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Program to Implement Convolution Network in Python Assignment Solution

July 01, 2024
Prof. James Harper
Prof. James
🇦🇪 United Arab Emirates
Python
Prof. James Harper is an experienced software developer and educator with a Master's degree in Computer Science from the University of Melbourne. With over 900 completed assignments, he specializes in Python programming and application development. Prof. Harper's passion for teaching and extensive industry experience ensure that his solutions are not only functional but also well-documented and easy to understand.
Key Topics
  • Instructions
  • Requirements and Specifications
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Instructions

Objective

Write a program to implement convolution network in python.

Requirements and Specifications

program to implement convolution network in python

Source Code

ACTIVISION

import numpy as np from layer import Layer class Activation(Layer): def __init__(self, activation, activation_prime): self.activation = activation self.activation_prime = activation_prime def forward(self, input): self.input = input return self.activation(self.input) def backward(self, output_gradient, learning_rate): return np.multiply(output_gradient, self.activation_prime(self.input))

CONVOLUTION

import numpy as np from scipy import signal from layer import Layer class Convolutional(Layer): def __init__(self, input_shape, kernel_size, depth): input_depth, input_height, input_width = input_shape self.depth = depth self.input_shape = input_shape self.input_depth = input_depth self.output_shape = (depth, input_height - kernel_size + 1, input_width - kernel_size + 1) self.kernels_shape = (depth, input_depth, kernel_size, kernel_size) self.kernels = np.random.randn(*self.kernels_shape) self.biases = np.random.randn(*self.output_shape) def forward(self, input): self.input = input self.output = np.copy(self.biases) # TODO: Implement the forward method using the formula provided in the powerpoint. # You may add or remove any variables that you wish. for i in range(self.depth): for j in range(self.input_depth): self.output[i] += signal.correlate2d(self.input[j], self.kernels[i, j], "valid") return self.output def backward(self, output_gradient, learning_rate): # TODO: initialize the kernels_gradient and input_gradient. kernels_gradient = np.zeros(self.kernels_shape) input_gradient = np.zeros(self.input_shape) # TODO: implement the back pass here. The equations in the ppt may help, but you're free to # add as much or as little code as you'd like. for i in range(self.depth): for j in range(self.input_depth): kernels_gradient[i, j] = signal.correlate2d(self.input[j], output_gradient[i], "valid") input_gradient[j] += signal.convolve2d(output_gradient[i], self.kernels[i, j], "full") # TODO: update the kernels and biases self.kernels -= learning_rate * kernels_gradient self.biases -= learning_rate * output_gradient return input_gradient

DENSE

import numpy as np from layer import Layer class Dense(Layer): def __init__(self, input_size, output_size): self.weights = np.random.randn(output_size, input_size) self.bias = np.random.randn(output_size, 1) def forward(self, input): # TODO: apply linear transformation to the input. see ppt for equation. self.input = input self.output = (np.dot(self.weights,input) + self.bias) return self.output def backward(self, output_gradient, learning_rate): # TODO: update the weights and bias weights_gradient = np.dot(output_gradient,self.input.T) X_gradient = np.dot(self.weights.T,output_gradient) self.weights -= learning_rate * weights_gradient self.bias -= learning_rate * output_gradient return X_gradient

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