Nesta lição, além de serem apresentados novos detalhes sobre as bibliotecas utilizadas no curso, serão abordados três assuntos principais: visualização das convoluções em CNNs; classificação Multi-Label; e classificação de dados não-estruturados (assunto que será melhor explorado nas próximas lições). Durante a aula, três competições do Kaggle são mencionadas, que servirão de deixa para a parte prática pós-intervalo.
A atividade proposta consiste em participar de uma competição do Kaggle utilizando a biblioteca do FastAI no PaperSpace. No Jupyter Notebook do link a seguir, é disponibilizado um esqueleto que pode ser utilizado como ponto de partida para participação na competição Dog Breed Identification:
Link para o Jupyter Notebook de apoio:
JH shows how to put the training code in compact form (and reviews the code).
A quick discution is made about the confusion which has been around precompute=true: “it only makes things faster” and “you can always skip it”.
When using precompute=True, data augmentation doesnt work, bacause it uses the cached non augmented activations.
The parameter bn_freeze(True) causes the “batch normalization moving averages to not be updated” (in the second half of the course we gonna learn why we want to do that. It is sth that is not supported by any other library, but is very important). It should be used if we have more than 49 layers and the dataset is similar to image net.
Softmax: “wants to pick a thing”. Use softmax just when you want to assign a single class to an image. You can’t use it to multi-lab classification.
FastAI can do “multi-label classification automaically”. If there is more than one label in a CSV associated to an image, it will switch automatically toulti-label mode.
In multi-label classification, you can’t use keras style data loading (i.e. from folders), you need to use ImageClassifierData.from_csv approach.
In Pytorch there are all 8 possible transforms we can apply to images (“dihedral group”).
Most ImageNet models are trained with 224x224 images. If we resize it to 64x64, we are destructing the pre-trained weights.
Start with a small sz parameter, train quickly to a reasonable weights value then increase the sz parameter (as powers of 2) until the original dimensions of the image.
Sped up convenience function: data.resize(int(sz*1.3), 'tmp').
Uma correção no arquivo do desafio do dog breeds. Na biblioteca do fastai os dados de teste não ficam ordenados na ordem alfabética como é esperado pelo arquivo de submissão.
Na célula onde gera o arquivo para submissão, troque para:
# Cria data frame para submissão, com as probabilidades calculadas pelo modelo
df=pd.DataFrame(
data=probs,
columns=d.columns[1:], # Excluir primeira coluna, que é o ID
index=[f[5:-4] for f in data.test_dl.dataset.fnames]
)
df.index.name = 'id'
Explicando melhor, só troquei a linha onde está index. Ao invés de pegar do index da submissão, peguei o nome dos arquivos de teste do objeto data, onde carregamos todos os dados (data.test_dl.dataset.fnames). O [5:-4] é para tirar o começo do caminho do arquivo (test/) e o final (.jpg), ficando só o id da imagem, que é o formato esperado pelo kaggle
No TTA o log_preds vem em uma tupla, onde a primeira entrada dessa tupla é o array com log_preds. E depois como são várias predições para a mesma imagem, precisa dar um np.mean.
@saulberardo@lucasosouza, percebi que na competição de Breed Identification não existe validation set, apenas training e test set, mas quando chamo o learn.fit ele apresenta um val_loss. Então a função val_idxs = get_cv_idxs(n) pega aleatoriamente algumas imagens e define elas como parte do validation set? Ao fazer isso, as imagens que foram setadas para validation também são utilizadas no treinamento ou não?
Então a função val_idxs = get_cv_idxs(n) pega aleatoriamente algumas imagens e define elas como parte do validation set?
Exatamente!
Ao fazer isso, as imagens que foram setadas para validation também são utilizadas no treinamento ou não?
Não são. Mas você pode fazer isso antes de enviar, retreinar o modelo no dataset completo, para aumentar a generalização. Para isso crie um novo objeto data. Como validação, passe uma quantidade pequena de índices (não sei se tem como desligar o validation no fastai, acho que não, vi em outra thread sugerindo criar uma pasta ‘validation’ e colocar apenas uma imagem dentro). E depois mude o dataset no objet learn, dando learn.set_data(new_data).
Non-Brazilian here, so forgive me for the English . I just wanted to share a funding opportunity for applying data science and DL tools to public health in Brazil. You must be Brazilian and based in Brazil to apply.
Eu assisti novamente ao video da lição 3 (parte 1) para melhorar meu entendimento dela e tomei notas do vocabulário usado pelo @jeremy.
Vamos jogar um pouquinho ! Concorda ?
Você pode dar uma definição / uma URL / uma explicação para todos os termos e expressões a seguir?
Se sim, você entendeu perfeitamente a terceira lição!
