So I've taken the Deep Learning AI course by Andrew NG on coursera. I am currently working the last assignment in week 2. I reached the part where I have to write the forward and backward propagation function. I managed to write the fwd_propagate function which is fairly easy. This is the code below :
def fwd_propagate(w,b,X,y):
m = X.shape[1]
A = sigmoid(np.dot(w.T,X)+b)
J = (-1/m)*np.sum(y * np.log(A) + (1-y) * np.log(1-A))
return J
Now I have to write the bwd_propagation function but I don't know where and how to start. Can someone help and explain to me what I should write.
This is everything I wrote so far with the tests.
import numpy as np
import matplotlib.pyplot as plt
import h5py
import scipy
from PIL import Image
from scipy import ndimage
%matplotlib inline
def load_dataset():
train_dataset = h5py.File('C:/Users/Univ/Desktop/ML Intern/Logistic-Regression-with-a-Neural-Network-mindset-master/train_catvnoncat.h5', "r")
train_set_x_orig = np.array(train_dataset["train_set_x"][:]) # your train set features
train_set_y_orig = np.array(train_dataset["train_set_y"][:]) # your train set labels
test_dataset = h5py.File('C:/Users/Univ/Desktop/ML Intern/Logistic-Regression-with-a-Neural-Network-mindset-master/test_catvnoncat.h5', "r")
test_set_x_orig = np.array(test_dataset["test_set_x"][:]) # your test set features
test_set_y_orig = np.array(test_dataset["test_set_y"][:]) # your test set labels
classes = np.array(test_dataset["list_classes"][:]) # the list of classes
train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))
return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes
train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dataset()
index = 25
plt.imshow(train_set_x_orig[index])
print ("y = " + str(train_set_y[:,index]) + ", it's a '" + classes[np.squeeze(train_set_y[:,index])].decode("utf-8") + "' picture.")
print(str(train_set_y.shape[1]) + " This is the amount of elements in the training set")
print(str(test_set_y.shape[1]) + " This is the amount of elements in the test set")
print(str(train_set_x_orig.shape[1]) + " This is the Num_px")
print(f"{train_set_x_orig.shape[1]} This is the Num_px")
print(train_set_x_orig.shape)
X_flatten1 = train_set_x_orig.reshape(train_set_x_orig.shape[0], -1).T
X_flatten2 = train_set_y.reshape(train_set_y.shape[0], -1).T
X_flatten3 = test_set_x_orig.reshape(test_set_x_orig.shape[0], -1).T
X_flatten4 = test_set_y.reshape(test_set_y.shape[0], -1).T
print(X_flatten1)
print(X_flatten2)
print(X_flatten3)
print(X_flatten4)
print(X_flatten1.shape)
print(X_flatten2.shape)
print(X_flatten3.shape)
print(X_flatten4.shape)
print(" Let's standardize our date")
train_set_x = X_flatten1/256
test_set_x = X_flatten3/256
print(train_set_x)
print(test_set_x)
def sigmoid(x):
s = 1/(1+np.exp(-x))
return s
print ("sigmoid(0) = " + str(sigmoid(0)))
print ("sigmoid(9.2) = " + str(sigmoid(9.2)))
def initialize_with_zeros(dim):
shp=(dim,1)
w = np.zeros(shp)
b = 0
assert(w.shape == (dim, 1))
assert(isinstance(b, float) or isinstance(b, int))
return w,b
dim = 2
w, b = initialize_with_zeros(dim)
print ("w = " + str(w))
print ("b = " + str(b))
def fwd_propagate(w,b,X,y):
m = X.shape[1]
A = sigmoid(np.dot(w.T,X)+b)
J = (-1/m)*np.sum(y * np.log(A) + (1-y) * np.log(1-A))
return J
