GPU内存在顺序模型初始化后直接填满，在训练期间不会更改(GPU Memory is full directly after sequential model initialization and w

GPU内存在顺序模型初始化后直接填满，在训练期间不会更改(GPU Memory is full directly after sequential model initialization and wont change during training)

我正在使用Keras，在Windows 7的后端张量流上使用NVIDIA Quadro M2000M GPU。

当我初始化包含5个GRU，5个Dropout和1个Dense图层的模型时，GPU内存使用量会跳至3800MB的4096MB并保持不变，直到我重新开始我的spyder会话。在spyder中清除会话：

K.clear_session()

不起作用。内存使用率保持在较高水平。

这样的模型分配GPU的这么多内存是正常的吗？我可以改变什么，以便可以使用内存使用情况？我想提高训练速度，我认为这种高内存使用会阻碍GPU充分发挥其潜力。

更新

我的模型看起来像这样：

model = Sequential() layers = [1, 70, 50,100, 50,20, 1] model.add(GRU( layers[1], #batch_size = 32, input_shape=(sequence_length, anzahl_features), return_sequences=True)) model.add(Dropout(dropout_1)) model.add(GRU( layers[2], #batch_size = 32, return_sequences=True)) model.add(Dropout(dropout_2)) model.add(GRU( layers[3], #batch_size = 32, return_sequences=True)) model.add(Dropout(dropout_3)) model.add(GRU( layers[4], #batch_size = 32, return_sequences=True)) model.add(Dropout(dropout_4)) model.add(GRU( layers[5], #batch_size = 32, return_sequences=False)) model.add(Dropout(dropout_5)) model.add(Dense( layers[6])) model.add(Activation('sigmoid'))

我的特征矩阵的大小为506x500x35（506个示例，500个序列长度和35个特征）。批量大小设置为128.站点说明：我不是说这是完美的特征矩阵或模型配置。

这里还有GPU-Z的截图，我重新启动spyder并启动模型直到第二个时期：

I am using Keras, on the backend tensorflow on windows 7 with the NVIDIA Quadro M2000M GPU.

When i initialization my model which contains 5 GRU, 5 Dropout and 1 Dense layers the GPU memory usage jumps to 3800MB of 4096MB and stays there until i restart my spyder session. Clearing the session within spyder with:

K.clear_session()

does not work. The Memory usage stays at that high level.

Is it normal that such a model allocate this much memory of the GPU? What can i change so the memory usage can be used proberly? I want to improve the training speed and i think this high memory usage hinder the GPU to use her full potential.

Update

My model looks like this:

model = Sequential() layers = [1, 70, 50,100, 50,20, 1] model.add(GRU( layers[1], #batch_size = 32, input_shape=(sequence_length, anzahl_features), return_sequences=True)) model.add(Dropout(dropout_1)) model.add(GRU( layers[2], #batch_size = 32, return_sequences=True)) model.add(Dropout(dropout_2)) model.add(GRU( layers[3], #batch_size = 32, return_sequences=True)) model.add(Dropout(dropout_3)) model.add(GRU( layers[4], #batch_size = 32, return_sequences=True)) model.add(Dropout(dropout_4)) model.add(GRU( layers[5], #batch_size = 32, return_sequences=False)) model.add(Dropout(dropout_5)) model.add(Dense( layers[6])) model.add(Activation('sigmoid'))

My feature matrix has the size 506x500x35 (506 examples, 500 sequence length and 35 features). The batch size is set to 128. Site note: I am not saying that that is the perfect feature matrix or model configuration.

Here also a screenshot of GPU-Z where i restarted spyder and started the model until the second epoch: