In recent years, deep neural networks have been used to solve complex machine-learning problems. They have achieved significant state-of-the-art 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 state-of-the-art results, for example in named entity recognition, dependency parsing, machine translation, image labeling or in playing video games). No previous knowledge of artificial neural networks is required, but basic understanding of machine learning is advisable.
SIS code: NPFL114
Semester: summer
E-credits: 7
Examination: 3/2 C+Ex
Guarantor: Milan Straka
1. Introduction to Deep Learning Slides PDF Slides 2018 Video numpy_entropy pca_first mnist_layers_activations
2. Training Neural Networks Slides PDF Slides 2018 Video Questions sgd_backpropagation sgd_manual mnist_training gym_cartpole
3. Training Neural Networks II Slides PDF Slides 2018 Video Questions explore_examples mnist_regularization mnist_ensemble uppercase
4. Convolutional Neural Networks Slides PDF Slides Video 2018 Video Questions mnist_cnn image_augmentation tf_dataset cifar_competition
5. Convolutional Neural Networks II Slides PDF Slides Video 2018 Video Questions mnist_web cags_classification cags_segmentation
6. Object Detection Slides PDF Slides Video 2018 Video I 2018 Video II Questions cnn_manual bboxes_utils svhn_competition
7. Recurrent Neural Networks Slides PDF Slides Video Video – Practicals 2018 Video I 2018 Video II Questions 3d_recognition sequence_classification tagger_we
8. Word Embeddings, CRF, CTC Slides PDF Slides Video Video – Practicals 2018 Video I 2018 Video II Questions mnist_multiple tagger_cle_rnn tagger_cle_cnn tagger_competition speech_recognition
9. Word2Vec, Seq2seq, NMT Slides PDF Slides Video Video – Practicals 2018 Video I 2018 Video II Questions tensorboard_projector lemmatizer_noattn lemmatizer_attn lemmatizer_competition
10. Deep Generative Models Slides PDF Slides Video Video – Practicals 2018 Video Questions vae gan dcgan
11. Introduction to Deep Reinforcement Learning Slides PDF Slides Video Video – Practicals 2018 Video I 2018 Video II Questions omr_competition monte_carlo reinforce reinforce_baseline reinforce_pixels
12. Speech Synthesis, External Memory Networks Slides PDF Slides Video Video – Practicals 2018 Video Questions
13. Transformer, BERT Slides PDF Slides Video Questions sentiment_analysis
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.
Feb 24 Slides PDF Slides 2018 Video numpy_entropy pca_first mnist_layers_activations
Mar 2 Slides PDF Slides 2018 Video Questions sgd_backpropagation sgd_manual mnist_training gym_cartpole
Mar 9 Slides PDF Slides 2018 Video Questions explore_examples mnist_regularization mnist_ensemble uppercase
Mar 23 Slides PDF Slides Video 2018 Video Questions mnist_cnn image_augmentation tf_dataset cifar_competition
Mar 30 Slides PDF Slides Video 2018 Video Questions mnist_web cags_classification cags_segmentation
Apr 06 Slides PDF Slides Video 2018 Video I 2018 Video II Questions cnn_manual bboxes_utils svhn_competition
Apr 14 Slides PDF Slides Video Video – Practicals 2018 Video I 2018 Video II Questions 3d_recognition sequence_classification tagger_we
Apr 20 Slides PDF Slides Video Video – Practicals 2018 Video I 2018 Video II Questions mnist_multiple tagger_cle_rnn tagger_cle_cnn tagger_competition speech_recognition
Apr 27 Slides PDF Slides Video Video – Practicals 2018 Video I 2018 Video II Questions tensorboard_projector lemmatizer_noattn lemmatizer_attn lemmatizer_competition
Word2vec
word embeddings, notably the CBOW and Skip-gram architectures [Efficient Estimation of Word Representations in Vector Space]
May 04 Slides PDF Slides Video Video – Practicals 2018 Video Questions vae gan dcgan
May 11 Slides PDF Slides Video Video – Practicals 2018 Video I 2018 Video II Questions omr_competition monte_carlo reinforce reinforce_baseline reinforce_pixels
Study material for Reinforcement Learning is the Reinforcement Learning: An Introduction; second edition by Richard S. Sutton and Andrew G. Barto (reffered to as RLB), available online.
May 18 Slides PDF Slides Video Video – Practicals 2018 Video Questions
May 25 Slides PDF Slides Video Questions sentiment_analysis
To pass the practicals, you need to obtain at least 80 points, excluding the bonus points. Note that up to 40 points above 80 (including the bonus points) will be transfered to the exam.
The tasks are evaluated automatically using the ReCodEx Code Examiner. The evaluation is performed using Python 3.6, TensorFlow 2.1.0, TensorFlow Addons 0.8.1, TensorFlow Hub 0.7.0, TensorFlow Probability 0.9.0, OpenAI Gym 0.15.4 and NumPy 1.18.1.
You can install all required packages to central user packages repository using
pip3 install --user --upgrade pip setuptools
followed by
pip3 install --user tensorflow==2.1.0 tensorflow-addons==0.8.1 tensorflow-hub==0.7.0 tensorflow-probability==0.9.0 gym==0.15.4
.
Python supports virtual environments, which are directories containing
independent sets of installed packages. You can create the virtual environment
by running python3 -m venv VENV_DIR
followed by
VENV_DIR/bin/pip3 install --upgrade pip setuptools
and
VENV_DIR/bin/pip3 install tensorflow==2.1.0 tensorflow-addons==0.8.1 tensorflow-hub==0.7.0 tensorflow-probability==0.9.0 gym==0.15.4
.
If your Windows TensorFlow fails with ImportError: DLL load failed
,
you are probably missing
Visual C++ 2019 Redistributable.
If tensorboard
cannot be found, make sure the directory with pip installed
packages is in your PATH (that directory is either in your virtual environment
if you use a virtual environment, or it should be ~/.local/bin
on Linux
and %UserProfile%\AppData\Roaming\Python\Python3[5-7]
and
%UserProfile%\AppData\Roaming\Python\Python3[5-7]\Scripts
on Windows).
Some programs (even VS and VS code) erroneously change Windows system-wide MIME
type of Javascript files to text/plain
, which causes problems for tensorboard.
If you encounter the issue, the easiest is to uninstall tensorboard (pip3 uninstall tensorboard
) and then install a development version (pip3 install [--user] tb-nightly
) which contains a fix. The development version is then
started exactly as a stable one using a tensorboard
command.
TensorFlow 2.1 eagerly checks for availability of TensorRT during the first
import tensorflow
. In case you do not have it, a three-line warning is printed.
You can safely ignore the warning, both the CPU and the GPU backends work without TensorRT.
To access Tensorboard in DeepNote, first make sure you have
labs/deepnote_ngrok
– if you do not, run labs/deepnote_ngrok_get
script to
download it. Then start Tensorboard and finally run labs/deepnote_ngrok 6006
(or a different port on which you started tensorboard) to get a public
URL you can open in another browser tab to access Tensorboard.
Solving assignments in teams of size 2 or 3 is encouraged, but everyone has to participate (it is forbidden not to work on an assignment and then submit a solution created by other team members). All members of the team must submit in ReCodEx individually, but can have exactly the same sources/models/results. Each such solution must explicitly list all members of the team to allow plagiarism detection using this template.
Deadline: Mar 8, 23:59 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 numpy_entropy_data.txt
, whose lines consist of data points of our
dataset, and load numpy_entropy_model.txt
, which describes a model probability distribution,
with each line being a tab-separated pair of (data point, probability).
Then compute the following quantities using NumPy, and print them each on
a separate line rounded on two decimal places (or inf
for positive infinity,
which happens when an element of data distribution has zero probability
under the model distribution):
Use natural logarithms to compute the entropies and the divergence.
For data distribution file numpy_entropy_data.txt
A
BB
A
A
BB
A
CCC
and model distribution file numpy_entropy_model.txt
A 0.5
BB 0.3
CCC 0.1
D 0.1
the output should be
0.96
1.07
0.11
If we remove the CCC 0.1
line from the model distribution, the output should
change to
0.96
inf
inf
Deadline: Mar 8, 23:59 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.
