[英]Tensorflow to Pytorch CNN(Use nn.Conv1d)
input_size = [765, 500, 72]
model = Sequential()
add = model.add
add(l.Conv1D(256, kernel_size=3, strides=2, activation='relu')
add(l.Dropout(0.5))
add(l.Conv1D(256, kernel_size=3, strides=2, activation='relu')
add(l.Dropout(0.5))
add(l.GlobalAveragePooling1D())
add(l.Dense(100, activation="relu"))
add(l.Dense(3, activation="softmax"))
(None, 249, 256)
(None, 249, 256)
(None, 124, 256)
(None, 124, 256)
(None, 256)
(None, 100)
(None, 3)
This is tensorflow model struc and summary.这是 tensorflow model 结构和摘要。 Tensorflow to Pytorch CNN model. Use Conv1D Tensorflow 到 Pytorch CNN model。使用 Conv1D
[Tensorflow Model summary] [Tensorflow Model总结]
To jump-start your research, here is an example usage of nn.Conv1d
:为了快速启动您的研究,这里有一个nn.Conv1d
的用法示例:
>>> f = nn.Conv1d(72, 256, kernel_size=3, stride=2)
>>> f(torch.rand(765, 72, 500)).shape
torch.Size([765, 256, 249])
Regarding this case keep in mind a few PyTorch-related things:关于这种情况,请记住一些与 PyTorch 相关的事情:
Unlike Tensorflow, it handles data in the BHC
format.与 Tensorflow 不同,它处理BHC
格式的数据。
You have to provide the input feature sizes for each linear layer.您必须为每个线性层提供输入特征尺寸。
The activation function is not included in nn.Conv1d
, you have to use a dedicated module for that ( eg. nn.ReLU
).激活 function 不包含在nn.Conv1d
中,您必须为此使用专用模块(例如nn.ReLU
)。
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.