【发布时间】：2017-10-11 22:48:57
【问题描述】：
我正在尝试填补半小时的碳通量时间序列。我想使用训练-测试-验证交叉验证来识别最简约的 LSTM 模型，方法是使用所有可用输入训练模型，然后对其进行修剪，直到分数停止提高。对于每个模型，我使用 k-fold CV 来分割 90% 的训练，10% 的验证，然后在 model.fit() 中，将训练进一步分割成训练和测试集。我正在使用提前停止来帮助最小化运行时间，并使用 ModelCheckpoint 来保存最佳权重（“val_loss”最低的时期）。然后我想加载这些模型权重，并使用在测试集上表现最佳的权重计算为模型外验证留出的 10% 数据的验证分数 (MSE)。
这是我的代码训练 LSTM 的一个工作示例，其中包含 9 个因素和 13 次（每次观察前的 6 小时）
import time
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from itertools import combinations
from functools import partial
from multiprocessing import Pool
from sklearn.neural_network import MLPRegressor as MPR
from sklearn.preprocessing import StandardScaler
from sklearn import metrics
from sklearn.model_selection import RepeatedKFold
import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import LSTM
from keras.wrappers.scikit_learn import KerasRegressor
from keras.callbacks import EarlyStopping,ModelCheckpoint
import warnings
warnings.filterwarnings('ignore')
import tensorflow as tf
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.9
session = tf.Session(config=config)
def TimeShape(rolls,X1):
X = np.zeros(shape = (X1.shape[0],rolls+1,X1.shape[1]))
X[:,0,:] = X1
if rolls > 0:
for roll in range(0,rolls):
X2 = np.roll(X1,(roll+1),axis=0)
X[:,roll+1,:] = X2
return(X)
def LSTM_Model(time_steps,inputs,load=None):
model = Sequential()
model.add(LSTM(12, input_shape=(time_steps+1,inputs),return_sequences=True,init='normal', activation='tanh'))
model.add(LSTM(6,init='normal', activation='tanh'))
model.add(Dense(1, init='normal',activation='linear'))
NUM_GPU = 1 # or the number of GPUs available on your machine
gpu_list = []
for i in range(NUM_GPU): gpu_list.append('gpu(%d)' % i)
model.compile(loss='mean_squared_error', optimizer='adam',context=gpu_list) # - Add if using MXNET
return(model)
class LossHistory(keras.callbacks.Callback):
def on_train_begin(self, logs={}):
self.train_losses = []
self.test_losses = []
def on_epoch_end(self, batch, logs={}):
self.train_losses.append(logs.get('loss'))
self.test_losses.append(logs.get('val_loss'))
class LSTM_Optimize:
def __init__(self,Path,y_var):
# **Read and prep Data Data**
self.Master = pd.read_csv(Path,delimiter = ',',header = 0,na_values = -9999)
self.Master = self.Master.set_index(pd.DatetimeIndex(pd.to_datetime(self.Master['datetime'])))
self.Master['DOY'] = self.Master.index.dayofyear*1.0
self.Master['HR'] = self.Master.index.hour*1.0
self.Data = self.Master[np.isfinite(self.Master[y_var])]
self.Data = self.Data.interpolate().bfill()
self.Data = self.Data.interpolate().ffill()
# ** Nomralize Y variable**
# ** Pipeline takes care of X, but not Y, I've foun the models work better when normalizing Y **
self.y = self.Data[y_var].values
self.YStandard = StandardScaler()
self.YScaled = self.YStandard.fit(self.y.reshape(-1, 1))
Yscale = self.YScaled.transform(self.y.reshape(-1, 1))
self.y = np.ndarray.flatten(Yscale)
self.Ytru = self.YScaled.inverse_transform(self.y.reshape(-1,1))
def Run(self,Inputs):
# Preparing the input data
time_steps = 12
X = self.Data[Inputs]
input_shape = len(Inputs)
self.XStandard = StandardScaler()
self.XScaled= self.XStandard.fit(X)
Xscale = self.XScaled.transform(X)
Xscale = TimeShape(time_steps,Xscale)
Xscale = Xscale[time_steps+1:,:,:]
self.y = self.y[time_steps+1:]
ES = EarlyStopping(monitor='val_loss', min_delta=0.0, patience=25, verbose=1, mode='auto')
CH = ModelCheckpoint(filepath='weights.hdf5',monitor='val_loss', verbose=0, save_best_only=True)
HS=LossHistory()
MSE = []
kf = RepeatedKFold(n_splits=10,n_repeats=2)
batch_size=25
Mod = LSTM_Model(time_steps,input_shape)
plt.figure(figsize = (7,7))
for train,test in kf.split(Xscale,self.y):
Mod.fit(Xscale[train],self.y[train],batch_size=batch_size, nb_epoch=1000,validation_split=0.1,
shuffle=True,callbacks=[ES,CH,HS],verbose=0)
Y = Mod.predict(Xscale[test],batch_size = batch_size)