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.
Note that the results might be slightly different, depending on your CPU type and whether you use GPU.
python3 pca_first.py --examples=1024 --iterations=64 --seed=7 --threads=1
51.52 9.94
python3 pca_first.py --examples=8192 --iterations=128 --seed=7 --threads=1
52.58 10.20
python3 pca_first.py --examples=55000 --iterations=1024 --seed=7 --threads=1
52.74 9.71
Deadline: Mar 8, 23:59 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:
layers
.activation
, with supported values of none
, relu
, tanh
and sigmoid
.In addition to submitting the task in ReCodEx, please also run the following variations and observe the results in TensorBoard:
0
layers, activation none
1
layer, activation none
, relu
, tanh
, sigmoid
10
layers, activation sigmoid
, relu
Note that the results might be slightly different, depending on your CPU type and whether you use GPU.
python3 mnist_layers_activations.py --recodex --seed=7 --threads=1 --epochs=1 --batch_size=50 --layers=0 --activation=none
91.22
python3 mnist_layers_activations.py --recodex --seed=7 --threads=1 --epochs=1 --batch_size=50 --layers=1 --activation=none
91.96
python3 mnist_layers_activations.py --recodex --seed=7 --threads=1 --epochs=1 --batch_size=50 --layers=1 --activation=relu
94.84
python3 mnist_layers_activations.py --recodex --seed=7 --threads=1 --epochs=1 --batch_size=50 --layers=1 --activation=tanh
94.19
python3 mnist_layers_activations.py --recodex --seed=7 --threads=1 --epochs=1 --batch_size=50 --layers=1 --activation=sigmoid
92.32
python3 mnist_layers_activations.py --recodex --seed=7 --threads=1 --epochs=1 --batch_size=50 --layers=3 --activation=relu
96.06
python3 mnist_layers_activations.py --recodex --seed=7 --threads=1 --epochs=1 --batch_size=50 --layers=5 --activation=tanh
94.67
Deadline: Mar 15 22, 23:59
3 points
In this exercise you will learn how to compute gradients using the so-called automatic differentiation, which is implemented by an automated backpropagation algorithm in TensorFlow. You will then perform training by running manually implemented minibatch stochastic gradient descent.
Starting with the sgd_backpropagation.py template, you should:
tf.GradientTape
to automatically compute the gradient of the loss
with respect to all variables;Note that the results might be slightly different, depending on your CPU type and whether you use GPU.
python3 sgd_backpropagation.py --batch_size=64 --epochs=2 --hidden_layer=20 --learning_rate=0.1 --seed=7 --threads=1
92.38
python3 sgd_backpropagation.py --batch_size=100 --epochs=2 --hidden_layer=32 --learning_rate=0.2 --seed=7 --threads=1
93.77
Deadline: Mar 15 22, 23:59
2 points
The goal in this exercise is to extend your solution to the sgd_backpropagation assignment by manually computing the gradient.
Note that this assignment is the only one where we will compute the gradient manually, we will otherwise always use the automatic differentiation. Therefore, the assignment is more of a mathematical exercise and it is definitely not required to pass the course. Furthermore, we will compute the derivative of the output functions later on the Mar 9 lecture.
Start with the sgd_manual.py template, which is based on sgd_backpropagation.py one. Be aware that these templates generates each a different output file.
In order to check that you do not use automatic differentiation, ReCodEx checks
that there is no GradientTape
string in your source (except in the comments).
The outputs should be exactly the same as in the sgd_backpropagation assignment.
Deadline: Mar 15 22, 23:59
3 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:
SGD
or Adam
).SGD
optimizer.exponential
or polynomial
(with degree 1, so inverse time decay).
Additionally, the final learning rate is given and the decay should gradually
decrease the learning rate to reach the final learning rate just after the
training.In addition to submitting the task in ReCodEx, please also run the following variations and observe the results in TensorBoard:
SGD
optimizer, learning_rate
0.01;SGD
optimizer, learning_rate
0.01, momentum
0.9;SGD
optimizer, learning_rate
0.1;Adam
optimizer, learning_rate
0.001;Adam
optimizer, learning_rate
0.01;Adam
optimizer, exponential
decay, learning_rate
0.01 and learning_rate_final
0.001;Adam
optimizer, polynomial
decay, learning_rate
0.01 and learning_rate_final
0.0001.Note that the results might be slightly different, depending on your CPU type and whether you use GPU.
python3 mnist_training.py --recodex --threads=1 --seed=7 --epochs=1 --batch_size=100 --hidden_layer=50 --optimizer SGD --learning_rate 0.03
90.10
python3 mnist_training.py --recodex --threads=1 --seed=7 --epochs=1 --batch_size=100 --hidden_layer=50 --optimizer SGD --learning_rate 0.2 --momentum 0.9
94.42
python3 mnist_training.py --recodex --threads=1 --seed=7 --epochs=1 --batch_size=100 --hidden_layer=50 --optimizer Adam --learning_rate 0.007
94.90
python3 mnist_training.py --recodex --threads=1 --seed=7 --epochs=2 --batch_size=100 --hidden_layer=50 --optimizer SGD --learning_rate 0.09 --decay polynomial --learning_rate_final 0.005
92.53
python3 mnist_training.py --recodex --threads=1 --seed=7 --epochs=2 --batch_size=100 --hidden_layer=50 --optimizer Adam --learning_rate 0.02 --decay exponential --learning_rate_final 0.0005
96.37
Deadline: Mar 15 22, 23:59
3 points
Solve the CartPole-v1 environment from the OpenAI Gym, utilizing only provided supervised training data. The data is available in gym_cartpole-data.txt file, each line containing one observation (four space separated floats) and a corresponding action (the last space separated integer). Start with the gym_cartpole.py.
The solution to this task should be a model which passes evaluation on random
inputs. This evaluation is performed by running the
gym_cartpole_evaluate.py
script, which loads a model and then evaluates it on 100 random episodes
(optionally rendering if --render
option is provided; note that the script
can be also imported as a module and evaluate any given tf.keras.Model
).
In order to pass, you must achieve an average reward of at least 475 on 100
episodes. Your model should have either one or two outputs (i.e., using either
sigmoid or softmax output function).
The size of the training data is very small and you should consider it when designing the model.
When submitting your model to ReCodEx, submit:
h5
suffix),py
suffix),
and possibly indicating teams.Your goal in this zero-point assignment is to explore the prepared examples.
Model.compile
, with custom Optimizer
, loss function and metric.
However, this example is 2-3 times slower than the previous two ones.tf.function
annotation to speed up execution of the previous
example back to the level of Model.fit
. See the
official tf.function
documentation
for details.Deadline: Mar 22, 23:59 6 points
You will learn how to implement three regularization methods in this assignment. Start with the mnist_regularization.py template and implement the following:
args.dropout
. Add a dropout layer after the
first Flatten
and also after all Dense
hidden layers (but not after the
output layer).args.l2
. Use
tf.keras.regularizers.L1L2
as a regularizer for all kernels (but not
biases) of all Dense
layers (including the last one).args.label_smoothing
. Instead
of SparseCategoricalCrossentropy
, you will need to use
CategoricalCrossentropy
which offers label_smoothing
argument.In ReCodEx, there will be three tests (one for each regularization methods) and you will get 2 points for passing each one.
In addition to submitting the task in ReCodEx, also run the following variations and observe the results in TensorBoard (notably training, development and test set accuracy and loss):
0
, 0.3
, 0.5
, 0.6
, 0.8
;0
, 0.001
, 0.0001
, 0.00001
;0
, 0.1
, 0.3
, 0.5
.Note that the results might be slightly different, depending on your CPU type and whether you use GPU.
python3 mnist_regularization.py --recodex --seed=7 --threads=1 --epochs=10 --batch_size=50 --hidden_layers=20 --dropout 0.2
90.00
python3 mnist_regularization.py --recodex --seed=7 --threads=1 --epochs=10 --batch_size=50 --hidden_layers=20 --l2 0.01
89.05
python3 mnist_regularization.py --recodex --seed=7 --threads=1 --epochs=10 --batch_size=50 --hidden_layers=20 --label_smoothing 0.2
91.09
Deadline: Mar 22, 23:59 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 development set.
