首先,我们定义一个TCN类,用于创建TCN模型。 classTCN:def__init__(self,input_shape,output_size,num_channels,kernel_size,dropout_rate):self.input_shape=input_shape self.output_size=output_size self.num_channels=num_channels self.kernel_size=kernel_size self.dropout_rate=dropout_ratedefbuild_model(...
def __init__(self, input_size, num_channels, kernel_size=2, dropout=0.2): # input_size : 输入的预期特征数 # num_channels: 通道数 # kernel_size: 卷积核大小 super(TCNEncoder, self).__init__() self._input_size = input_size self._output_dim = num_channels[-1] layers = nn.Layer...
relu(out + res) class TemporalConvNet(nn.Module): def __init__(self, num_inputs, num_channels, kernel_size=2, dropout=0.2): super(TemporalConvNet, self).__init__() layers = [] num_levels = len(num_channels) for i in range(num_levels): dilation_size = 2 ** i in_channels ...
__init__() """ :param num_inputs: int, 输入通道数或者特征数 :param num_channels: list, 每层的hidden_channel数. 例如[5,12,3], 代表有3个block, block1的输出channel数量为5; block2的输出channel数量为12; block3的输出channel数量为3. :param kernel_size: int, 卷积核尺寸 :param dropout...
在这个示例中,TemporalConvNet类的构造函数接受输入维度和输出维度作为参数,并允许通过num_channels参数来设置卷积层的数量(即层数)和每层的通道数。kernel_size参数用于设置卷积核的高度,而dropout参数则用于防止过拟合。
网络深度n就是有多少个block,反应到源代码的变量为num_channels的长度,即len(numchannels)len(numchannels)。 classTemporalConvNet(nn.Module): def__init__(self, num_inputs, num_channels, kernel_size=2, dropout=0.2): super(TemporalConvNet, self).__init__() ...
就是num_inputs就是输入数据的通道数,一般就是1; num_channels应该是个列表,其他的np.array也行,比方说是[2,1]。那么整个TCN模型包含两个TemporalBlock,整个模型共有4个卷积层,第一个TemporalBlock的两个卷积层的膨胀系数dilation=20=1dilation=20=1,第二个TemporalBlock的两个卷积层的膨胀系数是dilation=21=...
""" def __init__(self, input_size, num_channels, kernel_size=2, dropout=0.2): super(TCNEncoder, self).__init__() self._input_size = input_size self._output_dim = num_channels[-1] layers = nn.LayerList() num_levels = len(num_channels) for i in range(num_levels): dilation...
num_inputs就是输入数据的通道数,一般就是1; num_channels应该是个列表,其他的np.array也行,比方说是[2,1]。那么整个TCN模型包含两个TemporalBlock,第一个TemporalBlock会把模型的通道从1变成2,然后第二个会把通道数从2变成1. 没了,整个TCN挺简单的,如果之前学过PyTorch和图像处理的一些内容,然后用TCN来上手...
def__init__(self, num_inputs, num_channels, kernel_size=2, dropout=0.2):super(TemporalConvNet, self).__init__() layers = [] num_levels = len(num_channels)foriinrange(num_levels): dilation_size =2** i in_channels = num_inputsifi ==0elsenum_channels[i-1] out_channels = num...