精确度和val_acc在各个时代停滞不前

我构建了一个用于二进制文本分类的CNN模型，我的模型如下 . 作为参考，embedding_matrix包含预先训练的字矢量权重 .

model = Sequential()
model.add(Embedding(num_words, EMBEDDING_DIM, weights = 
[embedding_matrix], input_length=MAX_SEQUENCE_LENGTH, trainable = False)) #numbers match up to tokenizer
model.add(Dropout(0.3))
model.add(Conv1D(64, 5, activation='relu')) 
model.add(MaxPooling1D(pool_size=4))
model.add(Flatten())
model.add(Dense(1, activation='softmax'))

该模型被编译和评估 .

print(model.summary())
model.compile(loss='binary_crossentropy', optimizer="adam", metrics=['acc'])
model.fit(data, np.array(HIT), validation_split = VALIDATION_SPLIT, epochs = 3, batch_size=20, verbose = 2)
score = model.evaluate(data2, np.array(HIT2), verbose = 2)
print("Test Loss = ", score[0])
print("Test Accuracy = ", score[1])

时代不会发生变化 . 模型摘要和培训日志发布在下面 .

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
embedding_1 (Embedding)      (None, 300, 100)          1813200   
_________________________________________________________________
dropout_1 (Dropout)          (None, 300, 100)          0         
_________________________________________________________________
conv1d_1 (Conv1D)            (None, 296, 64)           32064     
_________________________________________________________________
max_pooling1d_1 (MaxPooling1 (None, 74, 64)            0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 4736)              0         
_________________________________________________________________
dense_1 (Dense)              (None, 1)                 4737      
=================================================================
Total params: 1,850,001
Trainable params: 36,801
Non-trainable params: 1,813,200
_________________________________________________________________


Train on 3919 samples, validate on 560 samples
Epoch 1/5
 - 4s - loss: 9.5353 - acc: 0.4019 - val_loss: 10.1633 - val_acc: 0.3625
Epoch 2/5
 - 3s - loss: 9.5353 - acc: 0.4019 - val_loss: 10.1633 - val_acc: 0.3625
Epoch 3/5
 - 3s - loss: 9.5353 - acc: 0.4019 - val_loss: 10.1633 - val_acc: 0.3625
Epoch 4/5
 - 3s - loss: 9.5353 - acc: 0.4019 - val_loss: 10.1633 - val_acc: 0.3625
Epoch 5/5
 - 3s - loss: 9.5353 - acc: 0.4019 - val_loss: 10.1633 - val_acc: 0.3625

我尝试使用不同的优化器，改变批量大小，以及更改CNN超参数，尽管loss，acc，val_loss和val_acc在各个时期仍然保持不变 . 但是，当将sigmoid函数用于最后一层时，精确度和损耗会在不同时期得到改善 .