Note that the results might be slightly different, depending on your CPU type and whether you use GPU.
python3 mnist_ensemble.py --recodex --seed=7 --threads=1 --epochs=2 --batch_size=50 --hidden_layers=20 --models=3
94.96 94.96
95.54 95.58
94.90 95.54
python3 mnist_ensemble.py --recodex --seed=7 --threads=1 --epochs=1 --batch_size=50 --hidden_layers=20 --models=5
94.08 94.08
94.36 94.34
93.94 94.20
94.02 94.20
93.94 94.16
Deadline: Mar 22, 23:59 4 points+5 bonus
This assignment introduces first NLP task. Your goal is to implement a model which is given Czech lowercased text and tries to uppercase appropriate letters. To load the dataset, use uppercase_data.py module which loads (and if required also downloads) the data. While the training and the development sets are in correct case, the test set is lowercased.
This is an open-data task, where you submit only the uppercased test set together with the training script (which will not be executed, it will be only used to understand the approach you took, and to indicate teams). Explicitly, submit exactly one .txt file and at least one .py file.
The task is also a competition. Everyone who submits a solution which achieves at least 97.0% accuracy will get 4 basic points; the 5 bonus points will be distributed depending on relative ordering of your solutions. The accuracy is computed per-character and can be evaluated by uppercase_eval.py script.
You may want to start with the uppercase.py template, which uses the uppercase_data.py to load the data, generate an alphabet of given size containing most frequent characters, and generate sliding window view on the data. The template also comments on possibilities of character representation.
Do not use RNNs, CNNs or Transformer in this task (if you have doubts, contact me).
Deadline: Apr 05, 23:59 5 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 comma-separated specifications of the following layers:
C-filters-kernel_size-stride-padding
: Add a convolutional layer with ReLU
activation and specified number of filters, kernel size, stride and padding.
Example: C-10-3-1-same
CB-filters-kernel_size-stride-padding
: Same as
C-filters-kernel_size-stride-padding
, but use batch normalization.
In detail, start with a convolutional layer without bias and activation,
then add batch normalization layer, and finally ReLU activation.
Example: CB-10-3-1-same
M-kernel_size-stride
: Add max pooling with specified size and stride.
Example: M-3-2
R-[layers]
: Add a residual connection. The layers
contain a specification
of at least one convolutional layer (but not a recursive residual connection R
).
The input to the specified layers is then added to their output (after the
ReLU nonlinearity of the last one).
Example: R-[C-16-3-1-same,C-16-3-1-same]
F
: Flatten inputs. Must appear exactly once in the architecture.H-hidden_layer_size
: Add a dense layer with ReLU activation and specified
size. Example: H-100
D-dropout_rate
: Apply dropout with the given dropout rate. Example: D-0.5
An example architecture might be --cnn=CB-16-5-2-same,M-3-2,F,H-100,D-0.5
.
You can assume the resulting network is valid; it is fine to crash if it is not.
After a successful ReCodEx submission, you can try obtaining the best accuracy
on MNIST and then advance to cifar_competition
.
Note that the results might be slightly different, depending on your CPU type and whether you use GPU.
python3 mnist_cnn.py --seed=7 --recodex --threads=1 --epochs=1 --batch_size=50 --cnn=F,H-100
94.84
python3 mnist_cnn.py --seed=7 --recodex --threads=1 --epochs=1 --batch_size=50 --cnn=F,H-100,D-0.5
94.17
python3 mnist_cnn.py --seed=7 --recodex --threads=1 --epochs=1 --batch_size=50 --cnn=M-5-2,F,H-50
87.18
python3 mnist_cnn.py --seed=7 --recodex --threads=1 --epochs=1 --batch_size=50 --cnn=C-8-3-5-same,C-8-3-2-valid,F,H-50
86.18
python3 mnist_cnn.py --seed=7 --recodex --threads=1 --epochs=1 --batch_size=50 --cnn=CB-6-3-5-valid,F,H-32
90.23
python3 mnist_cnn.py --seed=7 --recodex --threads=1 --epochs=1 --batch_size=50 --cnn=C-8-3-5-valid,R-[C-8-3-1-same,C-8-3-1-same],F,H-50
91.15
Deadline: Apr 05, 23:59 1 points
The template image_augmentation.py creates a simple convolutional network for classifying CIFAR-10. Your goal is to perform image data augmentation operations using ImageDataGenerator and to utilize these data during training.
Deadline: Apr 05, 23:59 2 points
In this assignment you will familiarize yourselves with tf.data
, which is
TensorFlow high-level API for constructing input pipelines. If you want,
you can read an official TensorFlow tf.data guide
or reference API manual.
The goal of this assignment is to implement image augmentation preprocessing
similar to image_augmentation
, but with tf.data
. Start with the
tf_dataset.py
template and implement the input pipelines employing the tf.data.Dataset
.
Deadline: Apr 05, 23:59 5 points+5 bonus
The goal of this assignment is to devise the best possible model for CIFAR-10. You can load the data using the cifar10.py module. Note that the test set is different than that of official CIFAR-10.
This is an open-data task, where you submit only the test set labels together with the training script (which will not be executed, it will be only used to understand the approach you took, and to indicate teams). Explicitly, submit exactly one .txt file and at least one .py file.
The task is also a competition. Everyone who submits a solution which achieves at least 60% test set accuracy will get 5 points; the rest 5 points will be distributed depending on relative ordering of your solutions. Note that my solutions usually need to achieve at least ~73% on the development set to score 60% on the test set.
You may want to start with the cifar_competition.py template which generates the test set annotation in the required format.
You can try a
Javascript-based demo of MNIST classification.
This demo uses a neural network trained in TensorFlow
using the mnist_web.py module,
whose output was converted for Tensorflow.js
with tensorflowjs_converter --input_format=keras
command and is then utilized
by mnist_web.html.
Deadline: Apr 12, 23:59 6 points+5 bonus
The goal of this assignment is to use pretrained EfficientNet-B0 model to achieve best accuracy in CAGS classification.
The CAGS dataset consists
of images of cats and dogs of size $224×224$, each classified in one of
the 34 breeds and each containing a mask indicating the presence of the animal.
To load the dataset, use the cags_dataset.py
module. The dataset is stored in a
TFRecord file
and each element is encoded as a
tf.train.Example.
Therefore the dataset is loaded using tf.data
API and each entry can be
decoded using .map(CAGS.parse)
call.
To load the EfficientNet-B0, use the the provided
efficient_net.py
module. Its method pretrained_efficientnet_b0(include_top)
:
tf.keras.Model
processing image of shape $(224, 224, 3)$ with
float values in range $[0, 1]$ and producing a list of results:
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);include_top == False
, the network will return image features (the result
of the last global average pooling);An example performing classification of given images is available in image_classification.py.
A note on finetuning: each tf.keras.layers.Layer
has a mutable trainable
property indicating whether its variables should be updated – however, after
changing it, you need to call .compile
again (or otherwise make sure the list
of trainable variables for the optimizer is updated). Furthermore, training
argument passed to the invocation call decides whether the layer is executed in
training regime (neurons gets dropped in dropout, batch normalization computes
estimates on the batch) or in inference regime. There is one exception though
– if trainable == False
on a batch normalization layer, it runs in the
inference regime even when training == True
.
This is an open-data task, where you submit only the test set labels together with the training script (which will not be executed, it will be only used to understand the approach you took, and to indicate teams). Explicitly, submit exactly one .txt file and at least one .py file.
The task is also a competition. Everyone who submits a solution which achieves at least 90% test set accuracy will get 6 points; the rest 5 points will be distributed depending on relative ordering of your solutions.
You may want to start with the cags_classification.py template which generates the test set annotation in the required format.
