2137_keras.py

import tensorflow as tf
import numpy as np
import random

# neural network predicting if input hour is 21:37
# network created with layers of size: [73 21]
# because of small number of neurons if may take few tries to read good results (around 0.98 accuracy)

X, y = [], []
for _ in range(10000): 
    h, m = random.randint(0, 23), random.randint(0, 59)
    if h == 21 and m == 37:
        y.append([1, 0])
    else:
        y.append([0, 1])
    X.append([h, m])

for _ in range(10000): 
    X.append([21, 37])
    y.append([1, 0])

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)


model = tf.keras.Sequential([
    tf.keras.layers.Dense(7, activation='relu', input_shape=(2,)),
    tf.keras.layers.Dense(3, activation='relu'),
    tf.keras.layers.Dense(1, activation='relu'),
    tf.keras.layers.Dense(2, activation='softmax')
])

model.compile(optimizer='rmsprop',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

n = model.fit(np.array(X_train), np.array(y_train),
              batch_size=100, epochs=10,
              validation_data=(np.array(X_test), np.array(y_test)))

y_pred = model.predict(np.array([
    [10, 20],  # 0.07371756
    [21, 20],  # 0.07371756
    [20, 37],  # 0.89896834
    [21, 38],  # 0.96462727
    [21, 39],  # 0.92450625
    [21, 37],  # 0.9837998
]))

for _y in y_pred:
    print(_y[0])