def predict(self, feed):
        feed = Feed.from_any(feed)
        self.phase = 'test'

        if isinstance(self.loss, SoftmaxCrossEntropy):
            # Add softmax from SoftmaxCrossEntropy
            self.layers += [self.loss]

        y = []
        for x_batch, in feed.batches():
            y.append(np.array(self.fprop(x_batch)))
        y = np.concatenate(y)[:feed.n_samples]

        if isinstance(self.loss, SoftmaxCrossEntropy):
            self.layers = self.layers[:-1]
        return y
Add a new node to the tree.
dataset/ETHPy150Open andersbll/deeppy/deeppy/feedforward/neural_network.py/NeuralNetwork.predict--crossover with--dataset/ETHPy150Open kivy/kivy/kivy/uix/treeview.py/TreeView.add_node