Venkatesh venkatesh-1729

Applied Functional Programming with Scala - Notes

1. Mastering Functions

A function is a mapping from one set, called a domain, to another set, called the codomain. A function associates every element in the domain with exactly one element in the codomain. In Scala, both domain and codomain are types.

val square : Int => Int = x => x * x

VGG-Face model for Keras

This is the Keras model of VGG-Face.

It has been obtained through the following method:

vgg-face-keras:directly convert the vgg-face matconvnet model to keras model
vgg-face-keras-fc:first convert vgg-face caffe model to mxnet model,and then convert it to keras model

Details about the network architecture can be found in the following paper:

	"""Short and sweet LSTM implementation in Tensorflow.

	Motivation:
	When Tensorflow was released, adding RNNs was a bit of a hack - it required
	building separate graphs for every number of timesteps and was a bit obscure
	to use. Since then TF devs added things like `dynamic_rnn`, `scan` and `map_fn`.
	Currently the APIs are decent, but all the tutorials that I am aware of are not
	making the best use of the new APIs.

	Advantages of this implementation:

	# Working example for my blog post at:
	# http://danijar.com/variable-sequence-lengths-in-tensorflow/
	import functools
	import sets
	import tensorflow as tf
	from tensorflow.models.rnn import rnn_cell
	from tensorflow.models.rnn import rnn


	def lazy_property(function):

	# Example for my blog post at:
	# https://danijar.com/introduction-to-recurrent-networks-in-tensorflow/
	import functools
	import sets
	import tensorflow as tf


	def lazy_property(function):
	attribute = '_' + function.__name__

	import tensorflow as tf
	import numpy as np

	if __name__ == '__main__':
	np.random.seed(1)
	# the size of the hidden state for the lstm (notice the lstm uses 2x of this amount so actually lstm will have state of size 2)
	size = 1
	# 2 different sequences total
	batch_size= 2
	# the maximum steps for both sequences is 10

	import json
	import urlparse
	from itertools import chain
	flatten = chain.from_iterable

	from nltk import word_tokenize

	from gensim.corpora import Dictionary
	from gensim.models.ldamodel import LdaModel
	from gensim.models.tfidfmodel import TfidfModel

	#!/usr/bin/env python
	# -- coding: utf-8 --

	'''

	This script just show the basic workflow to compute TF-IDF similarity matrix with Gensim


	OUTPUT :