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Last active April 15, 2022 23:01
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CNN from scratch using numpy
filter_update = []
for i in range(f.shape[2]):
for j in range(f.shape[0]):
for k in range(f.shape[1]):
temp = 0
spos_row = j
spos_col = k
epos_row = spos_row + s_row
epos_col = spos_col + s_col
for l in range(X.shape[2]):
temp = temp + (X[spos_row:epos_row,spos_col:epos_col,l]*error_wrt_filter_output[:,:,i]).sum()
filter_update_array = np.array(filter_update)
filter_update_array = np.resize(filter_update_array,(f.shape[2],f.shape[0],f.shape[1]))
# converting into binary classification
for i in range(y.shape[1]):
if y[0][i] >4:
y[0][i] = 1
y[0][i] = 0
#checking value counts
# generating output of convolution layer
filter_output = []
# for each image
for i in range(len(new_image)):
# apply each filter
for k in range(f.shape[2]):
# do element wise multiplication
for j in range(new_image.shape[1]):
filter_output = np.resize(np.array(filter_output), (len(new_image),f.shape[2],new_image.shape[1]))
# applying activation over convolution output
filter_output_sigmoid = sigmoid(filter_output)
filter_output.shape, filter_output_sigmoid.shape
# Generating patches from images
new_image = []
# for number of images
for k in range(X.shape[2]):
# sliding in horizontal direction
for i in range(X.shape[0]-f.shape[0]+1):
# sliding in vertical direction
for j in range(X.shape[1]-f.shape[1]+1):
# resizing the generated patches as per number of images
new_image = np.array(new_image)
#Error w.r.t sigmoid output
output_layer_input_wrt_filter_output_sigmoid = wo.T
#Error w.r.t sigmoid transformation
filter_output_sigmoid_wrt_filter_output = filter_output_sigmoid * (1-filter_output_sigmoid)
# cvalculating derivatives for backprop convolution
error_wrt_filter_output =,error_wrt_output*output_wrt_output_layer_input) * filter_output_sigmoid_wrt_filter_output
error_wrt_filter_output = np.average(error_wrt_filter_output, axis=1)
error_wrt_filter_output = np.resize(error_wrt_filter_output,(X.shape[0]-f.shape[0]+1,X.shape[1]-f.shape[1]+1, f.shape[2]))
error = np.square(y-output)/2
#Error w.r.t Output (Gradient)
error_wrt_output = -(y-output)
#Error w.r.t sigmoid transformation (output_layer_input)
#Error w.r.t weight
# initializing filter
f = f.T
print('Filter 1', '\n', f[:,:,0], '\n')
print('Filter 2', '\n', f[:,:,1], '\n')
print('Filter 3', '\n', f[:,:,2], '\n')
for i in range(f.shape[2]):
f[:,:,i] = f[:,:,i] - lr*filter_update_array[i]
# generating input for fully connected layer
filter_output_sigmoid = filter_output_sigmoid.reshape((filter_output_sigmoid.shape[0],filter_output_sigmoid.shape[1]*filter_output_sigmoid.shape[2]))
filter_output_sigmoid = filter_output_sigmoid.T
# Linear trasnformation for fully Connected Layer
output_layer_input = (output_layer_input - np.average(output_layer_input))/np.std(output_layer_input)
# activation function
output = sigmoid(output_layer_input)
# importing required libraries
import numpy as np
import pandas as pd
from tqdm import tqdm
from keras.datasets import mnist
# loading dataset
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# selecting a subset of data (200 images)
x_train = x_train[:200]
y = y_train[:200]
X = x_train.T
X = X/255
y = y.T
#checking value
# defining the Sigmoid Function
def sigmoid (x):
return 1/(1 + np.exp(-x))
# derivative of Sigmoid Function
def derivatives_sigmoid(x):
return x * (1 - x)
# number of features in data set
s_row = X.shape[0] - f.shape[0] + 1
s_col = X.shape[1] - f.shape[1] + 1
num_filter = f.shape[2]
inputlayer_neurons = (s_row)*(s_col)*(num_filter)
output_neurons = 1
# initializing weight
#delta change in w for fully connected layer
delta_error_fcp =,(error_wrt_output * output_wrt_output_layer_input).T)
wo = wo - lr*delta_error_fcp
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how do we print out the accuracy and predicted and expected output?

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