Tensorflow：试图让玩具神经网络``学习''

Question

Trying to get very simple ('toy') 2-layer neural network to build model, as a learning example to make sure the math flows correctly.

The model should learn that a '1' on the first and last feature equates to a '1' output.

features = []
features.append([[0, 0, 0, 0, 0], [0]])
features.append([[0, 0, 0, 0, 1], [0]])
features.append([[0, 0, 0, 1, 1], [0]])
features.append([[0, 0, 1, 1, 1], [0]])
features.append([[0, 1, 1, 1, 1], [0]])
features.append([[1, 1, 1, 1, 0], [0]])
features.append([[1, 1, 1, 0, 0], [0]])
features.append([[1, 1, 0, 0, 0], [0]])
features.append([[1, 0, 0, 0, 0], [0]])
features.append([[1, 0, 0, 1, 0], [0]])
features.append([[1, 0, 1, 1, 0], [0]])
features.append([[1, 1, 0, 1, 0], [0]])
features.append([[0, 1, 0, 1, 1], [0]])
features.append([[0, 0, 1, 0, 1], [0]])
# output of [1] of positions [0,4]==1
features.append([[1, 0, 0, 0, 1], [1]])
features.append([[1, 1, 0, 0, 1], [1]])
features.append([[1, 1, 1, 0, 1], [1]])
features.append([[1, 1, 1, 1, 1], [1]])
features.append([[1, 0, 0, 1, 1], [1]])
features.append([[1, 0, 1, 1, 1], [1]])
features.append([[1, 1, 0, 1, 1], [1]])
features.append([[1, 0, 1, 0, 1], [1]])

However I cannot get any error/cost to show up...

Epoch 3 completed out of 10 cost: 0.0
Epoch 5 completed out of 10 cost: 0.0
Epoch 7 completed out of 10 cost: 0.0
Epoch 9 completed out of 10 cost: 0.0
Accuracy: 1.0

Thanks in advance for having a quick look: here is the notebook ...

Answer 1

The problem turned out to be the output, it needed to be a 2-class array. Not sure why this is necessarily the case.

features.append([[0, 0, 0, 0, 0], [0,1]])
features.append([[0, 0, 0, 0, 1], [0,1]])
features.append([[0, 0, 0, 1, 1], [0,1]])
features.append([[0, 0, 1, 1, 1], [0,1]])
features.append([[0, 1, 1, 1, 1], [0,1]])

the working notebook is here .