WK#2 final code assignment-code passed tests but I keep getting errors with Model function

JamesW22879 · January 24, 2024, 1:40am

I am stuck in the place it appears most get stuck at. My code passed all individual tests but when I try to combine all in the model function, I get the below errors:

AssertionError 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)
131 assert type(d[‘w’]) == np.ndarray, f"Wrong type for d[‘w’]. {type(d[‘w’])} != np.ndarray"
132 assert d[‘w’].shape == (X.shape[0], 1), f"Wrong shape for d[‘w’]. {d[‘w’].shape} != {(X.shape[0], 1)}"
→ 133 assert np.allclose(d[‘w’], expected_output[‘w’]), f"Wrong values for d[‘w’]. {d[‘w’]} != {expected_output[‘w’]}"
134
135 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.]

I went through the code and removed all hardcoded items in the args to the subroutines. Please help.

thank you,

James

TMosh · January 24, 2024, 1:45am

You can use the File menu to open the public_tests.py file, and read the model_test() function.

That will let you inspect the condition that your code is failing. It’s the line labeled “-> 133” in the error summary.

I think in your error report, you did not display the entire contents of the assert output for the expected ‘w’ values. A screen capture image would be helpful.

JamesW22879 · January 24, 2024, 1:52am

TMosh · January 24, 2024, 2:29am

The error indicates your code returned all zeros for the weights, instead of the expected values.

JamesW22879 · January 24, 2024, 2:45am

That’s what is confusing me. The W printout before the start of the AssertionError is a printout of my w prior to calling predict and that subroutine does not return w. My w prior to the call match what is expected.

TMosh · January 24, 2024, 3:27am

What code did you add to print the w and b values? And where did you put that code?

paulinpaloalto · January 24, 2024, 3:47am

Note that the optimize function returns the w and b values to you as a python dictionary. My guess is that you are using incorrect variable names for that parameters dictionary or perhaps you are not extracting the returned values from the dictionary. Note that w will be all zeros after the call to initialize_with_zeros, so how is it going to get changed later in the scope of the model function?

JamesW22879 · January 24, 2024, 11:07am

here is the code I am using for Model:

{moderator edit - solution code removed}

JamesW22879 · January 24, 2024, 11:09am

Here is the last output after running above:
(4, 1)
<class ‘numpy.ndarray’>
{‘w’: array([[ 0.08639757],
[-0.08231268],
[-0.11798927],
[ 0.12866053]]), ‘b’: -0.039832360948163205}

AssertionError 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)
131 assert type(d[‘w’]) == np.ndarray, f"Wrong type for d[‘w’]. {type(d[‘w’])} != np.ndarray"
132 assert d[‘w’].shape == (X.shape[0], 1), f"Wrong shape for d[‘w’]. {d[‘w’].shape} != {(X.shape[0], 1)}"
→ 133 assert np.allclose(d[‘w’], expected_output[‘w’]), f"Wrong values for d[‘w’]. {d[‘w’]} != {expected_output[‘w’]}"
134
135 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.]
[0.]
[0.]
[0.]] != [[ 0.08639757]
[-0.08231268]
[-0.11798927]
[ 0.12866053]]

JamesW22879 · January 24, 2024, 11:22am

I just wanted to confirm that the below code is suppose to be in the predict function. Only thing I see changing w in predict and I thought that as a copy that was passed from model and stayed a local variable?

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

m = X.shape[1]
Y_prediction = np.zeros((1, m))
w  = w.reshape(X.shape[0], 1)

# Compute vector "A" predicting the probabilities of a cat being present in the picture
#(≈ 1 line of code)
# A = ...

Kic · January 24, 2024, 11:39am

Hi @JamesW22879 ,

Please check your direct message, and reply.

Kic · January 24, 2024, 12:04pm

Hi @JamesW22879 ,

You did not pass the updated ‘w’ and ‘b’ to the predict()function. What you have passed to the predict() function are the values returned from the initialize_with_zeros(). The updated ‘w’ and ‘b’ are stored in the variable params, returned from calling the optimize() function. The variable params is a python dictionary. To access the w and b, you have to do:
w = params[“w”]
b = params[“b”]

I suggest you update your code, then do a clean run:
kernel → restart and clear all output
cell → run all code from start

JamesW22879 · January 24, 2024, 12:09pm

I thought I tried that by passing params[“w”] but it did not work.

I just tried the below code with nearly the same results.

YOUR CODE STARTS HERE

w, b = initialize_with_zeros(np.shape(X_train)[0])
print(np.shape(w))
print(type(w))
params, grads, costs = optimize(w, b, X_train, Y_train, num_iterations, learning_rate, print_cost)
print(params)
w = params[“w”]
b = params[“b”]
Y_prediction_test = predict(w, b, X_test)
Y_prediction_train = predict(w, b, X_train)
# YOUR CODE ENDS HERE

(4, 1)
<class ‘numpy.ndarray’>
{‘w’: array([[ 0.08639757],
[-0.08231268],
[-0.11798927],
[ 0.12866053]]), ‘b’: -0.039832360948163205}

AssertionError 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)
131 assert type(d[‘w’]) == np.ndarray, f"Wrong type for d[‘w’]. {type(d[‘w’])} != np.ndarray"
132 assert d[‘w’].shape == (X.shape[0], 1), f"Wrong shape for d[‘w’]. {d[‘w’].shape} != {(X.shape[0], 1)}"
→ 133 assert np.allclose(d[‘w’], expected_output[‘w’]), f"Wrong values for d[‘w’]. {d[‘w’]} != {expected_output[‘w’]}"
134
135 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.]
[0.]
[0.]
[0.]] != [[ 0.08639757]
[-0.08231268]
[-0.11798927]
[ 0.12866053]]

JamesW22879 · January 24, 2024, 12:15pm

Never mind!!! got it to work once I but in the correct quote symbol!

