def forward(self, z): z = torch.relu(self.fc1(z)) z = z.view(-1, 128, 28, 28) x = torch.relu(self.conv1(z)) x = torch.sigmoid(self.conv2(x)) return x
import torch import torch.nn as nn import torchvision.transforms as transforms sketchypath
def forward(self, x): x = torch.relu(self.conv1(x)) x = torch.relu(self.conv2(x)) x = x.view(-1, 128*28*28) x = torch.relu(self.fc1(x)) return x def forward(self, z): z = torch
The SketchyPath architecture consists of the following components: sketchypath
SketchyPath is a learning tool that utilizes visual associations to help students remember complex information, particularly in the fields of medicine and other sciences. It is an extension of the popular SketchyMedical platform, focusing on pathology.
After watching a video, students can click on specific parts of the "sketch" to see a summary of the associated medical fact.