My error part:
NameError Traceback (most recent call last)
in
1 # Compute and display gradient with w and b initialized to zeros
----> 2 initial_w = np.zeros(n)
3 initial_b = 0.
4
5 dj_db, dj_dw = compute_gradient(X_train, y_train, initial_w, initial_b)
NameError: name ‘n’ is not defined
Expected Output:
dj_db at initial w and b (zeros) -0.1
dj_dw at initial w and b (zeros): [-12.00921658929115, -11.262842205513591]
Compute and display cost and gradient with non-zero w and b
test_w = np.array([ 0.2, -0.5])
test_b = -24
dj_db, dj_dw = compute_gradient(X_train, y_train, test_w, test_b)
print(‘dj_db at test w and b:’, dj_db)
print(‘dj_dw at test w and b:’, dj_dw.tolist())
UNIT TESTS
compute_gradient_test(compute_gradient)
NameError Traceback (most recent call last)
in
2 test_w = np.array([ 0.2, -0.5])
3 test_b = -24
----> 4 dj_db, dj_dw = compute_gradient(X_train, y_train, test_w, test_b)
5
6 print(‘dj_db at test w and b:’, dj_db)
NameError: name ‘X_train’ is not defined
Expected Output:
dj_db at test w and b (non-zeros) -0.5999999999991071
dj_dw at test w and b (non-zeros): [-44.8313536178737957, -44.37384124953978]
initial_w = 0.01 * (np.random.rand(2) - 0.5)
initial_b = -8
Some gradient descent settings
iterations = 10000
alpha = 0.001
w,b, J_history,_ = gradient_descent(X_train ,y_train, initial_w, initial_b,
compute_cost, compute_gradient, alpha, iterations, 0)
ameError Traceback (most recent call last)
in
----> 1 np.random.seed(1)
2 initial_w = 0.01 * (np.random.rand(2) - 0.5)
3 initial_b = -8
4
5 # Some gradient descent settings
NameError: name ‘np’ is not defined
Expected Output: Cost 0.30, (Click to see details):
plot_decision_boundary(w, b, X_train, y_train)
Set the y-axis label
plt.ylabel(‘Exam 2 score’)
Set the x-axis label
plt.xlabel(‘Exam 1 score’)
plt.legend(loc=“upper right”)
plt.show()
NameError Traceback (most recent call last)
in
----> 1 plot_decision_boundary(w, b, X_train, y_train)
2 # Set the y-axis label
3 plt.ylabel(‘Exam 2 score’)
4 # Set the x-axis label
5 plt.xlabel(‘Exam 1 score’)
NameError: name ‘w’ is not defined
UNQ_C4
GRADED FUNCTION: predict
TypeError Traceback (most recent call last)
in
5 tmp_X = np.random.randn(4, 2) - 0.5
6
----> 7 tmp_p = predict(tmp_X, tmp_w, tmp_b)
8 print(f’Output of predict: shape {tmp_p.shape}, value {tmp_p}')
9
in predict(tmp_X, tmp_w, tmp_b)
11
12
—> 13 for iteration in range(tmp_X):
14 # Compute predictions
15 tmp_X = np.dot(tmp_X, theta)
TypeError: only integer scalar arrays can be converted to a scalar index
Expected output
Output of predict: shape (4,),value [0. 1. 1. 1.]
NameError Traceback (most recent call last)
in
1 #Compute accuracy on our training set
----> 2 p = predict(X_train, w,b)
3 print('Train Accuracy: f'(np.mean(p == y_train) * 100))
NameError: name ‘X_train’ is not defined
Train Accuracy (approx): 92.00
NameError Traceback (most recent call last)
in
1 # load dataset
----> 2 X_train, y_train = load_data(“data/ex2data2.txt”)
NameError: name ‘load_data’ is not defined
NameError Traceback (most recent call last)
in
1 # print X_train
----> 2 print(“X_train:”, X_train[:5])
3 print(“Type of X_train:”,type(X_train))
4
5 # print y_train
NameError: name ‘X_train’ is not defined
NameError Traceback (most recent call last)
in
----> 1 print ('The shape of X_train is: ’ + str(X_train.shape))
2 print ('The shape of y_train is: ’ + str(y_train.shape))
3 print ('We have m = d training examples' (len(y_train)))
NameError: name ‘X_train’ is not defined
NameError Traceback (most recent call last)
in
1 # Plot examples
----> 2 plot_data(X_train, y_train[:], pos_label=“Accepted”, neg_label=“Rejected”)
3
4 # Set the y-axis label
5 plt.ylabel(‘Microchip Test 2’)
NameError Traceback (most recent call last)
in
1 # Plot examples
----> 2 plot_data(X_train, y_train[:], pos_label=“Accepted”, neg_label=“Rejected”)
3
4 # Set the y-axis label
5 plt.ylabel(‘Microchip Test 2’)
NameError: name ‘plot_data’ is not defined
NameError: name ‘plot_data’ is not defined
NameError Traceback (most recent call last)
in
----> 1 print(“X_train[0]:”, X_train[0])
2 print(“mapped X_train[0]:”, mapped_X[0])
NameError: name ‘X_train’ is not defined
UNQ_C5
File “”, line 2
def compute_cost_reg(X, y, w, b, lambda_ = 1):
^
IndentationError: unexpected indent
NameError Traceback (most recent call last)
in
----> 1 X_mapped = map_feature(X_train[:, 0], X_train[:, 1])
2 np.random.seed(1)
3 initial_w = np.random.rand(X_mapped.shape[1]) - 0.5
4 initial_b = 0.5
5 lambda_ = 0.5
NameError: name ‘map_feature’ is not defined
Expected Output:
Regularized cost : 0.6618252552483948
UNQ_C6
NameError Traceback (most recent call last)
in
----> 1 X_mapped = map_feature(X_train[:, 0], X_train[:, 1])
2 np.random.seed(1)
3 initial_w = np.random.rand(X_mapped.shape[1]) - 0.5
4 initial_b = 0.5
5
NameError: name ‘map_feature’ is not defined
Expected Output:
**dj_db:**0.07138288792343
First few elements of regularized dj_dw:
[[-0.010386028450548], [0.011409852883280], [0.0536273463274], [0.003140278267313]]
NameError Traceback (most recent call last)
in
2 import numpy as np
3 np.random.seed(1)
----> 4 initial_w = np.random.rand(X_mapped.shape[1])-0.5
5 initial_b = 1.
6
NameError: name ‘X_mapped’ is not defined
Expected Output: Cost < 0.5 (Click for details)
NameError Traceback (most recent call last)
in
----> 1 plot_decision_boundary(w, b, X_mapped, y_train)
2 # Set the y-axis label
3 plt.ylabel(‘Microchip Test 2’)
4 # Set the x-axis label
5 plt.xlabel(‘Microchip Test 1’)
NameError: name ‘plot_decision_boundary’ is not defined
NameError Traceback (most recent call last)
in
1 #Compute accuracy on the training set
----> 2 p = predict(X_mapped, w, b)
3
4 print('Train Accuracy: f'(np.mean(p == y_train) * 100))
NameError: name ‘predict’ is not defined
Expected Output:
Train Accuracy:~ 80%
