35 lines
750 B
Python
35 lines
750 B
Python
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import tensorflow as tf
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import numpy as np
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x = np.array([
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[0, 0, 0],
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[0, 0, 1],
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[0, 1, 0],
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[0, 1, 1],
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[1, 0, 0],
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[1, 0, 1],
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[1, 1, 0],
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[1, 1, 1],
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])
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y = np.array([sum(i) % 2 for i in x])
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model = tf.keras.Sequential([
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tf.keras.layers.Input(shape = (3,)),
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tf.keras.layers.Dense(3, activation = "tanh"),
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tf.keras.layers.Dense(1, activation = "sigmoid")
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])
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model.compile(
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optimizer = tf.keras.optimizers.Adam(learning_rate = 0.05),
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loss = "binary_crossentropy",
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metrics = ["accuracy"]
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)
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model.load_weights("mod1_final.weights.h5")
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prediction = model.predict(x)
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for i, o, t in zip(x, prediction, y):
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print(f"i = {i}, o = {round(o[0])}, t = {t} : {'Good' if t == round(o[0]) else 'Bad'}")
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