## Overview of the Problem set

You will learn to:

• Build the general architecture of a learning algorithm, including:
• Initializing parameters
• Calculating the cost function and its gradient
• Using an optimization algorithm (gradient descent)
• Gather all three functions above into a main model function, in the right order.

Problem Statement: You are given a dataset (“data.h5”) containing: - a training set of m_train images labeled as cat (y=1) or non-cat (y=0) - a test set of m_test images labeled as cat or non-cat - each image is of shape (num_px, num_px, 3) where 3 is for the 3 channels (RGB). Thus, each image is square (height = num_px) and (width = num_px). 例如：64x64x3

# Reshape the training and test examples
train_set_x_flatten = train_set_x_orig.reshape(num_px*num_px*3, -1).T
test_set_x_flatten = test_set_x_orig.reshape(num_px*num_px*3, -1).T


Common steps for pre-processing a new dataset are:

• Figure out the dimensions and shapes of the problem (m_train, m_test, num_px, …)
• Reshape the datasets such that each example is now a vector of size (num_px * num_px * 3, 1)
• “Standardize” the data
train_set_x = train_set_x_flatten/255.
test_set_x = test_set_x_flatten/255.


## General Architecture of the learning algorithm

The following Figure explains why Logistic Regression is actually a very simple Neural Network! Mathematical expression of the algorithm:

For one example $x^{(i)}$: $z^{(i)} = w^T x^{(i)} + b \tag{1}$ $\hat{y}^{(i)} = a^{(i)} = sigmoid(z^{(i)})\tag{2}$ $\mathcal{L}(a^{(i)}, y^{(i)}) = - y^{(i)} \log(a^{(i)}) - (1-y^{(i)} ) \log(1-a^{(i)})\tag{3}$

The cost is then computed by summing over all training examples: $J = \frac{1}{m} \sum_{i=1}^m \mathcal{L}(a^{(i)}, y^{(i)})\tag{6}$

Key steps: - Initialize the parameters of the model - Learn the parameters for the model by minimizing the cost
- Use the learned parameters to make predictions (on the test set) - Analyse the results and conclude

## Building the parts of our algorithm

### Helper functions

def sigmoid(z):
s = 1/(1+np.exp(-z))
return s


### Initializing parameters

def initialize_with_zeros(dim):

w = np.zeros((dim, 1))
b = 0

assert(w.shape == (dim, 1))
assert(isinstance(b, float) or isinstance(b, int))

return w, b


### Forward and Backward propagation

Forward Propagation:

• You get X
• You compute $A = \sigma(w^T X + b) = (a^{(0)}, a^{(1)}, …, a^{(m-1)}, a^{(m)})$
• You calculate the cost function: $J = -\frac{1}{m}\sum_{i=1}^{m}y^{(i)}\log(a^{(i)})+(1-y^{(i)})\log(1-a^{(i)})$

Backward propagation:

$\frac{\partial J}{\partial w} = \frac{1}{m}X(A-Y)^T\tag{7}$ $\frac{\partial J}{\partial b} = \frac{1}{m} \sum_{i=1}^m (a^{(i)}-y^{(i)})\tag{8}$

# GRADED FUNCTION: propagate

def propagate(w, b, X, Y):

m = X.shape

# FORWARD PROPAGATION (FROM X TO COST)
A = sigmoid(np.dot(w.T, X) + b)                                    # compute activation
cost = -(1/m)*np.sum(Y*np.log(A) + (1-Y)*np.log(1-A))                                # compute cost

# BACKWARD PROPAGATION (TO FIND GRAD)
dw = (1/m)*np.dot(X, (A-Y).T)
db = (1/m)*np.sum(A-Y)

assert(dw.shape == w.shape)
assert(db.dtype == float)
cost = np.squeeze(cost)
assert(cost.shape == ())

grads = {"dw": dw,
"db": db}

return grads, cost


### Optimization

The goal is to learn $w$ and $b$ by minimizing the cost function $J$. For a parameter $\theta$, the update rule is $\theta = \theta - \alpha \text{ } d\theta$, where $\alpha$ is the learning rate.

