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  10. 模型-MLP分類法One Hot Encoding

模型-MLP分類法One Hot Encoding

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    t=2
    category=2
    dim=x_train.shape[1]
     
     
    y_train2=tf.keras.utils.to_categorical(y_train, num_classes=(category))
    y_test2=tf.keras.utils.to_categorical(y_test, num_classes=(category))
    # 建立模型
    model = tf.keras.models.Sequential()
    model.add(tf.keras.layers.Dense(units=200,
        activation=tf.nn.relu,
        input_dim=dim))
    model.add(tf.keras.layers.Dense(units=40*t,
        activation=tf.nn.relu ))
    model.add(tf.keras.layers.Dense(units=80*t,
        activation=tf.nn.relu ))
    model.add(tf.keras.layers.Dense(units=100*t,
        activation=tf.nn.relu ))
    model.add(tf.keras.layers.Dense(units=category,
        activation=tf.nn.softmax ))
    model.compile(optimizer='adam',
        loss=tf.keras.losses.categorical_crossentropy,
        metrics=['accuracy'])
    model.fit(x_train, y_train2,
              epochs=300*t,
              batch_size=64)
     
    #測試
    model.summary()
     
    score = model.evaluate(x_test, y_test2, batch_size=64)
    print("score:",score)
     
    predict2 = model.predict_classes(x_test)
    print("predict_classes:",predict2)
    print("y_test",y_test[:])