自定义的tcn为def tcnBlock(incoming, filters, kernel_size, dilation_rate): net = incoming identity = incoming net = keras.layers.LeakyReLU(alpha=0.2)(net) net = keras.layers.Dropout(0.3)(net) net = Conv1D(filters, kernel_size, padding='causal', dilation_rate=dilation_rate, kernel_regularizer=regularizers.l2(1e-3))(net) if identity.shape[-1] == filters: shortcut = identity else: shortcut = Conv1D(filters, kernel_size, padding='same')(identity) # shortcut（捷径） net = keras.layers.add([net, shortcut]) return net

keras-tcn：Keras时间卷积网络

keras-tcn-master.zip

【Keras-TCN 深度学习框架详解】 Keras-TCN 是一个基于 Keras 的深度学习库，专门用于构建和训练 Temporal Convolutional Networks（时序卷积网络，简称TCN）。TCN 是一种在序列数据处理中表现出色的深度学习架构，...

时域卷积网络（TCN）案例模型

2. **Lambda层**：Lambda层是Keras框架中定义自定义操作的工具，它可以提供更大的灵活性。在这个案例中，可能使用Lambda层来进行特定的转换，比如计算全局平均池化（Global Average Pooling）或者最大池化，以减少...

能给我讲讲这段代码吗def tcnBlock(incoming, filters, kernel_size, dilation_rate): net = incoming identity = incoming # net = BatchNormalization()(net) # net = Activation('relu')(net) net = keras.layers.LeakyReLU(alpha=0.2)(net) net = keras.layers.Dropout(0.3)(net) net = Conv1D(filters, kernel_size, padding='causal', dilation_rate=dilation_rate, kernel_regularizer=regularizers.l2(1e-3))(net) # net = BatchNormalization()(net) net = Activation('relu')(net) # net = keras.layers.LeakyReLU(alpha=0.2)(net) net = keras.layers.Dropout(0.3)(net) net = Conv1D(filters, kernel_size, padding='causal', dilation_rate=dilation_rate, kernel_regularizer=regularizers.l2(1e-3))(net) # 计算全局均值 net_abs = Lambda(abs_backend)(net) abs_mean = GlobalAveragePooling1D()(net_abs) # 计算系数 # 输出通道数 scales = Dense(filters, activation=None, kernel_initializer='he_normal', kernel_regularizer=regularizers.l2(1e-4))(abs_mean) # scales = BatchNormalization()(scales) scales = Activation('relu')(scales) scales = Dense(filters, activation='sigmoid', kernel_regularizer=regularizers.l2(1e-4))(scales) scales = Lambda(expand_dim_backend)(scales) # 计算阈值 thres = keras.layers.multiply([abs_mean, scales]) # 软阈值函数 sub = keras.layers.subtract([net_abs, thres]) zeros = keras.layers.subtract([sub, sub]) n_sub = keras.layers.maximum([sub, zeros]) net = keras.layers.multiply([Lambda(sign_backend)(net), n_sub]) if identity.shape[-1] == filters: shortcut = identity else: shortcut = Conv1D(filters, kernel_size, padding='same')(identity) # shortcut（捷径） net = keras.layers.add([net, shortcut]) return net

这两个卷积层的输出通道数为 filters，卷积核大小为 kernel_size，扩张率为 dilation_rate。这里使用了因果卷积（causal convolution），保证了模型能够有效处理时间序列数据。 3. 全局均值池化层：计算了卷积层输出...

这段代码哪里有问题# 定义输入数据的shape batch_size = 32 input_shape = (None, 24) inputs = Input(shape=input_shape) # 定义TCN网络的中间层，可以设置多个卷积层和池化层 tcn_layer = TCN(nb_filters=4, kernel_size=3, dilations=[1, 2, 4])(inputs) # 定义模型，将输入层和TCN网络的中间层连接起来 model = Model(inputs=inputs, outputs=tcn_layer) # 使用predict函数获取特征，将数据分成多个batch进行处理 num_samples = x_data.shape[0] features = [] for i in range(0, num_samples, batch_size): batch = x_data[i:i + batch_size] if i + batch_size > num_samples: batch_size = num_samples - i if batch_size == 1: feature_batch = model.predict(batch.reshape(1, *input_shape), batch_size=batch_size) else: feature_batch = model.predict(batch, batch_size=batch_size) features.append(feature_batch) features = np.concatenate(features, axis=0)

