Need Help with this code completion. class neural_network: def init(self,Nhidden,Ninput,Noutput,Nonlinearities): self.layer = [] # BUILD THE NETWORK # Add a linear layer with Ninput input features and Nhidden output features # YOUR CODE HERE # Add a non-linearity if(Nonlinearities[0]=='relu'): self.layer.append(relu()) else: self.layer.append(sigmoid()) # Add a linear layer # YOUR CODE HERE # Add a non-linearity def forward(self,X): # Forward propagation # YOUR CODE HERE return X def backward(self,dY): # Backward propagation # YOUR CODE HERE return def update(self, learning_rate): # Updating the parameters of the network according to algorithm # YOUR CODE HERE self.layer[0].W = self.layer[0].b = self.layer[2].W = self.layer[2].b =

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Question

Need Help with this code completion.

class neural_network:
def __init__(self,Nhidden,Ninput,Noutput,Nonlinearities):


self.layer = []

# BUILD THE NETWORK

# Add a linear layer with Ninput input features and Nhidden output features
# YOUR CODE HERE

# Add a non-linearity
if(Nonlinearities[0]=='relu'):
self.layer.append(relu())
else:
self.layer.append(sigmoid())

# Add a linear layer
# YOUR CODE HERE

# Add a non-linearity


def forward(self,X):

# Forward propagation
# YOUR CODE HERE
return X

def backward(self,dY):
# Backward propagation
# YOUR CODE HERE

return

def update(self, learning_rate):
# Updating the parameters of the network according to algorithm

# YOUR CODE HERE

self.layer[0].W =
self.layer[0].b =
self.layer[2].W =
self.layer[2].b =

Expert Solution

Step 1: Here's the completed code with comments explaining each step:

Here's the completed code with comments explaining each step:

CODE in Python:

class neural_network:
def __init__(self, Nhidden, Ninput, Noutput, Nonlinearities):

self.layer = []

# BUILD THE NETWORK

# Add a linear layer with Ninput input features and Nhidden output features
# YOUR CODE HERE
self.layer.append(linear(Ninput, Nhidden))

# Add a non-linearity
if Nonlinearities[0] == 'relu':
self.layer.append(relu())
else:
self.layer.append(sigmoid())

# Add a linear layer with Nhidden input features and Noutput output features
# YOUR CODE HERE
self.layer.append(linear(Nhidden, Noutput))

# Add a non-linearity
if Nonlinearities[1] == 'relu':
self.layer.append(relu())
else:
self.layer.append(sigmoid())

def forward(self, X):

# Forward propagation
# YOUR CODE HERE
for layer in self.layer:
X = layer.forward(X)
return X

def backward(self, dY):
# Backward propagation
# YOUR CODE HERE
for layer in reversed(self.layer):
dY = layer.backward(dY)
return dY

def update(self, learning_rate):
# Updating the parameters of the network according to algorithm

# YOUR CODE HERE
self.layer[0].W = self.layer[0].W - learning_rate * self.layer[0].dW
self.layer[0].b = self.layer[0].b - learning_rate * self.layer[0].db
self.layer[2].W = self.layer[2].W - learning_rate * self.layer[2].dW
self.layer[2].b = self.layer[2].b - learning_rate * self.layer[2].db

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