cnn_out = TimeDistributed(Conv1D(filters=hparams[CONV_FILTERS], kernel_size=hparams[CONV_KERNEL], strides=hparams[CONV_STRIDE], activation=hparams[CONV_ACTIVATION], padding='same'), name='convolution')(detail_concat)其中 padding='same'是什么意思
时间: 2023-04-07 08:01:56 浏览: 56
padding='same'是指在卷积操作中,对输入数据进行填充,使得输出的大小与输入的大小相同。具体来说,如果卷积核的大小为k,那么在输入数据的边缘填充k/2个0,这样卷积后输出的大小就与输入的大小相同了。
相关问题
def model(self): num_classes = self.config.get("CNN_training_rule", "num_classes") seq_length = self.config.get("CNN_training_rule", "seq_length") conv1_num_filters = self.config.get("CNN_training_rule", "conv1_num_filters") conv1_kernel_size = self.config.get("CNN_training_rule", "conv1_kernel_size") conv2_num_filters = self.config.get("CNN_training_rule", "conv2_num_filters") conv2_kernel_size = self.config.get("CNN_training_rule", "conv2_kernel_size") hidden_dim = self.config.get("CNN_training_rule", "hidden_dim") dropout_keep_prob = self.config.get("CNN_training_rule", "dropout_keep_prob") model_input = keras.layers.Input((seq_length,1), dtype='float64') # conv1形状[batch_size, seq_length, conv1_num_filters] conv_1 = keras.layers.Conv1D(conv1_num_filters, conv1_kernel_size, padding="SAME")(model_input) conv_2 = keras.layers.Conv1D(conv2_num_filters, conv2_kernel_size, padding="SAME")(conv_1) max_poolinged = keras.layers.GlobalMaxPool1D()(conv_2) full_connect = keras.layers.Dense(hidden_dim)(max_poolinged) droped = keras.layers.Dropout(dropout_keep_prob)(full_connect) relued = keras.layers.ReLU()(droped) model_output = keras.layers.Dense(num_classes, activation="softmax")(relued) model = keras.models.Model(inputs=model_input, outputs=model_output) # model.compile(loss="categorical_crossentropy", # optimizer="adam", # metrics=["accuracy"]) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) print(model.summary()) return model给这段代码每行加上注释
```
def model(self):
# 获取配置文件中的参数
num_classes = self.config.get("CNN_training_rule", "num_classes") #分类数
seq_length = self.config.get("CNN_training_rule", "seq_length") #序列长度
conv1_num_filters = self.config.get("CNN_training_rule", "conv1_num_filters") #第一层卷积核数量
conv1_kernel_size = self.config.get("CNN_training_rule", "conv1_kernel_size") #第一层卷积核大小
conv2_num_filters = self.config.get("CNN_training_rule", "conv2_num_filters") #第二层卷积核数量
conv2_kernel_size = self.config.get("CNN_training_rule", "conv2_kernel_size") #第二层卷积核大小
hidden_dim = self.config.get("CNN_training_rule", "hidden_dim") #全连接层隐藏层大小
dropout_keep_prob = self.config.get("CNN_training_rule", "dropout_keep_prob") #dropout保留率
# 定义模型输入
model_input = keras.layers.Input((seq_length,1), dtype='float64')
# 第一层卷积
conv_1 = keras.layers.Conv1D(conv1_num_filters, conv1_kernel_size, padding="SAME")(model_input)
# 第二层卷积
conv_2 = keras.layers.Conv1D(conv2_num_filters, conv2_kernel_size, padding="SAME")(conv_1)
# 全局最大池化
max_poolinged = keras.layers.GlobalMaxPool1D()(conv_2)
# 全连接层
full_connect = keras.layers.Dense(hidden_dim)(max_poolinged)
# dropout层
droped = keras.layers.Dropout(dropout_keep_prob)(full_connect)
# relu激活层
relued = keras.layers.ReLU()(droped)
# 输出层
model_output = keras.layers.Dense(num_classes, activation="softmax")(relued)
# 定义模型
model = keras.models.Model(inputs=model_input, outputs=model_output)
# 编译模型
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# 打印模型结构
print(model.summary())
return model
```
def CNN_LSTM_model(input_size): model = Sequential() model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(input_size, 1))) model.add(Conv1D(filters=64, kernel_size=3, activation='relu')) model.add(MaxPooling1D(pool_size=2)) model.add(Dropout(0.5)) model.add(Conv1D(filters=128, kernel_size=3, activation='relu')) model.add(Conv1D(filters=128, kernel_size=3, activation='relu')) model.add(MaxPooling1D(pool_size=2)) model.add(Dropout(0.5)) model.add(Conv1D(filters=256, kernel_size=3, activation='relu')) model.add(Conv1D(filters=256, kernel_size=3, activation='relu')) model.add(MaxPooling1D(pool_size=2)) model.add(Dropout(0.5)) model.add(LSTM(64, return_sequences=True)) model.add(LSTM(32)) model.add(Dense(10, activation='relu')) model.add(Dense(1, activation='sigmoid')) return model改写成更适合5g异常流量数据分析的代码
可以考虑以下改进:
1. 修改模型输入形状,将原来的 `(input_size, 1)` 修改为 `(input_size, 3)`,表示每个时间步的输入特征包括当前时间步、前一时间步和后一时间步的流量数据。
2. 将最后一层的 `sigmoid` 激活函数改为 `softmax`,以便输出对不同类型异常流量的分类结果。
3. 修改模型输出层的神经元数量为 5,对应于可能的异常流量类型数目。
4. 修改损失函数为 `categorical_crossentropy`,适合多分类问题。
修改后的代码如下所示:
```
def CNN_LSTM_model(input_size):
model = Sequential()
model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(input_size, 3)))
model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))
model.add(MaxPooling1D(pool_size=2))
model.add(Dropout(0.5))
model.add(Conv1D(filters=128, kernel_size=3, activation='relu'))
model.add(Conv1D(filters=128, kernel_size=3, activation='relu'))
model.add(MaxPooling1D(pool_size=2))
model.add(Dropout(0.5))
model.add(Conv1D(filters=256, kernel_size=3, activation='relu'))
model.add(Conv1D(filters=256, kernel_size=3, activation='relu'))
model.add(MaxPooling1D(pool_size=2))
model.add(Dropout(0.5))
model.add(LSTM(64, return_sequences=True))
model.add(LSTM(32))
model.add(Dense(10, activation='relu'))
model.add(Dense(5, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
```
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