这段代码的问题在于，使用了和输入数据的batch_size同名的变量来存储每个batch的大小，导致在处理最后一个batch时，batch_size的值会变成1，从而使得model.predict函数的batch_size参数变为1，而不是预期的32，这...

input_shape = (None, 24) inputs = Input(shape=input_shape) # 定义TCN网络的中间层，可以设置多个卷积层和池化层 tcn_layer = TCN(nb_filters=4, kernel_size=3, dilations=[1, 2, 4])(inputs) # 定义模型，将输入层和TCN网络的中间层连接起来 model = Model(inputs=inputs, outputs=tcn_layer) # 使用predict函数获取特征，将数据分成多个batch进行处理 num_samples = x_data.shape[0] features = [] batch_size = 32 for i in range(0, num_samples, batch_size): batch = x_data[i:i + batch_size] if i + batch_size > num_samples: batch_num = num_samples - i if batch_num == 1: feature_batch = model.predict(batch.reshape(1, *input_shape), batch_size=batch_num) else: feature_batch = model.predict(batch, batch_size=batch_num) features.append(feature_batch) else: feature_batch = model.predict(batch, batch_size=batch_size) features.append(feature_batch) features = np.concatenate(features, axis=0)出现问题

这段代码是使用Keras框架定义了一个TCN网络模型，并对数据进行了处理获取特征。但是在使用predict函数获取特征时，代码可能存在一些问题。具体来说，当数据最后一个batch的大小小于batch_size时，使用batch_num进行...

语句tcn_1 = tcn.TCN(nb_filters=64,kernel_size=3,dilations=(1, 2, 4, 8, 16),padding='causal',use_skip_connections=True ,use_batch_norm=True,dropout_rate=0.1,activation='relu') tcn_2 = tcn.TCN(nb_filters=128, kernel_size=3, dilations=(1, 2, 4), padding='causal',use_skip_connections=True, use_batch_norm=True, dropout_rate=0.1, activation='relu') 有错误吗

每个层都使用了一些参数，如 nb_filters、kernel_size、dilations、padding 等。这些参数在 TCN 层中是常见的配置选项。但是，请确保您在代码中导入了正确的 tcn 模块。如果出现 "ModuleNotFoundError" 或 ...

定义输入数据的shape input_shape = (None, 24) # 定义模型的输入层 inputs = Input(shape=input_shape) # 定义TCN网络的中间层，可以设置多个卷积层和池化层 tcn_layer = TCN(nb_filters=4, kernel_size=3, dilations=[1, 2, 4, 8, 16])(inputs) # 定义模型，将输入层和TCN网络的中间层连接起来 model = Model(inputs=inputs, outputs=tcn_layer) # 使用predict函数获取特征，将数据分成多个batch进行处理 batch_size = 32 num_samples = train11s.shape[0] features = [] for i in range(0, num_samples, batch_size): batch = train11s[i:i + batch_size] if i + batch_size > num_samples: batch_size = num_samples - i if batch_size == 1: feature_batch = model.predict(batch.reshape(1, *input_shape), batch_size=batch_size) else: feature_batch = model.predict(batch, batch_size=batch_size) features.append(feature_batch) features = np.concatenate(features, axis=0) print(features.shape)出现错误

这段代码可能会出现错误，因为在循环中修改了batch_size的值。这可能会导致在处理最后一个batch时，batch_size的值小于32，从而导致维度不匹配的错误。建议将batch_size的值保存在另一个变量中，以避免这种错误。...

