shgidi

## marker_listener.js
  map.data.addListener('click', event => {

      let name = event.feature.getProperty('name');
      let desc = event.feature.getProperty('desc');
      let position = event.feature.getGeometry().get();
      let content = `
        <img style="float:left; width:10px; margin-top:30px">
        <div style="margin-left:120px; margin-bottom:20px;">
          <h2>${name}</h2>
          <p>${desc}</p>

## map.js
function initMap (){
    const map = new google.maps.Map(document.querySelector('#map'), {
    zoom: 14,

    center: {
      // TA
      lat: 32.0705, // N-S
      lng: 34.7691  // W-E
    },

## index.html
<html>
  <head>
    <title>TA Artist Map</title>
    <link rel="stylesheet" type="text/css" href="style.css">
  <head>
  <body>
    <div id="map" class="map"></div>

    <script src="map.js"></script>
    <script async defer

## 0_reuse_code.js
// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console

## plot_loss+sample.py
#https://gist.github.com/stared/dfb4dfaf6d9a8501cd1cc8b8cb806d2e
class PlotLosses(keras.callbacks.Callback):

    def __init__(self,imgs):
        super(PlotLosses, self).__init__()
        self.imgs=imgs

    def on_train_begin(self, logs={}):
        self.i = 0
        self.x = []

## save_load_keras_model.py
from keras.models import model_from_json

def save_keras_model(model,path):
  model_json = model.to_json()
  with open(path+"json", "w") as json_file:
    json_file.write(model_json)
  model.save_weights(path+'.hdf5')


def load_keras_model(path):

## 1_layer_nn.py
#simple model
model = Sequential([
    #Embedding(vocab_size, vec_size, input_length=seq_len,weights=[emb]),
    #Flatten(),
    Dense(100, input_dim=test_data.shape[1], init='uniform', activation='relu'),
    #Dropout(0.7),
    Dense(1, activation='sigmoid')])
model.compile(loss='binary_crossentropy', optimizer=Adam(), metrics=['accuracy'])

## vgg_finetune.py
from keras.applications.vgg16 import VGG16
from keras.layers import Conv2D
from keras.models import Sequential
from keras.layers import BatchNormalization
from keras.optimizers import Adam

vgg=VGG16()

p=0.4 #dropout
label_count=17

## resnet+xgb.py
from keras.applications.resnet50 import ResNet50
from scipy.misc import imresize

resnet=ResNet50()
imgs_new=[]

for i,img in enumerate(imgs_new_rand):
    imgs_new[i]=imresize(img,(224,224,3))

resnet_preds=resnet.predict(imgs_new)

## plot_bounding_box.py
plt.imshow(np.array(Image.open(g[0]))[y-30:y+30,x-30:x+30])
currentAxis = plt.gca()
coords=[10,10],20,20
currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor=color, linewidth=2))
	map.data.addListener('click', event => {

	let name = event.feature.getProperty('name');
	let desc = event.feature.getProperty('desc');
	let position = event.feature.getGeometry().get();
	let content = `
	<img style="float:left; width:10px; margin-top:30px">
	<div style="margin-left:120px; margin-bottom:20px;">
	<h2>${name}</h2>
	<p>${desc}</p>
	function initMap (){
	const map = new google.maps.Map(document.querySelector('#map'), {
	zoom: 14,

	center: {
	// TA
	lat: 32.0705, // N-S
	lng: 34.7691 // W-E
	},
	<html>
	<head>
	<title>TA Artist Map</title>
	<link rel="stylesheet" type="text/css" href="style.css">
	<head>
	<body>
	<div id="map" class="map"></div>

	<script src="map.js"></script>
	<script async defer
	// Use Gists to store code you would like to remember later on
	console.log(window); // log the "window" object to the console
	#https://gist.github.com/stared/dfb4dfaf6d9a8501cd1cc8b8cb806d2e
	class PlotLosses(keras.callbacks.Callback):

	def __init__(self,imgs):
	super(PlotLosses, self).__init__()
	self.imgs=imgs

	def on_train_begin(self, logs={}):
	self.i = 0
	self.x = []
	from keras.models import model_from_json

	def save_keras_model(model,path):
	model_json = model.to_json()
	with open(path+"json", "w") as json_file:
	json_file.write(model_json)
	model.save_weights(path+'.hdf5')


	def load_keras_model(path):
	#simple model
	model = Sequential([
	#Embedding(vocab_size, vec_size, input_length=seq_len,weights=[emb]),
	#Flatten(),
	Dense(100, input_dim=test_data.shape[1], init='uniform', activation='relu'),
	#Dropout(0.7),
	Dense(1, activation='sigmoid')])
	model.compile(loss='binary_crossentropy', optimizer=Adam(), metrics=['accuracy'])
	from keras.applications.vgg16 import VGG16
	from keras.layers import Conv2D
	from keras.models import Sequential
	from keras.layers import BatchNormalization
	from keras.optimizers import Adam

	vgg=VGG16()

	p=0.4 #dropout
	label_count=17
	from keras.applications.resnet50 import ResNet50
	from scipy.misc import imresize

	resnet=ResNet50()
	imgs_new=[]

	for i,img in enumerate(imgs_new_rand):
	imgs_new[i]=imresize(img,(224,224,3))

	resnet_preds=resnet.predict(imgs_new)
	plt.imshow(np.array(Image.open(g[0]))[y-30:y+30,x-30:x+30])
	currentAxis = plt.gca()
	coords=[10,10],20,20
	currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor=color, linewidth=2))