Deep learning 丢失、val_丢失、acc和val_acc不会在所有历代更新

我为序列分类（二进制）创建了一个LSTM网络，其中每个样本有25个时间步和4个特征。以下是我的keras网络拓扑：

上面，密集层之后的激活层使用softmax功能。我使用二进制交叉熵作为损失函数，Adam作为优化器来编译keras模型。使用batch_size=256、shuffle=True和validation_split=0.05对模型进行训练，以下是训练日志：

Train on 618196 samples, validate on 32537 samples
2017-09-15 01:23:34.407434: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:893] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2017-09-15 01:23:34.407719: I tensorflow/core/common_runtime/gpu/gpu_device.cc:955] Found device 0 with properties: 
name: GeForce GTX 1050
major: 6 minor: 1 memoryClockRate (GHz) 1.493
pciBusID 0000:01:00.0
Total memory: 3.95GiB
Free memory: 3.47GiB
2017-09-15 01:23:34.407735: I tensorflow/core/common_runtime/gpu/gpu_device.cc:976] DMA: 0 
2017-09-15 01:23:34.407757: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 0:   Y 
2017-09-15 01:23:34.407764: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1050, pci bus id: 0000:01:00.0)
618196/618196 [==============================] - 139s - loss: 4.3489 - acc: 0.7302 - val_loss: 4.4316 - val_acc: 0.7251
Epoch 2/50
618196/618196 [==============================] - 132s - loss: 4.3489 - acc: 0.7302 - val_loss: 4.4316 - val_acc: 0.7251
Epoch 3/50
618196/618196 [==============================] - 134s - loss: 4.3489 - acc: 0.7302 - val_loss: 4.4316 - val_acc: 0.7251
Epoch 4/50
618196/618196 [==============================] - 133s - loss: 4.3489 - acc: 0.7302 - val_loss: 4.4316 - val_acc: 0.7251
Epoch 5/50
618196/618196 [==============================] - 132s - loss: 4.3489 - acc: 0.7302 - val_loss: 4.4316 - val_acc: 0.7251
Epoch 6/50
618196/618196 [==============================] - 132s - loss: 4.3489 - acc: 0.7302 - val_loss: 4.4316 - val_acc: 0.7251
Epoch 7/50
618196/618196 [==============================] - 132s - loss: 4.3489 - acc: 0.7302 - val_loss: 4.4316 - val_acc: 0.7251
Epoch 8/50
618196/618196 [==============================] - 132s - loss: 4.3489 - acc: 0.7302 - val_loss: 4.4316 - val_acc: 0.7251

... and so on through 50 epochs with same numbers

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到目前为止，我还尝试使用rmsprop、nadam优化器和批处理大小128、512、1024，但损失、val_损失、acc、val_acc在所有时代都保持不变，每次尝试的精度都在0.72到0.74之间。

softmax激活确保输出的总和为1。它有助于确保在许多类中只输出一个类

因为您只有一个输出（只有一个类），所以这肯定不是一个好主意。所有样本的结果可能都是1

改用

sigmoid

。它与

二进制交叉熵

配合得很好