Tensorflow keras模型培训的最高损失额是多少？

我正在用Keras训练我的模型，我正在尝试阅读评估统计数据。我知道损失函数的作用，但可能的最大值是什么？越接近零越好，但我不知道0.2是否好。 我可以看到，经过多次迭代后，损失正在下降，精度也在提高

我的模型培训代码：

def trainModel(bow,unitlabels,units):
    x_train = np.array(bow)
    print("X_train: ", x_train)
    y_train = np.array(unitlabels)
    print("Y_train: ", y_train)
    model = tf.keras.models.Sequential([
            tf.keras.layers.Dense(256, activation=tf.nn.relu),
            tf.keras.layers.Dropout(0.2),
            tf.keras.layers.Dense(len(units), activation=tf.nn.softmax)])
    model.compile(optimizer='adam',
                         loss='sparse_categorical_crossentropy',
                         metrics=['accuracy'])
    model.fit(x_train, y_train, epochs=50)
    return model

我的结果是：

Epoch 1/50
1249/1249 [==============================] - 0s 361us/sample - loss: 0.8800 - acc: 0.7590
Epoch 2/50
1249/1249 [==============================] - 0s 90us/sample - loss: 0.4689 - acc: 0.8519
Epoch 3/50
1249/1249 [==============================] - 0s 90us/sample - loss: 0.3766 - acc: 0.8687
Epoch 4/50
1249/1249 [==============================] - 0s 92us/sample - loss: 0.3339 - acc: 0.8663
Epoch 5/50
1249/1249 [==============================] - 0s 89us/sample - loss: 0.3057 - acc: 0.8719
Epoch 6/50
1249/1249 [==============================] - 0s 87us/sample - loss: 0.2877 - acc: 0.8799
Epoch 7/50
1249/1249 [==============================] - 0s 88us/sample - loss: 0.2752 - acc: 0.8815
Epoch 8/50
1249/1249 [==============================] - 0s 89us/sample - loss: 0.2650 - acc: 0.8783
Epoch 9/50
1249/1249 [==============================] - 0s 92us/sample - loss: 0.2562 - acc: 0.8847
Epoch 10/50
1249/1249 [==============================] - 0s 91us/sample - loss: 0.2537 - acc: 0.8799
Epoch 11/50
1249/1249 [==============================] - 0s 89us/sample - loss: 0.2468 - acc: 0.8903
Epoch 12/50
1249/1249 [==============================] - 0s 88us/sample - loss: 0.2436 - acc: 0.8927
Epoch 13/50
1249/1249 [==============================] - 0s 89us/sample - loss: 0.2420 - acc: 0.8935
Epoch 14/50
1249/1249 [==============================] - 0s 88us/sample - loss: 0.2366 - acc: 0.8935
Epoch 15/50
1249/1249 [==============================] - 0s 94us/sample - loss: 0.2305 - acc: 0.8951
Epoch 16/50
1249/1249 [==============================] - 0s 98us/sample - loss: 0.2265 - acc: 0.8991
Epoch 17/50
1249/1249 [==============================] - 0s 90us/sample - loss: 0.2280 - acc: 0.8967
Epoch 18/50
1249/1249 [==============================] - 0s 90us/sample - loss: 0.2247 - acc: 0.8951
Epoch 19/50
1249/1249 [==============================] - 0s 92us/sample - loss: 0.2237 - acc: 0.8975
Epoch 20/50
1249/1249 [==============================] - 0s 102us/sample - loss: 0.2196 - acc: 0.8991
Epoch 21/50
1249/1249 [==============================] - 0s 102us/sample - loss: 0.2223 - acc: 0.8983
Epoch 22/50
1249/1249 [==============================] - 0s 102us/sample - loss: 0.2163 - acc: 0.8943
Epoch 23/50
1249/1249 [==============================] - 0s 100us/sample - loss: 0.2177 - acc: 0.8983
Epoch 24/50
1249/1249 [==============================] - 0s 101us/sample - loss: 0.2165 - acc: 0.8983
Epoch 25/50
1249/1249 [==============================] - 0s 100us/sample - loss: 0.2148 - acc: 0.9007
Epoch 26/50
1249/1249 [==============================] - 0s 98us/sample - loss: 0.2189 - acc: 0.8903
Epoch 27/50
1249/1249 [==============================] - 0s 98us/sample - loss: 0.2099 - acc: 0.9023
Epoch 28/50
1249/1249 [==============================] - 0s 98us/sample - loss: 0.2102 - acc: 0.9023
Epoch 29/50
1249/1249 [==============================] - 0s 94us/sample - loss: 0.2091 - acc: 0.8975
Epoch 30/50
1249/1249 [==============================] - 0s 90us/sample - loss: 0.2064 - acc: 0.9015
Epoch 31/50
1249/1249 [==============================] - 0s 90us/sample - loss: 0.2044 - acc: 0.9023
Epoch 32/50
1249/1249 [==============================] - 0s 90us/sample - loss: 0.2070 - acc: 0.9031
Epoch 33/50
1249/1249 [==============================] - 0s 90us/sample - loss: 0.2045 - acc: 0.9039
Epoch 34/50
1249/1249 [==============================] - 0s 94us/sample - loss: 0.2007 - acc: 0.9063
Epoch 35/50
1249/1249 [==============================] - 0s 90us/sample - loss: 0.1999 - acc: 0.9055
Epoch 36/50
1249/1249 [==============================] - 0s 103us/sample - loss: 0.2010 - acc: 0.9039
Epoch 37/50
1249/1249 [==============================] - 0s 111us/sample - loss: 0.2053 - acc: 0.9031
Epoch 38/50
1249/1249 [==============================] - 0s 99us/sample - loss: 0.2018 - acc: 0.9039
Epoch 39/50
1249/1249 [==============================] - 0s 90us/sample - loss: 0.2023 - acc: 0.9055
Epoch 40/50
1249/1249 [==============================] - 0s 90us/sample - loss: 0.2019 - acc: 0.9015
Epoch 41/50
1249/1249 [==============================] - 0s 92us/sample - loss: 0.2040 - acc: 0.8983
Epoch 42/50
1249/1249 [==============================] - 0s 103us/sample - loss: 0.2033 - acc: 0.8943
Epoch 43/50
1249/1249 [==============================] - 0s 97us/sample - loss: 0.2024 - acc: 0.9039
Epoch 44/50
1249/1249 [==============================] - 0s 90us/sample - loss: 0.2047 - acc: 0.9079
Epoch 45/50
1249/1249 [==============================] - 0s 90us/sample - loss: 0.1996 - acc: 0.9039
Epoch 46/50
1249/1249 [==============================] - 0s 91us/sample - loss: 0.1979 - acc: 0.9079
Epoch 47/50
1249/1249 [==============================] - 0s 90us/sample - loss: 0.1960 - acc: 0.9087
Epoch 48/50
1249/1249 [==============================] - 0s 97us/sample - loss: 0.1969 - acc: 0.9055
Epoch 49/50
1249/1249 [==============================] - 0s 99us/sample - loss: 0.1950 - acc: 0.9087
Epoch 50/50
1249/1249 [==============================] - 0s 98us/sample - loss: 0.1956 - acc: 0.9071

