关于BN应该应用在当前层的非线性之前还是应用在前一层的激活之前，这个线程有一些相当大的争论。

虽然没有正确答案，但批处理规范化的作者是这么说的 它应立即应用于当前层的非线性之前。原因(引自原文)-

"We add the BN transform immediately before the nonlinearity, by normalizing x = Wu+b. We could have also normalized the layer inputs u, but since u is likely the output of another nonlinearity, the shape of its distribution is likely to change during training, and constraining its first and second moments would not eliminate the covariate shift. In contrast, Wu + b is more likely to have a symmetric, non-sparse distribution, that is “more Gaussian” (Hyv¨arinen & Oja, 2000); normalizing it is likely to produce activations with a stable distribution."

Keras现在支持use_bias=False选项，所以我们可以通过编写这样的代码来节省一些计算

model.add(Dense(64, use_bias=False))
model.add(BatchNormalization(axis=bn_axis))
model.add(Activation('tanh'))

or

model.add(Convolution2D(64, 3, 3, use_bias=False))
model.add(BatchNormalization(axis=bn_axis))
model.add(Activation('relu'))

为了更详细地回答这个问题，正如Pavel所说，批处理规范化只是另一层，因此您可以使用它来创建所需的网络架构。

一般的用例是在网络中的线性层和非线性层之间使用BN，因为它将激活函数的输入归一化，这样您就位于激活函数的线性部分的中心(例如Sigmoid)。这里有一个小讨论

在你上面的例子中，这可能是这样的:

# import BatchNormalization
from keras.layers.normalization import BatchNormalization

# instantiate model
model = Sequential()

# we can think of this chunk as the input layer
model.add(Dense(64, input_dim=14, init='uniform'))
model.add(BatchNormalization())
model.add(Activation('tanh'))
model.add(Dropout(0.5))

# we can think of this chunk as the hidden layer    
model.add(Dense(64, init='uniform'))
model.add(BatchNormalization())
model.add(Activation('tanh'))
model.add(Dropout(0.5))

# we can think of this chunk as the output layer
model.add(Dense(2, init='uniform'))
model.add(BatchNormalization())
model.add(Activation('softmax'))

# setting up the optimization of our weights 
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='binary_crossentropy', optimizer=sgd)

# running the fitting
model.fit(X_train, y_train, nb_epoch=20, batch_size=16, show_accuracy=True, validation_split=0.2, verbose = 2)

希望这能让你更清楚一些。

这个帖子有误导性。我试着评论卢卡斯·拉马丹的回答，但我还没有权限，所以我把这个放在这里。

Batch normalization works best after the activation function, and here or here is why: it was developed to prevent internal covariate shift. Internal covariate shift occurs when the distribution of the activations of a layer shifts significantly throughout training. Batch normalization is used so that the distribution of the inputs (and these inputs are literally the result of an activation function) to a specific layer doesn't change over time due to parameter updates from each batch (or at least, allows it to change in an advantageous way). It uses batch statistics to do the normalizing, and then uses the batch normalization parameters (gamma and beta in the original paper) "to make sure that the transformation inserted in the network can represent the identity transform" (quote from original paper). But the point is that we're trying to normalize the inputs to a layer, so it should always go immediately before the next layer in the network. Whether or not that's after an activation function is dependent on the architecture in question.

它是另一种类型的层，所以你应该把它作为一个层添加到你的模型的适当位置

model.add(keras.layers.normalization.BatchNormalization())

请看一个例子:https://github.com/fchollet/keras/blob/master/examples/kaggle_otto_nn.py

批处理归一化是通过调整激活的平均值和缩放来归一化输入层和隐藏层。由于这种在深度神经网络中附加层的归一化效应，网络可以使用更高的学习率而不会消失或爆炸梯度。此外，批归一化对网络进行了正则化，使其更容易泛化，因此不需要使用dropout来缓解过拟合。

在使用Keras中的Dense()或Conv2D()计算线性函数后，我们使用BatchNormalization()来计算层中的线性函数，然后使用Activation()将非线性添加到层中。

from keras.layers.normalization import BatchNormalization
model = Sequential()
model.add(Dense(64, input_dim=14, init='uniform'))
model.add(BatchNormalization(epsilon=1e-06, mode=0, momentum=0.9, weights=None))
model.add(Activation('tanh'))
model.add(Dropout(0.5))
model.add(Dense(64, init='uniform'))
model.add(BatchNormalization(epsilon=1e-06, mode=0, momentum=0.9, weights=None))
model.add(Activation('tanh'))
model.add(Dropout(0.5))
model.add(Dense(2, init='uniform'))
model.add(BatchNormalization(epsilon=1e-06, mode=0, momentum=0.9, weights=None))
model.add(Activation('softmax'))

sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='binary_crossentropy', optimizer=sgd)
model.fit(X_train, y_train, nb_epoch=20, batch_size=16, show_accuracy=True, 
validation_split=0.2, verbose = 2)

如何应用批处理规范化?

假设我们向层l输入a[l-1]，并且我们有层l的权值W[l]和偏置单位b[l]。添加非线性后)为层l, z[l]为添加非线性前的向量

Using a[l-1] and W[l] we can calculate z[l] for the layer l Usually in feed-forward propagation we will add bias unit to the z[l] at this stage like this z[l]+b[l], but in Batch Normalization this step of addition of b[l] is not required and no b[l] parameter is used. Calculate z[l] means and subtract it from each element Divide (z[l] - mean) using standard deviation. Call it Z_temp[l] Now define new parameters γ and β that will change the scale of the hidden layer as follows: z_norm[l] = γ.Z_temp[l] + β

在这段代码摘录中，Dense()取a[l-1]，使用W[l]并计算z[l]。然后立即的BatchNormalization()将执行上述步骤以得到z_norm[l]。然后立即激活()将计算tanh(z_norm[l])给出一个[l]，即。

a[l] = tanh(z_norm[l])