tensorflow - 通过神经网络 (Keras) 进行价格预测

Question

我正在尝试进行通话价格预测，我的数据集看起来像这样

call_category_id,duration,Number 1,Number 2,price
9,24,77348,70000,0.01
9,144,77348,70000,0.08
9,138,77348,70000,0.08
9,12,77348,70000,0.01

拨打的号码分为两个号码（号码 1，号码 2），因为我认为这会改善预测结果。通常呼叫的前几位数字决定了每分钟的价格。

我的模型看起来像这样：

def get_model():
    model = Sequential([
        Dense(40,
              activation='relu',
              kernel_initializer='uniform',
              input_shape=(4,)),
        Dropout(0.3),
        Dense(36,
              activation='relu',
              kernel_initializer='uniform'),
        Dropout(0.3),
        Dense(32,
              activation='relu',
              kernel_initializer='uniform'),
        Dropout(0.3),
        Dense(28,
              activation='relu',
              kernel_initializer='uniform'),
        Dropout(0.3),
        Dense(24,
              activation='relu',
              kernel_initializer='uniform'),
        Dense(32,
              activation='relu',
              kernel_initializer='uniform'),
        Dropout(0.3),
        Dense(20,
              activation='relu',
              kernel_initializer='uniform'),
        Dropout(0.3),
        Dense(1, activation='linear'),
    ])
    c_optimizers = optimizers.Adam()
    model.compile(optimizer=c_optimizers,
                  loss='mean_squared_error',
                  metrics=['accuracy'])

    return model

model.fit(
    x_train,
    y_train,
    batch_size=1024,
    epochs=1000,
    validation_data=(x_test, y_test),
    shuffle=True,
    callbacks=[tensor_board])

然而，挑战在于准确性永远不会提高它停留在 19.6%。

39879/39879 [==============================] - 0s 5us/step - loss: 0.1646 - acc: 0.1969 - val_loss: 0.1003 - val_acc: 0.2065

标签： tensorflowmachine-learningkeraslinear-regression