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Activation Functions - PyTorch Beginner 12

22 Jan 2020

Learn all the basics you need to get started with this deep learning framework! In this part we learn about activation functions in neural nets. What are activation functions, why are they needed, and how do we apply them in PyTorch.

I go over following activation functions: - Binary Step - Sigmoid - TanH (Hyperbolic Tangent) - ReLU - Leaky ReLU - Softmax

All code from this course can be found on GitHub.

Activation Functions in PyTorch

import torch import torch.nn as nn import torch.nn.functional as F x = torch.tensor([-1.0, 1.0, 2.0, 3.0]) # sofmax output = torch.softmax(x, dim=0) print(output) sm = nn.Softmax(dim=0) output = sm(x) print(output) # sigmoid output = torch.sigmoid(x) print(output) s = nn.Sigmoid() output = s(x) print(output) #tanh output = torch.tanh(x) print(output) t = nn.Tanh() output = t(x) print(output) # relu output = torch.relu(x) print(output) relu = nn.ReLU() output = relu(x) print(output) # leaky relu output = F.leaky_relu(x) print(output) lrelu = nn.LeakyReLU() output = lrelu(x) print(output) #nn.ReLU() creates an nn.Module which you can add e.g. to an nn.Sequential model. #torch.relu on the other side is just the functional API call to the relu function, #so that you can add it e.g. in your forward method yourself. # option 1 (create nn modules) class NeuralNet(nn.Module): def __init__(self, input_size, hidden_size): super(NeuralNet, self).__init__() self.linear1 = nn.Linear(input_size, hidden_size) self.relu = nn.ReLU() self.linear2 = nn.Linear(hidden_size, 1) self.sigmoid = nn.Sigmoid() def forward(self, x): out = self.linear1(x) out = self.relu(out) out = self.linear2(out) out = self.sigmoid(out) return out # option 2 (use activation functions directly in forward pass) class NeuralNet(nn.Module): def __init__(self, input_size, hidden_size): super(NeuralNet, self).__init__() self.linear1 = nn.Linear(input_size, hidden_size) self.linear2 = nn.Linear(hidden_size, 1) def forward(self, x): out = torch.relu(self.linear1(x)) out = torch.sigmoid(self.linear2(out)) return out