keras.datasets.mnist和tensorflow.examples.tutorials.mnist有什么区别?

问题描述

我正在分析此DCGAN .当我使用tensorflow.examples.tutorials.mnist中的input_data时，如第144行:

I am analysing this DCGAN. When I use input_data from tensorflow.examples.tutorials.mnist, as seen in line 144:

self.x_train = input_data.read_data_sets("mnist",\
        one_hot=True).train.images

我获得了相当不错的成绩:虽然当我使用keras.datasets中的mnist并且第144行看起来像这样:

I obtain reasonably good results:Though when I use mnist from keras.datasets and the 144th line looks like this:

(xtr, ytr), (xte, yte) = mnist.load_data();
    self.x_train = xtr

我得到了非常糟糕的结果:我已经从两个数据集中手动检查了一些图像，它们非常相似.

I get horribly bad results:I have checked manually a few images from both datasets and they are quite similar.

那么keras.datasets.mnist和tensorflow.examples.tutorials.mnist有什么区别?为什么生成的图像如此不同? keras.datasets.mnist我怎么了?

So what is the difference between keras.datasets.mnist and tensorflow.examples.tutorials.mnist? Why are the resulting images so different? What am I doing wrong with keras.datasets.mnist?

推荐答案

tensorflow.examples.tutorials.mnist中的图像很可能已被归一化为[0，1]范围，因此可以获得更好的结果.而Keras中MNIST数据集中的值在[0，255]范围内，并且您需要对其进行归一化(当然，如果需要).试试这个:

It is very likely that the images in tensorflow.examples.tutorials.mnist have been normalized to the range [0, 1] and therefore you obtain better results. Whereas, the values in MNIST dataset in Keras are in the range [0, 255] and you are expected to normalize them (if needed, of course). Try this:

(xtr, ytr), (xte, yte) = mnist.load_data()
xtr = xtr.astype('float32') / 255.0
xte = xte.astype('float32') / 255.0

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