The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() python numpy using ReLu function

import numpy as np

class NeuralNetwork():

    def __init__(self):
        np.random.seed(1)

        self.synaptic_weights = np.random.random((8, 5))

    def rectified(self, x):

        return max(0, x)

    def rectified_derivative(x):
         x[x<=0] = 0
         x[x>0] = 1
         return x

    def train(self, training_inputs, training_outputs, training_iterations):

        for iteration in range(training_iterations):
            output = self.think(training_inputs)

            error = training_outputs - output

            adjustments = np.dot(training_inputs.T, error * self.rectified_derivative(output))

            self.synaptic_weights += adjustments

    def think(self, inputs):

        inputs = inputs.astype(float)
        output = self.rectified(np.dot(inputs, self.synaptic_weights))
        return output

Not sure why i am receiving this error. Could someone please point me in the right direction? Error is on this line:

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

    return max(0, x)

If you are trying to clamp all the values to be non-negative, use numpy.clip as follows:

x.clip(0)

Python's builtin max operator does not play well with numpy arrays