Numpy.dot issue: Am I the idiot or there is a real issue with the runtime? /u/NormalEarth5293 Python Education

Cell 1:

# UNQ_C2 # GRADED FUNCTION: compute_gradient def compute_gradient(x, y, w, b): """ Computes the gradient for linear regression Args: x (ndarray): Shape (m,) Input to the model (Population of cities) y (ndarray): Shape (m,) Label (Actual profits for the cities) w, b (scalar): Parameters of the model Returns dj_dw (scalar): The gradient of the cost w.r.t. the parameters w dj_db (scalar): The gradient of the cost w.r.t. the parameter b """ # Number of training examples m = x.shape[0] # You need to return the following variables correctly dj_dw = 0 dj_db = 0 ### START CODE HERE ### f_w_b = np.dot(w,x)+b print(f_w_b[:5]) dj_dw_temp = f_w_b-y print(dj_dw_temp[:5]) temp = f_w_b-y print(np.shape(temp),np.shape(x)) dj_db_temp = temp * x print("hi",np.dot(temp,x)) dj_dw = np.sum(dj_dw_temp)/m dj_db = np.sum(dj_db_temp)/m ### END CODE HERE ### return dj_dw, dj_db Cell 1: # UNQ_C2 # GRADED FUNCTION: compute_gradient def compute_gradient(x, y, w, b): """ Computes the gradient for linear regression Args: x (ndarray): Shape (m,) Input to the model (Population of cities) y (ndarray): Shape (m,) Label (Actual profits for the cities) w, b (scalar): Parameters of the model Returns dj_dw (scalar): The gradient of the cost w.r.t. the parameters w dj_db (scalar): The gradient of the cost w.r.t. the parameter b """ # Number of training examples m = x.shape[0] # You need to return the following variables correctly dj_dw = 0 dj_db = 0 ### START CODE HERE ### f_w_b = np.dot(w,x)+b print(f_w_b[:5]) dj_dw_temp = f_w_b-y print(dj_dw_temp[:5]) temp = f_w_b-y print(np.shape(temp),np.shape(x)) dj_db_temp = temp * x print("hi",np.dot(temp,x)) dj_dw = np.sum(dj_dw_temp)/m dj_db = np.sum(dj_db_temp)/m ### END CODE HERE ### return dj_dw, dj_db 

Cell 2:

# Compute and display gradient with w initialized to zeroes initial_w = 0 initial_b = 0 tmp_dj_dw, tmp_dj_db = compute_gradient(x_train, y_train, initial_w, initial_b) print('Gradient at initial w, b (zeros):', tmp_dj_dw, tmp_dj_db) compute_gradient_test(compute_gradient) 

Results:

[0. 0. 0. 0. 0.]

[-17.592 -9.1302 -13.662 -11.854 -6.8233]

(97,) (97,)

hi -6336.898425318999

Gradient at initial w, b (zeros): -5.839135051546393 -65.32884974555671

Using X with shape (4, 1)

[ 4.5 8.5 12.5 16.5]

[0. 0. 0. 0.]

(4,) (4,)

hi 0.0

[ 4. 7. 10. 13.]

[-2. -2. -2. -2.]

(4,) (4,)

hi -40.0

—————————————————————————

AssertionError Traceback (most recent call last)

<ipython-input-98-dd854b16287c> in <module>

6 print(‘Gradient at initial w, b (zeros):’, tmp_dj_dw, tmp_dj_db)

7

—-> 8 compute_gradient_test(compute_gradient)

~/work/public_tests.py in compute_gradient_test(target)

60 dj_dw, dj_db = target(x, y, initial_w, initial_b)

61 #assert dj_dw.shape == initial_w.shape, f”Wrong shape for dj_dw. {dj_dw} != {initial_w.shape}”

—> 62 assert dj_db == -2, f”Case 2: dj_db is wrong: {dj_db} != -2″

63 assert np.allclose(dj_dw, -10.0), f”Case 1: dj_dw is wrong: {dj_dw} != -10.0″

64

AssertionError: Case 2: dj_db is wrong: -10.0 != -2

Can somene please explain why the numpy.dot() gives in the first time the vector product and in the 2nd time it gives me the product summed up?

