Program to Implement Convolution Network in Python Assignment Solution

Instructions

Objective

Write a program to implement convolution network in python.

Requirements and Specifications

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