Deadline: Apr 19, 23:59 6 points+5 bonus
The goal of this assignment is to use pretrained EfficientNet-B0 model to
achieve best image segmentation IoU score on the CAGS dataset.
The dataset and the EfficientNet-B0 is described in the cags_classification
assignment.
This is an open-data task, where you submit only the test set masks together with the training script (which will not be executed, it will be only used to understand the approach you took, and to indicate teams). Explicitly, submit exactly one .txt file and at least one .py file.
A mask is evaluated using intersection over union (IoU) metric, which is the
intersection of the gold and predicted mask divided by their union, and the
whole test set score is the average of its masks' IoU. A TensorFlow compatible
metric is implemented by the class CAGSMaskIoU
of the
cags_segmentation_eval.py
module, which can further be used to evaluate a file with predicted masks.
The task is also a competition. Everyone who submits a solution which achieves at least 85% test set IoU will get 6 points; the rest 5 points will be distributed depending on relative ordering of your solutions.
You may want to start with the cags_segmentation.py template, which generates the test set annotation in the required format – each mask should be encoded on a single line as a space separated sequence of integers indicating the length of alternating runs of zeros and ones.
Deadline: Apr 19, 23:59 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 construct a series of 2D convolutional layers with ReLU
activation and valid
padding, specified in the args.cnn
option.
The args.cnn
contains comma separater layer specifications in the format
filters-kernel_size-stride
.
Of course, you cannot use any TensorFlow convolutional operation (instead,
implement the forward and backward pass using matrix multiplication and other
operations) nor the GradientTape
for gradient computation.
Note that the results might be slightly different, depending on your CPU type and whether you use GPU.
python3 cnn_manual.py --seed=7 --recodex --threads=1 --learning_rate=0.01 --epochs=1 --batch_size=50 --cnn=5-1-1
89.62
python3 cnn_manual.py --seed=7 --recodex --threads=1 --learning_rate=0.01 --epochs=1 --batch_size=50 --cnn=5-3-1
92.83
python3 cnn_manual.py --seed=7 --recodex --threads=1 --learning_rate=0.01 --epochs=1 --batch_size=50 --cnn=5-3-2
90.62
python3 cnn_manual.py --seed=7 --recodex --threads=1 --learning_rate=0.01 --epochs=1 --batch_size=50 --cnn=5-3-2,10-3-2
92.58
Deadline: Apr 19, 23:59 2 points
This is a preparatory assignment for svhn_competition
. The goal is to
implement several bounding box manipulation routines in the
bboxes_utils.py
module. Notably, you need to implement the following methods:
bbox_to_fast_rcnn
: convert a bounding box to a Fast R-CNN-like
representation relative to a given anchor;bbox_from_fast_rcnn
: convert a Fast R-CNN-like representation relative to an
anchor back to a bounding box;bboxes_training
: given a list of anchors and gold objects, assign gold
objects to anchors and generate suitable training data (the exact algorithm
is described in the template).The bboxes_utils.py contains simple unit tests, which are evaluated when executing the module, which you can use to check the validity of your implementation.
When submitting to ReCodEx, you must submit exactly one Python source with
methods bbox_to_fast_rcnn
, bbox_to_fast_rcnn
and bboxes_training
.
These methods are then executed and compared to the reference implementation.
Deadline: Apr 26, 23:59 May 03, 23:59
5 points+5 bonus
The goal of this assignment is to implement a system performing object recognition, optionally utilizing pretrained EfficientNet-B0 backbone.
The Street View House Numbers (SVHN) dataset
annotates for every photo all digits appearing on it, including their bounding
boxes. The dataset can be loaded using the svhn_dataset.py
module. Similarly to the CAGS
dataset, it is stored in a
TFRecord file
with tf.train.Example
elements, which can be decoded using .map(SVHN.parse)
call. Every element
is a dictionary with the following keys:
"image"
: a square 3-channel image,"classes"
: a 1D tensor with all digit labels appearing in the image,"bboxes"
: a [num_digits, 4]
2D tensor with bounding boxes of every
digit in the image.Given that the dataset elements are each of possibly different size and you
want to preprocess them using a NumPy function bboxes_training
, it might be
more comfortable to convert the dataset to NumPy. Alternatively, you can
call bboxes_training
directly in tf.data.Dataset.map
by using tf.numpy_function
,
see FAQ.
Similarly to the cags_classification
, you can load the EfficientNet-B0 using the provided
efficient_net.py
module. Its method pretrained_efficientnet_b0(include_top, dynamic_shape=False)
has gotten
a new argument dynamic_shape
, and with dynamic_shape=True
it constructs
a model capable of processing an input image of any size.
This is an open-data task, where you submit only the test set annotations together with the training script (which will not be executed, it will be only used to understand the approach you took, and to indicate teams). Explicitly, submit exactly one .txt file and at least one .py file.
Each test set image annotation consists of a sequence of space separated five-tuples label top left bottom right, and the annotation is considered correct, if exactly the gold digits are predicted, each with IoU at least 0.5. The whole test set score is then the prediction accuracy of individual images. An evaluation of a file with the predictions can be performed by the svhn_eval.py module.
The task is also a competition. Everyone submitting a solution with at least 20% test set accuracy will get 5 points; the rest 5 points will be distributed depending on relative ordering of your solutions. Note that I usually need at least 35% development set accuracy to achieve the required test set performance.
You should start with the svhn_competition.py template, which generates the test set annotation in the required format.
A baseline solution can use RetinaNet-like single stage detector,
using only a single level of convolutional features (no FPN)
with single-scale and single-aspect anchors. Focal loss is available
as tfa.losses.SigmoidFocalCrossEntropy
(using reduction=tf.losses.Reduction.SUM_OVER_BATCH_SIZE
option is a good
idea) and non-maximum suppression as
tf.image.non_max_suppression or
tf.image.combined_non_max_suppression.
Deadline: Apr 26, 23:59 5 points+5 bonus
Your goal in this assignment is to perform 3D object recognition. The input is voxelized representation of an object, stored as a 3D grid of either empty or occupied voxels, and your goal is to classify the object into one of 10 classes. The data is available in two resolutions, either as 20×20×20 data or 32×32×32 data. To load the dataset, use the modelnet.py module.
The official dataset offers only train and test sets, with the test set having a different distributions of labels. Our dataset contains also a development set, which has nearly the same label distribution as the test set.
The assignment is again an open-data task, where you submit only the test set labels together with the training script (which will not be executed, it will be only used to understand the approach you took, and to indicate teams). Explicitly, submit exactly one .txt file and at least one .py file.
The task is also a competition. Everyone submitting a solution with at least 85% test set accuracy will get 5 points; the rest 5 points will be distributed depending on relative ordering of your solutions.
You can start with the 3d_recognition.py template, which among others generates test set annotations in the required format.
Deadline: Apr 26, 23:59 6 points
The goal of this assignment is to introduce recurrent neural networks, manual TensorBoard log collection, and manual gradient clipping. Considering recurrent neural network, the assignment shows convergence speed and illustrates exploding gradient issue. The network should process sequences of 50 small integers and compute parity for each prefix of the sequence. The inputs are either 0/1, or vectors with one-hot representation of small integer.
