TensorFlow操作和Numpy乘法的时间比较

问题描述

我一直在尝试优化我的计算；对于我尝试过的大多数操作，tensorflow都快得多.我正在尝试做一个相当简单的操作...变换一个矩阵(将每个值乘以1/2，然后将该值加1/2).

I've been trying to optimize my computations; and for most operations that I've tried, tensorflow is much faster. I'm trying to do a fairly simple operation...Transform a matrix (multiply each value by 1/2 and then add 1/2 to that value).

借助@mrry，我能够在tensorflow中执行这些操作.但是令我惊讶的是，numpy方法显着更快?！

With the help of @mrry , I was able to do these operations in tensorflow. However to my surprise, the numpy method was significantly faster?!

tensorflow似乎是一个非常有用的工具，我认为这可以帮助阐明其用途和优势.

tensorflow seems like an extremely useful tool for data scientists and I think this could help clarify it's use and advantages.

我不是以最有效的方式使用tensorflow数据结构和操作吗?我不确定使用非张量流方法会更快吗?我正在使用2012年中的Macbook Air 4GB RAM

Am I not using tensorflow data structures and operations in the most efficient way? I'm not sure how non-tensorflow methods would be faster. I'm using a Mid-2012 Macbook Air 4GB RAM

trans1是张量流版本，而trans2是numpy. DF_var是熊猫数据框对象

import pandas as pd
import tensorflow as tf
import numpy as np

def trans1(DF_var):
    #Total user time is 31.8532807827 seconds

    #Create placeholder 
    T_feed = tf.placeholder(tf.float32,DF_var.shape)

    #Matrix transformation
    T_signed = tf.add(
                      tf.constant(0.5,dtype=tf.float32),
                      tf.mul(T_feed,tf.constant(0.5,dtype=tf.float32))
                      ) 

    #Get rid of of top triangle
    T_ones = tf.constant(np.tril(np.ones(DF_var.shape)),dtype=tf.float32)
    T_tril = tf.mul(T_signed,T_ones)

    #Start Graph Session
    sess = tf.Session()

    DF_signed = pd.DataFrame(
                          sess.run(T_tril,feed_dict={T_feed: DF_var.as_matrix()}),
                          columns = DF_var.columns, index = DF_var.index
                          )
    #Close Graph Session
    sess.close() 
    return(DF_signed)

def trans2(DF_var):
    #Total user time is 1.71233415604 seconds
    M_computed = np.tril(np.ones(DF_var.shape))*(0.5 + 0.5*DF_var.as_matrix())
    DF_signed = pd.DataFrame(M_computed,columns=DF_var.columns, index=DF_var.index)
    return(DF_signed)

我的计时方法是:

import time
start_time = time.time()
#operation
print str(time.time() - start_time)

推荐答案

您的结果与另一个人的基准测试.

在基准测试中，他在

他的加法结果表明:

His results for addition shows that:

他还测试了其他一些功能，例如矩阵乘法:

He also tested a few other functions such as matrix multiplication:

结果是:

在Theano中矩阵的最小值和平均值很慢，而在 Tensorflow.目前尚不清楚为什么Theano这么慢(比NumPy还差) 这些操作.

The minimum and mean of matrices are slow in Theano and quick in Tensorflow. It is not clear why Theano is as slow (worse than NumPy) for these operations.

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