Tensorflow:在占位符中引入不同大小的矩阵 [英] Tensorflow: introducing a matrix of different size in a placeholder

问题描述

我正在尝试使用矩阵乘法的张量流做一个简单的操作，但我必须使用其列大小可变的矩阵(如下面的示例所示)

I'm trying to do a simple operation with tensorflow of matrix multiplication but I have to use a matrix of variable size of its columns (as it can be seen in the example below)

import tensorflow as tf

input1 = tf.placeholder("float", [None,None]) 
    input2 = tf.placeholder(tf.float32) 
    output = tf.mul(input1, input2)

with tf.Session() as sess:   
    print(sess.run([output], feed_dict={input1:[[1,2],[3,4,5]], input2:[2.]}))

问题是，一旦执行此操作，就会收到一条错误消息，告诉我:

The thing is that once I do this, I receive an error message telling me:

ValueError:设置具有序列的数组元素.

ValueError: setting an array element with a sequence.

我知道在第一行中添加任何数字或无(可以生成mxn形状)很容易解决，但是我想为实验训练更大的数据，并且不确定0是否会影响数据或不是.

I know this can be easily solvable adding any number or None in the first row (to produce an m x n shape), however I want to train bigger data for an experiment and I'm not sure whether a 0 could affect data or not.

推荐答案

tf.placeholder() op为密集张量定义了一个占位符，因此您必须在值中定义所有元素正在尝试喂食.

The tf.placeholder() op defines a placeholder for a dense tensor, so you must define all of the elements in the value that you are trying to feed.

一种替代方法(在最新版本的TensorFlow中，如果您从源代码构建或下载夜间版本，则可用)是使用

An alternative (in the latest version of TensorFlow, available if you build from source or download a nightly release) is to use a tf.sparse_placeholder() op, which allows you to feed a tf.SparseTensor with a tf.SparseTensorValue. This allows you to represent an object in which not all elements are defined, but the ones that are undefined are interpreted as zeros.

请注意，TensorFlow对稀疏数据和可变大小示例的支持仍是初步的，并且大多数操作—如

Note that TensorFlow's support for sparse data and variable-sized examples is still preliminary, and most of the operations—like tf.mul()—are currently only defined for dense tensors. An alternative approach, which we use for variable-sized image data, is to process one (variable-sized) record at a time in an input pipeline, before converting it to a constant shape, and using the batching functions to make a single dense batch.

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