Why are 2 test cases failing

Course Q&A Deep Learning Specialization Neural Networks and Deep Learning
1

GRADED FUNCTION: two_layer_model

def two_layer_model(X, Y, layers_dims, learning_rate = 0.0075, num_iterations = 3000, print_cost=False):
“”"
Implements a two-layer neural network: LINEAR->RELU->LINEAR->SIGMOID.

Arguments:
X -- input data, of shape (n_x, number of examples)
Y -- true "label" vector (containing 1 if cat, 0 if non-cat), of shape (1, number of examples)
layers_dims -- dimensions of the layers (n_x, n_h, n_y)
num_iterations -- number of iterations of the optimization loop
learning_rate -- learning rate of the gradient descent update rule
print_cost -- If set to True, this will print the cost every 100 iterations 

Returns:
parameters -- a dictionary containing W1, W2, b1, and b2
"""

np.random.seed(1)
grads = {}
costs = []                              # to keep track of the cost
m = X.shape[1]                           # number of examples
(n_x, n_h, n_y) = layers_dims

# Initialize parameters dictionary, by calling one of the functions you'd previously implemented
#(≈ 1 line of code)
# parameters = ...
# YOUR CODE STARTS HERE

Moderator Edit: Solution Code Removed.
return parameters, costs

def plot_costs(costs, learning_rate=0.0075):
plt.plot(np.squeeze(costs))
plt.ylabel(‘cost’)
plt.xlabel(‘iterations (per hundreds)’)
plt.title(“Learning rate =” + str(learning_rate))
plt.show()

Cost after iteration 1: 0.6930054580300078
Cost after first iteration: 0.693049735659989
Cost after iteration 1: 0.6927286108981437
Cost after iteration 1: 0.6927286108981437
Cost after iteration 1: 0.6927286108981437
Error: Wrong output for variable W1.
Error: Wrong output for variable b1.
Error: Wrong output for variable W2.
Error: Wrong output for variable b2.
Cost after iteration 2: 0.6922979697910279
Error: Wrong output for variable W1.
Error: Wrong output for variable b1.
Error: Wrong output for variable W2.
Error: Wrong output for variable b2.
2 Tests passed
2 Tests failed

AssertionError Traceback (most recent call last)
in
3 print("Cost after first iteration: " + str(costs[0]))
4
----> 5 two_layer_model_test(two_layer_model)

~/work/release/W4A2/public_tests.py in two_layer_model_test(target)
75 ]
76
—> 77 multiple_test(test_cases, target)
78
79

~/work/release/W4A2/test_utils.py in multiple_test(test_cases, target)
140 print(‘\033[92m’, success," Tests passed")
141 print(‘\033[91m’, len(test_cases) - success, " Tests failed")
→ 142 raise AssertionError(“Not all tests were passed for {}. Check your equations and avoid using global variables inside the function.”.format(target.name))
143

AssertionError: Not all tests were passed for two_layer_model. Check your equations and avoid using global variables inside the function.

2

First, sharing your code is not allowed by the community code of conduct. So, avoid sharing your code.

Second, it is not enough to say, “Why are 2 test cases failing” and share your whole code. You have to share the Week number, assignment number, and full error only.

Regarding your error, your argument to the linear_activation_backward function is wrong. Check the instructions again:

def linear_activation_backward(dA, cache, activation):
    ...
    return dA_prev, dW, db