Michele Pratusevich mprat

siemanko / tf_lstm.py

Last active July 26, 2023 06:57

Simple implementation of LSTM in Tensorflow in 50 lines (+ 130 lines of data generation and comments)

	"""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:

kaushikpavani / compute_caffe_parameters.py

Created September 25, 2016 14:14

Calculate number of parameters in a Caffe model

	import caffe
	caffe.set_mode_cpu()
	import numpy as np
	from numpy import prod, sum
	from pprint import pprint

	def print_net_parameters (deploy_file):
	print "Net: " + deploy_file
	net = caffe.Net(deploy_file, caffe.TEST)
	print "Layer-wise parameters: "

onauparc / C++ Predict with caffe

Created June 17, 2014 13:36

sample code for caffe C++ prediction

nrk / command.txt

Created April 2, 2012 19:19

Using ffprobe to get info from a file in a nice JSON format

ffprobe -v quiet -print_format json -show_format -show_streams "lolwut.mp4" > "lolwut.mp4.json"