizer_attn.py epochs=1 max_sentences=1000 batch_size=2 cle_dim=24 rnn_dim=24
500/500  39s  loss: 2.8889  val_accuracy: 0.1451  39s/epoch  78ms/step
python3 lemmatizer_attn.py epochs=1 max_sentences=500 batch_size=2 cle_dim=32 rnn_dim=32
250/250  29s  loss: 3.0417  val_accuracy: 0.1471  29s/epoch  114ms/step
lemmatizer_competition
Deadline: Apr 25, 7:59 a.m. 4 points+5 bonus
In this assignment, you should extend lemmatizer_noattn
or lemmatizer_attn
into a realworld Czech lemmatizer. As in tagger_competition
, we will use
Czech PDT dataset loadable using the morpho_dataset.py
module.
You can also use the same additional data as in the tagger_competition
assignment.
The task is a competition. Everyone who submits a solution a solution with at least 96% 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 preneuralnetwork stateoftheart of 98.76%.
You can start with the
lemmatizer_competition.py
template, which among others generates test set annotations in the required format. Note that
you can evaluate the predictions as usual using the morpho_dataset.py
module, either by running with task=lemmatizer corpus=czech_pdt_lemmas evaluate=path
arguments, or using its
evaluate_file
method.
3d_recognition
Deadline: May 02, 7:59 a.m. 3 points+4 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.
If you want, it is possible to use the EfficientNetB0 in this assignment; however, I do not know of a straightforward way to utilize it, apart from rendering the object to a 2D image (or several of them).
The task is a competition. Everyone who submits a solution which achieves at least 88% test set accuracy gets 3 points; the rest 4 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.
homr_competition
Deadline: May 02 May 09, 7:59 a.m.
3 points+5 bonus
Tackle handwritten optical music recognition in this assignment. The inputs are grayscale images of monophonic scores starting with a clef, key signature, and a time signature, followed by several staves. The dataset is loadable using the homr_dataset.py module, and is downloaded automatically if missing (note that is has ~500MB, so it might take a while). No other data or pretrained models are allowed for training.
The task is a competition.
The evaluation is performed using the same metric as in speech_recognition
, by
computing edit distance to the gold sequence, normalized by its length (the
EditDistanceMetric
is again provided by the
homr_dataset.py).
Everyone who submits a solution with at most
3% test set edit distance will get 3 points; the rest 5 points will be
distributed depending on relative ordering of your solutions.
You can evaluate the predictions as usual using the
homr_dataset.py
module, either by running with evaluate=path
arguments, or using its
evaluate_file
method.
You can start with the homr_competition.py template, which among others generates test set annotations in the required format.
tagger_transformer
Deadline: May 09, 7:59 a.m. 3 points
This assignment is a continuation of tagger_we
. Using the
tagger_transformer.py
template, implement a PreLN Transformer encoder.
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 tagger_transformer.py max_sentences=5000 transformer_layers=0
Epoch 1/5 loss: 1.5585  accuracy: 0.5335  val_loss: 0.8724  val_accuracy: 0.7128
Epoch 2/5 loss: 0.5397  accuracy: 0.8519  val_loss: 0.5609  val_accuracy: 0.8247
Epoch 3/5 loss: 0.2519  accuracy: 0.9560  val_loss: 0.4491  val_accuracy: 0.8407
Epoch 4/5 loss: 0.1310  accuracy: 0.9775  val_loss: 0.4135  val_accuracy: 0.8476
Epoch 5/5 loss: 0.0796  accuracy: 0.9843  val_loss: 0.4004  val_accuracy: 0.8478
python3 tagger_transformer.py max_sentences=5000 transformer_heads=1
Epoch 1/5 loss: 1.0680  accuracy: 0.6571  val_loss: 0.6104  val_accuracy: 0.7975
Epoch 2/5 loss: 0.2136  accuracy: 0.9307  val_loss: 0.5002  val_accuracy: 0.8464
Epoch 3/5 loss: 0.0605  accuracy: 0.9811  val_loss: 0.7676  val_accuracy: 0.8461
Epoch 4/5 loss: 0.0361  accuracy: 0.9878  val_loss: 0.9315  val_accuracy: 0.8388
Epoch 5/5 loss: 0.0263  accuracy: 0.9906  val_loss: 0.9784  val_accuracy: 0.8446
python3 tagger_transformer.py max_sentences=5000 transformer_heads=4
Epoch 1/5 loss: 1.0682  accuracy: 0.6598  val_loss: 0.5239  val_accuracy: 0.8123
Epoch 2/5 loss: 0.1897  accuracy: 0.9391  val_loss: 0.4625  val_accuracy: 0.8380
Epoch 3/5 loss: 0.0556  accuracy: 0.9824  val_loss: 0.6330  val_accuracy: 0.8226
Epoch 4/5 loss: 0.0337  accuracy: 0.9885  val_loss: 0.7936  val_accuracy: 0.8145
Epoch 5/5 loss: 0.0266  accuracy: 0.9904  val_loss: 0.7206  val_accuracy: 0.8370
python3 tagger_transformer.py max_sentences=5000 transformer_heads=4 transformer_dropout=0.1
Epoch 1/5 loss: 1.1690  accuracy: 0.6259  val_loss: 0.5695  val_accuracy: 0.7975
Epoch 2/5 loss: 0.2457  accuracy: 0.9220  val_loss: 0.4771  val_accuracy: 0.8281
Epoch 3/5 loss: 0.0870  accuracy: 0.9730  val_loss: 0.6044  val_accuracy: 0.8413
Epoch 4/5 loss: 0.0525  accuracy: 0.9828  val_loss: 0.7615  val_accuracy: 0.8355
Epoch 5/5 loss: 0.0430  accuracy: 0.9854  val_loss: 0.7607  val_accuracy: 0.8403
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 tagger_transformer.py epochs=1 max_sentences=800 transformer_layers=0
loss: 2.6393  accuracy: 0.2170  val_loss: 2.1583  val_accuracy: 0.3447
python3 tagger_transformer.py epochs=1 max_sentences=800 transformer_heads=1
loss: 2.1624  accuracy: 0.3235  val_loss: 1.9781  val_accuracy: 0.3119
python3 tagger_transformer.py epochs=1 max_sentences=800 transformer_heads=4
loss: 2.1716  accuracy: 0.3277  val_loss: 1.9632  val_accuracy: 0.3381
python3 tagger_transformer.py epochs=1 max_sentences=800 transformer_heads=4 transformer_dropout=0.1
loss: 2.2652  accuracy: 0.3063  val_loss: 1.9840  val_accuracy: 0.3606
sentiment_analysis
Deadline: May 09, 7:59 a.m. 3 points
Perform sentiment analysis on Czech Facebook data using a provided pretrained
Czech Electra model eleczechlcsmall
.
The dataset consists of pairs of (document, label) and can be (down)loaded using the
text_classification_dataset.py
module. When loading the dataset, a tokenizer
might be provided, and if it is,
the document is also passed through the tokenizer and the resulting tokens are
added to the dataset.
Even though this assignment is not a competition, your goal is to submit test
set annotations with at least 77% accuracy. As usual, you can evaluate your
predictions using the text_classification_dataset.py
module, either by running with evaluate=path
arguments, or using its
evaluate_file
method.
Note that contrary to working with EfficientNet, you need to finetune the Electra model in order to achieve the required accuracy.
You can start with the sentiment_analysis.py template, which among others loads the Electra Czech model and generates test set annotations in the required format. Note that bert_example.py module illustrate the usage of both the Electra tokenizer and the Electra model.
reading_comprehension
Deadline: May 16, 7:59 a.m. 4 points+5 bonus
Wed May 11, 14:45: Unfortunately, several contexts in the initial version of the dataset were incorrect (not belonging to the questions). The dataset has been fixed just now, so please redownload it. More details in Piazza.
Implement the best possible model for reading comprehension task using
a translated version of the SQuAD 1.1 dataset, utilizing a provided
Czech RoBERTa model ufal/robeczechbase
.
The dataset can be loaded using the
reading_comprehension_dataset.py
module. The loaded dataset is the direct reprentation of the data and not yet
