Lei Mao leimao

In this article, I will share some of my experience on installing NVIDIA driver and CUDA on Linux OS. Here I mainly use Ubuntu as example. Comments for CentOS/Fedora are also provided as much as I can.

-ss 14:55 gives the timestamp where I want FFmpeg to start, as a duration string.
-t 5 says how much I want FFmpeg to decode, using the same duration syntax as for -ss.
-s 480x270 tells FFmpeg to resize the video output to 480 by 270 pixels.
-f image2 selects the output format, a series of still images — make sure there are leading zeros in filename.

##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

	# @Author: xiewenqian <int>
	# @Date: 2016-11-28T20:35:09+08:00
	# @Email: [email protected]
	# @Last modified by: int
	# @Last modified time: 2016-12-01T19:32:48+08:00


	import pandas as pd
	from pymongo import MongoClient

	# Create a simple TF Graph
	# By Omid Alemi - Jan 2017
	# Works with TF <r1.0

	import tensorflow as tf

	I = tf.placeholder(tf.float32, shape=[None,3], name='I') # input
	W = tf.Variable(tf.zeros_initializer(shape=[3,2]), dtype=tf.float32, name='W') # weights
	b = tf.Variable(tf.zeros_initializer(shape=[2]), dtype=tf.float32, name='b') # biases
	O = tf.nn.relu(tf.matmul(I, W) + b, name='O') # activation / output

	""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
	import numpy as np
	import cPickle as pickle
	import gym

	# hyperparameters
	H = 200 # number of hidden layer neurons
	batch_size = 10 # every how many episodes to do a param update?
	learning_rate = 1e-4
	gamma = 0.99 # discount factor for reward

	from pyspark.sql.types import StringType
	from pyspark.sql.functions import udf

	maturity_udf = udf(lambda age: "adult" if age >=18 else "child", StringType())

	df = spark.createDataFrame([{'name': 'Alice', 'age': 1}])
	df.withColumn("maturity", maturity_udf(df.age))

	df.show()

	Copyright 2025 Chun-Min Chang

	Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

	The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

	THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OT

Lei Mao leimao

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Install ImageMagick

Pull specific region of frames from video file w/ ffmpeg