Deep Learning Network Code Question


A1 ) Add mini-batch stochastic gradient decent for both networks, add a function to calculate prediction accuracy for both networks.¶A2) Plot the histogram of activation functions for each layer in network 1 after the training is done.A3) Add two more hidden layers to the first network and define backpropegation accordinglyA4) add 1-2-3 more hidden layers to the network 2 and plot cost for each epoch as a line in the line plot. The color for each line should be unique based on the number of hidden layers.Your line plot should have 4 lines, Original (2 layers), 3 layers, 4 layers and 5 layers networks. Please see the code in the attached document and modify it according to the above mentioned questions. Please do in a ipynb file (jupyter) or google colab

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[1] Do it in ipynb file
[2] from sklearn import datasets
from sklearn import preprocessing
import torch
import numpy as np
[3] iris = datasets.load_iris()
samples = preprocessing.normalize([:,:4]
labels =, 1)
[4] print(samples.shape,labels.shape)
[5] ### to have a binary classification get the only class 1 and 2
i, j = np.where(labels == 2)
samples = np.delete(samples, i, axis =0)
labels = np.delete(labels, i,axis =0)
[6] print(samples.shape,labels.shape)
[7] #### Loading the data into torch tensor
samples = torch.tensor(samples, dtype=torch.float)
labels = torch.tensor(labels, dtype=torch.float)
[8] class TwoLayerNN(torch.nn.Module):
def __init__(self, ):
self.input_dim = 4
self.hidden_dim = 32
self.output_dim = 1
self.learningRate = 0.01
self.w1 = torch.randn(self.input_dim, self.hidden_dim)
self.w2 = torch.randn(self.hidden_dim, self.output_dim)
### activation functions
def sigmoid(self, z):
return torch.sigmoid(z)
### derivative of activation functions
def reluPrime(self,z):
z_clone = z.clone()
z_clone[z < 0] = 0 return z_clone def sigmoidPrime(self, x): return x * (1 - x) # Forward propagation def forward(self, X): self.z1 = torch.matmul(X, self.w1) # 3 X 3 ".dot" does not broadcast in PyTorch self.a1 = torch.nn.functional.relu(self.z1) self.z2 = torch.matmul(self.a1, self.w2) self.a2 = self.sigmoid(self.z2) return self.a2 #backpropagation def backward(self, samples, labels, y_hat): self.dz2 = self.a2 - labels self.dw2 = self.a1.t().mm(self.dz2) self.da1 = self.dz1 = self.da1* self.reluPrime(self.z1) self.dw1 = samples.t().mm(self.dz1) self.w1 -= self.learningRate * self.dw1 self.w2 -= self.learningRate * self.dw2 [9] model = TwoLayerNN() num_epochs = 50 cost=torch.nn.BCELoss() for epoch in range(num_epochs): y_hat = model(samples) epoch_cost = cost(y_hat,labels) if epoch % 5 == 0: print('Epoch {} | Loss: {}'.format(epoch, epoch_cost)) model.backward(samples, labels, y_hat) [10] #Implementing the same neural network using pytorch internal backpropagation class TwoLayerNN(torch.nn.Module): def __init__(self): super().__init__() self.fc1 = torch.nn.Linear(4, 32) self.fc2 = torch.nn.Linear(32, 1) self.relu = torch.nn.ReLU() self.sigmoid = torch.nn.Sigmoid() def forward(self,x): x = self.relu(self.fc1(x)) x = self.sigmoid(self.fc2(x)) return x [11] model = TwoLayerNN() cost = torch.nn.BCELoss() optimizer = torch.optim.SGD(model.parameters(), lr=0.1) num_epochs = 50 for epoch in range(num_epochs): optimizer.zero_grad() y_hat = model(samples) epoch_cost = cost(y_hat, labels) epoch_cost.backward() optimizer.step() if epoch % 5 == 0: print('Epoch {} | Loss: {}'.format(epoch, epoch_cost)) Purchase answer to see full attachment Tags: cisco network Network settings hidden layers User generated content is uploaded by users for the purposes of learning and should be used following Studypool's honor code & terms of service.

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