ready to be directly trained on. Each of the train
, dev
and test
datasets
are composed of a list of paragraphs, each consisting of:
context
: text with the information;qas
: list of questions and answers, where each item consists of:question
: text of the question;answers
: a list of answers, each answer is composed of:text
: string of the text, exactly as appearing in the context;start
: character offset of the answer text in the context.
In the train
and dev
sets, each question has exactly one answer, while in
the test
set there might be several answers. We evaluate the reading
comprehension task using accuracy, where an answer is considered correct if
its text is exactly equal to some correct answer. You can evaluate your
predictions as usual with the
reading_comprehension_dataset.py
module, either by running with evaluate=path
arguments, or using its
evaluate_file
method.
The task is a competition. Everyone who submits a solution
a solution with at least 65% answer accuracy gets 4 points; the rest 5 points
will be distributed depending on relative ordering of your solutions. Note that
usually achieving 62% on the dev
set is enough to get 65% on the test
set (because of multiple references in the test
set).
Note that contrary to working with EfficientNet, you need to finetune the RobeCzech model in order to achieve the required accuracy.
You can start with the reading_comprehension.py template, which among others (down)loads the data and the RobeCzech model, and describes the format of the required test set annotations.
vae
Deadline: Jun 30, 23:59 3 points
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
, mnistfashion
, and mnistcifarcars
) and
different latent variable dimensionality (z_dim=2
and z_dim=100
).
The generated images are available in TensorBoard logs, and the images
generated by the reference solution can be watched
here.
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 vae.py dataset=mnist train_size 500 epochs=3 z_dim=2
Epoch 1/3 reconstruction_loss: 0.5445  latent_loss: 7.7449  loss: 306.6298
Epoch 2/3 reconstruction_loss: 0.2925  latent_loss: 8.9848  loss: 221.4592
Epoch 3/3 reconstruction_loss: 0.2629  latent_loss: 3.2707  loss: 205.3799
python3 vae.py dataset=mnist train_size 500 epochs=3 z_dim=100
Epoch 1/3 reconstruction_loss: 0.4632  latent_loss: 0.0891  loss: 246.3138
Epoch 2/3 reconstruction_loss: 0.2757  latent_loss: 0.0152  loss: 207.8753
Epoch 3/3 reconstruction_loss: 0.2634  latent_loss: 0.0058  loss: 202.1027
gan
Deadline: Jun 30, 23:59 2 points
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
, mnistfashion
, and mnistcifarcars
) and
maybe try different latent variable dimensionality. The generated images are
available in TensorBoard logs, and the images generated by the reference
solution can be watched
here.
You can also continue with dcgan
assignment.
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 gan.py dataset=mnist train_size=490 epochs=5 z_dim=2
Epoch 1/5 discriminator_loss: 0.4933  generator_loss: 3.2423  loss: 1.2273  discriminator_accuracy: 0.8306
Epoch 2/5 discriminator_loss: 0.0334  generator_loss: 4.8349  loss: 1.6104  discriminator_accuracy: 1.0000
Epoch 3/5 discriminator_loss: 0.0131  generator_loss: 5.3357  loss: 1.7719  discriminator_accuracy: 1.0000
Epoch 4/5 discriminator_loss: 0.0323  generator_loss: 5.5625  loss: 1.8544  discriminator_accuracy: 0.9980
Epoch 5/5 discriminator_loss: 0.0152  generator_loss: 6.4666  loss: 2.1581  discriminator_accuracy: 0.9990
python3 gan.py dataset=mnist train_size=490 epochs=5 z_dim=100
Epoch 1/5 discriminator_loss: 0.5071  generator_loss: 2.8793  loss: 1.1304  discriminator_accuracy: 0.8286
Epoch 2/5 discriminator_loss: 0.0562  generator_loss: 3.8137  loss: 1.2801  discriminator_accuracy: 1.0000
Epoch 3/5 discriminator_loss: 0.0451  generator_loss: 4.1467  loss: 1.3939  discriminator_accuracy: 1.0000
Epoch 4/5 discriminator_loss: 0.0436  generator_loss: 5.1381  loss: 1.7160  discriminator_accuracy: 0.9980
Epoch 5/5 discriminator_loss: 0.0865  generator_loss: 4.8655  loss: 1.6472  discriminator_accuracy: 0.9918
dcgan
Deadline: Jun 30, 23:59 1 points
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
, mnistfashion
, and mnistcifarcars
). However,
note that you will need a lot of computational power (preferably a GPU) to
generate the images; the example outputs below were also generated on a GPU,
which means the results are nondeterministic. The images generated by the
reference solution can be watched
here.
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 dcgan.py dataset=mnist train_size=490 epochs=2 z_dim=2
Epoch 1/2 discriminator_loss: 2.0305  generator_loss: 1.1844  loss: 1.1044  discriminator_accuracy: 0.5469
Epoch 2/2 discriminator_loss: 1.1953  generator_loss: 0.9264  loss: 0.7119  discriminator_accuracy: 0.7082
python3 dcgan.py dataset=mnist train_size=490 epochs=2 z_dim=100
Epoch 1/2 discriminator_loss: 1.8414  generator_loss: 0.9347  loss: 0.9271  discriminator_accuracy: 0.5418
Epoch 2/2 discriminator_loss: 1.4439  generator_loss: 0.9677  loss: 0.8049  discriminator_accuracy: 0.5816
crac2022
Deadline: Evaluation Jun 18
If you would like to try participating in a real shared task, right now CRAC 2022 Shared Task on Multilingual Coreference Resolution is running, with the evaluation phase in Jun 18.
The goal is to perform coreference resolution on 13 datasets in 10 languages,
where coreference resolution is the task of clustering together multiple
mentions of the same entity appearing in a textual document (e.g., Joe Biden
,
the U.S. President
, and he
).
Note that you should then also write a system description paper – however, it might be possible to send a joint paper, so you would only need to write several paragraphs about your approach.
monte_carlo
Deadline: Jun 30, 23:59 2 points
Solve the discretized CartPolev1 environment
environment from the Gym library using the Monte Carlo
reinforcement learning algorithm. The gym
environments have the followng
methods and properties:
observation_space
: the description of environment observationsaction_space
: the description of environment actionsreset() → new_state
: starts a new episodestep(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 environmentspecific informationrender()
: render current environment state
We additionaly extend the gym
environment by:
episode
: number of the current episode (zerobased)reset(start_evaluation=False) → new_state
: ifstart_evaluation
isTrue
, an evaluation is started
Once you finish training (which you indicate by passing start_evaluation=True
to reset
), your goal is to reach an average return of 475 during 100
evaluation episodes. Note that the environment prints your 100episode
average return each 10 episodes even during training.
Start with the monte_carlo.py template, which parses several useful parameters, creates the environment and illustrates the overall usage.
You will also need the wrappers.py
module, which wraps the standard gym
API with the abovementioned added features we use.
During evaluation in ReCodEx, three 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.
reinforce
Deadline: Jun 30, 23:59 2 points
Solve the continuous CartPolev1 environment
environment from the Gym library using the REINFORCE
algorithm. The continuous environment is very similar to the discrete one, except
that the states are vectors of realvalued observations with shape
env.observation_space.shape
.
Your goal is to reach an average return of 475 during 100 evaluation episodes.
