AssertionError: Wrong values for d['w'] | deeplearning specialization

Question

I was completing the first course of the deeplearning specialization, where the first programming assignment was to build a logistic regression model from scratch. Since it was the first time for me to build a model from scratch and it took me some time to gulp the advanced mathematics, I had lots of errors. Among them, I found a one I am completely unable to fix and just cannot understand. It was an assertion error saying that the shape of dw (derivative of cost with respect to weight) is actually wrong.

The codes :

import numpy as np 

def sigmoid(x):
    return 1 / 1 + np.exp(x)

def propagate(w, b, X, Y):
    m = X.shape[1] 
    A = sigmoid(np.dot(w.T,X) + b)
    cost = np.sum(np.abs(Y * np.log(A) + (1-Y)*(np.log(1-A))) / m)
    dw = np.dot(X,(A-Y).T) / m
    db = np.sum(A - Y) /m
    cost = np.squeeze(np.array(cost))
    grads = {"dw": dw,"db": db}
    return grads, cost

def optimize(w, b, X, Y, num_iterations=100, learning_rate=0.009, print_cost=False):
    w = copy.deepcopy(w)
    b = copy.deepcopy(b)
    costs = []
    for i in range(num_iterations):      
        grads, cost = propagate(w, b ,X, Y)
        dw = grads["dw"]
        db = grads["db"]
        w = w - learning_rate * grads["dw"]
        b = b - learning_rate * grads["db"]
        if i % 100 == 0:
            costs.append(cost)
            if print_cost:
                print ("Cost after iteration %i: %f" %(i, cost))
    params = {"w": w,
              "b": b}
    grads = {"dw": dw,"db": db}
    return params, grads, costs

def predict(w, b, X):
    m = X.shape[1]
    Y_prediction = np.zeros((1, m))
    w = w.reshape(x[0], 1)
    A = sigmoid(np.dot(w.T, X) + b)   
    for i in range(A.shape[1]):
        if A[0, i] > 0.5:
            Y_prediction[0,i] = 1.0
        else:
            Y_prediction[0,i] = 0.0
    return Y_prediction

def model(X_train, Y_train, X_test, Y_test, num_iterations=2000, learning_rate=0.5, print_cost=False):
    w = np.zeros(shape=(X_train.shape[0],1))
    b = np.zeros(shape=(1,1))
    params, gards, costs = optimize(w, b, X_train, Y_train)
    b = params["b"]
    w = params["w"]
    Y_prediction_train = predict(w, b, X_train)
    Y_prediction_test = predict(w, b, X_test)
    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

model_test(model)

the model_test function wasn't defined anywhere in the course and I think that it was built-in to the exercise I guess.. But here's the issue:

---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
<ipython-input-36-7f17a31b22cb> in <module>
----> 1 model_test(model)

~/work/release/W2A2/public_tests.py in model_test(target)
    117     assert type(d['w']) == np.ndarray, f"Wrong type for d['w']. {type(d['w'])} != np.ndarray"
    118     assert d['w'].shape == (X.shape[0], 1), f"Wrong shape for d['w']. {d['w'].shape} != {(X.shape[0], 1)}"
--> 119     assert np.allclose(d['w'], expected_output['w']), f"Wrong values for d['w']. {d['w']} != {expected_output['w']}"
    120 
    121     assert np.allclose(d['b'], expected_output['b']), f"Wrong values for d['b']. {d['b']} != {expected_output['b']}"

---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
<ipython-input-36-7f17a31b22cb> in <module>
----> 1 model_test(model)

~/work/release/W2A2/public_tests.py in model_test(target)
    117     assert type(d['w']) == np.ndarray, f"Wrong type for d['w']. {type(d['w'])} != np.ndarray"
    118     assert d['w'].shape == (X.shape[0], 1), f"Wrong shape for d['w']. {d['w'].shape} != {(X.shape[0], 1)}"
--> 119     assert np.allclose(d['w'], expected_output['w']), f"Wrong values for d['w']. {d['w']} != {expected_output['w']}"
    120 
    121     assert np.allclose(d['b'], expected_output['b']), f"Wrong values for d['b']. {d['b']} != {expected_output['b']}"

AssertionError: Wrong values for d['w']. [[ 0.28154433]
 [-0.11519574]
 [ 0.13142694]
 [ 0.20526551]] != [[ 0.00194946]
 [-0.0005046 ]
 [ 0.00083111]
 [ 0.00143207]]

At this point I am completely lost and I have no idea what to do..

Answer 1

The problem comes from this line:

params, gards, costs = optimize(w, b, X_train, Y_train)

You will still need to specify the parameters for the optimize function. Ignoring the last parameter will let the model use the default values, which do not equal the parameters specified in the model. So the line above should be:

params, grads, costs = optimize(w, b, X_train, Y_train, num_iterations, learning_rate, print_cost=print_cost)

Answer 2

with respect to Arthur L, I think the problem does come from the line

params, gards, costs = optimize(w, b, X_train, Y_train)

But I believe you simply misspelled your grads variable. I believe it will work without specifying the additional parameters. It doesn't hurt to have them in there though.