我构建了一个用于二元文本分类的LSTM模型。这里的标签采用了一键编码方式。以下是我的模型结构：

model = Sequential()model.add(Embedding(input_dim=dictionary_length, output_dim=60, input_length=max_word_count))model.add(LSTM(600))model.add(Dense(units=max_word_count, activation='tanh', kernel_regularizer=regularizers.l2(0.04), activity_regularizer=regularizers.l2(0.015)))model.add(Dense(units=max_word_count, activation='relu', kernel_regularizer=regularizers.l2(0.01), bias_regularizer=regularizers.l2(0.01)))model.add(Dense(2, activation='softmax', kernel_regularizer=regularizers.l2(0.001)))adam_optimizer = Adam(lr=0.001, decay=0.0001)model.compile(loss='categorical_crossentropy', optimizer=adam_optimizer,               metrics=[tf.keras.metrics.Accuracy(), metrics.AUC(), metrics.Precision(), metrics.Recall()])

当我训练这个模型时，准确率始终保持为0，但其他指标却有所改善。这里有什么问题吗？

Epoch 1/394/94 [==============================] - 5s 26ms/step - loss: 3.4845 - accuracy: 0.0000e+00 - auc_4: 0.7583 - precision_4: 0.7251 - recall_4: 0.7251Epoch 2/394/94 [==============================] - 2s 24ms/step - loss: 0.4772 - accuracy: 0.0000e+00 - auc_4: 0.9739 - precision_4: 0.9249 - recall_4: 0.9249Epoch 3/394/94 [==============================] - 3s 27ms/step - loss: 0.1786 - accuracy: 0.0000e+00 - auc_4: 0.9985 - precision_4: 0.9860 - recall_4: 0.9860

回答：

因为你需要使用CategoricalAccuracy：https://www.tensorflow.org/api_docs/python/tf/keras/metrics/CategoricalAccuracy 来测量这个问题的准确率。

model.compile(loss='categorical_crossentropy', optimizer=adam_optimizer,               metrics=[tf.keras.metrics.CategoricalAccuracy(), metrics.AUC(), metrics.Precision(), metrics.Recall()])