stgcn里的1d卷积和glu门怎么通过代码实现
时间: 2023-03-15 07:10:46 浏览: 80
首先,要实现1D卷积,可以使用Keras中的Conv1D函数,示例代码为:model = Sequential()model.add(Conv1D(filters=32, kernel_size=3, activation='relu', input_shape=(1000,1))) 。要实现GLU门,可以使用Keras中的GLU函数,示例代码为:model.add(keras.layers.GLU(units=16, activation='relu')) 。
相关问题
深度卷积和深度可分离卷积代码实现
深度卷积和深度可分离卷积的代码实现会有所不同。以下是它们的代码实现示例:
深度卷积的代码实现:
```python
import tensorflow as tf
# 输入张量
input_tensor = tf.placeholder(tf.float32, shape=[None, height, width, channels])
# 卷积核
kernel = tf.Variable(tf.truncated_normal([kernel_size, kernel_size, input_channels, output_channels], stddev=0.1))
# 深度卷积
conv_output = tf.nn.conv2d(input_tensor, kernel, strides=[1, stride, stride, 1], padding='SAME')
```
深度可分离卷积的代码实现:
```python
import tensorflow as tf
# 输入张量
input_tensor = tf.placeholder(tf.float32, shape=[None, height, width, channels])
# 深度可分离卷积
depthwise_output = tf.nn.depthwise_conv2d(input_tensor, depthwise_filter, strides=[1, stride, stride, 1], padding='SAME')
pointwise_output = tf.nn.conv2d(depthwise_output, pointwise_filter, strides=[1, 1, 1, 1], padding='SAME')
```
在以上代码示例中,`input_tensor`表示输入张量,`kernel`表示卷积核,`conv_output`表示深度卷积的输出。对于深度可分离卷积,`depthwise_output`表示Depthwise Convolution的输出,`pointwise_output`表示Pointwise Convolution的输出。这里的代码只是示例,实际实现可能会有所不同,具体的实现方式会根据使用的深度学习框架而有所差异。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* *3* [深度可分离卷积](https://blog.csdn.net/zfjBIT/article/details/127521956)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"]
- *2* [深度可分离卷积(计算过程及代码实现)](https://blog.csdn.net/m0_45267220/article/details/130291855)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"]
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1d卷积神经网络matlab代码
以下是一个简单的1D卷积神经网络的MATLAB代码示例:
```matlab
% 读取数据
load('data.mat');
% 定义卷积神经网络结构
net = network;
net.numInputs = 1;
net.numLayers = 3;
% 定义输入层
net.inputs{1}.size = size(data, 2);
% 定义卷积层
net.layers{1}.type = 'conv';
net.layers{1}.size = 5;
net.layers{1}.stride = 1;
net.layers{1}.padding = 0;
net.layers{1}.transferFcn = 'relu';
% 定义池化层
net.layers{2}.type = 'pool';
net.layers{2}.size = 2;
net.layers{2}.stride = 2;
% 定义输出层
net.layers{3}.type = 'output';
net.layers{3}.size = 1;
net.layers{3}.transferFcn = 'softmax';
% 连接各层
net = connect(net, 1, 2);
net = connect(net, 2, 3);
% 训练网络
net.trainFcn = 'trainscg';
net.trainParam.epochs = 100;
net.trainParam.show = 10;
net.trainParam.lr = 0.01;
net = train(net, data, labels);
% 测试网络
predictions = net(data);
accuracy = sum(predictions == labels) / length(labels);
disp(['Accuracy: ', num2str(accuracy)]);
```
请注意,此代码仅供参考,您需要根据您的数据和任务进行适当的修改和调整。此外,您需要确保您的MATLAB版本支持深度学习工具箱。