Thank you

Cell 1:

# UNQ_C2 # GRADED FUNCTION: compute_gradient def compute_gradient(x, y, w, b): """ Computes the gradient for linear regression Args: x (ndarray): Shape (m,) Input to the model (Population of cities) y (ndarray): Shape (m,) Label (Actual profits for the cities) w, b (scalar): Parameters of the model Returns dj_dw (scalar): The gradient of the cost w.r.t. the parameters w dj_db (scalar): The gradient of the cost w.r.t. the parameter b """ # Number of training examples m = x.shape[0] # You need to return the following variables correctly dj_dw = 0 dj_db = 0 ### START CODE HERE ### f_w_b = np.dot(w,x)+b print(f_w_b[:5]) dj_dw_temp = f_w_b-y print(dj_dw_temp[:5]) temp = f_w_b-y print(np.shape(temp),np.shape(x)) dj_db_temp = temp * x print("hi",np.dot(temp,x)) dj_dw = np.sum(dj_dw_temp)/m dj_db = np.sum(dj_db_temp)/m ### END CODE HERE ### return dj_dw, dj_db Cell 1: # UNQ_C2 # GRADED FUNCTION: compute_gradient def compute_gradient(x, y, w, b): """ Computes the gradient for linear regression Args: x (ndarray): Shape (m,) Input to the model (Population of cities) y (ndarray): Shape (m,) Label (Actual profits for the cities) w, b (scalar): Parameters of the model Returns dj_dw (scalar): The gradient of the cost w.r.t. the parameters w dj_db (scalar): The gradient of the cost w.r.t. the parameter b """ # Number of training examples m = x.shape[0] # You need to return the following variables correctly dj_dw = 0 dj_db = 0 ### START CODE HERE ### f_w_b = np.dot(w,x)+b print(f_w_b[:5]) dj_dw_temp = f_w_b-y print(dj_dw_temp[:5]) temp = f_w_b-y print(np.shape(temp),np.shape(x)) dj_db_temp = temp * x print("hi",np.dot(temp,x)) dj_dw = np.sum(dj_dw_temp)/m dj_db = np.sum(dj_db_temp)/m ### END CODE HERE ### return dj_dw, dj_db 

Cell 2:

# Compute and display gradient with w initialized to zeroes initial_w = 0 initial_b = 0 tmp_dj_dw, tmp_dj_db = compute_gradient(x_train, y_train, initial_w, initial_b) print('Gradient at initial w, b (zeros):', tmp_dj_dw, tmp_dj_db) compute_gradient_test(compute_gradient) 

Results:

[0. 0. 0. 0. 0.]

[-17.592 -9.1302 -13.662 -11.854 -6.8233]

(97,) (97,)

hi -6336.898425318999

Gradient at initial w, b (zeros): -5.839135051546393 -65.32884974555671

Using X with shape (4, 1)

[ 4.5 8.5 12.5 16.5]

[0. 0. 0. 0.]

(4,) (4,)

hi 0.0

[ 4. 7. 10. 13.]

[-2. -2. -2. -2.]

(4,) (4,)

hi -40.0

—————————————————————————

AssertionError Traceback (most recent call last)

<ipython-input-98-dd854b16287c> in <module>

6 print(‘Gradient at initial w, b (zeros):’, tmp_dj_dw, tmp_dj_db)

7

—-> 8 compute_gradient_test(compute_gradient)

~/work/public_tests.py in compute_gradient_test(target)

60 dj_dw, dj_db = target(x, y, initial_w, initial_b)

61 #assert dj_dw.shape == initial_w.shape, f”Wrong shape for dj_dw. {dj_dw} != {initial_w.shape}”

—> 62 assert dj_db == -2, f”Case 2: dj_db is wrong: {dj_db} != -2″

63 assert np.allclose(dj_dw, -10.0), f”Case 1: dj_dw is wrong: {dj_dw} != -10.0″

64

AssertionError: Case 2: dj_db is wrong: -10.0 != -2

Can somene please explain why the numpy.dot() gives in the first time the vector product and in the 2nd time it gives me the product summed up?

Thank you