I've just learned a little about pytorch. I built a CNN to calculate the effects of various optimization algorithms with the official documents of pytorch (I've just finished from SGD to adagrad). However, most of the official documents and tutorial videos ended when the accuracy and time-consuming were calculated, and the code of model visualization ，I had no idea at all. I would like to ask what is used for visualization similar to the following two figures. Is it Matplotlib pyplot or the visualization tool corresponding to pytorch?enter image description here
I can not tell you what library is used to generate the plot you linked to.
There are plenty of options, all of which you can use once you have the data.
One of these options is matplotlib. Others include using Matlab or pgfplots if you want to include your plots in a LaTeX document. These are the tools I use somewhat frequently. They are purely subjective choices.
However, pytorch also supports tensorboard, which is especially useful for live tracking of the training progress.
Have a look at this tutorial: https://pytorch.org/tutorials/recipes/recipes/tensorboard_with_pytorch.html
it looks like matplotlib pyplot to me. While training, you can store all the loss values and accuracy values and plot them after the network is trained.
Related
I'm using Windows 10 machine. Libraries: Keras with Tensorflow 2.0 Embeddings: Glove(100 dimensions).
I am trying to implement an LSTM architecture for multi-label text classification.
I am using different types of fine-tuning to achieve better results but with no luck so far.
The main problem I believe is the difference in class distributions of my dataset but after a lot of tries and errors, I couldn't implement stratified-k-split in Keras.
I am also experimenting with dropout layers, batch sizes, # of layers, learning rates, clip values, validation splits but I get a minimum boost or worst performance sometimes.
For metrics, I use mainly ROC and F1.
I also followed the suggestion from a StackOverflow member who said to delete some of my examples so I can balance my dataset but if I do that I will have a very low number of examples.
What would you suggest to me?
If someone can provide code based on my implementation for
stratified-k-split I would be grateful cause I have checked all the
online resources but can't implement it.
Any tips, suggestions will be really helpful.
Metrics Plots
Dataset form+Embedings form+train-test-split form
Dataset's labels distribution
My LSTM implementation
I want to train a facial recognition CNN from scratch. I can write a Keras Sequential() model following popular architectures and copying their networks.
I wish to use the LFW dataset, however I am confused regarding the technical methodology. Do I have to crop each face to a tight-fitting box? That seems impractical, as the dataset has 13000+ faces.
Lastly, I know it's stupid, but all I have to do is preprocess the images (of course), then fit the model to these images? What's the exact procedure?
Your question is very open ended. Before preprocessing and fitting the model, you need to understand Object Detection. Once you understand what object detection you will get answer to your 1st question whether you are required to manually crop every 13000 image. The answer is no. However, you will have to draw bounding boxes around faces and assign label to images if they are not available in the training data.
Your second question is very vague . What do you mean by exact procedure? Is it the steps you need to do or how to do preprocessing and fitting of the model in python/or any other language? There are lots of references available on the internet about how to do preprocessing and model training for every specific problem. There are no universal steps which can be applied to any problem
I am working on a binary logistic regression data set in python. I want to know if there are any numerical methods to calculate how well the model fits the data.
please don't include graphical methods like plotting etc.
Thanks :)
read through 3.3.2. Classification metrics in sklearn documentation.
http://scikit-learn.org/stable/modules/model_evaluation.html
hope it helps.
I want to extract features using caffe and train those features using SVM. I have gone through this link: http://caffe.berkeleyvision.org/gathered/examples/feature_extraction.html. This links provides how we can extract features using caffenet. But I want to use Lenet architecture here. I am unable to change this line of command for Lenet:
./build/tools/extract_features.bin models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel examples/_temp/imagenet_val.prototxt fc7 examples/_temp/features 10 leveldb
And also, after extracting the features, how to train these features using SVM? I want to use python for this. For eg: If I get features from this code:
features = net.blobs['pool2'].data.copy()
Then, how can I train these features using SVM by defining my own classes?
You have two questions here:
Extracting features using LeNet
Training an SVM
Extracting features using LeNet
To extract the features from LeNet using the extract_features.bin script you need to have the model file (.caffemodel) and the model definition for testing (.prototxt).
The signature of extract_features.bin is here:
Usage: extract_features pretrained_net_param feature_extraction_proto_file extract_feature_blob_name1[,name2,...] save_feature_dataset_name1[,name2,...] num_mini_batches db_type [CPU/GPU] [DEVICE_ID=0]
So if you take as an example val prototxt file this one (https://github.com/BVLC/caffe/blob/master/models/bvlc_alexnet/train_val.prototxt), you can change it to the LeNet architecture and point it to your LMDB / LevelDB. That should get you most of the way there. Once you did that and get stuck, you can re-update your question or post a comment here so we can help.
Training SVM on top of features
I highly recommend using Python's scikit-learn for training an SVM from the features. It is super easy to get started, including reading in features saved from Caffe's format.
Very lagged reply, but should help.
Not 100% what you want, but I have used the VGG-16 net to extract face features using caffe and perform a accuracy test on a small subset of the LFW dataset. Exactly what you needed is in the code. The code creates classes for training and testing and pushes them into the SVM for classification.
https://github.com/wajihullahbaig/VGGFaceMatching
I'm new in the field of Deep Neural Network. There are various deep learning frameworks nearby. Notably Theano, Torch7, Caffe, and recently open sourced TensorFlow. I have tried out a couple of tutorials with TensorFlow provided on their site. Specifically the MNIST dataset. I guess this is the hello world of every deep learning framework out there. I also viewed tutorials from here. This one was explained in detail, but they do not provide hands on experience with any deep learning frameworks. So which framework should be better for beginners? I looked up similar questions asked on Quora. Some said that theano is tougher to learn but it gives more control, Caffe is easier, but it gives less control over the network. And nothing on Tensorflow, as it is new, but from what i've seen the documentation is not That well written, also it seems tougher to understand. So as a newbie what should i choose to learn?
Another question, As I said, MNIST is the hello world of every deep learning framework, and many neural networks can be found for recognizing MNIST dataset. So, if I use the same network to detect other dataset, say CIFAR-10 dataset, will it work?? Let's just say that i turn the CIFAR-10 dataset to grayscale images and convert it to same dimension as MNIST dataset. Will the model be invalid or fail to learn? or have bad accuracy or what?