Keras模型训练中损失函数的最大值是多少？附训练代码与结果

Hey there! Let's unpack your questions about loss functions, their maximum values, and whether your model's performance is on track.

1. Maximum Value of sparse_categorical_crossentropy #

The loss function you're using—sparse_categorical_crossentropy—calculates the negative log-likelihood of the true class. Here's the key thing: it doesn't have a hard upper bound.

If your model assigns a probability approaching 0 to the correct category, the log(0) term tends toward negative infinity. Since we take the negative of that value, the loss can grow arbitrarily large. In practice, you'll see higher initial losses (like your 0.88 in epoch 1) when the model is still guessing, but as it learns to predict the correct classes more reliably, the loss will drop.

2. Is a Loss of 0.2 "Good"? #

This depends on your specific task and dataset, but based on your training progress, 0.2 is absolutely a strong result—especially paired with your final accuracy of ~90.7%.

For multi-class classification tasks (which this appears to be, given the softmax output layer and sparse categorical loss), a loss in the 0.2 range with accuracy around 90% indicates solid predictive performance. The bigger win here is the trend: your loss steadily decreases while accuracy consistently rises, which means your model is learning effectively without obvious overfitting (from the training logs we see).

3. Your Training Trend Looks Great! #

The pattern you observed—loss going down and accuracy going up with each epoch—is exactly what we want during model training. Your model starts with 75.9% accuracy and a loss of 0.88, and by epoch 50, it hits 90.7% accuracy with a loss of ~0.195. That's a clear sign your model is improving and adapting well to your data.

Your Training Code #

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

Training Results #

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

内容的提问来源于stack exchange，提问作者thomas dees