Question 1Consider the constructor for the following neural network class :1234567class 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([2,3,4,4])How many hidden layers are there in this model?
Question
Question 1Consider the constructor for the following neural network class :1234567class 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([2,3,4,4])How many hidden layers are there in this model?
Solution
The model has 3 hidden layers.
Here's the step-by-step explanation:
The Net class takes a list of integers as input for its constructor. This list represents the number of neurons in each layer of the neural network.
When you create an object with model = Net([2,3,4,4]), you're specifying a neural network with four layers. The first layer has 2 neurons, the second layer has 3 neurons, the third layer has 4 neurons, and the fourth layer also has 4 neurons.
However, in the context of neural networks, the input layer is not typically counted as a 'hidden' layer. So, the number of hidden layers in this model is 3 (the second, third, and fourth layers).
Similar Questions
Question 1Consider the constructor for the following neural network class :1234567class 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([2,3,4,4])How many hidden layers are there in this model?
Consider the following neural network model or class:1234567891011class Net(nn.Module): def __init__(self,D_in,H,D_out): super(Net,self).__init__() self.linear1=nn.Linear(D_in,H) self.linear2=nn.Linear(H,D_out) def forward(self,x): x=torch.sigmoid(self.linear1(x)) x=torch.sigmoid(self.linear2(x)) return xHow many hidden neurons does the following neural network object have?1model=Net(1,6,1)
In PyTorch, the ______________ class is used to define a custom neural network architecture.
Question 2What's wrong with the following function :123456789101112 ]:class Net(nn.Module): def __init__(self,D_in,H,D_out): super(Net,self).__init__() self.linear1=nn.Linear(D_in,H) self.linear2=nn.Linear(H,D_out) def forward(self,x): x=torch.sigmoid(linear1(x)) x=torch.sigmoid(linear2(x)) return x1 pointyou did not call self.linear1(x) and self .linear2(x)nothing
What activation function is used in the following class 123456789class NetRelu(nn.Module): def __init__(self,D_in,H,D_out): super(NetRelu,self).__init__() self.linear1=nn.Linear(D_in,H) self.linear2=nn.Linear(H,D_out) def forward(self,x): x=torch.relu(self.linear1(x))) x=self.linear2(x) return x 1 pointrelutanh Sigmoid
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