Module auton_survival.models.cph.dcph_torch

Classes

class DeepCoxPHTorch (inputdim, layers=None, optimizer='Adam')

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

Initializes internal Module state, shared by both nn.Module and ScriptModule.

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Class variables

var dump_patches : bool

var training : bool

Methods

def forward(self, x) ‑> Callable[..., Any]

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class DeepRecurrentCoxPHTorch (inputdim, typ='LSTM', layers=1, hidden=None, optimizer='Adam')

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Browse git

Class variables

var dump_patches : bool

var training : bool

Methods

def forward(self, x) ‑> Callable[..., Any]

Browse git