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(2i) 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

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...

input_2 = keras.Input(shape=(176,)) x = keras.layers.Reshape((-1, 176, 1))(input_2) x = keras.layers.LayerNormalization()(x) x = Conv1D(filters=16, kernel_size=12, strides=4, padding='causal')(x) x = tf.keras.layers.Dropout(0.4)(x) x = tcnBlock(x, 16, 3, 1) x = tcnBlock(x, 8, 3, 2) x = tcnBlock(x, 4, 3, 4) x = GlobalAveragePooling1D()(x) x = keras.layers.LayerNormalization()(x) output_2 = keras.layers.Dense(1, activation='sigmoid')(x) model2 = keras.Model(inputs=input_2, outputs=output_2) model2.summary()结合上边的自定义，这段代码有什么问题

3. 在第 6 行中，tf.keras.layers.Dropout 应该改为 keras.layers.Dropout，因为前面已经使用了 keras 的命名空间。 4. 在第 7 行中，tcnBlock 是自定义的函数，需要检查该函数的实现是否正确并且已经导入...

from tensorflow.keras.layers import Input from tensorflow.keras.models import Model from tcn import TCN # 定义输入数据的shape input_shape = (None, 160) # 定义模型的输入层 inputs = Input(shape=input_shape) # 定义TCN网络的中间层，可以设置多个卷积层和池化层 tcn_layer = TCN(nb_filters=64, kernel_size=3, dilations=[1, 2, 4, 8, 16])(inputs) # 定义模型，将输入层和TCN网络的中间层连接起来 model = Model(inputs=inputs, outputs=tcn_layer) # 使用predict函数获取特征，将数据分成多个batch进行处理 batch_size = 32 num_samples = x_data.shape[0] features = [] for i in range(0, num_samples, batch_size): batch = x_data[i:i + batch_size] feature_batch = model.predict(batch) features.append(feature_batch) # 将多个batch的特征拼接成一个大的特征矩阵 features = np.concatenate(features, axis=0)这段代码循环的时候会不会超出范围

在这段代码中，循环的终止条件是 i < num_samples，并且每次循环处理的数据量都是 batch_size，因此在最后一次循环时，i 的值会等于 num_samples - batch_size，而此时 batch 的大小为 batch_size，不会...

input_2 = keras.Input(shape=(160,)) x = keras.layers.LayerNormalization()(input_2) x = keras.layers.Reshape((160, 1))(x) x = Conv1D(filters=16, kernel_size=12, strides=4, padding='causal')(x) x = tf.keras.layers.Dropout(0.4)(x) x = tcnBlock(x, 12, 3, 1) x = tcnBlock(x, 6, 3, 2) x = tcnBlock(x, 4, 3, 4) x = GlobalAveragePooling1D()(x) x = keras.layers.LayerNormalization()(x) output_2 = keras.layers.Dense(1, activation='sigmoid')(x) model2 = keras.Model(inputs=input_2, outputs=output_2) model2.summary()这段代码正确吗

def tcnBlock(incoming, filters, kernel_size, dilation_rate): net = incoming identity = incoming net = LayerNormalization()(net) net = keras.layers.LeakyReLU(alpha=0.2)(net) net = Dropout(0.3)(net...

请帮我解释以下代码中时域卷积神经网络的层数是怎么确定的tcn_1 = tcn.TCN(nb_filters=64, kernel_size=3, padding='causal', dilations=(1, 2, 4, 8),use_skip_connections=True,use_batch_norm=True,dropout_rate=0.1 ,activation='relu')

在这段代码中，dilations=(1, 2, 4, 8)表示有4个卷积层，并且每个卷积层的扩张率（dilation rate）分别为1、2、4和8。扩张率决定了卷积操作在输入序列上的感受野大小，即每个卷积核能够看到的输入序列长度。较大的...

请帮我解释以下代码中时域卷积神经网络的残差块数量是怎么确定的以及如何设置残差块数目，tcn_1 = tcn.TCN(nb_filters=64, kernel_size=3, padding='causal', dilations=(1, 2, 4, 8),use_skip_connections=True,use_batch_norm=True,dropout_rate=0.1 ,activation='relu')

在这段代码中，并未直接指定时域卷积神经网络（TCN）的残差块数量。相反，残差块的数量是通过设置参数dilations的长度来确定的。在TCN中，每个卷积层都会产生一个残差块。因此，dilations的长度决定了TCN中残差块...

