Question 2How many dimensions is the input for the following neural network object:45678910111213321        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 x        model=Net(4,10,1)        super(Net,self).__init__()    def __init__(self,D_in,H,D_out):class Net(nn.Module):

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)

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

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 Module or class :123456789101112class Net(nn.Module):    def __init__(self, in_size, n_hidden, out_size, p)        super(Net, self).__init__()        self.drop=nn.Dropout(p=p)        self.linear1=nn.Linear(in_size, n_hidden)        self.linear2=nn.Linear(n_hidden, out_size)    def forward(self, x):        x=torch.relu(self.linear1(x))        x=self.drop(x)        x=self.linear2(x)        return x how would you create a neural network with a dropout parameter of 0.9 1 pointmodel =Net( in_size=10, n_hidden=100, out_size=10, p=0.9)model =Net( in_size=0.9, n_hidden=100, out_size=10, p=10)model =Net( in_size=0.9, n_hidden=0.9, out_size=10, p=10)

In a Fully Connected NN, if the input volume is 32x32x3 connected to a singlelayer of 5 neurons, how many parameters must be learned?