Your goal is to modify the sequence_classification.py template and implement the following:
SimpleRNN
, GRU
and LSTM
) and dimensionality.In addition to submitting the task in ReCodEx, please also run the following variations and observe the results in TensorBoard. Concentrate on the way how the RNNs converge, convergence speed, exploding gradient issues and how gradient clipping helps:
--rnn_cell=SimpleRNN --sequence_dim=1
, --rnn_cell=GRU --sequence_dim=1
, --rnn_cell=LSTM --sequence_dim=1
--sequence_dim=2
--sequence_dim=10
--rnn_cell=LSTM --hidden_layer=70 --rnn_cell_dim=30 --sequence_dim=30
and the same with --clip_gradient=1
--rnn_cell=SimpleRNN
--rnn_cell=GRU --hidden_layer=90
Note that the results might be slightly different, depending on your CPU type and whether you use GPU.
python3 sequence_classification.py --recodex --seed=7 --batch_size=16 --epochs=1 --threads=1 --train_sequences=1000 --test_sequences=100 --sequence_length=20 --sequence_dim=1 --rnn_cell=SimpleRNN --rnn_cell_dim=16 --hidden_layer=0 --clip_gradient=0
52.85
python3 sequence_classification.py --recodex --seed=7 --batch_size=16 --epochs=1 --threads=1 --train_sequences=1000 --test_sequences=100 --sequence_length=20 --sequence_dim=1 --rnn_cell=LSTM --rnn_cell_dim=10 --hidden_layer=0 --clip_gradient=0
54.80
python3 sequence_classification.py --recodex --seed=7 --batch_size=16 --epochs=1 --threads=1 --train_sequences=1000 --test_sequences=100 --sequence_length=20 --sequence_dim=1 --rnn_cell=GRU --rnn_cell_dim=12 --hidden_layer=0 --clip_gradient=0
47.95
python3 sequence_classification.py --recodex --seed=7 --batch_size=16 --epochs=1 --threads=1 --train_sequences=1000 --test_sequences=100 --sequence_length=20 --sequence_dim=1 --rnn_cell=LSTM --rnn_cell_dim=16 --hidden_layer=50 --clip_gradient=0
54.10
python3 sequence_classification.py --recodex --seed=7 --batch_size=16 --epochs=1 --threads=1 --train_sequences=1000 --test_sequences=100 --sequence_length=20 --sequence_dim=1 --rnn_cell=LSTM --rnn_cell_dim=16 --hidden_layer=50 --clip_gradient=0.01
53.85
Deadline: Apr 26, 23:59 3 points
In this assignment you will create a simple part-of-speech tagger. For training and evaluation, we will use Czech dataset containing tokenized sentences, each word annotated by gold lemma and part-of-speech tag. The morpho_dataset.py module (down)loads the dataset and can generate batches.
Your goal is to modify the tagger_we.py template and implement the following:
GRU
and LSTM
) and dimensionality.Note that the results might be slightly different, depending on your CPU type and whether you use GPU.
python3 tagger_we.py --recodex --seed=7 --batch_size=2 --epochs=1 --threads=1 --max_sentences=200 --rnn_cell=LSTM --rnn_cell_dim=16 --we_dim=64
29.34
python3 tagger_we.py --recodex --seed=7 --batch_size=2 --epochs=1 --threads=1 --max_sentences=200 --rnn_cell=GRU --rnn_cell_dim=20 --we_dim=64
46.29
Deadline: May 03, 23:59 2 points
In this assignment you will implement a model with multiple inputs. Start with the mnist_multiple.py template and:
Note that the results might be slightly different, depending on your CPU type and whether you use GPU.
python3 mnist_multiple.py --recodex --seed=7 --batch_size=50 --epochs=1 --threads=1
93.94 97.32
python3 mnist_multiple.py --recodex --seed=7 --batch_size=100 --epochs=1 --threads=1
91.86 96.28
Deadline: May 03, 23:59 2 points
This assignment is a continuation of tagger_we
. Using the
tagger_cle_rnn.py
template, implement character-level word embedding computation using
a bidirectional character-level GRU.
Once submitted to ReCodEx, you should experiment with the effect of CLEs
compared to a plain tagger_we
, and the influence of their dimensionality. Note
that tagger_we
has by default word embeddings twice the size of word
embeddings in tagger_cle_rnn
.
Note that the results might be slightly different, depending on your CPU type and whether you use GPU.
python3 tagger_cle_rnn.py --recodex --seed=7 --batch_size=3 --epochs=2 --threads=1 --max_sentences=90 --rnn_cell=LSTM --rnn_cell_dim=16 --we_dim=32 --cle_dim=16
25.85
python3 tagger_cle_rnn.py --recodex --seed=7 --batch_size=3 --epochs=2 --threads=1 --max_sentences=90 --rnn_cell=GRU --rnn_cell_dim=20 --we_dim=32 --cle_dim=16
33.90
Deadline: May 03, 23:59 2 points
This task is a continuation of tagger_cle_rnn
assignment. Using the
tagger_cle_cnn.py
template, instead of using RNNs to generate character-level embeddings,
process character sequences with 1D convolutional filters with varying kernel
sizes and obtain fixed-size representations using global max-pooling.
Compute the final embeddings by using a highway layer.
Note that the results might be slightly different, depending on your CPU type and whether you use GPU.
python3 tagger_cle_cnn.py --recodex --seed=7 --batch_size=3 --epochs=4 --threads=1 --max_sentences=90 --rnn_cell=LSTM --rnn_cell_dim=16 --we_dim=32 --cle_dim=16 --cnn_filters=16 --cnn_max_width=3
38.01
python3 tagger_cle_cnn.py --recodex --seed=7 --batch_size=3 --epochs=4 --threads=1 --max_sentences=90 --rnn_cell=GRU --rnn_cell_dim=20 --we_dim=32 --cle_dim=16 --cnn_filters=16 --cnn_max_width=3
53.85
Deadline: May 03, 23:59 4 points+5 bonus
In this assignment, you should extend
tagger_we
/tagger_cle_rnn
/tagger_cle_cnn
into a real-world Czech part-of-speech tagger. We will use
Czech PDT dataset loadable using the morpho_dataset.py
module. Note that the dataset contains more than 1500 unique POS tags and that
the POS tags have a fixed structure of 15 positions (so it is possible to
generate the POS tag characters independently).
You can use the following additional data in this assignment:
The assignment is again an open-data task, where you submit only the test set annotations
together with the training script (which will not be executed, it will be
only used to understand the approach you took, and to indicate teams).
Explicitly, submit exactly one .txt file and at least one .py file.
Note that all .zip
files you submit will be extracted first.
The task is also a competition. Everyone submitting a solution with at least 92% label accuracy gets 4 points; the rest 5 points will be distributed depending on relative ordering of your solutions. Lastly, 3 bonus points will be given to anyone surpassing pre-neural-network state-of-the-art of 95.89% from Spoustová et al., 2009. You can evaluate a generated file using the morpho_evaluator.py module.
You can start with the tagger_competition.py template, which among others generates test set annotations in the required format.
Deadline: May 03, 23:59 May 10, 23:59
6 points+5 bonus
This assignment is a competition task in speech recognition area. Specifically, your goal is to predict a sequence of letters given a spoken utterance. We will be using TIMIT corpus, with input sound waves passed through the usual preprocessing – computing Mel-frequency cepstral coefficients (MFCCs). You can repeat exactly this preprocessing on a given audio using the timit_mfcc_preprocess.py script.
Because the data is not publicly available, you can download it only through ReCodEx. Please do not distribute it. To load the dataset using the timit_mfcc.py module.
This is an open-data task, where you submit only the test set annotations together with the training script (which will not be executed, it will be only used to understand the approach you took, and to indicate teams). Explicitly, submit exactly one .txt file and at least one .py file.
The task is also a competition. The evaluation is performed
by computing edit distance to the gold letter sequence, normalized by its length
(i.e., exactly as tf.edit_distance
). Everyone submitting a solution with at
most 50% test set edit distance will get 6 points; the rest 5 points will be distributed
depending on relative ordering of your solutions. An evaluation (using for example development data)
can be performed by
speech_recognition_eval.py.
You should start with the speech_recognition.py template.
tf.nn.ctc_loss
),tf.nn.ctc_beam_search_decoder
)
and possibly evaluate the results using normalized edit distance (tf.edit_distance
).You can try exploring the TensorBoard Projector with pre-trained embeddings
for 20k most frequent lemmas in
Czech
and English
– after extracting the archive, start
tensorboard --logdir dir_where_the_archive_is_extracted
.
In order to use the Projector tab yourself, you can take inspiration from the projector_export.py script, which was used to export the above pre-trained embeddings.
Deadline: May 10, 23:59 4 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
morpho_dataset.py
module.
