I'm implementing a cost function for a neural network, which takes in the predictions made on the training set by the forward propagation step, the true output labels Y, and calculates the cost of the model. The function definition is -

def calculate_cost (Y, prediction, activation_output = 'sigmoid'):
    """
    Calculates the cost.

    Takes in the output labels and the prediction from forward propagation, as well as the activation function of the output layer.
    Returns the cost.
    """
    m = Y.shape[1]

    if activation_output.lower() == 'sigmoid':
        cost = (1/m)*np.sum(Y*np.log(prediction) + (1-Y)*np.log(1-prediction))

    if activation_output.lower() == 'softmax':
        cost = (1/m)*np.sum(np.sum(Y*np.log(prediction), axis = 0, keepdims = True))

    return cost

I get an error on the line where I declare m as the second dimension of the input parameter Y.

<ipython-input-6-0c38b0031612> in calculate_cost(Y, prediction, activation_output)     
     10     """
---> 11     m = Y.shape[1]
     12 
     13     if activation_output.lower() == 'sigmoid':

AttributeError: 'function' object has no attribute 'shape'

Why is this happening? Y is clearly a numpy array.

I am calling a function model() which I have defined which takes in the input data set, the corresponding output labels, and a few hyperparameters and trains the model. This is the function definition for the model() function -

def model (X, Y, architecture, activation_functions, learning_rate = 0.001, print_cost = True, number_of_iterations = 10000):
    """
    Takes in the training set X, the labels Y, and all the required parameters and trains the defined model for the given number of iterations.
    Prints the cost if print_cost is true.
    """
    costs = []
    number_of_layers = len(architecture) - 1

    parameters = initialize_parameters(architecture)

    for i in range(number_of_iterations):

        prediction, cache = forward_propagation(X, parameters, number_of_layers, activation_functions)
        cost = calculate_cost(Y, prediction, activation_functions[-1])

        costs.append(cost)
        gradients = backward_propagation(X, Y, cache, number_of_layers)

        parameters = update_parameters(parameters, number_of_layers, gradients, alpha=0.01)

        if print_cost and i%100 == 0:
            print(f'Completed {i} iterarions.\n')
            print(f'Cost after iteration {i} = {cost}')

    plt.plot(costs)
    plt.xlabel('Iterations (in hundereds)')
    plt.ylabel('Cost')
    plt.title(f'Learning rate = {learning_rate}')
    plt.show()

    print(f'Ran {number_of_iterations} iterations. Returning parameters now.')
    print(f'The final cost of the model was: {costs[-1]}')
    print(f'The training accuracy was: {calculate_accuracy(X, Y, parameters, number_of_layers, activation_functions)}')

    return parameters

I am calling the function like this -

X_train = pd.read_csv('training_set.csv', header = None).to_numpy()
Y_train = pd.read_csv('training_labels.csv', header = None).to_numpy

model(X_train, Y_train, [10,5,5,1], ['relu','relu','relu','relu','sigmoid'])

The second parameter Y_train gets passed to the calculate_cost() function. And Y_train is clearly an array:/