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Home Forums Assignment courserra IBM AI Engineering Professional Certificate Deep Neural Networks with PyTorch Week 5 – Deeper Neural Networks : nn.ModuleList()

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Anonymous

Consider the constructor for the following neural network class :
class Net(nn.Module):
# Section 1:
def __init__(self, Layers):
super(Net,self).__init__()
self.hidden = nn.ModuleList()
for input_size,output_size in zip(Layers,Layers[1:]):
self.hidden.append(nn.Linear(input_size,output_size))
Let us create an object model = Net([2,3,4,5])

How many neurons are in the first hidden layers ?
answer: 3

True or False we use the following Class or . Module for classification :
class Net(nn.Module):

# Constructor
def __init__(self, Layers):
super(Net, self).__init__()
self.hidden = nn.ModuleList()
for input_size, output_size in zip(Layers, Layers[1:]):
self.hidden.append(nn.Linear(input_size, output_size))

# Prediction
def forward(self, activation):
L = len(self.hidden)
for (l, linear_transform) in zip(range(L), self.hidden):
if l < L – 1:
activation = torch.relu(linear_transform(activation))
else:
activation = torch.relu(linear_transform(activation))
return activation

answer: False

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