self.linear = nn.Linear(num_channels[-1], output_size)转为tensorflow格式

在TensorFlow中，可以使用以下代码将self.linear = nn.Linear(num_channels[-1], output_size)转换为类似的操作：

import tensorflow as tf

self.linear = tf.keras.layers.Dense(units=output_size, input_shape=(num_channels[-1],))

这里我们使用了`tf.keras.layers.Dense`来创建一个全连接层，`units`参数指定输出的维度，`input_shape`参数指定输入的维度，即`num_channels[-1]`。

相关问题

self.linear = nn.Linear(num_channels[-1], output_size)将这句改为tensorflow格式

以下是将该句代码转换为 TensorFlow 格式的代码：

self.linear = tf.keras.layers.Dense(output_size, input_shape=(num_channels[-1],))

class ResidualBlock(nn.Module): def init(self, in_channels, out_channels, dilation): super(ResidualBlock, self).init() self.conv = nn.Sequential( nn.Conv1d(in_channels, out_channels, kernel_size=3, padding=dilation, dilation=dilation), nn.BatchNorm1d(out_channels), nn.ReLU(), nn.Conv1d(out_channels, out_channels, kernel_size=3, padding=dilation, dilation=dilation), nn.BatchNorm1d(out_channels), nn.ReLU() ) self.attention = nn.Sequential( nn.Conv1d(out_channels, out_channels, kernel_size=1), nn.Sigmoid() ) self.downsample = nn.Conv1d(in_channels, out_channels, kernel_size=1) if in_channels != out_channels else None def forward(self, x): residual = x out = self.conv(x) attention = self.attention(out) out = out * attention if self.downsample: residual = self.downsample(residual) out += residual return out class VMD_TCN(nn.Module): def init(self, input_size, output_size, n_k=1, num_channels=16, dropout=0.2): super(VMD_TCN, self).init() self.input_size = input_size self.nk = n_k if isinstance(num_channels, int): num_channels = [num_channels*(2**i) for i in range(4)] self.layers = nn.ModuleList() self.layers.append(nn.utils.weight_norm(nn.Conv1d(input_size, num_channels[0], kernel_size=1))) for i in range(len(num_channels)): dilation_size = 2 ** i in_channels = num_channels[i-1] if i > 0 else num_channels[0] out_channels = num_channels[i] self.layers.append(ResidualBlock(in_channels, out_channels, dilation_size)) self.pool = nn.AdaptiveMaxPool1d(1) self.fc = nn.Linear(num_channels[-1], output_size) self.w = nn.Sequential(nn.Conv1d(num_channels[-1], num_channels[-1], kernel_size=1), nn.Sigmoid()) # 特征融合 门控系统 # self.fc1 = nn.Linear(output_size * (n_k + 1), output_size) # 全部融合 self.fc1 = nn.Linear(output_size * 2, output_size) # 只选择其中两个融合 self.dropout = nn.Dropout(dropout) # self.weight_fc = nn.Linear(num_channels[-1] * (n_k + 1), n_k + 1) # 置信度系数，对各个结果加权平均 软投票思路 def vmd(self, x): x_imfs = [] signal = np.array(x).flatten() # flatten()必须加上 否则最后一个batch报错size不匹配！ u, u_hat, omega = VMD(signal, alpha=512, tau=0, K=self.nk, DC=0, init=1, tol=1e-7) for i in range(u.shape[0]): imf = torch.tensor(u[i], dtype=torch.float32) imf = imf.reshape(-1, 1, self.input_size) x_imfs.append(imf) x_imfs.append(x) return x_imfs def forward(self, x): x_imfs = self.vmd(x) total_out = [] # for data in x_imfs: for data in [x_imfs[0], x_imfs[-1]]: out = data.transpose(1, 2) for layer in self.layers: out = layer(out) out = self.pool(out) # torch.Size([96, 56, 1]) w = self.w(out) out = w * out # torch.Size([96, 56, 1]) out = out.view(out.size(0), -1) out = self.dropout(out) out = self.fc(out) total_out.append(out) total_out = torch.cat(total_out, dim=1) # 考虑w1total_out[0]+ w2total_out[1],在第一维，权重相加得到最终结果，不用cat total_out = self.dropout(total_out) output = self.fc1(total_out) return output优化代码

1. 代码中的注释最好用英文，这样可以方便其他国家的程序员阅读和理解。

2. 在ResidualBlock类中，应该将init()改为__init__()，这是Python中的一个特殊方法，用于初始化类的实例变量。

3. 对于VMD_TCN类中的layers部分，可以使用一个for循环来代替多次重复的代码。例如：

for i in range(len(num_channels)):
    dilation_size = 2 ** i
    in_channels = num_channels[i-1] if i > 0 else num_channels[0]
    out_channels = num_channels[i]
    self.layers.append(ResidualBlock(in_channels, out_channels, dilation_size))

4. 不建议在forward()函数中使用numpy数组，应该使用PyTorch张量来保证代码的可重复性和GPU加速。例如，将self.vmd(x)中的signal变量改为torch.tensor(signal, dtype=torch.float32)。

5. 对于全连接层的输入尺寸，可以使用num_channels[-1] * self.nk代替output_size * (self.nk + 1)，这样可以避免使用self.nk + 1这个魔数。

6. 在vmd()函数中，x_imfs可以使用PyTorch张量来存储，而不是使用Python列表。例如，可以使用torch.zeros((self.nk+1, self.input_size))来创建一个张量，并将每个u[i]复制到对应的张量中。这样可以避免在循环中多次创建张量，提高代码的效率。

7. 在forward()函数中，可以使用torch.cat()函数来将所有输出张量连接起来，而不是使用Python列表。例如，可以将total_out定义为一个空的张量，然后在每次迭代中使用torch.cat()函数将输出张量连接到total_out中。这样可以避免在循环中多次分配内存，提高代码的效率。