import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt

def fTest(x_arg):
    return 5 - 3*x_arg + 2*(x_arg)**2

# training data
t = np.random.choice(np.arange(-10,10, .01),5000 )
t1 = []
for i in range(len(t)):
    t1.append([t[i], t[i]**2])
s = []
for i in range(len(t)):
    s.append(fTest(t[i]))
t1 = np.array(t1)
s = np.array(s)

# validation set
v = np.random.choice(np.arange(-10,10, .01),5000 )
v1 = []
for i in range(len(v)):
    v1.append([v[i], v[i]**2])
u = []
for i in range(len(v)):
    u.append(fTest(v[i]))
v1 = np.array(v1)
u = np.array(u)

model = keras.Sequential([
    keras.layers.Dense(1, input_shape=(2,) , use_bias=True),
])

model.compile(optimizer='adam',
              loss='mean_squared_logarithmic_error',
              metrics=['mae','accuracy'])

model.fit(t1, s, batch_size=50, epochs=2000, validation_data=(v1,u))

Epoch 2000/2000
200/200 [==============================] - 0s 20us/step - loss: 4.5276e-13 - mean_absolute_error: 3.3994e-05 - acc: 0.0000e+00 - val_loss: 3.3360e-13 - val_mean_absolute_error: 3.1792e-05 - val_acc: 0.0000e+00