Start with the reinforce.py template, which provides a simple network implementation in TensorFlow. However, feel free to use PyTorch instead, if you like.
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.
reinforce_baseline
Deadline: Jun 30, 23:59 2 points
This is a continuation of the reinforce
assignment.
Using the reinforce_baseline.py template, solve the continuous CartPolev1 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. In this assignment, you must train your agent in ReCodEx using the provided environment only.
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.
reinforce_pixels
Deadline: Jun 30, 23:59 2 points
This is a continuation of the reinforce_baseline
assignment.
The supplied cart_pole_pixels_environment.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 400 in 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 should probably train the model locally and submit the already pretrained model to ReCodEx.
Start with the reinforce_pixels.py template, which parses several parameters and creates the correct environment.
learning_to_learn
Deadline: Jun 30, 23:59 4 points
Implement a simple variant of learningtolearn architecture using the learning_to_learn.py template. Utilizing the Omniglot dataset loadable using the omniglot_dataset.py module, the goal is to learn to classify a sequence of images using a custom hierarchy by employing external memory.
The inputs image sequences consists of args.classes
random chosen Omniglot
classes, each class being assigned a randomly chosen label. For every chosen
class, args.images_per_class
images are randomly selected. Apart from the
images, the input contain the random labels one step after the corresponding
images (with the first label being 1). The gold outputs are also the labels,
but without the onestep offset.
The input images should be passed through a CNN feature extraction module
and then processed using memory augmented LSTM controller; the external memory
contains enough memory cells, each with args.cell_size
units. In each step,
the controller emits:
args.read_heads
read keys, each used to perform a read from memory as a weighted combination of cells according to the softmax of cosine similarities of the read key and the memory cells; a write value, which is prepended to the memory (dropping the last cell).
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 learning_to_learn.py epochs=50 classes=2
Epoch 1/50 loss: 0.5964  acc: 0.6422  acc1: 0.6768  acc2: 0.6529  acc5: 0.6360  acc10: 0.6299  val_loss: 0.4335  val_acc: 0.7825  val_acc1: 0.5195  val_acc2: 0.7460  val_acc5: 0.8010  val_acc10: 0.8725
Epoch 2/50 loss: 0.2660  acc: 0.8759  acc1: 0.6137  acc2: 0.8349  acc5: 0.9097  acc10: 0.9272  val_loss: 0.2693  val_acc: 0.8744  val_acc1: 0.6390  val_acc2: 0.8225  val_acc5: 0.9135  val_acc10: 0.9275
Epoch 3/50 loss: 0.1792  acc: 0.9164  acc1: 0.6364  acc2: 0.8870  acc5: 0.9550  acc10: 0.9609  val_loss: 0.2980  val_acc: 0.8770  val_acc1: 0.6250  val_acc2: 0.8460  val_acc5: 0.9130  val_acc10: 0.9260
Epoch 4/50 loss: 0.1504  acc: 0.9296  acc1: 0.6544  acc2: 0.9133  acc5: 0.9643  acc10: 0.9725  val_loss: 0.2037  val_acc: 0.9083  val_acc1: 0.6390  val_acc2: 0.8860  val_acc5: 0.9420  val_acc10: 0.9555
Epoch 5/50 loss: 0.1326  acc: 0.9369  acc1: 0.6741  acc2: 0.9261  acc5: 0.9686  acc10: 0.9772  val_loss: 0.1829  val_acc: 0.9168  val_acc1: 0.6485  val_acc2: 0.9025  val_acc5: 0.9560  val_acc10: 0.9600
Epoch 10/50 loss: 0.0952  acc: 0.9525  acc1: 0.6985  acc2: 0.9542  acc5: 0.9825  acc10: 0.9880  val_loss: 0.1709  val_acc: 0.9240  val_acc1: 0.6280  val_acc2: 0.9140  val_acc5: 0.9655  val_acc10: 0.9685
Epoch 20/50 loss: 0.0729  acc: 0.9613  acc1: 0.7106  acc2: 0.9732  acc5: 0.9916  acc10: 0.9937  val_loss: 0.1401  val_acc: 0.9383  val_acc1: 0.6845  val_acc2: 0.9310  val_acc5: 0.9690  val_acc10: 0.9805
Epoch 50/50 loss: 0.0579  acc: 0.9668  acc1: 0.7243  acc2: 0.9833  acc5: 0.9948  acc10: 0.9961  val_loss: 0.1271  val_acc: 0.9444  val_acc1: 0.7110  val_acc2: 0.9385  val_acc5: 0.9760  val_acc10: 0.9835
python3 learning_to_learn.py epochs=50 read_heads=2 classes=5
Epoch 1/50 loss: 1.5479  acc: 0.2698  acc1: 0.3502  acc2: 0.2777  acc5: 0.2588  acc10: 0.2571  val_loss: 1.4092  val_acc: 0.3719  val_acc1: 0.3176  val_acc2: 0.3430  val_acc5: 0.3568  val_acc10: 0.4202
Epoch 2/50 loss: 0.8753  acc: 0.6209  acc1: 0.2889  acc2: 0.4895  acc5: 0.6703  acc10: 0.7216  val_loss: 0.7641  val_acc: 0.6890  val_acc1: 0.2538  val_acc2: 0.5340  val_acc5: 0.7508  val_acc10: 0.8050
Epoch 3/50 loss: 0.5346  acc: 0.7813  acc1: 0.2553  acc2: 0.6352  acc5: 0.8657  acc10: 0.8919  val_loss: 0.6430  val_acc: 0.7511  val_acc1: 0.2608  val_acc2: 0.6134  val_acc5: 0.8286  val_acc10: 0.8614
Epoch 4/50 loss: 0.4314  acc: 0.8231  acc1: 0.2716  acc2: 0.6970  acc5: 0.9090  acc10: 0.9250  val_loss: 0.5841  val_acc: 0.7696  val_acc1: 0.2796  val_acc2: 0.6414  val_acc5: 0.8390  val_acc10: 0.8760
Epoch 5/50 loss: 0.3852  acc: 0.8410  acc1: 0.2851  acc2: 0.7280  acc5: 0.9260  acc10: 0.9400  val_loss: 0.7275  val_acc: 0.7390  val_acc1: 0.2836  val_acc2: 0.6138  val_acc5: 0.8024  val_acc10: 0.8456
Epoch 10/50 loss: 0.2885  acc: 0.8799  acc1: 0.3195  acc2: 0.8274  acc5: 0.9569  acc10: 0.9656  val_loss: 0.8520  val_acc: 0.7335  val_acc1: 0.2994  val_acc2: 0.6314  val_acc5: 0.7852  val_acc10: 0.8416
Epoch 20/50 loss: 0.2252  acc: 0.9049  acc1: 0.3511  acc2: 0.9009  acc5: 0.9750  acc10: 0.9805  val_loss: 0.5483  val_acc: 0.8216  val_acc1: 0.3182  val_acc2: 0.7828  val_acc5: 0.8828  val_acc10: 0.9152
Epoch 50/50 loss: 0.1720  acc: 0.9233  acc1: 0.3859  acc2: 0.9518  acc5: 0.9870  acc10: 0.9895  val_loss: 0.5175  val_acc: 0.8478  val_acc1: 0.3636  val_acc2: 0.8288  val_acc5: 0.9006  val_acc10: 0.9324
Note that your results may be slightly different, depending on your CPU type and whether you use a GPU.
python3 learning_to_learn.py train_episodes=160 test_episodes=160 epochs=3 classes=2
Epoch 1/3 loss: 0.7764  acc: 0.5078  acc1: 0.5375  acc2: 0.5063  acc5: 0.5031  acc10: 0.5000  val_loss: 0.6923  val_acc: 0.5175  val_acc1: 0.7531  val_acc2: 0.5688  val_acc5: 0.4500  val_acc10: 0.4969
Epoch 2/3 loss: 0.6992  acc: 0.5034  acc1: 0.5250  acc2: 0.5031  acc5: 0.4906  acc10: 0.5063  val_loss: 0.6914  val_acc: 0.5397  val_acc1: 0.7469  val_acc2: 0.5844  val_acc5: 0.5031  val_acc10: 0.4875
Epoch 3/3 loss: 0.6969  acc: 0.4975  acc1: 0.5594  acc2: 0.5063  acc5: 0.4844  acc10: 0.5094  val_loss: 0.6907  val_acc: 0.5272  val_acc1: 0.6781  val_acc2: 0.5312  val_acc5: 0.5219  val_acc10: 0.5000
python3 learning_to_learn.py train_episodes=160 test_episodes=160 epochs=3 read_heads=2 classes=5
Epoch 1/3 loss: 1.6505  acc: 0.2004  acc1: 0.1937  acc2: 0.2025  acc5: 0.2050  acc10: 0.2087  val_loss: 1.6086  val_acc: 0.2075  val_acc1: 0.2837  val_acc2: 0.2325  val_acc5: 0.1900  val_acc10: 0.1900
Epoch 2/3 loss: 1.6146  acc: 0.2042  acc1: 0.2237  acc2: 0.1912  acc5: 0.1950  acc10: 0.2138  val_loss: 1.6075  val_acc: 0.2156  val_acc1: 0.3050  val_acc2: 0.2325  val_acc5: 0.1912  val_acc10: 0.2100
Epoch 3/3 loss: 1.6114  acc: 0.2031  acc1: 0.2275  acc2: 0.2138  acc5: 0.1838  acc10: 0.1912  val_loss: 1.6061  val_acc: 0.2261  val_acc1: 0.3363  val_acc2: 0.2387  val_acc5: 0.2163  val_acc10: 0.2013
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 individually 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/ipynb
) containing the model training and prediction.
The Python sources are not executed, but must be included for inspection.
Competition Evaluation