Your goal is to modify the lemmatizer_noattn.py template and implement the following:
Note that the results might be slightly different, depending on your CPU type and whether you use GPU.
python3 lemmatizer_noattn.py --recodex --seed=7 --batch_size=2 --epochs=3 --threads=1 --max_sentences=200 --rnn_dim=24 --cle_dim=64
20.47
Deadline: May 10, 23:59 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
:
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 the results might be slightly different, depending on your CPU type and whether you use GPU.
python3 lemmatizer_attn.py --recodex --seed=7 --batch_size=2 --epochs=3 --threads=1 --max_sentences=200 --rnn_dim=24 --cle_dim=64
22.14
Deadline: May 10, 23:59 May 17, 23:59
5 points+5 bonus
In this assignment, you should extend lemmatizer_noattn
or lemmatizer_attn
into a real-world Czech lemmatizer. As in tagger_competition
, we will use
Czech PDT dataset loadable using the morpho_dataset.py
module.
You can also use the following additional data as in the tagger_competition
assignment.
The assignment is again an open-data task, where you submit only the test set annotations
together with the training script (which will not be executed, it will be
only used to understand the approach you took, and to indicate teams).
Explicitly, submit exactly one .txt file and at least one .py file.
Note that all .zip
files you submit will be extracted first.
The task is also a competition. Everyone submitting a solution with at least 92% accuracy will get 5 points; the rest 5 points will be distributed depending on relative ordering of your solutions. Lastly, 3 bonus points will be given to anyone surpassing pre-neural-network state-of-the-art of 97.86%. You can evaluate a generated file using the morpho_evaluator.py module.
You can start with the lemmatizer_competition.py template, which among others generates test set annotations in the required format.
Deadline: May 17, 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
, mnist-fashion
, and mnist-cifarcars
) and
different latent variable dimensionality (z_dim=2
and z_dim=100
).
The generated images are available in TensorBoard logs.
Note that the results might be slightly different, depending on your CPU type and whether you use GPU.
python3 vae.py --recodex --seed=7 --batch_size=50 --dataset=mnist-recodex --decoder_layers=500,500 --encoder_layers=500,500 --epochs=2 --threads=1 --z_dim=2
2357.67
python3 vae.py --recodex --seed=7 --batch_size=50 --dataset=mnist-recodex --decoder_layers=500,500 --encoder_layers=500,500 --epochs=2 --threads=1 --z_dim=100
2174.10
Deadline: May 17, 23:59 3 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
, mnist-fashion
, and mnist-cifarcars
) and
maybe try different latent variable dimensionality. The generated images are
available in TensorBoard logs.
You can also continue with dcgan
assignment.
Note that the results might be slightly different, depending on your CPU type and whether you use GPU.
python3 gan.py --recodex --seed=7 --batch_size=50 --dataset=mnist-recodex --discriminator_layers=128 --generator_layers=128 --epochs=2 --threads=1 --z_dim=2
57.75
python3 gan.py --recodex --seed=7 --batch_size=50 --dataset=mnist-recodex --discriminator_layers=128 --generator_layers=128 --epochs=2 --threads=1 --z_dim=100
49.24
Deadline: May 17, 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
, mnist-fashion
, and mnist-cifarcars
). However,
note that you will need a lot of computational power (preferably a GPU) to
generate the images.
Note that the results might be slightly different, depending on your CPU type and whether you use GPU.
python3 dcgan.py --recodex --seed=7 --batch_size=50 --dataset=mnist-recodex --epochs=1 --threads=1 --z_dim=2
30.34
python3 dcgan.py --recodex --seed=7 --batch_size=50 --dataset=mnist-recodex --epochs=1 --threads=1 --z_dim=100
27.20
Deadline: May 24, 23:59 5 points+5 bonus
You should implement optical music recognition in your final competition assignment. The inputs are PNG images of monophonic scores starting with a clef, key signature, and a time signature, followed by several staves. The dataset is loadable using the omr_dataset.py module and is downloaded automatically if missing (note that is has 185MB). No other data or pretrained models are allowed for training.
The assignment is again an open-data task, where you submit only the annotated test set
together with the training script (which will not be executed, it will be
only used to understand the approach you took, and to indicate teams).
Explicitly, submit exactly one .txt file and at least one .py file.
Note that all .zip
files you submit will be extracted first.
The task is also a competition. The evaluation is performed by computing edit distance to the gold letter sequence, normalized by its length. Everyone submitting a solution with at most 10% test set edit distance will get 5 points; the rest 5 points will be distributed depending on relative ordering of your solutions. Furthermore, 3 bonus points will be given to anyone surpassing current state-of-the-art of 0.80%. An evaluation (using for example development data) can be performed by omr_competition_eval.py.
You can start with the omr_competition.py template, which among others generates test set annotations in the required format.
Deadline: May 24, 23:59 2 points
Solve the discretized CartPole-v1 environment environment from the OpenAI Gym using the Monte Carlo reinforcement learning algorithm.
Use the supplied cart_pole_evaluator.py module (depending on gym_evaluator.py) to interact with the discretized environment. The environment has the following methods and properties:
states
: number of states of the environmentactions
: number of actions of the environmentepisode
: number of the current episode (zero-based)reset(start_evaluate=False) → 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 environment-specific
informationrender()
: render current environment stateOnce you finish training (which you indicate by passing start_evaluate=True
to reset
), your goal is to reach an average return of 475 during 100
evaluation episodes. Note that the environment prints your 100-episode
average return each 10 episodes even during training.
You can start with the monte_carlo.py template, which parses several useful parameters, creates the environment and illustrates the overall usage.
During evaluation in ReCodEx, two different random seeds will be employed, and you need to reach the required return on all of them. Time limit for each test is 5 minutes.
Note that gym_evaluator.py
and cart_pole_evaluator.py
must not be submitted to ReCodEx.
Deadline: May 24, 23:59 2 points
Solve the continuous CartPole-v1 environment environment from the OpenAI Gym using the REINFORCE algorithm.
The supplied cart_pole_evaluator.py
module (depending on gym_evaluator.py)
can create a continuous environment using environment(discrete=False)
.
The continuous environment is very similar to the discrete environment, except
that the states are vectors of real-valued observations with shape environment.state_shape
.
Your goal is to reach an average return of 475 during 100 evaluation episodes. Start with the reinforce.py template.
During evaluation in ReCodEx, two different random seeds will be employed, and you need to reach the required return on all of them. Time limit for each test is 5 minutes.
Note that gym_evaluator.py
and cart_pole_evaluator.py
must not be submitted to ReCodEx.
Deadline: May 24, 23:59 2 points
This is a continuation of the reinforce
assignment.
Using the reinforce_baseline.py template, solve the CartPole-v1 environment environment using the REINFORCE with baseline algorithm.
Using a baseline lowers the variance of the value function gradient estimator, which allows faster training and decreases sensitivity to hyperparameter values. To reflect this effect in ReCodEx, note that the evaluation phase will automatically start after 200 episodes. Using only 200 episodes for training in this setting is probably too little for the REINFORCE algorithm, but suffices for the variant with a baseline.
Your goal is to reach an average return of 475 during 100 evaluation episodes.
During evaluation in ReCodEx, two different random seeds will be employed, and you need to reach the required return on all of them. Time limit for each test is 5 minutes.
Note that gym_evaluator.py
and cart_pole_evaluator.py
must not be submitted to ReCodEx.
Deadline: May 24, 23:59 2 points
This is a continuation of the reinforce
or reinforce_baseline
assignments.
The supplied cart_pole_pixels_evaluator.py
module (depending on gym_evaluator.py)
generates a pixel representation of the CartPole
environment
as an $80×80$ image with three channels, with each channel representing one time step
(i.e., the current observation and the two previous ones).
To pass the assignment, you need to reach an average return of 250 during 100 evaluation episodes. During evaluation in ReCodEx, two different random seeds will be employed, and you need to reach the required return on all of them. Time limit for each test is 10 minutes.
You can start with the reinforce_pixels.py template using the correct environment.
Note that gym_evaluator.py
and cart_pole_pixels_evaluator.py
must not be submitted to ReCodEx.