Thank you for your assistance!!

Kic · January 24, 2024, 12:26pm

Hi @JamesW22879 ,

The code you attached on the DM didn’t have the lines extracting w and b. Please check your code carefully.

JamesW22879 · January 24, 2024, 1:21pm

I got it to work with no errors. I submitted the assignment and received a grade of 83% and the feedback showed the following:

[ValidateApp | INFO] Validating ‘/home/jovyan/work/submitted/courseraLearner/W2A2/Logistic_Regression_with_a_Neural_Network_mindset.ipynb’
[ValidateApp | INFO] Executing notebook with kernel: python3
[ValidateApp | ERROR] Timeout waiting for execute reply (30s).
[ValidateApp | ERROR] Interrupting kernel
[ValidateApp | ERROR] Timeout waiting for execute reply (30s).
[ValidateApp | ERROR] Interrupting kernel
Tests failed on 1 cell(s)! These tests could be hidden. Please check your submission.

The following cell failed:

from public_tests import *

model_test(model)

The error was:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-20-9408a3dffbf6> in <module>
      1 from public_tests import *
      2 
----> 3 model_test(model)

~/work/submitted/courseraLearner/W2A2/public_tests.py in model_test(target)
    123     y_test = np.array([[0, 1, 0]])
    124 
--> 125     d = target(X, Y, x_test, y_test, num_iterations=50, learning_rate=0.01)
    126 
    127     assert type(d['costs']) == list, f"Wrong type for d['costs']. {type(d['...

TypeError: model() missing 1 required positional argument: 'print_cost'

Now when I go back to my code I get the same error. Why did the error only show up after submitting the code? It ran clean prior to that. I made no changed to costs in my code.

saifkhanengr · January 24, 2024, 1:57pm

This seems a grader (or Coursera) issue. Please try Kernel → Restart & Run all. Let us know the result…

saifkhanengr · January 24, 2024, 2:28pm

Alright! After reviewing the code, it turns out that the model function was missing some default values. Maybe James deleted it by mistake. See the below figure. Adding print_cost = False to the model function will resolve the issue. It is extremely important not to change the prewritten code.

Edward_Rand · January 26, 2024, 12:58am

Have been having issues with this for several days now… on the verge of becoming bald… Seems no matter what I do I just end up with a new error. Please help. here is my current error.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)
123 y_test = np.array([[0, 1, 0]])
124
→ 125 d = target(X, Y, x_test, y_test, num_iterations=50, learning_rate=0.01)
126
127 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)
36 # YOUR CODE STARTS HERE
37 w, b = initialize_with_zeros(X_train.shape[0])
—> 38 params, grads, costs = optimize(X_train, Y_train, X_test, Y_test, num_iterations, learning_rate, print_cost=False)
39 Y_prediction_test = predict(w, b, X_test)
40 Y_prediction_train = predict(w, b, X_train)

in optimize(w, b, X, Y, num_iterations, learning_rate, print_cost)
35 # grads, cost = …
36 # YOUR CODE STARTS HERE
—> 37 grads, cost = propagate(w, b, X, Y)
38
39 # YOUR CODE ENDS HERE

in propagate(w, b, X, Y)
31 # cost = …
32 # YOUR CODE STARTS HERE
—> 33 A = sigmoid(np.dot(w.T, X) + b)
34 cost = -( 1 / m) * np.sum(Y * np.log(A) + (1 - Y) * (np.log(1 - A)))
35 # YOUR CODE ENDS HERE

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

TMosh · January 26, 2024, 1:07am

Some things to check in your model() function:

Check if you have the correct parameters and in the correct order in the call to optimize().
Don’t hard-code print_cost to False. Just use the value that’s provided when model() is called.

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WK#2 final code assignment-code passed tests but I keep getting errors with Model function

I am stuck in the place it appears most get stuck at. My code passed all individual tests but when I try to combine all in the model function, I get the below errors:

Here is the last output after running above: (4, 1) <class ‘numpy.ndarray’> {‘w’: array([[ 0.08639757], [-0.08231268], [-0.11798927], [ 0.12866053]]), ‘b’: -0.039832360948163205}

YOUR CODE STARTS HERE

(4, 1) <class ‘numpy.ndarray’> {‘w’: array([[ 0.08639757], [-0.08231268], [-0.11798927], [ 0.12866053]]), ‘b’: -0.039832360948163205}

Related topics

Here is the last output after running above:
(4, 1)
<class ‘numpy.ndarray’>
{‘w’: array([[ 0.08639757],
[-0.08231268],
[-0.11798927],
[ 0.12866053]]), ‘b’: -0.039832360948163205}

(4, 1)
<class ‘numpy.ndarray’>
{‘w’: array([[ 0.08639757],
[-0.08231268],
[-0.11798927],
[ 0.12866053]]), ‘b’: -0.039832360948163205}