For every submission, ReCodEx checks the above conditions (exactly one
.txt
, at least one.py/ipynb
) and whether the given annotations can be evaluated without error. If not, it will report a corresponding error in the logs. 
Before the deadline, ReCodEx prints the exact achieved score, but only if it is worse than the baseline.
If you surpass the baseline, the assignment is marked as solved in ReCodEx and you immediately get regular points for the assignment. However, ReCodEx does not print the reached score.

After the competition deadline, the latest submission of every user surpassing the required baseline participates in a competition. Additional bonus points are then awarded according to the ordering of the performance of the participating submissions.

After the competition results announcement, ReCodEx starts to show the exact performance for all the already submitted solutions and also for the solutions submitted later.
What Is Allowed
 You can use only the given annotated data for training and evaluation.
 You can use the given annotated training data in any way.
 You can use the given annotated development data for evaluation or hyperparameter tuning, but not for the training itself.
 Additionally, you can use any unannotated or manually created data for training and 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 trained models, like handwritten rules).
 Do not use test set annotations in any way, if you somehow get access to them.
 Unless stated otherwise, you can use any algorithm to solve the competition task at hand. The implementation should be either created by you or it can be based on some publicly available implementation, in which case you must reference it and you must understand it fully.
 If you utilize an already trained model, it must be trained only on the allowed training data, unless stated otherwise.
Install

