Prediction

\[y_{prediction} = 1(activation > 0.5)\]

As an example, if we would like to set the entries of a matrix X to 0 and 1 based on a threshold we need to:

\[X_{new} = (X > threshold)\]

def predict(parameters, X):
    """
    Using the learned parameters, predicts a class for each example in X
    Arguments:
    parameters -- python dictionary containing your parameters
    X -- input data of size (n_x, m)
    Returns
    predictions -- vector of predictions of our model (red: 0 / blue: 1)
    """

    return predictions

Accuracy

predictions = predict(parameters, X)

Expected Output

• Cost after iteration 9000 0.218607
• Accuracy 90 %

Accuracy is really high compared to Logistic Regression. The model has learnt the leaf patterns of the flower! Neural networks are able to learn even highly non-linear decision boundaries, unlike logistic regression.