Source code for draugr.torch_utilities.exporting.summary

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

__author__ = "Christian Heider Nielsen"
__doc__ = r"""

           Created on 26-06-2021
           """

__all__ = ["to_latex_table"]

from torch import nn


[docs]def to_latex_table(m: nn.Module) -> str: """ :param m: :return: """ return m.__repr__()
if __name__ == "__main__": from torch.nn import functional def aiasujd(): """description :return: """ class Model(nn.Module): """description""" def __init__(self): super().__init__() self.conv0 = nn.Conv2d(1, 16, kernel_size=3, padding=5) self.conv1 = nn.Conv2d(16, 32, kernel_size=3) def forward(self, x): """ :param x: :return: """ h = self.conv0(x) h = self.conv1(h) return h model = Model() print(to_latex_table(model)) def uiahsduhaw(): """description :return: """ class Model(nn.Module): """description""" def __init__(self): super().__init__() self.conv1 = nn.Conv2d(1, 10, kernel_size=5) self.conv2 = nn.Conv2d(10, 20, kernel_size=5) self.conv2_drop = nn.Dropout2d() self.fc1 = nn.Linear(320, 50) self.fc2 = nn.Linear(50, 10) def forward(self, x): """ :param x: :return: """ x = functional.relu(functional.max_pool2d(self.conv1(x), 2)) x = functional.relu( functional.max_pool2d(self.conv2_drop(self.conv2(x)), 2) ) x = x.view(-1, 320) x = functional.relu(self.fc1(x)) x = functional.dropout(x, training=self.training) x = self.fc2(x) return functional.log_softmax(x, dim=1) model = Model() print(to_latex_table(model)) uiahsduhaw()