Installing to central user packages repository
You can install all required packages to central user packages repository using
pip3 install user tensorflow==2.8.0 tensorflowaddons==0.16.1 tensorflowprobability==0.16.0 tensorflowhub==0.12.0 gym==0.20.0 scipy transformers==4.18.0 protobuf~=3.20.1
. 
Installing to a virtual environment
Python supports virtual environments, which are directories containing independent sets of installed packages. You can create a virtual environment by running
python3 m venv VENV_DIR
followed byVENV_DIR/bin/pip3 install tensorflow==2.8.0 tensorflowaddons==0.16.1 tensorflowprobability==0.16.0 tensorflowhub==0.12.0 gym==0.20.0 scipy transformers==4.18.0 protobuf~=3.20.1
(orVENV_DIR/Scripts/pip3
on Windows). 
Windows installation

On Windows, it can happen that
python3
is not in PATH, whilepy
command is – in that case you can usepy m venv VENV_DIR
, which uses the newest Python available, or for examplepy 3.9 m venv VENV_DIR
, which uses Python version 3.9. 
If your Windows TensorFlow fails with
ImportError: DLL load failed
, you are probably missing Visual C++ 2019 Redistributable. 
If you encounter a problem creating the logs in the
args.logdir
directory, a possible cause is that the path is longer than 260 characters, which is the default maximum length of a complete path on Windows. However, you can increase this limit on Windows 10, version 1607 or later, by following the instructions.


macOS installation

With an Intel processor, you should not need anything special.

If you have Apple Silicon, the installation is a bit more involved, because some Python packages do not yet have an official Arm64 binary build. The easiest workaround is to use Conda, which contains all the required dependencies.

Download Conda env.

Install it and activate it.
chmod +x ~/Downloads/Miniforge3MacOSXarm64.sh sh ~/Downloads/Miniforge3MacOSXarm64.sh source ~/miniforge3/bin/activate

Install the Arm64 TensorFlow dependencies.
conda install c apple tensorflowdeps==2.8.0

Install the Arm64 build of TensorFlow.
python m pip install tensorflowmacos==2.8.0



GPU support on Linux and Windows
TensorFlow 2.8 supports NVIDIA GPU out of the box, but you need to install CUDA 11.2 and cuDNN 8.1 libraries yourself.

GPU support on macOS
The AMD and Apple Silicon GPUs can be used by installing a plugin providing the GPU acceleration using:
python m pip install tensorflowmetal

Errors when running with a GPU
If you encounter errors when running with a GPU:
 if you are using the GPU also for displaying, try using the following
environment variable:
export TF_FORCE_GPU_ALLOW_GROWTH=true
 you can rerun with
export TF_CPP_MIN_LOG_LEVEL=0
environmental variable, which increases verbosity of the log messages.
 if you are using the GPU also for displaying, try using the following
environment variable:
MetaCentrum

How to install TensorFlow dependencies on MetaCentrum?
To install CUDA, cuDNN and Python 3.8 on MetaCentrum, it is enough to run in every session the following command:
module add python/3.8.0gccrab6t cuda/cuda11.2.0intel19.0.4tn4edsz cudnn/cudnn8.1.0.7711.2linuxx64intel19.0.4wx22b5t

How to install TensorFlow on MetaCentrum?
Once you have the required dependencies, you can create a virtual environment and install TensorFlow in it. However, note that by default the MetaCentrum jobs have a little disk space, so read about how to ask for scratch storage when submitting a job, and about quotas,
TL;DR:

Run an interactive CPU job, asking for 16GB scratch space:
qsub l select=1:ncpus=1:mem=8gb:scratch_local=16gb I

In the job, use the allocated scratch space as a temporary directory:
export TMPDIR=$SCRATCHDIR

Finally, create the virtual environment and install TensorFlow in it:
module add python/3.8.0gccrab6t cuda/cuda11.2.0intel19.0.4tn4edsz cudnn/cudnn8.1.0.7711.2linuxx64intel19.0.4wx22b5t python3 m venv CHOSEN_VENV_DIR CHOSEN_VENV_DIR/bin/pip install nocachedir upgrade pip setuptools CHOSEN_VENV_DIR/bin/pip install nocachedir tensorflow==2.8.0 tensorflowaddons==0.16.1 tensorflowprobability==0.16.0 tensorflowhub==0.12.0 gym==0.20.0 scipy


How to run a GPU computation on MetaCentrum?
First, read the official MetaCentrum documentation: Beginners guide, About scheduling system, GPU clusters.
TL;DR: To run an interactive GPU job with 1 CPU, 1 GPU, 16GB RAM, and 8GB scatch space, run:
qsub q gpu l select=1:ncpus=1:ngpus=1:mem=16gb:scratch_local=8gb I
To run a script in a noninteractive way, replace the
I
option with the script to be executed.If you want to run a CPUonly computation, remove the
q gpu
andngpus=1:
from the above commands.
AIC

How to install TensorFlow dependencies on AIC?
To install CUDA, cuDNN and Python 3.9 on AIC, you should add the following to your
.profile
:export PATH="/lnet/aic/data/python/3.9.9/bin:$PATH" export LD_LIBRARY_PATH="/lnet/aic/opt/cuda/cuda11.2/lib64:/lnet/aic/opt/cuda/cuda11.2/cudnn/8.1.1/lib64:/lnet/aic/opt/cuda/cuda11.2/extras/CUPTI/lib64:$LD_LIBRARY_PATH"

How to run a GPU computation on AIC?
First, read the official AIC documentation: Submitting CPU Jobs, Submitting GPU Jobs.
TL;DR: To run an interactive GPU job with 1 CPU, 1 GPU, and 16GB RAM, run:
qrsh q gpu.q l gpu=1,mem_free=16G,h_data=16G pty yes bash l
To run a script requiring a GPU in a noninteractive way, use
qsub q gpu.q l gpu=1,mem_free=16G,h_data=16G cwd b y SCRIPT_PATH
If you want to run a CPUonly computation, remove the
q gpu.q
andgpu=1,
from the above commands.
Git

Is it possible to keep the solutions in a Git repository?
Definitely. Keeping the solutions in a branch of your repository, where you merge them with the course repository, is probably a good idea. However, please keep the cloned repository with your solutions private.

