View uart_echo_example_main.c
/* Uart Events Example
This example code is in the Public Domain (or CC0 licensed, at your option.)
Unless required by applicable law or agreed to in writing, this
software is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
CONDITIONS OF ANY KIND, either express or implied.
*/
#include <stdio.h>
#include <string.h>
View esp32_getChipVersion.ino
/*
esp32 ,chip Revision check
thanks:
https://www.esp32.com/viewtopic.php?t=1358
*/
#include "soc/efuse_reg.h"
//
View cha_iot_rand.py
# -*- coding: utf-8 -*-
import numpy as np
import chainer
from chainer import cuda, Function, gradient_check, Variable, optimizers, serializers, utils
from chainer import Link, Chain, ChainList
import chainer.functions as F
import chainer.links as L
import time
from matplotlib import pyplot as plt
View test_rnn2-blog.py
# encoding: utf-8
from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.layers.recurrent import LSTM
from keras.optimizers import Adam
from keras.callbacks import EarlyStopping
import sys
import numpy as np
View yosoku_update_one.py
# -*- coding: utf-8 -*-
import requests
import json
import time
import sys
import gc
import traceback
import api_func
import com_func
View yosoku_update.service
[Unit]
Description = tesorFlow Learning server
[Service]
ExecStart=/usr/bin/python /usr/local/yosoku_update/yosoku_update.py
Restart=always
Type=simple
[Install]
WantedBy=multi-user.target
View ai_func.py
import tensorflow as tf
import json
from urllib2 import urlopen
import api_func
#ai_func
class ai_funcClass:
def __init__(self):
print ""
View yosoku_update.py
#import tensorflow as tf
import json
from urllib2 import urlopen
import datetime
import threading
import time
import sys
import traceback
import api_func
View webhook.py
# -*- coding: utf-8 -*-
# 日本語
from urllib2 import urlopen
import webapp2
import json
import os
import sys
import traceback
import com_func
View gist:c5c9f8230b7265f330933865bc69333e
import tensorflow as tf
if __name__ == "__main__":
# Model parameters
W = tf.Variable([0.0], dtype=tf.float32)
b = tf.Variable([0.0], dtype=tf.float32)
# Model input and output
x = tf.placeholder(tf.float32)
y = tf.placeholder(tf.float32)
linear_model = W*x + b