cifar/models/tanh.py

import torch.nn as nn


def tanh_fc(in_ch=3, in_dim=32, width=100, depth=4, num_classes=10):
    layers = [nn.Flatten(), nn.Linear(in_ch*in_dim**2, width), nn.Tanh()]
    for _ in range(depth - 1):
        layers.extend([nn.Linear(width, width), nn.Tanh()])
    layers.append(nn.Linear(width, num_classes))
    return nn.Sequential(*layers)


def tanh_4fc_100(in_ch=3, in_dim=32):
    return tanh_fc(in_ch, in_dim, width=100, depth=4)


def tanh_6fc_100(in_ch=3, in_dim=32):
    return tanh_fc(in_ch, in_dim, width=100, depth=6)