Hi.
Attach the smallest executable source code.
To build a common network first, enable the commented out part and run it only once.
import numpy as np
from keras.models import Model
from keras.models import Sequential
from keras.layers import Input, Dense, Activation, Dropout
from keras.layers import BatchNormalization
from keras.models import load_model



# Enable this on the first run and build the network.
'''
model = Sequential(name='sample_01')

model.add(Input(shape=(3,)))
model.add(BatchNormalization())
model.add(Dense(10))
model.add(Activation('relu'))
model.add(Dense(4))

model.compile(optimizer='adam', loss='mean_squared_error')

model.summary()

model.save("model_sample_01.h5")

exit()
'''



model = load_model("model_sample_01.h5")

x_data = [
[0.0, 0.0, 0.0],
[0.0, 0.0, 1.0],
[0.0, 1.0, 0.0],
[0.0, 1.0, 1.0],
[1.0, 0.0, 0.0],
[1.0, 0.0, 1.0],
[1.0, 1.0, 0.0],
[1.0, 1.0, 1.0],
]

y_data = [
[0.0, 0.0, 0.0, 1.0],
[0.0, 1.0, 1.0, 1.0],
[0.0, 1.0, 1.0, 1.0],
[0.0, 1.0, 0.0, 0.0],
[0.0, 1.0, 1.0, 1.0],
[0.0, 1.0, 0.0, 0.0],
[0.0, 1.0, 0.0, 0.0],
[1.0, 1.0, 1.0, 0.0],
]

x_data_keras = np.array(x_data)
y_data_keras = np.array(y_data)

max_loop = 1
max_in_loop = 10





# training
for i in range(max_loop):
    for j in range(max_in_loop):
        result_1 = model.train_on_batch(x_data_keras, y_data_keras)
#        result_2 = model.test_on_batch(x_data_keras, y_data_keras)
        result_3 = model.evaluate(x_data_keras, y_data_keras, verbose = 0)
        print(str(result_1))


print("end")

BRs
take hamster