在非常简单的KERAS二进制分类器中，LOSS不变 [英] LOSS not changeing in very simple KERAS binary classifier

问题描述

我正在尝试运行一个非常(简化)的Keras二进制分类器神经网络，但没有成功.损失保持恒定.到目前为止，我一直在使用Optimizer(SGD，Adam，RMSProp)，Learningrate，Weight-Initializations，Batch Size和输入数据归一化.

I'm trying to get a very (over) simplified Keras binary classifier neural network running without success. The LOSS just stays constant. I've played around with Optimizers (SGD, Adam, RMSProp), Learningrates, Weight-Initializations, Batch Size and input data normalization so far.

什么都没有改变.我是从根本上做错了吗?这是代码:

Nothing changes at all. Am I doing something fundamentally wrong? Here is the code:

from tensorflow import keras
from keras import Sequential
from keras.layers import Dense
from keras.optimizers import SGD

data = np.array(
    [
        [100,35,35,12,0],
        [101,46,35,21,0],
        [130,56,46,3412,1],
        [131,58,48,3542,1]
    ]
)

x = data[:,1:-1]
y_target = data[:,-1]

x = x / np.linalg.norm(x)

model = Sequential()
model.add(Dense(3, input_shape=(3,), activation='softmax', kernel_initializer='lecun_normal',
                bias_initializer='lecun_normal'))
model.add(Dense(1, activation='softmax', kernel_initializer='lecun_normal',
                bias_initializer='lecun_normal'))

model.compile(optimizer=SGD(learning_rate=0.1),
              loss='binary_crossentropy',
              metrics=['accuracy'])

model.fit(x, y_target, batch_size=2, epochs=10,
          verbose=1)

推荐答案

Softmax 的定义是:

exp(a) / sum(exp(a)

因此，当您使用单个神经元时，您将得到:

so when you use with a single neuron you will get:

exp(a) / exp(a) = 1

这就是为什么您的分类器不适用于单个神经元的原因.

That is why your classifier doesn't work with a single neuron.

在这种特殊情况下，您可以改用 Sigmoid :

You can use sigmoid instead in this special case:

exp(a) / (exp(a) + 1)

此外， Sigmoid 函数适用于两个类别分类器. Softmax 是针对多类分类器的Sigmoid扩展.

Furthermore sigmoid function is for two class classifiers. Softmax is an extension of sigmoid for multiclass classifers.

对于第一层，您应该使用 relu 或 Sigmoid 函数而不是softmax.

For the first layer you should use relu or sigmoid function instead of softmax.

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