This common example is a simple way to dabble into the most basic neural network (i.e. two neurons, one input and one output).

I have been playing around with this using TensorFlow and keras, and I am wondering, it should be very simple for the NN to learn the weight (1.8) and bias (32) with little to no effort.

However, I find that (albeit, using only 13 examples) after running for 500 epochs I can get a weight of 1.8 but the bias is always off (29.2, for example). Obviously there is no activation function, given this linear problem.

My question is, for such a simple case, will a NN be able to solve this problem exactly?

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    $\begingroup$ Yes it should. Did you check the learning rate? Maybe it is not able to converge in the constant for some reason. $\endgroup$ Commented Mar 1, 2020 at 21:25
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    $\begingroup$ It did! Simply, 500 epochs was not enough. I ran it for 10,000 for good measure. It converged rather nicely. Thanks for the reassurance. Also, the learning rate was set to 0.01 for this example (using Adam). $\endgroup$
    – Shinobii
    Commented Mar 2, 2020 at 2:35

1 Answer 1


Hard to say without seeing code, but I can imagine several reasons:

  • You are using an activation function. You want 'linear' or no activation
  • Learning rate is much too low
  • Wrong network (should be one dense layer with one weight)
  • Poor batch size

This works pretty instantly:

from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Nadam

model = Sequential([Dense(1, activation=None, input_shape=[1])])
model.compile(loss='mean_squared_error', optimizer=Nadam(lr=0.1), metrics=['mean_absolute_error'])

F = list(range(-200,200))
C = [(f - 32) * 5 / 9 for f in F]

model.fit(F, C, batch_size=16, epochs=50)

[array([[0.5555556]], dtype=float32), array([-17.777737], dtype=float32)]

300 F -> 148.8889 C, good, and it has learned the conversion is C = 5/9 * F - 17.7778, or C = 5/9 * (F - 32)


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