tensor

Using the Matrix package I can create a two-dimensional sparse matrix. Can someone suggest a package that would allow me to create a multidimensional (specifically a 3-dimensional) sparse matrix (array, or technically a three-way tensor) in R? The slam package has a simple_sparse_array class: http://finzi.psych.upenn.edu/R/library/slam/html/array.html , although it only has support for indexing and coercion (if you wanted to do tensor operations, or elementwise arithmetic, without converting back to a regular dense array, you'd have to implement them yourself ...) I found this by doing library

获取和读取数据 初始化模型参数 实现softmax运算 定义模型 定义损失函数 计算分类准确率 训练模型 小结 import torch import torchvision import numpy as np import sys import torchvision.transforms as transforms sys.path.append('..') import d2lzh_pytorch as d2l 获取和读取数据 我们将使用Fahsion_MNIST数据集，并设置批量大小为256 batch_size= 256 mnist_train= torchvision.datasets.FashionMNIST(root='~/Datasets/FashionMNIST',download=True,train=True,transform=transforms.ToTensor()) mnist_test = torchvision.datasets.FashionMNIST(root='~/Datasets/FashionMNIST',download=True,train=False,transform=transforms.ToTensor()) if sys.platform.startswith('win'): num_worker=0 #

I'm trying to access a part of a multidimensional array in Matlab - it could be done like this: X(2:3, 1:20, 5, 4:7) However, neither the number of elements, nor the ranges are fixed, so I want to provide the indices from arrays - for the above example they'd be ind1 = [2 1 5 4]; ind2 = [3 20 5 7]; For a fixed number of dimensions this is not a problem ( X(ind1(1):ind2(1),...) , but since they are not I'm not sure how to implement this in Matlab. Is there a way? Or should I approach this differently? Using comma separated lists you can make it a more quick and friendly: % some test data ind1 =

举个简单的例子，当我们要实现一个这样的计算图时： 用TensorFlow是这样的： 而用pytorch是这样的： 当然，里面都包含了建立前向计算图，传入变量数据，求梯度等操作，但是显而易见，pytorch的代码更为凝练。TensorFlow我也用得比较少，我本文重点说说pytorch的一些学习心得，一是总结，而是若有缘人能看见此文，也能当个参考。 其实一个好的框架应该要具备三点：对大的计算图能方便的实现；能自动求变量的导数；能简单的运行在GPU上；pytorch都做到了，但是现在很多公司用的都是TensorFlow，而pytorch由于比较灵活，在学术科研上用得比较多一点。鄙人认为可能，Google可能下手早一些，而Facebook作后来者，虽然灵活，但是很多公司已经入了TensorFlow的坑了，要全部迁移出来还是很费功夫；而且，TensorFlow在GPU的分布式计算上更为出色，在数据量巨大时效率比pytorch要高一些，我觉得这个也是一个重要的原因吧。 好的，不闲扯了。pytorch的一些心得，我总结一下： 首先，pytorch包括了三个层次：tensor,variable,Module。tensor，即张量的意思，由于是矩阵的运算，十分适合在GPU上跑。但是这样一个tensor为什么还不够呢？要搞出来一个variable，其实variable只是tensor的一个封装

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 unstack a Tensor because I need a sequence as input for the RNN. I am using variable sequence lengths which prevents me from correctly using tf.unstack . def MapToSequences(x): # x.get_shape().as_list() = [64, 1, None, 512] x = tf.squeeze(x) # tf.shape(x) = [None, None, None], at runtime would be [64, seqlen, 512] x = tf.transpose(x, perm=[1, 0, 2]) # [seqlen, 64, 512] # Here I'd like to unstack with seqlen as num x = tf.unstack(x) # Cannot infer num from shape (?, ?, ?) return x I tried using tf.shape(x) to infer the seqlen

I am trying to add attention mechanism to stacked LSTMs implementation https://github.com/salesforce/awd-lstm-lm All examples online use encoder-decoder architecture, which I do not want to use (do I have to for the attention mechanism?). Basically, I have used https://webcache.googleusercontent.com/search?q=cache:81Q7u36DRPIJ:https://github.com/zhedongzheng/finch/blob/master/nlp-models/pytorch/rnn_attn_text_clf.py+&cd=2&hl=en&ct=clnk&gl=uk def __init__(self, rnn_type, ntoken, ninp, nhid, nlayers, dropout=0.5, dropouth=0.5, dropouti=0.5, dropoute=0.1, wdrop=0, tie_weights=False): super

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 am trying to follow the tensor flow tutorial as described in this link I am trying to print the predicted result as described : print ( "Predicted %d, Label: %d" % ( classifier . predict ( test_data [ 0 ]), test_labels [ 0 ])) But I am not able to print the result. I am getting the following error. print ( "Predicted %d, Label: %d" % ( classifier . predict ( test_data [ 0 ]), test_labels [ 0 ])) TypeError : % d format : a number is required , not generator How do I print the generator in python . I tried to write a loop and

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 add gradient clipping to my graph. I used the approach recommended here: How to effectively apply gradient clipping in tensor flow? optimizer = tf.train.GradientDescentOptimizer(learning_rate) if gradient_clipping: gradients = optimizer.compute_gradients(loss) clipped_gradients = [(tf.clip_by_value(grad, -1, 1), var) for grad, var in gradients] opt = optimizer.apply_gradients(clipped_gradients, global_step=global_step) else: opt = optimizer.minimize(loss, global_step=global_step) But when I turn on gradient clipping, I get the