| PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked | |
| 1 0 3 Braund, Mr. Owen Harris male 22 1 0 A/5 21171 7.25 S | |
| 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1 0 PC 17599 71.2833 C85 C | |
| 3 1 3 Heikkinen, Miss. Laina female 26 0 0 STON/O2. 3101282 7.925 S | |
| 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0 113803 53.1 C123 S | |
| 5 0 3 Allen, Mr. William Henry male 35 0 0 373450 8.05 S | |
| 6 0 3 Moran, Mr. James male 0 0 330877 8.4583 Q | |
| 7 0 1 McCarthy, Mr. Timothy J male 54 0 0 17463 51.8625 E46 S | |
| 8 0 3 Palsson, Master. Gosta Leonard male 2 3 1 349909 21.075 S | |
| 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27 0 2 347742 11.1333 S |
##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
##VGG19 model for Keras
This is the Keras model of the 19-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
Here's a simple implementation of bilinear interpolation on tensors using PyTorch.
I wrote this up since I ended up learning a lot about options for interpolation in both the numpy and PyTorch ecosystems. More generally than just interpolation, too, it's also a nice case study in how PyTorch magically can put very numpy-like code on the GPU (and by the way, do autodiff for you too).
For interpolation in PyTorch, this open issue calls for more interpolation features. There is now a nn.functional.grid_sample() feature but at least at first this didn't look like what I needed (but we'll come back to this later).
In particular I wanted to take an image, W x H x C, and sample it many times at different random locations. Note also that this is different than upsampling which exhaustively samples and also doesn't give us fle
| # derived from http://scikit-learn.org/stable/auto_examples/applications/topics_extraction_with_nmf_lda.html | |
| # explanations are located there : https://www.linkedin.com/pulse/dissociating-training-predicting-latent-dirichlet-lucien-tardres | |
| from sklearn.feature_extraction.text import CountVectorizer | |
| from sklearn.decomposition import LatentDirichletAllocation | |
| import pickle | |
| # create a blank model | |
| lda = LatentDirichletAllocation() |