My second NN which can see if a number is even or odd

even_or_odd.py

from keras.utils import to_categorical
from keras.models import Sequential
from keras.layers import Dense
from random import sample
import numpy as np
import os

# Train data (Number 0 to 99 in binary list format with maximum length of 10)
x_train = np.array(list(map(lambda x: [0 for _ in range(
    10 - len(format(x[0], "b")))] + list(format(x[0], "b")), sample([[x] for x in range(100)], 100))))
y_train = np.array([not bool(int(''.join(x), 2) % 2) for x in x_train])
y_train_binary = to_categorical(y_train)

# Test data same like train data
x_test = np.array(list(map(lambda x: [0 for _ in range(
    10 - len(format(x[0], "b")))] + list(format(x[0], "b")), sample([[x] for x in range(100)], 100))))
y_test = np.array([not bool(int(''.join(x), 2) % 2) for x in x_test])
y_test_binary = to_categorical(y_test)

# Neural Network
model = Sequential([
    Dense(units=2, activation="relu", input_dim=10),
    Dense(units=2, activation="softmax")
])

model.compile(optimizer="sgd", loss="binary_crossentropy",
              metrics=["accuracy"])

if os.path.exists("weights.h5"):
    model.load_weights("weights.h5")
else:
    model.fit(x_train, y_train_binary, epochs=30, batch_size=1)
    model.save_weights("weights.h5")

loss_metrics = model.evaluate(x_test, y_test_binary, batch_size=1)
print("Loss:", loss_metrics[0], "Accuracy: ", str(loss_metrics[1] * 100) + "%")

while True:
    x_predict = input("Give me a number: ")
    # Parse input to integer
    try:
        x_predict = int(x_predict)
    except ValueError:
        print("This wasn't a number.")
        continue
    # Check if number is too long
    if len(list(format(x_predict, "b"))) > 10:
        print("Number is too big.")
        continue
    # Check for negative number
    if x_predict < 0:
        x_predict *= -1
    x_predict = np.array(
        [[0 for _ in range(10 - len(format(x_predict, "b")))] + list(format(x_predict, "b"))])
    result = model.predict(x_predict)
    print("Number is even." if result[0][1] > result[0][0] else "Number is odd.", result[0])