mean

在运行到 loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(output,Y)) 时，报错： ValueError: Only call sigmoid_cross_entropy_with_logits with named arguments (labels=…, logits=…, …) 应该改为 loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=output,logits=Y)) 来源： CSDN 作者： Nani_xiao 链接： https://blog.csdn.net/xiao_lxl/article/details/77249209

I'm doing some statistics work, I have a (large) collection of random numbers to compute the mean of, I'd like to work with generators, because I just need to compute the mean, so I don't need to store the numbers. The problem is that numpy.mean breaks if you pass it a generator. I can write a simple function to do what I want, but I'm wondering if there's a proper, built-in way to do this? It would be nice if I could say "sum(values)/len(values)", but len doesn't work for genetators, and sum already consumed values. here's an example: import numpy def my_mean(values): n = 0 Sum = 0.0 try:

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 由 翻译 强力驱动 问题: I have the daily weather report like this. What I want is to calculate the monthly mean of the max, min and observation temperature and then plot these three lines . I already convert the date format like this: Date = as . POSIXlt ( Weather2011$Date , format = "%m/%d/%Y" ) Year = as . numeric ( format ( Date , format = "%Y" )) Month = as . numeric ( format ( Date , format = "%m" )) Week = as . numeric ( format ( Date , format = "%U" )) Weekday = as . numeric ( format ( Date , format = "%w" )) Weather2011 looks like this: Date Max .

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I am trying to adapt the code to achieve 1-D convolution using 1-D input. The model is compilable so you can see the layers and shapes in .summary() , but it throws the error when .fit() the model. it seems to occur in loss computation. Below is my code: import numpy as np from scipy.stats import norm from keras.layers import Input, Dense, Lambda, Flatten, Reshape from keras.layers import Conv1D, UpSampling1D from keras.models import Model from keras import backend as K from keras import metrics num_conv = 6 batch_size = 100 latent_dim = 2

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 由 翻译 强力驱动 问题: The following digitalmicrograph function tries to create an image by taking difference of neighboring pixel in a sub-row of a row of the image. The first pixel is replaced with a mean of the difference result of the sub-row thus created. E.g. If the input image is 8 pixels wide and 1 rows tall and the sub-row size is 4 - In_img = {8,9,2,4,9,8,7,5} Then the output image will be - Out_img = {mean(8,9,2,4)=5.75,9-8=1,2-9=-7,4-2=2,mean(9,8,7,5)=7.25,8-9=-1,7-8=-1,5-7=-2} When I run this script, the first pixel of the first row is

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I am trying to generate groupwise (hearing - my independent variable, so HL and NH are the two groups) summary statistics (mean, sd, min, max, standard error etc. ) for each of the 10 dependent variables. I was able to do this for one variable (R_PTA) using these 2 codes: 1. RightPTA <- mydata %>% group_by(NHL) %>% summarise(n=length(R_PTA), mean_R_PTA=mean(R_PTA), sd_R_PTA=sd(R_PTA), se_R_PTA=sd(R_PTA)/sqrt(length(R_PTA)), min_R_PTA=min(R_PTA), max_R_PTA=max(R_PTA)) 2. mydata mean<-tapply(mydata$R_PTA, mydata$NHL, mean) mean sd<-tapply

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: import tensorflow as tf import numpy as np isTrain = tf.placeholder(tf.bool) user_input = tf.placeholder(tf.float32) # ema = tf.train.ExponentialMovingAverage(decay=.5) with tf.device('/cpu:0'): beta = tf.Variable(tf.ones([1])) batch_mean = beta.assign(user_input) ema = tf.train.ExponentialMovingAverage(decay=0.5) ema_apply_op = ema.apply([batch_mean]) ema_mean = ema.average(batch_mean) def mean_var_with_update(): with tf.control_dependencies([ema_apply_op]): return tf.identity(batch_mean) mean = tf.cond(isTrain, mean_var_with_update, lambda

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: Some article I read once said that it means jumping (from one URI to another), but I detected this "302" even when there was actually no jumping at all! 回答1: A 302 redirect means that the page was temporarily moved, while a 301 means that it was permanently moved. 301s are good for SEO value, while 302s aren't because 301s instruct clients to forget the value of the original URL, while the 302 keeps the value of the original and can thus potentially reduce the value by creating two, logically-distinct URLs that each produce the same content

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: In the tutorial there is an example for finding prime numbers. >>> for n in range(2, 10): ... for x in range(2, n): ... if n % x == 0: ... print(n, 'equals', x, '*', n//x) ... break ... else: ... # loop fell through without finding a factor ... print(n, 'is a prime number') ... I understand that the double == is a test for equality, but I don't understand the "if n % x" part. Like I can verbally walk through each part and say what the statement does for the example. But I don't understand how the percentage sign falls in. What does "if n % x

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I'm trying to pixelate (\mosaic) an image by calculate the mean of a (non overlap) sliding window over the image. For this I try to implement a "window size" and a "step" parameters. Assuming my step won't exceed the image border. Means that if my image is a 32X32 dims the window can be 2x2\4x4\8x8\16x16 dims. Here an example I try to look for some combinations of mean operator\mask\convolution but didn't find anything relevant. Here some examples of what iI try to look for: Those links gave some parts of my question but iI didn't find out