我正在运行Tensorflow here中的一个基本文本分类示例。
有一件事我不明白，为什么我们需要使用from_logits=True与BinaryCrossentropy损失？当我试图删除它并将activation="sigmoid"添加到最后一个Dense层时，binary_accuracy在训练时根本不移动。
更改代码：
model = tf.keras.Sequential([
layers.Embedding(max_features + 1, embedding_dim),
layers.Dropout(0.2),
layers.GlobalAveragePooling1D(),
layers.Dropout(0.2),
layers.Dense(1, activation="sigmoid")]) # <-- Add activation = sigmoid here
model.compile(loss=losses.BinaryCrossentropy(), # <-- Remove from_logits=True here
optimizer='adam',
metrics=tf.metrics.BinaryAccuracy(threshold=0.0))
epochs = 10
history = model.fit(
train_ds,
validation_data=val_ds,
epochs=epochs)培训产出：
Epoch 1/10
625/625 [==============================] - 4s 4ms/step - loss: 0.6635 - binary_accuracy: 0.4981 - val_loss: 0.6149 - val_binary_accuracy: 0.5076
Epoch 2/10
625/625 [==============================] - 2s 4ms/step - loss: 0.5492 - binary_accuracy: 0.4981 - val_loss: 0.4990 - val_binary_accuracy: 0.5076
Epoch 3/10
625/625 [==============================] - 2s 4ms/step - loss: 0.4453 - binary_accuracy: 0.4981 - val_loss: 0.4208 - val_binary_accuracy: 0.5076
Epoch 4/10
625/625 [==============================] - 2s 4ms/step - loss: 0.3792 - binary_accuracy: 0.4981 - val_loss: 0.3741 - val_binary_accuracy: 0.5076
Epoch 5/10
625/625 [==============================] - 3s 4ms/step - loss: 0.3360 - binary_accuracy: 0.4981 - val_loss: 0.3454 - val_binary_accuracy: 0.5076
Epoch 6/10
625/625 [==============================] - 3s 4ms/step - loss: 0.3054 - binary_accuracy: 0.4981 - val_loss: 0.3262 - val_binary_accuracy: 0.5076
Epoch 7/10
625/625 [==============================] - 3s 4ms/step - loss: 0.2813 - binary_accuracy: 0.4981 - val_loss: 0.3126 - val_binary_accuracy: 0.5076
Epoch 8/10
625/625 [==============================] - 3s 4ms/step - loss: 0.2616 - binary_accuracy: 0.4981 - val_loss: 0.3033 - val_binary_accuracy: 0.5076
Epoch 9/10
625/625 [==============================] - 3s 4ms/step - loss: 0.2456 - binary_accuracy: 0.4981 - val_loss: 0.2967 - val_binary_accuracy: 0.5076
Epoch 10/10
625/625 [==============================] - 2s 4ms/step - loss: 0.2306 - binary_accuracy: 0.4981 - val_loss: 0.2920 - val_binary_accuracy: 0.5076
1条答案
按热度按时间bhmjp9jg1#
看起来模型正在正常训练，但目前显示模型如何训练的计算方法是错误的。
我认为
BinaryAccuracy中的threshold正在实现度量的结果。例如，因为您将损失函数的输入更改为sigmoid之后的一个，所以值的范围将在0和1之间，但您的BinaryAccuracythreshold现在是0.0，应该是0.5。如果您想根据需要修改模型架构，请尝试将该值更改为
0.5。