I’m trying to build a neural network to approximate the squares of numbers from -50 to 50. I’ve referred to the code in this answer to write mine:

import tensorflow as tf
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras import regularizers

x_train = np.random.random((10000,1))*100-50
y_train = np.square(x_train)

model = Sequential(
        [
            Dense(8, activation = 'relu', kernel_regularizer = regularizers.l2(0.001), input_shape = (1,)),
            Dense(8, activation = 'relu',  kernel_regularizer = regularizers.l2(0.001)),
            Dense(1, activation = 'relu')
            ]

        )

batch_size = 32
epochs = 100

model.compile(loss = 'mse', optimizer='adam')
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose = 1)

x = "n" 
while True:
    print("enter num:")
    x = input()
    if x == "end":
        break

    X = int(x)

    predicted_sum = model.predict(np.array([X]))
    print(predicted_sum)

The problem is, all inputs give rise to the output “[[0.]]”. I don’t know why this is caused and how to fix it, can someone help?

This message is displayed immediately after the code is executed, does it have something to do with the problem?

oneDNN custom operations are on. You may see slightly
different numerical results due to floating-point round-off errors from different computation orders. To turn them off
, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.