Jiwon jjone36

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I may be slow to respond.

jjone36 / tf_1_basic.py

Last active August 22, 2019 13:44

	# 1. Create variables as a tensor
	a = tf.constant(2, tf.int32)
	b = tf.Variable(10, tf.float32)

	# 2. Write opertaions between them
	c = tf.multiply(a, b)

	# 3. Initialize variables
	init = tf.global_variables_initializer()

jjone36 / window_basic2.py

Created March 30, 2019 07:43

	import cv2
	import numpy as np

	# Step 1. Define detect function
	face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_default.xml')

	def detect_face(img):

	img_copy = img.copy()
	face_rects = face_cascade.detectMultiScale(img_copy)

jjone36 / window_basic.py

Last active March 10, 2019 01:16

	import cv2
	import numpy as np

	# Step 1. Define callback function
	drawing = False
	ix = -1
	iy = -1

	def draw_rectangle(event, x, y, flags, params):

jjone36 / dash_basic2.py

Last active October 28, 2019 08:08

	import dash
	import dash_core_components as dcc
	import dash_html_components as html
	from dash.dependencies import Input, Output

	import pandas as pd
	import plotly.graph_objs as go

	# Step 1. Launch the application
	app = dash.Dash()

jjone36 / dash_basic.py

Last active March 4, 2019 04:34

	import dash
	import dash_core_components as dcc
	import dash_html_components as html
	from dash.dependencies import Input, Output

	import pandas as pd
	import plotly.graph_objs as go

	# Step 1. Launch the application
	app = dash.Dash()

jjone36 / DataCamp_Cosmetic.py

Created February 16, 2019 14:05

	# Create a function to vetorize all the ingredients and get t-SNE at once
	def cosmetic_map(option_1, option_2):
	''' Define a function creating a dataframe for each option '''
	df = cosm[cosm['Label'] == option_1][cosm[option_2] == 1]
	df = df.reset_index()

	# embedding each ingredients
	word_index_map = {}
	index_word_map = []
	current_index = 0

jjone36 / one_hot_encodding.py

Created January 21, 2019 01:32

	import numpy as np

	# one-hot-encoding
	categories = np.array(['shirt', 'dress', 'shoe'])
	labels = ['shoe', 'shirt', 'shoe', 'shirt', 'dress', 'dress', 'dress']

	ohe_labels = np.zeros([len(labels), len(categories)])

	for m in range(len(labels)):
	# find the location of the label

jjone36 / EasiestCNN_1.py

Created January 17, 2019 07:34

	import numpy as np

	# what is convolution? How to capture the edge by padding?
	array = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
	kernel = np.array([-1, 1])

	conv = np.array([0, 0, 0, 0, 0, 0, 0, 0])

	# convolution
	conv[1] = (kernel * array[0:2]).sum()

jjone36 / EasiestANN_2.py

Last active December 28, 2018 08:33

	# define activation function
	def relu(x):
	return np.where(x <= 0, 0, x)

	# input data
	input_layer = np.array([[5], [2]])
	weights_1 = np.array([[2, -2], [3, 1]])
	weights_2 = np.array([[1], [2]])

	# computation of first hidden layer

jjone36 / EasiestANN_1.py

Last active December 28, 2018 08:31