Deadline: Jun 7, 23:59 5 points
In this assignment you should try finetuning the mBERT model to perform sentiment analysis. We will use Czech dataset of Facebook comments, which can be loaded by the text_classification_dataset.py module.
Use the BERT implementation from the
🤗 Transformers library, which
you can install by pip3 install [--user] transformers
. Start by looking at the
bert_example.py
example demonstrating loading, tokenizing and calling a BERT model, and you can
also read the documentation, specifically
for the tokenizer
and for TFBertModel.call.
The assignment is an open-data task, where you submit only the test set annotations together with the training script (which will not be executed, it will be only used to understand the approach you took, and to indicate teams). Explicitly, submit exactly one .txt file and at least one .py file. You pass if your test set accuracy is at least 75%.
You can start with the sentiment_analysis.py template, which among others generates test set annotations in the required format.
In the competitions, your goal is to train a model and then predict target values on the given unannotated test set.
When submitting a competition solution to ReCodEx, you can include any
number of files of any kind, and either submit them individuall or
compess them in a .zip
file. However, there should be exactly one
text file with the test set annotation (.txt
) and at least one
Python source (.py
) containing the model training and prediction.
The Python sources are not executed, but must be included for inspection.
For every submission, ReCodEx checks the above conditions (exactly one .txt
,
at least one .py
), whether the given annotations can be evaluated without
error, and if the annotations surpass the required performance baseline.
If all these checks pass, the assignment is marked as solved in ReCodEx
and gets the regular points for the assignment.
Just after the deadline, the newest submission of every user passing ReCodEx evaluation participates in a course competition. Additional bonus points are then awarded according to the ordering of the participating submissions.
After the deadline, the exact performance becomes visible for all submissions.
How to look what is in a tf.data.Dataset
?
The tf.data.Dataset
is not just an array, but a description of a pipeline,
which can produce data if requested. A simple way to run the pipeline is
to iterate it using Python iterators:
dataset = tf.data.Dataset.range(10)
for entry in dataset:
print(entry)
How to use tf.data.Dataset
with model.fit
or model.evaluate
?
To use a tf.data.Dataset
in Keras, the dataset elements should be pairs
(input_data, gold_labels)
, where input_data
and gold_labels
must be
already batched. For example, given CAGS
dataset, you can preprocess
training data for cags_classification
as (for development data, you would
remove the .shuffle
):
train = cags.train.map(CAGS.parse)
train = train.map(lambda example: (example["image"], example["label"]))
train = train.shuffle(10000, seed=args.seed)
train = train.batch(args.batch_size)
Is every iteration through a tf.data.Dataset
the same?
No. Because the dataset is only a pipeline generating data, it is called
each time the dataset is iterated – therefore, every .shuffle
is called
in every iteration.
How to generate different random numbers each epoch during tf.data.Dataset.map
?
When a global random seed is set, methods like tf.random.uniform
generate
the same sequence of numbers on each iteration.
The easiest method I found is to create a Generator object and use it to produce random numbers.
generator = tf.random.experimental.Generator.from_seed(42)
data = tf.data.Dataset.from_tensor_slices(tf.zeros(10, tf.int32))
data = data.map(lambda x: x + generator.uniform([], maxval=10, dtype=tf.int32))
for _ in range(3):
print(*[element.numpy() for element in data])
How to call numpy methods or other non-tf functions in tf.data.Dataset.map
?
You can use tf.numpy_function to call a numpy function even in a computational graph. However, the results have no static shape information and you need to set it manually – ideally using tf.ensure_shape, which both sets the static shape and verifies during execution that the real shape mathes it.
For example, to use the bboxes_training
method from
bboxes_utils, you could do something like:
anchors = np.array(...)
def prepare_data(example):
anchor_classes, anchor_bboxes = tf.numpy_function(
bboxes_utils.bboxes_training, [anchors, example["classes"], example["bboxes"], 0.5], (tf.int32, tf.float32))
anchor_classes = tf.ensure_shape(anchor_classes, [len(anchors)])
anchor_bboxes = tf.ensure_shape(anchor_bboxes, [len(anchors), 4])
...
How to use ImageDataGenerator
in tf.data.Dataset.map
?
The ImageDataGenerator
offers a .random_transform
method, so we can use
tf.numpy_function
from the previous answer:
train_generator = tf.keras.preprocessing.image.ImageDataGenerator(...)
def augment(image, label):
return tf.ensure_shape(
tf.numpy_function(train_generator.random_transform, [image], tf.float32),
image.shape
), label
dataset.map(augment)
How to make a part of the network frozen, so that its weights are not updated?
Each tf.keras.layers.Layer
/tf.keras.Model
has a mutable trainable
property indicating whether its variables should be updated – however, after
changing it, you need to call .compile
again (or otherwise make sure the
list of trainable variables for the optimizer is updated).
Note that once trainable == False
, the insides of a layer are no longer
considered, even if some its sub-layers have trainable == True
. Therefore, if
you want to freeze only some sub-layers of a layer you use in your model, the
layer itself must have trainable == True
.
How to choose whether dropout/batch normalization is executed in training or inference regime?
When calling a tf.keras.layers.Layer
/tf.keras.Model
, a named option
training
can be specified, indicating whether training or inference regime
should be used. For a model, this option is automatically passed to its layers
which require it, and Keras automatically passes it during
model.{fit,evaluate,predict}
.
However, you can manually pass for example training=False
to a layer when
using Functional API, meaning that layer is executed in the inference
regime even when the whole model is training.
How does trainable
and training
interact?
The only layer, which is influenced by both these options, is batch normalization, for which:
trainable == False
, the layer is always executed in inference regime;trainable == True
, the training/inference regime is chosen according
to the training
option.How can sequences of different length be processed by a RNN?
Keras employs masking to indicate, which sequence elements are valid and which are just padding.
Usually, a mask is created using
a Embedding
or Masking layer
and is then automatically propagated. If model.compile
is used, it is also
automatically utilized in losses and metrics.
However, in order for the mask propagation to work, you can use only
tf.keras.layers
to process the data, not raw TF operations like tf.concat
or even the +
operator (see tf.keras.layers.Concatenate/Add/...
).
How to compute masked losses and masked metrics manually?
When you want to compute the losses and metrics manually, pass the mask as the
third argument to their __call__
method (each individual component of
loss/metric is then multiplied by the mask, zeroing out the ones for padding
elements).
How to print output masks of a tf.keras.Model
?
When you call the model directly, like model(input_batch)
, the mask of each
output is available in a private ._keras_mask
property, so for single-output
models you can print it with print(model(input_batch)._keras_mask)
.
How to create TensorBoard logs manually?
Start by creating a SummaryWriter using for example:
writer = tf.summary.create_file_writer(args.logdir, flush_millis=10 * 1000)
and then you can generate logs inside a with writer.as_default()
block.
You can either specify step
manually in each call, or you can use
tf.summary.experimental.set_step(step)
. Also, during training you
usually want to log only some batches, so the logging block during
training usually looks like:
if optimizer.iterations % 100 == 0:
tf.summary.experimental.set_step(optimizer.iterations)
with self._writer.as_default():
# logging
What can be logged in TensorBoard?
tf.summary.scalar(name like "train/loss", value, [step])
tf.summary.histogram(name like "train/output_layer", tensor value castable to `tf.float64`, [step])
[num_images, h, w, channels]
, where
channels
can be 1 (grayscale), 2 (grayscale + alpha), 3 (RGB), 4 (RGBA):tf.summary.image(name like "train/samples", images, [step], [max_outputs=at most this many images])
tf.summary.text(name like "hyperparameters", markdown, [step])
[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])
To pass the practicals, you need to obtain at least 80 points, excluding the bonus points. Note that up to 40 points above 80 (including the bonus points) will be transfered to the exam.
To pass the exam, you need to obtain at least 60, 75 and 90 out of 100-point exam, to obtain grades 3, 2 and 1, respectively. (PhD students with binary grades require 75 points.) The exam consists of five 20-point questions, which are randomly generated, but always cover the whole course. In addition, you can get at most 40 surplus points from the practicals and at most 10 points for community work (i.e., fixing slides or reporting issues) – but only the points you already have at the time of the exam count.