# GRADED FUNCTION: optimize

def optimize(w, b, X, Y, num_iterations, learning_rate, print_cost = False):

costs = []

for i in range(num_iterations):

# Cost and gradient calculation (≈ 1-4 lines of code)
grads, cost = propagate(w, b, X, Y)

# Retrieve derivatives from grads
dw = grads["dw"]
db = grads["db"]

# update rule (≈ 2 lines of code)

w = w - learning_rate*dw
b = b - learning_rate*db

# Record the costs
if i % 100 == 0:
costs.append(cost)

# Print the cost every 100 training examples
if print_cost and i % 100 == 0:
print ("Cost after iteration %i: %f" %(i, cost))

params = {"w": w,
"b": b}

grads = {"dw": dw,
"db": db}

return params, grads, costs


There is two steps to computing predictions:

1. Calculate $\hat{Y} = A = \sigma(w^T X + b)$
2. Convert the entries of a into 0 (if activation <= 0.5) or 1 (if activation > 0.5), stores the predictions in a vector Y_prediction. If you wish, you can use an if/else statement in a for loop (though there is also a way to vectorize this).
# GRADED FUNCTION: predict

def predict(w, b, X):
'''
Predict whether the label is 0 or 1 using learned logistic regression parameters (w, b)

Arguments:
w -- weights, a numpy array of size (num_px * num_px * 3, 1)
b -- bias, a scalar
X -- data of size (num_px * num_px * 3, number of examples)

Returns:
Y_prediction -- a numpy array (vector) containing all predictions (0/1) for the examples in X
'''

m = X.shape
Y_prediction = np.zeros((1,m))
w = w.reshape(X.shape, 1)

# Compute vector "A" predicting the probabilities of a cat being present in the picture
A = sigmoid(np.dot(w.T, X) + b)

for i in range(A.shape):

# Convert probabilities A[0,i] to actual predictions p[0,i]
if A[0,i] <= 0.5:
Y_prediction[0,i] = 0
else:
Y_prediction[0,i] = 1

assert(Y_prediction.shape == (1, m))

return Y_prediction


## Merge all functions into a model

Implement the model function. Use the following notation: - Y_prediction for your predictions on the test set - Y_prediction_train for your predictions on the train set - w, costs, grads for the outputs of optimize()

def model(X_train, Y_train, X_test, Y_test, num_iterations = 2000, learning_rate = 0.5, print_cost = False):
"""
Builds the logistic regression model by calling the function you've implemented previously

Arguments:
X_train -- training set represented by a numpy array of shape (num_px * num_px * 3, m_train)
Y_train -- training labels represented by a numpy array (vector) of shape (1, m_train)
X_test -- test set represented by a numpy array of shape (num_px * num_px * 3, m_test)
Y_test -- test labels represented by a numpy array (vector) of shape (1, m_test)
num_iterations -- hyperparameter representing the number of iterations to optimize the parameters
learning_rate -- hyperparameter representing the learning rate used in the update rule of optimize()
print_cost -- Set to true to print the cost every 100 iterations

Returns:
d -- dictionary containing information about the model.
"""

# initialize parameters with zeros (≈ 1 line of code)
w, b = initialize_with_zeros(X_train.shape)

# Gradient descent (≈ 1 line of code)
parameters, grads, costs = optimize(w, b, X_train, Y_train, num_iterations, learning_rate, print_cost)

# Retrieve parameters w and b from dictionary "parameters"
w = parameters["w"]
b = parameters["b"]

# Predict test/train set examples (≈ 2 lines of code)
Y_prediction_test = predict(w, b, X_test)
Y_prediction_train = predict(w, b, X_train)

# Print train/test Errors
print("train accuracy: {} %".format(100 - np.mean(np.abs(Y_prediction_train - Y_train)) * 100))
print("test accuracy: {} %".format(100 - np.mean(np.abs(Y_prediction_test - Y_test)) * 100))

d = {"costs": costs,
"Y_prediction_test": Y_prediction_test,
"Y_prediction_train" : Y_prediction_train,
"w" : w,
"b" : b,
"learning_rate" : learning_rate,
"num_iterations": num_iterations}

return d Interpretation: You can see the cost decreasing. It shows that the parameters are being learned. However, you see that you could train the model even more on the training set. Try to increase the number of iterations in the cell above and rerun the cells. You might see that the training set accuracy goes up, but the test set accuracy goes down. This is called overfitting.

## Reference 