I am getting the following error in model_test(model) error inside propagate function in

# Programming Assignment: Logistic Regression with a Neural Network Mindset

I am getting the ValueError: operands could not be broadcast together with shapes (1,3) (1,7)

I printed out different values in cost function and it seems that sometimes the the parameter A is of different shape than th shape of Y

Following is prints I am getting

w [[ 0.08483675]

[-0.08073906]

[-0.11582744]

[ 0.12636363]]

B -0.0389675014823217

shape m ()

shape Y (1, 7)

shape A (1, 7)

shape log A (1, 7)

w [[ 0.08639757]

[-0.08231268]

[-0.11798927]

[ 0.12866053]]

B -0.03983236094816321

shape m ()

shape Y (1, 3)

shape A (1, 3)

shape log A (1, 3)

w [[ 0.09027857]

[-0.0801878 ]

[-0.12002229]

[ 0.13012181]]

B -0.03855751694083141

shape m ()

shape Y (1, 3)

shape A (1, 3)

shape log A (1, 3)

w [[ 0.09414372]

[-0.07807089]

[-0.12204824]

[ 0.1315738 ]]

B -0.037289601637241196

shape m ()

shape Y (1, 3)

shape A (1, 3)

shape log A (1, 3)

w [[ 0.08639757]

[-0.08231268]

[-0.11798927]

[ 0.12866053]]

B -0.03983236094816321

shape m ()

shape Y (1, 3)

shape A (1, 7)

shape log A (1, 7)

---------------------------------------------------------------------------ValueError Traceback (most recent call last)

in 1 from public_tests import * 2

----> 3 model_test(model)

~/work/release/W2A2/public_tests.py in model_test(target) 113 y_test = np.array([0, 1, 0]) 114

→ 115 d = target(X, Y, x_test, y_test, num_iterations=50, learning_rate=0.01) 116

117 assert type(d[‘costs’]) == list, f"Wrong type for d[‘costs’]. {type(d[‘costs’])} != list"

in model(X_train, Y_train, X_test, Y_test, num_iterations, learning_rate, print_cost) 47 # Predict test/train set examples (≈ 2 lines of code) 48 Y_prediction_test = predict(w, b, X_test)—> 49 Y_prediction_train = predict(w, b, X_train) 50

51 # YOUR CODE ENDS HERE

in predict(w, b, X) 53 # Cost and gradient calculation (≈ 1-4 lines of code) 54 ### START CODE HERE ###—> 55 grads, cost = propagate(w, b, X, Y) 56 ### END CODE HERE ### 57

in propagate(w, b, X, Y) 33 print(‘shape A’, np.shape(A)) 34 print(‘shape log A’, np.shape(np.log(A)))—> 35 cost = (1/m)*np.sum(-Y*np.log(A)-(1-Y)*np.log(1-A)) 36 ### END CODE HERE ### 37