Can some one please help me with this, I am stuck here---->

# GRADED FUNCTION: model

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
# parameters, grads, costs = ...
# Retrieve parameters w and b from dictionary "parameters"
# w = ...
# b = ...
# Predict test/train set examples (≈ 2 lines of code)
# Y_prediction_test = ...
# Y_prediction_train = ...
# YOUR CODE STARTS HERE
w, b = initialize_with_zeros(4)
parameters, grads, costs = optimize(w,b,X_train, Y_train, num_iterations, learning_rate)
w = params["w"]
b = params["b"]
print(X_train.shape)
# YOUR CODE ENDS HERE
Y_prediction_test = predict(w, b, X_test)
# 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
```

I get an error →

in predict(w, b, X)

16 m = X.shape[1]

17 Y_prediction = np.zeros((1, m))

—> 18 w = w.reshape(X.shape[0], 1)

19

20 # Compute vector “A” predicting the probabilities of a cat being present in the picture

ValueError: cannot reshape array of size 2 into shape (4,1)