wes wesyoung

Keybase proof

I hereby claim:

I am wesyoung on github.
I am barely3am (https://keybase.io/barely3am) on keybase.
I have a public key ASBjkkySlQIsT8Oz5xEt6aNz_543h6wSr_dgAI__EZKLngo

To claim this, I am signing this object:

Keras Models Examples

一系列常用模型的Keras实现

DNN

Multilayer Perceptron (MLP) for multi-class softmax classification

from keras.models import Sequential

Deploying to Elastic Beanstalk via CircleCi 2.0

I got to here after spending hours trying to deploy to an Elastic Beanstalk instance via CircleCi 2.0 so I thought I'd write up what worked for me to hopefully help others. Shout out to RobertoSchneiders who's steps for getting it to work with CircleCi 1.0 were my starting point.

For the record, I'm not the most server-savvy of developers so there may be a better way of doing this.

Setup a user on AWS IAM to use for deployments

Add user here

	import os
	import autogen
	import memgpt.autogen.memgpt_agent as memgpt_autogen
	import memgpt.autogen.interface as autogen_interface
	import memgpt.agent as agent
	import memgpt.system as system
	import memgpt.utils as utils
	import memgpt.presets as presets
	import memgpt.constants as constants
	import memgpt.personas.personas as personas

	import requests
	import json
	import logging
	import os
	import re
	ENABLE_JA3 = os.environ.get('CIF_GATHERERS_JA3_ENABLED')
	class Ja3(object):
	def __init__(self, args, *kwargs):
	self.logger = logging.getLogger(__name__)
	self.enabled = kwargs.get('enabled', os.environ.get('CIF_GATHERERS_JA3_ENABLED'))

	#!/usr/bin/env python3

	"""
	# docker containers are your friend!

	$ pip install arrow chardet elasticsearch-dsl
	$ cat test.eml \| python3 spam_es.py
	"""

	"""

	import json
	import numpy as np
	import keras
	import keras.preprocessing.text as kpt
	from keras.preprocessing.text import Tokenizer
	from keras.models import model_from_json

	# we're still going to use a Tokenizer here, but we don't need to fit it
	tokenizer = Tokenizer(num_words=3000)
	# for human-friendly printing

	model = Sequential()
	model.add(Embedding(num_words, 32, input_length=max_log_length))
	# Prevent overfitting using dropout method of regularization
	model.add(Dropout(0.5))
	model.add(LSTM(64, recurrent_dropout=0.5))
	model.add(Dropout(0.5))
	# Condense to single binary output value
	model.add(Dense(1, activation='sigmoid'))
	model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
	# Training set automatically split 75/25 to check validation loss/accuracy at each epoch

	# Copyright 2018 SwiftComply.com
	commands:
	01_node_install:
	test: "[ `node --version` != 'v8.10.0' ]"
	command: "curl --silent --location https://rpm.nodesource.com/setup_8.x \| sudo bash -"
	02_yarn_repo:
	test: "[ ! -f /etc/yum.repos.d/yarn.repo ]"
	command: "curl --silent --location https://dl.yarnpkg.com/rpm/yarn.repo \| sudo tee /etc/yum.repos.d/yarn.repo"
	03_yarn_install:
	test: "[ ! -x /usr/bin/yarn ]"

	property theAddress : "[email protected]"
	property theReportSubject : "PHISHING report (message attached)"
	--
	using terms from application "Mail"
	on perform mail action with messages theMsgs for rule theRule
	repeat with eachMsg in theMsgs
	tell application "Mail"
	try
	set theSubject to subject of eachMsg
	on error