joelthchao’s gists

joelthchao / monitor.html

Last active March 18, 2018 11:20

DHT

	<head>
	<!-- Plotly.js -->
	<script src="https://cdn.plot.ly/plotly-latest.min.js"></script>
	</head>
	<body>
	<!-- Plotly chart will be drawn inside this DIV -->
	<div id="plot"></div>
	<script>
	Plotly.d3.csv("record/dht.csv", function(err, rows){

joelthchao / cron.py

Last active March 18, 2018 11:13

DHT

	#!/home/pi/miniconda3/bin/python
	from datetime import datetime
	import sys
	import Adafruit_DHT


	def main():
	sensor = Adafruit_DHT.AM2302
	pin = 4
	record_file = '/home/pi/joel/dht_monitor/record/dht.csv' # TODO: change to your own path

joelthchao / starter.py

Created March 18, 2018 11:05

DHT

	import Adafruit_DHT

	sensor = Adafruit_DHT.AM2302
	pin = 4
	humidity, temperature = Adafruit_DHT.read_retry(
	sensor, pin, retries=5, delay_seconds=1)
	print('{},{}'.format(humidity, temperature))

joelthchao / install_packages.sh

Last active March 18, 2018 11:05

DHT

	#!/bin/bash
	# Install Anaconda
	wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-armv7l.sh
	bash Miniconda3-latest-Linux-armv7l.sh

	# Install Adafruit_Python_DHT
	git clone https://github.com/adafruit/Adafruit_Python_DHT.git
	cd Adafruit_Python_DHT
	python setup.py install

joelthchao / dead_neuron_2.py

Last active September 16, 2017 14:07

Dead neuron

	import keras.backend as K
	from keras.initializers import Constant
	from keras.layers import Input, Dense
	from keras.models import Model
	from keras.optimizers import SGD

	b_init = -0.5 # [-1, -0.75, -0.5, -0.25, 0, 0.25, 0.5, 0.75, 1]
	net_input = Input(shape=(4,))
	net = Dense(2, activation='relu', bias_initializer=Constant(b_init))(net_input)
	net = Dense(1, activation='sigmoid')(net)

joelthchao / dead_neuron_1.py

Last active September 16, 2017 13:44

Dead neuron

joelthchao / keras_callback_medium_3.py

Last active July 2, 2017 15:44

Keras Callback

	from keras.callbacks import ProgbarLogger

	class ProgbarLoggerVerbose(ProgbarLogger):
	def on_train_begin(self, logs=None):
	super(ProgbarLoggerVerbose, self).on_train_begin(logs)
	self.verbose = True

	log_file = 'path/to/log.txt' # if you don't want to do logging, just leave the kwags unfilled
	my_callback = MyCallback(test_x, test_y, log_file=log_file, verbose=True)
	model.fit(X, Y, callbacks=[ProgbarLoggerVerbose('samples'), my_callback], verbose=0)

joelthchao / keras_callback_medium_2.py

Last active July 2, 2017 15:54

Keras Callback

	log_file = 'path/to/log.txt' # if you don't want to do logging, just leave the kwags unfilled
	my_callback = MyCallback(test_x, test_y, log_file=log_file, verbose=True)
	model.fit(train_x, train_y, callbacks=[my_callback])

	"""
	Output:
	59264/60000 [============================>.] - ETA: 0s - loss: 0.3520 - acc: 0.8925Epoch 0 acc= 0.9753
	60000/60000 [==============================] - 5s - loss: 0.3497 - acc: 0.8932 - val_loss: 0.0818 - val_acc: 0.9753
	"""

joelthchao / keras_callback_medium_1.py

Last active July 2, 2017 15:23

Keras callback

	from keras.callbacks import Callback
	import numpy as np

	class MyCallback(Callback):
	def __init__(self, test_data, test_label, log_file=None, verbose=True):
	self.test_data = test_data
	self.test_label = test_label
	self.result = []
	self.log = open(log_file, 'w') if log_file else None
	self.verbose = verbose

joelthchao / demo.py

Last active August 31, 2021 18:02

Keras uses TensorBoard Callback with train_on_batch

	import numpy as np
	import tensorflow as tf
	from keras.callbacks import TensorBoard
	from keras.layers import Input, Dense
	from keras.models import Model


	def write_log(callback, names, logs, batch_no):
	for name, value in zip(names, logs):
	summary = tf.Summary()

Joel joelthchao