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neuralTest.py
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neuralTest.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
class MultilayerPerceptron(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim):
"""
Args:
input_dim (int): the size of the input vectors
hidden_dim (int): the output size of the first Linear layer
output_dim (int): the output size of the second Linear layer
"""
super(MultilayerPerceptron, self).__init__()
self.fc1 = nn.Linear(input_dim, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, output_dim)
def forward(self, x_in, apply_softmax=False):
"""The forward pass of the MLP
Args:
x_in (torch.Tensor): an input data tensor.
x_in.shape should be (batch, input_dim)
apply_softmax (bool): a flag for the softmax activation
should be false if used with the Cross Entropy losses
Returns:
the resulting tensor. tensor.shape should be (batch, output_dim)
"""
intermediate = F.relu(self.fc1(x_in)) # F = functional
output = self.fc2(intermediate)
if apply_softmax:
output = F.softmax(output, dim=1)
return output