无论输入如何，Keras上的CNN都收敛到相同的值 [英] CNNs on Keras converge to the same value no matter the input

问题描述

我最近一直在学习Keras，并在带有CNN的CIFAR10数据集上进行了尝试.但是，我训练的模型(可以运行代码此处)返回相同的答案无论如何每次输入.我在模型定义中忘记了什么吗?

I've been learning Keras recently and I tried my hand at the CIFAR10 dataset with CNNs. However, the model I trained (you can run the code here) returns the same answer for every input, no matter what. Did I forget something in the model definition?

推荐答案

您忘记了对图像进行标准化.当前，x_train中的值在[0,255]范围内.这会导致较大的梯度更新并拖延训练过程.在这种情况下，一种简单的标准化方案是:

You have forgotten to normalize the images. Currently, the values in x_train are in the range [0,255]. This causes large gradient updates and stalls training process. One simple normalization scheme in this case would be:

x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255

这将导致这些值落在[0,1]范围内.然后，您肯定会看到培训的进展.

This causes the values to fall in the range [0,1]. Then you would surely see that the training progresses.

更复杂的归一化方案涉及按特征(即按像素)归一化或居中.在这种方法中，我们对所有图像进行归一化，以使所有图像中的每个像素的平均值为零，标准偏差为1(即，它们大部分落在[-1,1]范围内):

A more sophisticated normalization scheme involves feature-wise (i.e. pixel-wise) normalization or centering. In this approach we normalize all the images such that each pixel in all the images have a mean of zero and a standard deviation of one (i.e. they mostly fall in the range [-1,1]):

# make sure values are float
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')

x_mean = x_train.mean(axis=0)
x_train -= x_mean
x_std = x_train.std(axis=0)
x_train /= x_std + 1e-8  # add a small constant to prevent division by zero

# normalize test data using the mean and std of training data
x_test -= x_mean
x_test /= x_std + 1e-8

请注意最后一部分:从不通过其自身的均值和标准差对测试数据进行标准化.改用训练平均值和std.

Note the last part: NEVER EVER normalize test data by its own mean and std. Use the training mean and std instead.

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