On GitHub, do not create a public fork with your solutions
If you keep your solutions in a GitHub repository, please do not create a clone of the repository by using the Fork button – this way, the cloned repository would be public.
Of course, if you just want to create a pull request, GitHub requires a public fork and that is fine – just do not store your solutions in it.

How to clone the course repository?
To clone the course repository, run
git clone https://github.com/ufal/npfl114
This creates the repository in the
npfl114
subdirectory; if you want a different name, add it as a last parameter.To update the repository, run
git pull
inside the repository directory. 
How to keep the course repository as a branch in your repository?
If you want to store the course repository just in a local branch of your existing repository, you can run the following command while in it:
git remote add upstream https://github.com/ufal/npfl114 git fetch upstream git checkout t upstream/master
This creates a branch
master
; if you want a different name, addb BRANCH_NAME
to the last command.In both cases, you can update your checkout by running
git pull
while in it. 
How to merge the course repository with your modifications?
If you want to store your solutions in a branch merged with the course repository, you should start by
git remote add upstream https://github.com/ufal/npfl114 git pull upstream master
which creates a branch
master
; if you want a different name, change the last argument tomaster:BRANCH_NAME
.You can then commit to this branch and push it to your repository.
To merge the current course repository with your branch, run
git merge upstream master
while in your branch. Of course, it might be necessary to resolve conflicts if both you and I modified the same place in the templates.
ReCodEx

What files can be submitted to ReCodEx?
You can submit multiple files of any type to ReCodEx. There is a limit of 20 files per submission, with a total size of 20MB.

What file does ReCodEx execute and what arguments does it use?
Exactly one file with
py
suffix must contain a line starting withdef main(
. Such a file is imported by ReCodEx and themain
method is executed (during the import,__name__ == "__recodex__"
).The file must also export an argument parser called
parser
. ReCodEx uses its arguments and default values, but it overwrites some of the arguments depending on the test being executed – the template should always indicate which arguments are set by ReCodEx and which are left intact. 
What are the time and memory limits?
The memory limit during evaluation is 1.5GB. The time limit varies, but it should be at least 10 seconds and at least twice the running time of my solution.
Tensors

How to work with the usual
tf.Tensor
s?Read the TensorFlow Tensor guide and also the TensorFlow tensor indexing guide.

How to work with the
tf.RaggedTensor
s?Read the TensorFlow RaggedTensor guide.

How to convert the
tf.RaggedTensor
to atf.Tensor
and back?Often, you might want to convert a
tf.RaggedTensor
to atf.Tensor
and then back.
To obtain just the valid elements (so the rank of the resulting
tf.Tensor
is smaller by one):tensor_with_valid_elements = ragged_tensor.values ... new_ragged_tensor = ragged_tensor.with_values(new_tensor_with_valid_elements)

To obtain a
tf.Tensor
with the corresponding shape (so padding elements are added where needed):tensor_with_padding = ragged_tensor.to_tensor() ... new_ragged_tensor = tf.RaggedTensor.from_tensor(new_tensor_with_padding, ragged_tensor.row_lengths())

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
withmodel.fit
ormodel.evaluate
?To use a
tf.data.Dataset
in Keras, the dataset elements should be pairs(input_data, gold_labels)
, whereinput_data
andgold_labels
must be already batched. For example, givenCAGS
dataset, you can preprocess training data forcags_classification
as (for development data, you would remove the.shuffle
):train = cags.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.Instead, create a
Generator
object and use it to produce random numbers.generator = tf.random.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])
When a GPU is visible, you should create the
generator
explicitly on a CPU using awith tf.device("/cpu:0"):
block (on macOS, it will crash otherwise). 
How to call numpy methods or other nontf 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 matches it.
For example, to use the
bboxes_training
method from bboxes_utils, you could proceed as follows: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
intf.data.Dataset.map
?The
ImageDataGenerator
offers a.random_transform
method, so we can usetf.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)
Debugging

How to debug problems “inside” computation graphs with weird stack traces?
At the beginning of your program, run
tf.config.run_functions_eagerly(True)
The
tf.funcion
s (with the exception of the ones used intf.data
pipelines) are then not traced (i.e., no computation graphs are created) and the pure Python code is executed instead. 
How to debug problems “inside”
tf.data
pipelines with weird stack traces?Unfortunately, the solution above does not affect tracing in
tf.data
pipelines (for example intf.data.Dataset.map
). However, since TF 2.5, the commandtf.data.experimental.enable_debug_mode()
should disable any asynchrony, parallelism, or nondeterminism and forces Python execution (as opposed to tracecompiled graph execution) of userdefined functions passed into transformations such as
tf.data.Dataset.map
.
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 mutabletrainable
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 sublayers havetrainable == True
. Therefore, if you want to freeze only some sublayers of a layer you use in your model, the layer itself must havetrainable == 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 optiontraining
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 duringmodel.{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
andtraining
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 thetraining
option.
 if

How to use linear warmup?
You can prepend any
following_schedule
by using the followingLinearWarmup
schedule:class LinearWarmup(tf.optimizers.schedules.LearningRateSchedule): def __init__(self, warmup_steps, following_schedule): self._warmup_steps = warmup_steps self._warmup = tf.optimizers.schedules.PolynomialDecay(0., warmup_steps, following_schedule(0)) self._following = following_schedule def __call__(self, step): return tf.cond(step < self._warmup_steps, lambda: self._warmup(step), lambda: self._following(step  self._warmup_steps))
TensorBoard

Cannot start TensorBoard after installation
If
tensorboard
executable 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[79]
and%UserProfile%\AppData\Roaming\Python\Python3[79]\Scripts
on Windows). 
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 set it as the first argument ofas_default()
. 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: with self._writer.as_default(step=optimizer.iterations): # 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]
, wherechannels
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])
 scalar values:
Requirements
To pass the practicals, you need to obtain at least 80 points, excluding the bonus points. Note that all surplus points (both bonus and nonbonus) will be transfered to the exam. In total, assignments for at least 120 points (not including the bonus points) will be available, and if you solve all the assignments (any nonzero amount of points counts as solved), you automatically pass the exam with grade 1.
To pass the exam, you need to obtain at least 60, 75, or 90 points out of 100point exam to receive a grade 3, 2, or 1, respectively. The exam consists of 100pointworth questions from the list below (the questions are randomly generated, but in such a way that there is at least one question from every but the first lecture). In addition, you can get 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. You can take the exam without passing the practicals first.
Exam Questions
Lecture 1 Questions

