keras 的InstanceNormalization的使用

1.instanceNormalization在keras_contrib中，而keras_contrib不能以常规pip方式导入，而是

pip install git+https://www.github.com/keras-team/keras-contrib.git

但是这样还需要下载git工具，我先去官网下载，翻墙后下载速度还是接近10k的高速，然后我去github下载，然并卵，依旧慢，然后我发现腾讯的软件管家可以直接下，，，，和安装常规软件差不多。
ok，软件下好了，我发现我又不太想用了，，，，哈哈

2.然后我直接去github找到keras_contrib,把它下面的InstanceNormalization这个类直接ctrl c了下来放到我的代码里面，然后像BN一样使用即可。使用和地址如下：

o=InstanceNormalization()(input)

https://github.com/keras-team/keras-contrib/blob/master/keras_contrib/layers/normalization/instancenormalization.py

这个类如下：

from keras.layers import Layer, InputSpec
from keras import initializers, regularizers, constraints
from keras import backend as Kclass InstanceNormalization(Layer):"""Instance normalization layer.Normalize the activations of the previous layer at each step,i.e. applies a transformation that maintains the mean activationclose to 0 and the activation standard deviation close to 1.# Argumentsaxis: Integer, the axis that should be normalized(typically the features axis).For instance, after a `Conv2D` layer with`data_format="channels_first"`,set `axis=1` in `InstanceNormalization`.Setting `axis=None` will normalize all values in eachinstance of the batch.Axis 0 is the batch dimension. `axis` cannot be set to 0 to avoid errors.epsilon: Small float added to variance to avoid dividing by zero.center: If True, add offset of `beta` to normalized tensor.If False, `beta` is ignored.scale: If True, multiply by `gamma`.If False, `gamma` is not used.When the next layer is linear (also e.g. `nn.relu`),this can be disabled since the scalingwill be done by the next layer.beta_initializer: Initializer for the beta weight.gamma_initializer: Initializer for the gamma weight.beta_regularizer: Optional regularizer for the beta weight.gamma_regularizer: Optional regularizer for the gamma weight.beta_constraint: Optional constraint for the beta weight.gamma_constraint: Optional constraint for the gamma weight.# Input shapeArbitrary. Use the keyword argument `input_shape`(tuple of integers, does not include the samples axis)when using this layer as the first layer in a Sequential model.# Output shapeSame shape as input.# References- [Layer Normalization](https://arxiv.org/abs/1607.06450)- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://arxiv.org/abs/1607.08022)"""def __init__(self,axis=None,epsilon=1e-3,center=True,scale=True,beta_initializer='zeros',gamma_initializer='ones',beta_regularizer=None,gamma_regularizer=None,beta_constraint=None,gamma_constraint=None,**kwargs):super(InstanceNormalization, self).__init__(**kwargs)self.supports_masking = Trueself.axis = axisself.epsilon = epsilonself.center = centerself.scale = scaleself.beta_initializer = initializers.get(beta_initializer)self.gamma_initializer = initializers.get(gamma_initializer)self.beta_regularizer = regularizers.get(beta_regularizer)self.gamma_regularizer = regularizers.get(gamma_regularizer)self.beta_constraint = constraints.get(beta_constraint)self.gamma_constraint = constraints.get(gamma_constraint)def build(self, input_shape):ndim = len(input_shape)if self.axis == 0:raise ValueError('Axis cannot be zero')if (self.axis is not None) and (ndim == 2):raise ValueError('Cannot specify axis for rank 1 tensor')self.input_spec = InputSpec(ndim=ndim)if self.axis is None:shape = (1,)else:shape = (input_shape[self.axis],)if self.scale:self.gamma = self.add_weight(shape=shape,name='gamma',initializer=self.gamma_initializer,regularizer=self.gamma_regularizer,constraint=self.gamma_constraint)else:self.gamma = Noneif self.center:self.beta = self.add_weight(shape=shape,name='beta',initializer=self.beta_initializer,regularizer=self.beta_regularizer,constraint=self.beta_constraint)else:self.beta = Noneself.built = Truedef call(self, inputs, training=None):input_shape = K.int_shape(inputs)reduction_axes = list(range(0, len(input_shape)))if self.axis is not None:del reduction_axes[self.axis]del reduction_axes[0]mean = K.mean(inputs, reduction_axes, keepdims=True)stddev = K.std(inputs, reduction_axes, keepdims=True) + self.epsilonnormed = (inputs - mean) / stddevbroadcast_shape = [1] * len(input_shape)if self.axis is not None:broadcast_shape[self.axis] = input_shape[self.axis]if self.scale:broadcast_gamma = K.reshape(self.gamma, broadcast_shape)normed = normed * broadcast_gammaif self.center:broadcast_beta = K.reshape(self.beta, broadcast_shape)normed = normed + broadcast_betareturn normeddef get_config(self):config = {'axis': self.axis,'epsilon': self.epsilon,'center': self.center,'scale': self.scale,'beta_initializer': initializers.serialize(self.beta_initializer),'gamma_initializer': initializers.serialize(self.gamma_initializer),'beta_regularizer': regularizers.serialize(self.beta_regularizer),'gamma_regularizer': regularizers.serialize(self.gamma_regularizer),'beta_constraint': constraints.serialize(self.beta_constraint),'gamma_constraint': constraints.serialize(self.gamma_constraint)}base_config = super(InstanceNormalization, self).get_config()return dict(list(base_config.items()) + list(config.items()))

3.其余类可以类似的使用

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