Lecture 2 Questions
Training Neural Network
Assume the artificial neural network on the right, with mean square error loss
and gold output of 3. Compute the values of all weights $w_i$ after performing
an SGD update with learning rate 0.1.
Different networks architectures, activation functions (tanh
, sigmoid
,
softmax
) and losses (MSE
, NLL
) may appear in the exam.
Maximum Likelihood Estimation
Formulate maximum likelihood estimator for neural network parameters and derive
the following two losses:
Backpropagation Algorithm, SGD with Momentum
Write down the backpropagation algorithm. Then, write down the SGD algorithm
with momentum. Finally, formulate SGD with Nestorov momentum and explain the
difference to SGD with regular momentum.
Adagrad and RMSProp
Write down the AdaGrad algorithm and show that it tends to internally decay
learning rate by a factor of $1/\sqrt{t}$ in step $t$. Furthermore, write
down RMSProp algorithm and compare it to Adagrad.
Adam
Write down the Adam algorithm and explain the bias-correction terms
$(1-\beta^t)$.
Lecture 3 Questions
Regularization
Define overfitting and sketch what a regularization is. Then describe
basic regularization methods like early stopping, L2 and L1 regularization,
dataset augmentation, ensembling and label smoothing.
Dropout
Describe the dropout method and write down exactly how is it used during training and
during inference. Then explain why it cannot be used on RNN state,
describe the variational dropout variant, and also describe layer
normalization.
Network Convergence
Describe factors influencing network convergence, namely:
Lecture 4 Questions
Convolution
Write down equations of how convolution of a given image is computed. Assume the input
is an image $I$ of size $H \times W$ with $C$ channels, the kernel $K$
has size $N \times M$, the stride is $T \times S$, the operation performed is
in fact cross-correlation (as usual in convolutional neural networks)
and that $O$ output channels are computed. Explain both
$\textit{SAME}$ and $\textit{VALID}$ padding schemes and write down output
size of the operation for both these padding schemes.
Batch Normalization
Describe the batch normalization method and explain how it is used during
training and during inference. Explicitly write over what is being
normalized in case of fully connected layers, and in case of convolutional
layers. Compare batch normalization to layer normalization.
Lecture 5 Questions
VGG and ResNet
Describe overall architecture of VGG and ResNet (you do not need to remember
exact number of layers/filters, but you should know when a BatchNorm is
executed, when ReLU, and how residual connections work when the number of
channels increases). Then describe two ResNet extensions (WideNet, DenseNet,
PyramidNet, ResNeXt).
CNN Regularization, SE, MBConv
Describe CNN regularization methods (networks with stochastic depth, Cutout,
DropBlock). Then show a Squeeze and excitation block for a ResNet
and finally sketch mobile inverted bottleneck with separable convolutions.
Transposed Convolution
Write down equations of how convolution of a given image is computed. Assume the input
is an image $I$ of size $H \times W$ with $C$ channels, the kernel $K$
has size $N \times M$, the stride is $S$, the operation performed is
in fact cross-correlation (as usual in convolutional neural networks)
and that $O$ output channels are computed. Then write down the equation of
transposed convolution (or equivalently backpropagation through a convolution
to its inputs).
Lecture 6 Questions
Two-stage Object Detection
Define object detection task and describe Fast-RCNN and Faster-RCNN
architectures. Notably, show what the overall architectures of the networks
are, explain the RoI-pooling, show how the network parametrizes bounding
boxes, how do the losses looks like, how are RoI chosen during training,
how the objects are predicted, and what region proposal network does.
Image Segmentation
Define object detection and image segmentation tasks, and sketch a Faster-RCNN
and Mask-RCNN architectures. Notably, show what the overall architecture of
the networks is, explain the RoI-pooling and RoI-align layer, show how the network
parametrizes bounding boxes, how do the losses looks like, how are RoI chosen
during training and how the objects are predicted.
Single-stage Object Detection
Define object detection task and describe single-stage detector architecture.
Namely, show feature pyramid network, define focal loss and sketch RetinaNet
– the overall architecture including the convolutional classification and
bounding box prediction heads, overall loss, how the gold labels are
generated, and how the objects are predicted.
Lecture 7 Questions
LSTM
Write down how the Long Short-Term Memory cell operates.
GRU and Highway Networks
Show a basic RNN cell (using just one hidden layer) and then write down
how it is extended using gating into the Gated Recurrent Unit.
Finally, describe highway networks and compare them to RNN.
Lecture 8 Questions
Character-level word embeddings
Describe why are character-level word embeddings useful. Then describe the
two following methods:
Sequence classification and CRF
Describe how RNNs, bidirectional RNNs and multi-layer RNNs can be used to
classify every element of a given sequence (i.e., what the architecture of
a tagger might be; include also residual connections and suitable places
for dropout layers). Then, explain how a CRF layer works, define score
computation for a given sequence of inputs and sequence of labels,
describe the loss computation during training, and sketch the inference
algorithm.
CTC Loss
Describe CTC loss and the whole settings which can be solved utilizing CTC
loss. Then show how CTC loss can be computed. Finally, describe greedy
and beam search CTC decoding.
Lecture 9 Questions
Word2vec and Hierarchical and Negative Sampling
Explain how can word embeddings be precomputed using the CBOW and Skip-gram
models. First start with the variants where full softmax is performed, and
then describe how hierarchical softmax and negative sampling is used to speedup
training of word embeddings.
Neural Machine Translation and Attention
Draw/write how an encoder-decoder architecture is used for machine translation,
both during training and during inference. Then describe the architecture
of an attention module.
Neural Machine Translation and Subwords
Draw/write how an encoder-decoder architecture is used for machine translation,
both during training and during inference (without attention). Furthermore,
elaborate on how subword units are used to reduce out-of-vocabulary problem and
sketch BPE algorithm and WordPieces algorithm for constructing fixed number of
subword units.
Lecture 10 Questions
Variational Autoencoders
Describe deep generative modelling using variational autoencoders – show VAE
architecture, devise training algorithm, write training loss, and propose sampling
procedure.
Generative Adversarial Networks
Describe deep generative modelling using generative adversarial networks -- show GAN
architecture and describe training procedure and training loss. Mention also
CGAN (conditional GAN) and sketch generator and discriminator architecture in a DCGAN.
Lecture 11 Questions
Reinforcement learning
Describe the general reinforcement learning settings and formulate the Monte
Carlo algorithm. Then, formulate and prove the policy gradient theorem
and write down the REINFORCE algorithm.
REINFORCE with baseline
Describe the general reinforcement learning settings, formulate the
policy gradient theorem and write down the REINFORCE algorithm.
Then explain what is the baseline, show policy gradient theorem with the
baseline (including the proof of why the baseline can be included),
and write down the REINFORCE with baseline algorithm.
Lecture 12 Questions
Speech Synthesis
Describe the WaveNet network (what a dilated convolution and gated activations
are, how the residual block looks like, what the overall architecture is, and how
global and local conditioning work). Discuss parallelizability of training and
inference, show how Parallel WaveNet can speedup inference, and sketch how it is
trained.
Neural Turing Machines
Sketch an overall architecture of a Neural Turing Machine with an LSTM
controller, assuming $R$ reading heads and one write head. Describe the
addressing mechanism (content addressing and its combination with previous
weights, shifts, and sharpening) and reading and writing operations. Finally,
describe the inputs and the outputs of the controller.
Lecture 13 Questions
Transformer
Describe Transformer architecture, namely the self-attention layer, multi-head
self-attention layer, masked self-attention and overall architecture of an
encoder and a decoder. Describe positional embeddings, learning rate schedule
during training and parallelizability of training and inference.
BERT
Describe the BERT model architecture (including multi-head self-attention layer)
and its pre-training – format of input and output data, masked language model
and next sentence prediction. Define GELU and describe how the BERT model
can be finetuned to perform POS tagging, sentiment analysis and paraphrase
detection (detect if two sentences have the same meaning).