Considering a neural network with $D$ input neurons, a single hidden layer with $H$ neurons, $K$ output neurons, hidden activation $f$ and output activation $a$, list its parameters (including their size) and write down how the output is computed. [5]

List the definitions of frequently used MLP output layer activations (the ones producing parameters of a Bernoulli distribution and a categorical distribution). Then write down three commonly used hidden layer activations (sigmoid, tanh, ReLU). [5]

Formulate the Universal approximation theorem. [5]
Lecture 2 Questions

Describe maximum likelihood estimation, as minimizing NLL, crossentropy and KL divergence. [10]

Define mean squared error and show how it can be derived using MLE. [5]

Describe gradient descent and compare it to stochastic (i.e., online) gradient descent and minibatch stochastic gradient descent. [5]

Formulate conditions on the sequence of learning rates used in SGD to converge to optimum almost surely. [5]

Write down the backpropagation algorithm. [5]

Write down the minibatch SGD algorithm with momentum. Then, formulate SGD with Nesterov momentum and show the difference between them. [5]

Write down the AdaGrad algorithm and show that it tends to internally decay learning rate by a factor of $1/\sqrt{t}$ in step $t$. Then write down the RMSProp algorithm and explain how it solves the problem with the involuntary learning rate decay. [10]

Write down the Adam algorithm. Then show why the biascorrection terms $(1\beta^t)$ make the estimation of the first and second moment unbiased. [10]
Lecture 3 Questions

Considering a neural network with $D$ input neurons, a single ReLU hidden layer with $H$ units and softmax output layer with $K$ units, write down the explicit formulas of the gradient of all the MLP parameters (two weight matrices and two bias vectors), assuming input $\boldsymbol x$, target $g$ and negative log likelihood loss. [10]

Assume a network with MSE loss generated a single output $o \in \mathbb{R}$, and the target output is $g$. What is the value of the loss function itself, and what is the explicit formula of the gradient of the loss function with respect to $o$? [5]

Assume a binaryclassification network with crossentropy loss generated a single output $z \in \mathbb{R}$, which is passed through the sigmoid output activation function, producing $o = \sigma(z)$. If the target output is $g$, what is the value of the loss function itself, and what is the explicit formula of the gradient of the loss function with respect to $z$? [5]

Assume a $K$classclassification network with crossentropy loss generated a $K$element output $\boldsymbol z \in \mathbb{R}^K$, which is passed through the softmax output activation function, producing $\boldsymbol o=\operatorname{softmax}(\boldsymbol z)$. If the target distribution is $\boldsymbol g$, what is the value of the loss function itself, and what is the explicit formula of the gradient of the loss function with respect to $\boldsymbol z$? [5]

Define $L_2$ regularization and describe its effect both on the value of the loss function and on the value of the loss function gradient. [5]

Describe the dropout method and write down exactly how it is used during training and during inference. [5]

Describe how label smoothing works for crossentropy loss, both for sigmoid and softmax activations. [5]

How are weights and biases initialized using the default Glorot initialization? [5]
Lecture 4 Questions

Write down the equation 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 crosscorrelation (as usual in convolutional neural networks) and that $O$ output channels are computed. [5]

Explain both
SAME
andVALID
padding schemes and write down the output size of a convolutional operation with an $N \times M$ kernel on image of size $H \times W$ for both these padding schemes (stride is 1). [5] 
Describe batch normalization including all its parameters, and write down an algorithm how it is used during training and an algorithm how it is used during inference. Be sure to explicitly write over what is being normalized in case of fully connected layers and in case of convolutional layers. [10]

Describe overall architecture of VGG19 (you do not need to remember the exact number of layers/filters, but you should describe which layers are used). [5]
Lecture 5 Questions

Describe overall architecture of ResNet. You do not need to remember the exact number of layers/filters, but you should draw a bottleneck block (including the applications of BatchNorms and ReLUs) and state how residual connections work when the number of channels increases. [10]

Draw the original ResNet block (including the exact positions of BatchNorms and ReLUs) and also the improved variant with full preactivation. [5]

Compare the bottleneck block of ResNet and ResNeXt architectures (draw the latter using convolutions only, i.e., do not use grouped convolutions). [5]

Describe the CNN regularization method of networks with stochastic depth. [5]

Compare Cutout and DropBlock. [5]

Describe Squeeze and Excitation applied to a ResNet block. [5]

Draw the Mobile inverted bottleneck block (including explanation of separable convolutions, the expansion factor, exact positions of BatchNorms and ReLUs, but without describing Squeeze and excitation blocks). [5]

Assume an input image $I$ of size $H \times W$ with $C$ channels, and a convolutional kernel $K$ with size $N \times M$, stride $S$ and $O$ output channels. Write down (or derive) the equation of transposed convolution (or equivalently backpropagation through a convolution to its inputs). [5]
Lecture 6 Questions

Write down how $\mathit{AP}_{50}$ is computed. [5]

Considering a FastRCNN architecture, draw overall network architecture, explain what a RoIpooling layer is, show how the network parametrizes bounding boxes and write down the loss. Finally, describe nonmaximum suppression and how the FastRCNN prediction is performed. [10]

Considering a FasterRCNN architecture, describe the region proposal network (what are anchors, architecture including both heads, how are the coordinates of proposals parametrized, what does the loss look like). [10]

Considering MaskRCNN architecture, describe the additions to a FasterRCNN architecture (the RoIAlign layer, the new maskproducing head). [5]

Write down the focal loss with class weighting, including the commonly used hyperparameter values. [5]

Draw the overall architecture of a RetinaNet architecture (the FPN architecture including the block combining feature maps of different resolutions; the classification and bounding box generation heads, including their output size). [5]
Lecture 7 Questions

Write down how the Long ShortTerm Memory (LSTM) cell operates, including the explicit formulas. Also mention the forget gate bias. [10]

Write down how the Gated Recurrent Unit (GRU) operates, including the explicit formulas. [10]

Describe Highway network computation. [5]

Why the usual dropout cannot be used on recurrent state? Describe how the problem can be alleviated with variational dropout. [5]

Describe layer normalization including all its parameters, and write down how it is computed (be sure to explicitly state over what is being normalized in case of fully connected layers and convolutional layers). [5]