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交叉熵损失的最大损失发生在当你在你的类上有一个均匀的分布，没有倾向于任何类，你得到最大熵。看看公式：

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您可以计算最大损失，通常自然对数

ln

用于对数。由于您将有一个1个热目标，总和将减少到

-log（a^（i）_k）

，并且统一假设

a^（i）=1/len（单位）

。例如，在二进制分类中，将

a=0.5

和

-ln（0.5）~0.693147

设置为最大损失约为0.69。

损失通常定义在0和1之间。关于0.2是否好，没有直接的答案。为此，您需要查看其他指标，以了解您的模型的能力，即精确召回率、auc、混淆矩阵等。谢谢！此外，您还应该在训练运行中包含验证数据。只要培训和验证损失继续下降，您就可以认为培训时间越长模型越好。然而，通常在某个地方会有一个分歧点，即训练损失持续下降，但验证损失又开始上升。这就是你开始过度拟合训练数据的地方。我可以通过在我的model.fit（）函数中添加类似validation_split=0.2这样的内容来完成吗？是的，这会起作用，而且可能是实现这一点的最快方法。但从长远来看，我建议您自己进行此拆分，然后使用

validation\u data=…

将验证数据显式传递给

fit

。自己进行分割是一个好主意，因为您可以确保训练运行之间的一致性，即使（例如）您的训练数据发生变化