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hidden layer to output layer self.weights.append(np.random.randn(self.num_outputs, self.num_neurons_per_layer)) self.biases.append(np.random.randn(self.num_outputs, 1)) def forward_propagate(self, inputs): activations = [inputs] for i in range(self.num_hidden_layers + 1): z =