PS: se você não quiser se testar ou se quiser checar as suas respostas, vá para o post “Deep Learning 2: Part 1 Lesson 3” do blog de @hiromi : " super travail !!! "
try to teach what you learned by posting in a blog
wiki thread in the Fastai forum
AWS fastai AMI
Github
Tmux (Ubuntu, Mac Os)
Understand why there are validation images not well classified
learning rate
why a low learning rate is safer but slower for training a NN ?
why a high learning rate can increase the value of the loss function ?
learn.lr_find(); learn.sched.plot()
batch size
SGDR
fastai vs pytorch
CNN ou Convolutional Neural Network
Resnet
Beginner Fastai forum
Kaggle site
How to download data from Kaggle : script kaggle-cli
pip install kaggle-cli
accepts the competition rules in Kaggle site
kg download -u user -p ‘password’ -c competition
How to download images from any sites
CurlWget as Google Chrome extension
symlinks
ls -l in a terminal
Quick DogsCats
fastai.conv_learner
tfms, data transformation
data object
shift + tab
test_name=“test”
learn object
precompute=True
learn.unfreeze()
learn.bn_freeze(True) for deeper NN (resnet50 and above) with similar dataset that Imagenet dataset ( if are you using a deep network on a very similiar dataset to your target (ours is dogs and cats) - its causing the batch normalization not be updated)
kernel / filter of convolutional with a shape of 3 x 3
search for edges (left and top)
feature maps
non linearity, relu
max pooling
fastai/courses/dl1/excel
MNIST data base
filter to detects top edges
we get activation after the element-wise product by the convolutional filter
an activation is calculated
Relu means max(0, value)
pytorch stores convolutional filters as a tensor
a tensor is an array with more dimensions (additional axis)
the size of each hidden layer in a CNN is the number of convolutional filters used to get the feature maps
the size of a convolutional kernel has 3 dimensions and the third one is the number of feature maps in the input hidden layer
max pooling : kill the dimension by sub-sampling (keep the max) without over-lapping
fully connected layer (linear matrix product)
but big CNN gives big number of weights in the fully connected layers : risk of overfitting !
VGG (16 layers) : 138 millions of weights
VGG (19 layers) : more than 143 millions of weights
in theses CNN, the number of weights of the convolutional filters is about 20 millions : the majority of the weights comes from the fully connected layers
Resnet and ResNext do not use large fully connected layers
the fully connected layers do a classic matrice product
last layer : there is no Relu (than, we can have negative value)
softmax is an activation function that allows to get probabilities
softmax tends to take one thing out of the other (ie, with a probabilities clearly higher than the other ones) : its “personality” is to pick a thing (so, it is perfect for one or 2 label classifier)
sigmoid is an activation function uses for multi-label classifier because it gives a number between 0 and 1 (looks like a probability) for each label
Relu is an activation function too but it does not get probabilities
an activation function is a function applied on activations
in Deep Learning, an activation function adds a non-linearity
we must know log, exp
activation functions have a personality
we can not use softmax for multi-label classification
if your objective is to classify multi-labels images, you can not use ImageClassifierData.from_paths because an image can not be in more than a folder. Then, you need to use ImageClassifierData.from_csv
Good news : the Fastai library will recognize in your csv file if they are more than 2 labels (multi-label classification)
data.val_ds (ds como data set in pytorch) : gives you a single image (or object) back
data.val_dl (dl como data loader in pytorch) : gives you a transformed mini batch
in pytorch, to get the next mini batch, we use a generator (iterator) : next(iter(data.val_dl))
if you know python, you learn pytorch naturally
zip takes 2 lists and combines them : list(zip(data.classes,y[0]))
1 hot encoded vector
CatsDogs and DogsBreed were a single-label classification
images from The Planet competition are not like ones used in Imagenet competition
you can change the input image size during the training for the NN that have an adaptative pooling before the first fully connected layer like Resnet (but not VGG) : learn.set_data(get_data(sz))
get data (imagens) resize before to pass them to the data object thanks to data.resize(int(sz*1.3), ‘tmp’) : speed-up ! (faster than resize directly in the tfms)
after dogsbreed, try to run the Planet jupyter notebook
metrics for accuracy : metrics = [f2] (f2 uses fbeta_score) and pass it to the learn object : learn = ConvLearner.pretrained(arch, data, metrics=metrics)
in the Fastai library, everything can be changed
sigmoid function is used for logistic regression
fastai chooses automatically softmax or sigmoid activation function
when you use a pretrained CNN network, it means that the weight of the first layer of your new models are not random but the ones of the last fully connected layers you added, are random. Then, you need to train firstly theses last layers before to unfreeze and train teh whole network. If not, the random weight of the last layers will destroy the weights of the first layers (from the pretrained model)
the GPU takes a center crop on each input image of size sz. That’s why it is important to do Data Augmentation before on the input dataset
in the fastai library, there is a concept of layer groups
learn.summary()
tables of data : structured data
audio, images, natural linguaguem : unstructured
Grocery Sales Forecasting competition in Kaggle
Rossman data
from fastai.structured import *
from fastai.colum_data import *
pandas (book : Python for Data Analysis)
test = pd.read_csv(f’{PATH}test.csv’, parse_dates=[‘Date’])
there is a difference with the DogsCats dataset : we do a lot of preprocessing on these structured data
. I just wanted to share a funding opportunity for applying data science and DL tools to public health in Brazil. You must be Brazilian and based in Brazil to apply.