class GRU(nn.Module): def init(self, input_size, num_channels, num_classes, tcn_options, dropout, relu_type, dwpw=False): super(GRU, self).init() self.trunk = nn.GRU(512,1024, 3, batch_first=True, bidirectional=True, dropout=0.2) self.output = nn.Linear(21024, num_classes) def forward(self, x): # x needs to have dimension (N, C, L) in order to be passed into CNN gru,_ = self.trunk(x) if gru.shape[1]>2: gru=gru[:,0:2,:] elif gru.shape[1]<2: k=2-gru.shape[1] temp=gru[:,0:k,:] gru=torch.cat([gru,temp],dim=1) gru=gru.reshape(gru.shape[0],gru.shape[1]gru.shape[2]) out=self.output(gru) return out 修改这段代码

这是一个名为GRU的类，它继承了nn.Module。它的构造函数有6个参数：输入大小、通道数、类别数、tcn选项、dropout、ReLU类型和是否使用dwpw。在构造函数中，定义了一个名为trunk的GRU层，它有512个输入、1024个输出，...

TCN(input_size, output_size, num_channels, kernel_size, dropout)中每个参数应该怎么填

- input_size: 输入的特征维度大小，可以根据你的实际输入数据的维度来填写。 - output_size: 输出的特征维度大小，可以根据你的任务需要来填写。 - num_channels: 每个卷积层的输出通道数，可以根据你的实际需要来...

batch_size = 32 epoch = 100 process_num = 3 initial_learning_rate = 8e-5 decay_steps = 10000 decay_rate = 0.5 save_step = 1000 feature_dim = 257 win_length = 400 hop_length = 160 n_fft = 512 # 模型结构 pb_path = f'resources/crn_tcn' save_pb = False

其中，batch_size表示每个训练批次的数据量，epoch表示训练轮数，process_num表示使用的进程数，initial_learning_rate表示初始学习率，decay_steps表示学习率下降的步数，decay_rate表示学习率下降的比例，save_...

tcn.add(tf.keras.layers.Dense(64)) tcn.add(tf.keras.layers.LeakyReLU(alpha=0.3))

这段代码是在构建一个Temporal Convolutional Network（TCN）模型中添加了一个64个神经元的全连接层，并使用LeakyReLU激活函数进行激活。TCN是一种用于时间序列数据建模的深度学习架构，其主要特点是通过不同大小的...

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keras-tcn：Keras时间卷积网络

keras-tcn-master.zip

时域卷积网络（TCN）案例模型

请帮我解释以下代码中 时域卷积神经网络的层数是怎么确定的tcn_1 = tcn.TCN(nb_filters=64, kernel_size=3, padding='causal', dilations=(1, 2, 4, 8),use_skip_connections=True,use_batch_norm=True,dropout_rate=0.1 ,activation='relu')

请帮我解释以下代码中 时域卷积神经网络的残差块数量是怎么确定的以及如何设置残差块数目，tcn_1 = tcn.TCN(nb_filters=64, kernel_size=3, padding='causal', dilations=(1, 2, 4, 8),use_skip_connections=True,use_batch_norm=True,dropout_rate=0.1 ,activation='relu')

TCN(input_size, output_size, num_channels, kernel_size, dropout)中每个参数应该怎么填

batch_size = 32 epoch = 100 process_num = 3 initial_learning_rate = 8e-5 decay_steps = 10000 decay_rate = 0.5 save_step = 1000 feature_dim = 257 win_length = 400 hop_length = 160 n_fft = 512 # 模型结构 pb_path = f'resources/crn_tcn' save_pb = False

tcn.add(tf.keras.layers.Dense(64)) tcn.add(tf.keras.layers.LeakyReLU(alpha=0.3))

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请帮我解释以下代码中时域卷积神经网络的层数是怎么确定的tcn_1 = tcn.TCN(nb_filters=64, kernel_size=3, padding='causal', dilations=(1, 2, 4, 8),use_skip_connections=True,use_batch_norm=True,dropout_rate=0.1 ,activation='relu')

请帮我解释以下代码中时域卷积神经网络的残差块数量是怎么确定的以及如何设置残差块数目，tcn_1 = tcn.TCN(nb_filters=64, kernel_size=3, padding='causal', dilations=(1, 2, 4, 8),use_skip_connections=True,use_batch_norm=True,dropout_rate=0.1 ,activation='relu')