def predict(model, x):
    (W1, b1, W2, b2) = (model['W1'], model['b1'], model['W2'], model['b2'])
    z1 = (x.dot(W1) + b1)
    a1 = np.tanh(z1)
    z2 = (a1.dot(W2) + b2)
    exp_scores = np.exp(z2)
    probs = (exp_scores / np.sum(exp_scores, axis=1, keepdims=True))
    return np.argmax(probs, axis=1)
def predict(model, x):
    (W1, b1, W2, b2) = (model['W1'], model['b1'], model['W2'], model['b2'])
    z1 = (x.dot(W1) + b1)
    a1 = np.tanh(z1)
    z2 = (a1.dot(W2) + b2)
    exp_scores = np.exp(z2)
    probs = (exp_scores / np.sum(exp_scores, axis=1, keepdims=True))
    return np.argmax(probs, axis=1)