改进TCN模型的风力预测.zip

import inspect import tensorflow as tf from typing import List from tensorflow import keras from keras import backend as K from keras import Model, Input, optimizers from keras import layers from keras.layers import Activation, SpatialDropout1D, Lambda from keras.layers import Layer, Conv1D, Dense, BatchNormalization, LayerNormalization,PReLU from keras.layers import Layer,Permute, RepeatVector,Multiply,LSTM def is_power_of_two(num: int): return num != 0 and ((num & (num - 1)) == 0)#(num&(num-1)==0)num是2的指数 def adjust_dilations(dilations: list):#自适应的膨胀系数 if all([is_power_of_two(i) for i in dilations]): return dilations else: new_dilations = [2 ** i for i in dilations] return new_dilations #定义残差块 class ResidualBlock(Layer): def __init__(self, dilation_rate: int, nb_filters: int, kernel_size: int, padding: str, activation: str = 'PReLU', dropout_rate: float = 0, kernel_initializer: str = 'he_normal', use_batch_norm: bool = False, use_layer_norm: bool = False, **kwargs): """Defines the residual block for the WaveNet TCN,#为TCN网络定义残差块 Args:#命令行参数个数 x: The previous layer in the model模型中的上一层 training: boolean indicating whether the layer should behave in training mode or in inference mode指示图层在训练模式或推理模式下的行为是具有布尔值 dilation_rate: The dilation power of 2 we are using for this residual block我们用于此残余块的2的膨胀率 nb_filters: The number of convolutional filters to use in this block要在此块中使用的卷积滤波器的数量 kernel_size: The size of the convolutional kernel卷积核的大小 padding: The padding used in the convolutional layers, 'same' or 'causal'. 填充：卷积层中使用的填充，"相同"或"因果"。 activation: The final activation used in o = Activation(x + F(x)) dropout_rate: Float between 0 and 1. Fraction of the input units to drop.浮动在0到1之间。要丢弃的输入单位的分数。 kernel_initializer: Initializer for the kernel weights matrix (Conv1D).内核权重矩阵 （Conv1D） 的初始值设定项。 use_batch_norm: Whether to use batch normalization in the residual layers or not.是否在残差图层中使用批量归一化。 use_layer_norm: Whether to use layer normalization in the residual layers or not.是否在残差图层中使用图层归一化。 kwargs: Any initializers for Layer class.Layer 类的任何初始值设定项。 """ self.dilation_rate = dilation_rate self.nb_filters = nb_filters#过滤器即卷积核尺寸 self.kernel_size = kernel_size self.padding = padding self.activation = activation self.dropout_rate = dropout_rate self.use_batch_norm = use_batch_norm self.use_layer_norm = use_layer_norm self.kernel_initializer = kernel_initializer self.layers = [] self.layers_outputs = [] self.shape_match_conv = None self.res_output_shape = None self.final_activation = None super(ResidualBlock, self).__init__(**kwargs) #定义一个添加层的函数可以很容易的添加神经层,为之后的添加省下不少时间 def _add_and_activate_layer(self, layer): """Helper function for building layer Args: layer: Appends layer to internal layer list and builds it based on the current output shape of ResidualBlocK. Updates current output shape. #添加图层到内部图层列表，并根据当前的输出构建它残差块（ResidualBlocK）的形状，更新当前输出形状。 """ self.layers.append(layer)#将定义好的网络层添加进模型中 self.layers[-1].build(self.res_output_shape) self.res_output_shape = self.layers[-1].compute_output_shape(self.res_output_shape) def build(self, input_shape):#build() 可自定义网络的权重的维度，可以根据输入来指定权重的维度 with K.name_scope(self.name): # name scope used to make sure weights get unique names名称作用域，用于确保权重获得唯一的名称 self.layers = [] self.res_output_shape = input_shape for k in range(2): name = 'conv1D_{}'.format(k) #一维膨胀因果卷积网络，format(k)传递位置参数，conv1D_{k}。 with K.name_scope(name): # name scope used to make sure weights get unique names self._add_and_activate_layer(Conv1D(filters=self.nb_filters, kernel_size=self.kernel_size, dilation_rate=self.dilation_rate, padding=self.padding, name=name, kernel_initializer=self.kernel_initializer)) with K.name_scope('norm_{}'.format(k)):#正则化 if self.use_batch_norm: self._add_and_activate_layer(BatchNormalization()) elif self.use_layer_norm: self._add_and_activate_layer(LayerNormalization()) self._add_and_activate_layer(Activation(tf.keras.layers.PReLU(alpha_initializer=tf.initializers.constant(0.2), alpha_regularizer=None, alpha_constraint=None, shared_axes=None,)))#激活函数 self._add_and_activate_layer(SpatialDropout1D(rate=self.dropout_rate)) if self.nb_filters != input_shape[-1]: # 1x1 conv to match the shapes (channel dimension).# 1x1转换以匹配形状(通道尺寸)，即1x1全卷积连接 name = 'matching_conv1D' with K.name_scope(name): # make and build this layer separately because it directly uses input_shape，单独创建和构建这个层，因为它直接使用input_shape self.shape_match_conv = Conv1D(filters=self.nb_filters, kernel_size=1, padding='same',#padding='SAME'意味着卷积后的输出与输入size保持一致。 name=name, kernel_initializer=self.kernel_initializer) else: name = 'matching_identity' self.shape_match_conv = Lambda(lambda x: x, name=name) #lambda定义一个函数，x=x with K.name_scope(name): self.shape_match_conv.build(input_shape) self.res_output_shape = self.shape_match_conv.compute_output_shape(input_shape)#残差块的输出形状=卷积计算的输出形状（） self.final_activation = Activation(self.activation)#激活函数层 self.final_activation.build(self.res_output_shape) # probably isn't necessary # this is done to force Keras to add the layers in the list to self._layers #这样做是为了迫使Keras将列表中的图层添加到self._layers for layer in self.la