Sketch a tagger architecture utilizing word embeddings, recurrent characterlevel word embeddings and two sentencelevel bidirectional RNNs with a residual connection. [10]
Lecture 8 Questions

Considering a linearchain CRF, write down how a score of a label sequence $\boldsymbol y$ is defined, and how can a log probability be computed using the label sequence scores. [5]

Write down the dynamic programming algorithm for computing log probability of a linearchain CRF, including its asymptotic complexity. [10]

Write down the dynamic programming algorithm for linearchain CRF decoding, i.e., an algorithm computing the most probable label sequence $\boldsymbol y$. [10]

In the context of CTC loss, describe regular and extended labelings and write down an algorithm for computing the log probability of a gold label sequence $\boldsymbol y$. [10]

Describe how CTC predictions are performed using a beamsearch. [5]

Draw the CBOW architecture from
word2vec
, including the sizes of the inputs and the sizes of the outputs and used nonlinearities. Also make sure to indicate where the embeddings are being trained. [5] 
Draw the SkipGram architecture from
word2vec
, including the sizes of the inputs and the sizes of the outputs and used nonlinearities. Also make sure to indicate where the embeddings are being trained. [5] 
Describe the hierarchical softmax used in
word2vec
. [5] 
Describe the negative sampling proposed in
word2vec
, including the choice of distribution of negative samples. [5]
Lecture 9 Questions

Draw a sequencetosequence architecture for machine translation, both during training and during inference (without attention). [5]

Draw a sequencetosequence architecture for machine translation used during training, including the attention. Then write down how exactly is the attention computed. [10]

Explain how can word embeddings tying be used in a sequencetosequence architecture. [5]

Write down why are subword units used in text processing, and describe the BPE algorithm for constructing a subword dictionary from a large corpus. [5]

Write down why are subword units used in text processing, and describe the WordPieces algorithm for constructing a subword dictionary from a large corpus. [5]

Pinpoint the differences between the BPE and WordPieces algorithms, both during dictionary construction and during inference. [5]
Lecture 11 Questions

Describe the Transformer encoder architecture, including the description of selfattention (but you do not need to describe multihead attention), FFN and positions of LNs and dropouts. [10]

Write down the formula of Transformer selfattention, and then describe multihead selfattention in detail. [10]

Describe the Transformer decoder architecture, including the description of selfattention and masked selfattention (but you do not need to describe multihead attention), FFN and positions of LNs and dropouts. Also discuss the difference between training and prediction regimes. [10]

Why are positional embeddings needed in Transformer architecture? Write down the sinusoidal positional embeddings used in the Transformer. [5]

Compare RNN to Transformer – what are the strengths and weaknesses of these architectures? [5]

Explain how are ELMo embeddings trained and how are they used in downstream applications. [5]

Describe the BERT architecture (you do not need to describe the (multihead) selfattention operation). Elaborate also on what positional embeddings are used and what are the GELU activations. [10]

Describe the GELU activations and explain why are they a combination of ReLUs and Dropout. [5]

Elaborate on BERT training process (what are the two objectives used and how exactly are the corresponding losses computed). [10]
Lecture 12 Questions

Write down the variational lower bound (ELBO) in the form of a reconstruction error minus the KL divergence between the encoder and the prior. Then prove it is actually a lower bound on probability $\log P(\boldsymbol x)$ (you can use Jensen's inequality if you want). [10]

Draw an architecture of a variational autoencoder (VAE). Pay attention to the parametrization of the distribution from the encoder (including the used activation functions), and show how to perform latent variable sampling so that it is differentiable with respect to the encoder parameters (the reparametrization trick). [10]

Write down the minmax formulation of generative adversarial network (GAN) objective. Then describe what loss is actually used for training the generator in order to avoid vanishing gradients at the beginning of the training. [5]

Write down the training algorithm of generative adversarial networks (GAN), including the losses minimized by the discriminator and the generator. Be sure to use the version of generator loss which avoids vanishing gradients at the beginning of the training. [10]

Explain how the class label is used when training a conditional generative adversarial network (CGAN). [5]

Illustrate that alternating SGD steps are not guaranteed to converge for a minmax problem. [5]
Lecture 13 Questions

Show how to incrementally update a running average (how to compute an average of $N$ numbers using the average of the first $N1$ numbers). [5]

Describe multiarm bandits and write down the $\epsilon$greedy algorithm for solving it. [5]

Define the Markov Decision Process, including the definition of the return. [5]

Define the value function, such that all expectations are over simple random variables (actions, states, rewards), not trajectories. [5]

Define the actionvalue function, such that all expectations are over simple random variables (actions, states, rewards), not trajectories. [5]

Express the value function using the actionvalue function, and express the actionvalue function using the value function. [5]

Define the optimal value function and the optimal actionvalue function. Then define optimal policy in such a way that its existence is guaranteed. [5]

Write down the MonteCarlo onpolicy everyvisit $\epsilon$soft algorithm. [10]

Formulate the policy gradient theorem. [5]

Prove the part of the policy gradient theorem showing the value of $\nabla_{\boldsymbol\theta} v_\pi(s)$. [10]

Assuming the policy gradient theorem, formulate the loss used by the REINFORCE algorithm and show how can its gradient be expressed as an expectation over states and actions. [5]

Write down the REINFORCE algorithm. [10]

Show that introducing baseline does not influence validity of the policy gradient theorem. [5]

Write down the REINFORCE with baseline algorithm. [10]
Lecture 14 Questions

Sketch the overall structure and training procedure of the Neural Architecture Search. You do not need to describe how exactly is the block produced by the controller. [5]

Draw the WaveNet architecture (show the overall architecture, explain dilated convolutions, write down the gated activations, describe global and local conditioning). [10]

Define the Mixture of Logistic distribution used in the Teacher model of Parallel WaveNet, including the explicit formula of computing the likelihood of the data. [5]

Describe the changes in the Student model of Parallel WaveNet, which allow efficient sampling (how does the latent prior look like, how the output data distribution is modeled in a single iteration and then after multiple iterations). [5]

Describe the addressing mechanism used in Neural Turing Machines – show the overall structure including the required parameters, and explain content addressing, interpolation with location addressing, shifting and sharpening. [10]

Explain the overall architecture of a Neural Turing Machine with an LSTM controller, assuming $R$ reading heads and one write head. Describe the inputs and outputs of the LSTM controller itself, then how the memory is read from and written to, and how the final output is computed. You do not need to write down the implementation of the addressing mechanism (you can assume it is a function which gets parameters, memory and previous distribution, and computes a new distribution over memory cells). [10]