One Paragraph of project description goes here
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
""" Example using GenSim's LDA and sklearn. """ | |
import numpy as np | |
from gensim import matutils | |
from gensim.models.ldamodel import LdaModel | |
from sklearn import linear_model | |
from sklearn.datasets import fetch_20newsgroups | |
from sklearn.feature_extraction.text import CountVectorizer |
##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
import tensorflow as tf | |
from tensorflow.python.platform import gfile | |
with tf.Session() as sess: | |
model_filename ='PATH_TO_PB.pb' | |
with gfile.FastGFile(model_filename, 'rb') as f: | |
graph_def = tf.GraphDef() | |
graph_def.ParseFromString(f.read()) | |
g_in = tf.import_graph_def(graph_def) | |
LOGDIR='/logs/tests/1/' | |
train_writer = tf.summary.FileWriter(LOGDIR) |
import torch | |
import torch.nn as nn | |
from torch.nn import functional as F | |
from torch.autograd import Variable | |
from torch import optim | |
import numpy as np | |
import math, random | |
# Generating a noisy multi-sin wave |
#!/usr/bin/env python | |
# -*- coding: utf-8 -*- | |
""" | |
Restricted Boltzmann Machine (RBM) | |
References : | |
- Y. Bengio, P. Lamblin, D. Popovici, H. Larochelle: Greedy Layer-Wise | |
Training of Deep Networks, Advances in Neural Information Processing | |
Systems 19, 2007 |
#pragma once | |
struct Foo { | |
}; | |