Encoder-checkpoint

Module Contents

Classes

Encoder

Base class for all neural network modules.

class Encoder-checkpoint.Encoder(input_size, z_dim, hidden_dims_enc_common, hidden_dims_enc_ind, hidden_dims_enc_pre_Z, fix_Z, linearize=False, layers_independent_types=None, image_size=[256, 256], kernel_size=3, stride=1, padding=1, pool_size=2, pool_stride=2)

Bases: torch.nn.Module

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 to(), etc.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

forward(x)