每当我在Keras上试用LSTM模型时，由于训练时间长，似乎无法对模型进行训练。
例如，这样的模型每步训练需要80秒。
def create_model(self):
inputs = {}
inputs['input'] = []
lstm = []
placeholder = {}
for tf, v in self.env.timeframes.items():
inputs[tf] = Input(shape = v['shape'], name = tf)
lstm.append(LSTM(8)(inputs[tf]))
inputs['input'].append(inputs[tf])
account = Input(shape = (3,), name = 'account')
account_ = Dense(8, activation = 'relu')(account)
dt = Input(shape = (7,), name = 'dt')
dt_ = Dense(16, activation = 'relu')(dt)
inputs['input'].extend([account, dt])
data = Concatenate(axis = 1)(lstm)
data = Dense(128, activation = 'relu')(data)
y = Concatenate(axis = 1)([data, account, dt])
y = Dense(256, activation = 'relu')(y)
y = Dense(64, activation = 'relu')(y)
y = Dense(16, activation = 'relu')(y)
output = Dense(3, activation = 'linear')(y)
model = Model(inputs = inputs['input'], outputs = output)
model.compile(loss = 'mse', optimizer = 'adam', metrics = ['mae'])
return model
具有LSTM的Flatten + Dense贴图的模型如下：
def create_model(self):
inputs = {}
inputs['input'] = []
lstm = []
placeholder = {}
for tf, v in self.env.timeframes.items():
inputs[tf] = Input(shape = v['shape'], name = tf)
#lstm.append(LSTM(8)(inputs[tf]))
placeholder[tf] = Flatten()(inputs[tf])
lstm.append(Dense(32, activation = 'relu')(placeholder[tf]))
inputs['input'].append(inputs[tf])
account = Input(shape = (3,), name = 'account')
account_ = Dense(8, activation = 'relu')(account)
dt = Input(shape = (7,), name = 'dt')
dt_ = Dense(16, activation = 'relu')(dt)
inputs['input'].extend([account, dt])
data = Concatenate(axis = 1)(lstm)
data = Dense(128, activation = 'relu')(data)
y = Concatenate(axis = 1)([data, account, dt])
y = Dense(256, activation = 'relu')(y)
y = Dense(64, activation = 'relu')(y)
y = Dense(16, activation = 'relu')(y)
output = Dense(3, activation = 'linear')(y)
model = Model(inputs = inputs['input'], outputs = output)
model.compile(loss = 'mse', optimizer = 'adam', metrics = ['mae'])
return model
每步训练需要45-50毫秒。
模型中是否存在引起此问题的错误？还是这个模型运行的速度如此之快？
-self.env.timeframes看起来像这样：9个项的字典
timeframes = {
's1': {
'lookback': 86400,
'word': '1 s',
'unit': 1,
'offset': 12
},
's5': {
'lookback': 200,
'word': '5 s',
'unit': 5,
'offset': 2
},
'm1': {
'lookback': 100,
'word': '1 min',
'unit': 60,
'offset': 0
},
'm5': {
'lookback': 100,
'word': '5 min',
'unit': 300,
'offset': 0
},
'm30': {
'lookback': 100,
'word': '30 min',
'unit': 1800,
'offset': 0
},
'h1': {
'lookback': 200,
'word': '1 h',
'unit': 3600,
'offset': 0
},
'h4': {
'lookback': 200,
'word': '4 h',
'unit': 14400,
'offset': 0
},
'h12': {
'lookback': 100,
'word': '12 h',
'unit': 43200,
'offset': 0
},
'd1': {
'lookback': 200,
'word': '1 d',
'unit': 86400,
'offset': 0
}
}
提示符中的GPU信息-
2018-06-30 07:35:16.204320: I T:\src\github\tensorflow\tensorflow\core\platform\cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2018-06-30 07:35:16.495832: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1356] Found device 0 with properties:
name: GeForce GTX 1080 major: 6 minor: 1 memoryClockRate(GHz): 1.86
pciBusID: 0000:01:00.0
totalMemory: 8.00GiB freeMemory: 6.59GiB
2018-06-30 07:35:16.495981: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1435] Adding visible gpu devices: 0
2018-06-30 07:35:16.956743: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-06-30 07:35:16.956827: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:929] 0
2018-06-30 07:35:16.957540: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:942] 0: N
2018-06-30 07:35:16.957865: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6370 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0, compute capability: 6.1)
答案 0 :(得分：6)
如果您使用的是GPU，请用CuDNNLSTM图层替换所有LSTM图层。您可以从keras.layers导入它：
from keras.layers import CuDNNLSTM
def create_model(self):
inputs = {}
inputs['input'] = []
lstm = []
placeholder = {}
for tf, v in self.env.timeframes.items():
inputs[tf] = Input(shape = v['shape'], name = tf)
lstm.append(CuDNNLSTM(8)(inputs[tf]))
inputs['input'].append(inputs[tf])
account = Input(shape = (3,), name = 'account')
account_ = Dense(8, activation = 'relu')(account)
dt = Input(shape = (7,), name = 'dt')
dt_ = Dense(16, activation = 'relu')(dt)
inputs['input'].extend([account, dt])
data = Concatenate(axis = 1)(lstm)
data = Dense(128, activation = 'relu')(data)
y = Concatenate(axis = 1)([data, account, dt])
y = Dense(256, activation = 'relu')(y)
y = Dense(64, activation = 'relu')(y)
y = Dense(16, activation = 'relu')(y)
output = Dense(3, activation = 'linear')(y)
model = Model(inputs = inputs['input'], outputs = output)
model.compile(loss = 'mse', optimizer = 'adam', metrics = ['mae'])
return model
这里是更多信息：https://keras.io/layers/recurrent/#cudnnlstm
这将显着加快模型=）