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@@ -0,0 +1,24 @@
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+#!/usr/bin/python3
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+# -*- coding: utf-8 -*-
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+
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+import tensorflow as tf
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+from tensorflow.keras import layers, models
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+
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+trainingData = tf.constant([[0, 0], [0, 1], [1, 0], [1, 1]])
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+trainLabels = tf.constant([ 0, 1, 1, 0 ])
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+
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+model = models.Sequential()
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+model.add(layers.Dense(32, input_dim=2, activation='relu'))
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+model.add(layers.Dense(1, activation='sigmoid'))
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+
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+model.compile(loss='mean_squared_error',
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+ optimizer='adam',
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+ metrics=['binary_accuracy'])
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+
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+model.fit(trainingData, trainLabels, epochs=400)
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+prediction = model.predict(trainingData)
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+
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+for (a, b), (y,) in zip(trainingData, prediction):
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+ print("{:.0f} xor {:.0f} = {:.0f}".format(a, b, y))
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+
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+
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