Neural Networks and Deep Learning Error

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

In Assignment 2 for the following code
def model(X_train, Y_train, X_test, Y_test, num_iterations=2000, learning_rate=0.5, print_cost=False):
“”"
Builds the logistic regression model by calling the function you’ve implemented previously

Arguments:
X_train -- training set represented by a numpy array of shape (num_px * num_px * 3, m_train)
Y_train -- training labels represented by a numpy array (vector) of shape (1, m_train)
X_test -- test set represented by a numpy array of shape (num_px * num_px * 3, m_test)
Y_test -- test labels represented by a numpy array (vector) of shape (1, m_test)
num_iterations -- hyperparameter representing the number of iterations to optimize the parameters
learning_rate -- hyperparameter representing the learning rate used in the update rule of optimize()
print_cost -- Set to True to print the cost every 100 iterations

Returns:
d -- dictionary containing information about the model.
"""
# (≈ 1 line of code)   
# initialize parameters with zeros 
# w, b = ...

#(≈ 1 line of code)
# Gradient descent 
# params, grads, costs = ...

# Retrieve parameters w and b from dictionary "params"
# w = ...
# b = ...

# Predict test/train set examples (≈ 2 lines of code)
# Y_prediction_test = ...
# Y_prediction_train = ...

# YOUR CODE STARTS HERE

{Moderator Edit. Code Removed}
# YOUR CODE ENDS HERE

# Print train/test Errors
if print_cost:
    print("train accuracy: {} %".format(100 - np.mean(np.abs(Y_prediction_train - Y_train)) * 100))
    print("test accuracy: {} %".format(100 - np.mean(np.abs(Y_prediction_test - Y_test)) * 100))


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

from public_tests import *

model_test(model)
I have Error in model function though all other function works correct individually. I don’t know what to do. can anyone help?
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)
130
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

AssertionError: Wrong shape for d[‘w’]. (12288, 1) != (4, 1)

2

Your initialize_with_zeros() is wrong. Just grab the row of X_train using .shape. Test set and Train sets have different numbers for rows.

Also, we have X_train, Y_train. Our model don’t know about train_set_x and train_set_y.