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import networkx as nx | |
import numpy as np | |
import argparse | |
def remove_one_edge(G, src, goal): | |
threshold = 10 | |
Edge_Count = {} | |
pass | |
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source: https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdfcross_entropy = tf.reduce_mean( | |
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv)) | |
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) | |
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1)) | |
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) | |
with tf.Session() as sess: | |
sess.run(tf.global_variables_initializer()) | |
for i in range(20000): | |
batch = mnist.train.next_batch(50) |
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W_conv2 = weight_variable([5, 5, 32, 64]) | |
b_conv2 = bias_variable([64]) | |
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) | |
h_pool2 = max_pool_2x2(h_conv2) | |
W_fc1 = weight_variable([7 * 7 * 64, 1024]) | |
b_fc1 = bias_variable([1024]) | |
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64]) | |
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) | |
keep_prob = tf.placeholder(tf.float32) | |
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) |
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x = tf.placeholder(tf.float32, shape=[None, 784]) | |
y_ = tf.placeholder(tf.float32, shape=[None, 10]) | |
W_conv1 = weight_variable([5, 5, 1, 32]) | |
b_conv1 = bias_variable([32]) | |
x_image = tf.reshape(x, [-1, 28, 28, 1]) | |
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) | |
h_pool1 = max_pool_2x2(h_conv1) |
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from tensorflow.examples.tutorials.mnist import input_data | |
import tensorflow as tf | |
import numpy as np | |
mnist = input_data.read_data_sets('MNIST_data', one_hot=True) | |
def weight_variable(shape): | |
initial = tf.truncated_normal(shape, stddev=0.1) | |
return tf.Variable(initial) | |
def bias_variable(shape): |
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import argparse | |
import json | |
import logging | |
import os | |
import sqlite3 | |
import sys | |
from base64 import b64decode | |
from ctypes import c_uint, c_void_p, c_char_p, cast, byref, string_at |
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import smtplib | |
import socks | |
fromaddr = input('sender email: ') | |
toaddrs = input('receiver email: ') | |
msg = input('message') | |
username = input('your email: ') | |
password = input('your password: ') | |
server = smtplib.SMTP('smtp.gmail.com:587') |
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// Buoy_task.cpp : Defines the entry point for the console application. | |
// | |
#include "stdafx.h" | |
#include<opencv2/opencv.hpp> | |
#include<stdio.h> | |
#include<iostream> | |
using namespace std; | |
using namespace cv; |
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// Marker Task.cpp : Defines the entry point for the console application. | |
#include "stdafx.h" | |
#include<opencv2\opencv.hpp> | |
#include<stdio.h> | |
#include<iostream> | |
using namespace std; | |
using namespace cv; |