XOR gate exercise using Jupiter notebook

Create jupyter notebook file: Intro_NN_XORGATE.ipynb

****Exercise****
# Uncomment the xor_gate line and find out which neurons besides the or_gate neuron the

# network should have in its hidden and output layer to produce the right values.
in[]class Network():

def __init__(self, gate1, gate2, out_gate):

self.hidden_neuron1 = gate1

self.hidden_neuron2 = gate2

self.out_neuron = out_gate

def activate(self, x1, x2):

z1 = self.hidden_neuron1.activate(x1, x2)

z2 = self.hidden_neuron2.activate(x1, x2)

return self.out_neuron.activate(z1, z2)

#xor_gate = Network(…, …, and_gate)

make_truth_table(xor_gate)
****Exercise****
# Finish this version of an XOR gate that more closely resembles a neural network by determining the shapes the #weights and biases need to have.
#W1 = np.array(…)

#b1 = np.array(…)
#W2 = np.array(…)

#b2 = np.array(…)
in[?]hidden_layer = Layer(W1, b1)

output_layer = Layer(W2, b2)
in[]class Network():

def __init__(self, hidden, output):

self.hidden = hidden

self.output = output

def activate(self, X):

z = self.hidden.activate(X)

return self.output.activate(z)
xor_gate = Network(hidden_layer, output_layer)
xor_output = xor_gate.activate(X)

np.round(xor_output)

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