1.6 KiB
Ptn Conv Model
A convolutional model consisting of multiple convolutional layers.
Fields:
-
input_dim: A string representing the input dimensions in the format "(C,H,W)".
Example:"(3,28,28)"for a 3-channel 28x28 image. -
penultimate_dim: An integer specifying the number of features before the output layer. If you specify 0, the number is computed internally. Default:0 -
output_dim: An integer specifying the number of output features, which should match the number of target classes in classification. Default:10, Min:1, Max:1e6. -
channel_list: A string representing a Python list of integers specifying the number of channels per layer excluding the channel for the input (e.g. 3 for the color image). Example:"[32,64,128,256,512]". -
kernel_size_list: A string representing a Python list of kernel sizes per layer.
Example:"[3,3,3,3,1]". -
padding_list: A string representing a Python list of padding values per layer.
Example:"[1,1,1,1,0]". -
downsample_list: A string representing a Python list of boolean values indicating whether to downsample at each layer.
Example:"[True,True,True,True,False]".
Input
| Name | Data type |
|---|---|
| input_dim | String |
| penultimate_dim | Int |
| output_dim | Int |
| channel_list | String |
| kernel_size_list | String |
| padding_list | String |
| downsample_list | String |
Output
| Data type |
|---|
| Ptmodel |
Category: PyTorch wrapper - Training
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