ByteSizedMarius

Edit: This doesn't work for lists > 20 items, because pagination does not work. Please see here

This script allows extracting name and coordinates for gmaps shared lists. It is incredibly unstable and may break anytime. Good luck figuring out why, because the syntax is extremely confusing and basically makes no sense at all. Thanks to google for not providing an api for this after LITERALLY 12 YEARS

How to use this script:

1. Share a list and open the link in a browser window. It will redirect. The new link will look like this: google.com/maps/@<your coords>/data=....
2. Take the data-portion and paste it into the following link: https://google.com/maps/@/data=?ucbcb=1

khanhnamle1994

#--------------------------
# USER-SPECIFIED DATA
#--------------------------

# Tune these parameters

num_classes = 2
image_shape = (160, 576)
EPOCHS = 40
BATCH_SIZE = 16

terabyte

Prologue

I wrote this answer on stackexchange, here: https://stackoverflow.com/posts/12597919/

It was wrongly deleted for containing "proprietary information" years later. I think that's bullshit so I am posting it here. Come at me.

The Question

Amazon is a SOA system with 100s of services (or so says Amazon Chief Technology Officer Werner Vogels). How do they handle build and release?

omoindrot

"""
Example TensorFlow script for finetuning a VGG model on your own data.
Uses tf.contrib.data module which is in release v1.2
Based on PyTorch example from Justin Johnson
(https://gist.github.com/jcjohnson/6e41e8512c17eae5da50aebef3378a4c)

Required packages: tensorflow (v1.2)
Download the weights trained on ImageNet for VGG:
```
wget http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz

khacanh

# From http://blog.otoro.net/2015/11/24/mixture-density-networks-with-tensorflow/

import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import math

NHIDDEN = 24
STDEV = 0.5
KMIX = 24 # number of mixtures

ASvyatkovskiy

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import math
import sys
import tempfile
import time
import os

tomrunia

# Initialize placeholders for feeding in to the queue
pl_queue_screens = tf.placeholder(tf.float32, shape=[config.seq_length, config.image_size, config.image_size, config.input_channels], name="queue_inputs")
pl_queue_targets = tf.placeholder(tf.uint8, shape=[config.seq_length], name="queue_targets_cnt")

#  ...

capacity = config.min_after_dequeue + 10 * (config.num_gpus*config.batch_size)
q = tf.RandomShuffleQueue(

jaron

import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.optimizers import SGD
from keras.regularizers import l2, activity_l2
from keras.utils import np_utils
from sklearn import metrics 

# to run this code, you'll need to load the following data: 

jaron

import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.optimizers import Adam
from keras.utils import np_utils
from sklearn import metrics 

# to run this code, you'll need to load the following data: 
# train_x, train_y

tony-gutierrez

# Dont forget to set the env variable "certdomain", and either fill in your email below or use an env variable for that too.
# Also note that this config is using the LetsEncrypt staging server, remove the flag when ready!

Resources:
  sslSecurityGroupIngress: 
    Type: AWS::EC2::SecurityGroupIngress
    Properties:
      GroupId: {"Fn::GetAtt" : ["AWSEBSecurityGroup", "GroupId"]}
      IpProtocol: tcp
      ToPort: 443