Mod.load_weights('weights.hdf5')
Y = Mod.predict(Xscale[test],batch_size = batch_size)
MSE.append(metrics.mean_squared_error(self.y[test],Y))
plt.plot(HS.test_losses,linestyle='--')
plt.plot(HS.train_losses)
print(Mod.summary())
print(np.asanyarray(MSE).mean())
Path = 'FluxData.csv'
% matplotlib inline
start_time = time.time()
if __name__ == '__main__':
CH4_Model = ['Sedge','Shrubby','Temp','VWC','ustar','wind_speed','air_pressure',
'PPFD_Avg','NR_Wm2_Avg','AirTC_Avg']
y_var = 'ch4_flux'
Model = CH4_Model
Best = LSTM_Optimize(Path,y_var)
Best.Run(Model)
print()
print("--- %s seconds ---" % (time.time() - start_time))
这是我的数据集的几行 - 实际系列有 1000 个观察结果
datetime,co2_flux,ch4_flux,ustar,wind_speed,AirTC_Avg,air_pressure,AirTC_Min,RH,PPFD_Avg,NR_Wm2_Avg,VWC,Temp,Sedge,Shrubby
7/11/2016 8:00,-0.337747167,0.011732699,0.404379747,3.887986435,15.07,101118.6513,15.03,92.7,414.2,225.1,0.5895,7.950660426,0.001292044,0.823794007
7/11/2016 8:30,-1.021087283,0.010256442,0.424094541,3.94983083,14.89,101144.0926,14.84,92.8,339.7,177.1,0.5895,8.24119905,0.001058732,0.826866339
7/11/2016 9:00,-0.146511388,0.008503355,0.456274817,4.687202214,14.71,101177.3176,14.63,93.4,354.4,183.7,0.5895,8.146344257,0.000474955,0.84272365
7/11/2016 9:30,0.144368521,0.009458078,0.462915317,4.810986576,14.27,101203.9191,14.2,93.3,370.2,188.4,0.5895,7.995179025,0.00147768,0.854715683
7/11/2016 10:00,1.471425801,0.014895985,0.47095652,5.098075355,13.7,101235.9171,13.62,94.3,462.9,233.9,0.5895,7.521166721,4.64E-05,0.871581919
7/11/2016 10:30,0.889911286,0.01564225,0.487227522,4.969666239,13.13,101277.0195,13.04,96,309.9,155.2,0.5895,7.923818563,8.14E-06,0.880709962
当我在支持 Tensorflow 的情况下运行它时，一切都很顺利，我得到了 .但是，如果我尝试使用 MXNet 后端运行它，它无法加载保存的模型权重，并且我得到了这个回溯：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-1-14c6597a2feb> in <module>()
114 Model = CH4_Model
115 Best = LSTM_Optimize(Path,y_var)
--> 116 Best.Run(Model)
117 print()
118 print("--- %s seconds ---" % (time.time() - start_time))
<ipython-input-1-14c6597a2feb> in Run(self, Inputs)
96 shuffle=True,callbacks=[ES,CH,HS],verbose=0)
97 Y = Mod.predict(Xscale[test],batch_size = batch_size)
---> 98 Mod.load_weights('weights.hdf5')
99 Y = Mod.predict(Xscale[test],batch_size = batch_size)
100 MSE.append(metrics.mean_squared_error(self.y[test],Y))
/usr/local/lib/python3.5/dist-packages/Keras-1.2.2-py3.5.egg/keras/engine/topology.py in load_weights(self, filepath, by_name)
2718 self.load_weights_from_hdf5_group_by_name(f)
2719 else:
-> 2720 self.load_weights_from_hdf5_group(f)
2721
2722 if hasattr(f, 'close'):
/usr/local/lib/python3.5/dist-packages/Keras-1.2.2-py3.5.egg/keras/engine/topology.py in load_weights_from_hdf5_group(self, f)
2804 weight_values[0] = w
2805 weight_value_tuples += zip(symbolic_weights, weight_values)
-> 2806 K.batch_set_value(weight_value_tuples)
2807
2808 def load_weights_from_hdf5_group_by_name(self, f):
/usr/local/lib/python3.5/dist-packages/Keras-1.2.2-py3.5.egg/keras/backend/mxnet_backend.py in batch_set_value(tuples)
2205 """
2206 for p, w in tuples:
-> 2207 set_value(p, w)
2208
2209
/usr/local/lib/python3.5/dist-packages/Keras-1.2.2-py3.5.egg/keras/backend/mxnet_backend.py in set_value(x, value)
2193 if isinstance(value, Number):
2194 value = [value]
-> 2195 x.bind(mx.nd.array(value))
2196
2197
/usr/local/lib/python3.5/dist-packages/mxnet-0.11.0-py3.5.egg/mxnet/ndarray.py in array(source_array, ctx, dtype)
1295 raise TypeError('source_array must be array like object')
1296 arr = empty(source_array.shape, ctx, dtype)
-> 1297 arr[:] = source_array
1298 return arr
1299
/usr/local/lib/python3.5/dist-packages/mxnet-0.11.0-py3.5.egg/mxnet/ndarray.py in __setitem__(self, key, value)
384 _internal._set_value(float(value), out=self)
385 elif isinstance(value, (np.ndarray, np.generic)):
--> 386 self._sync_copyfrom(value)
387 else:
388 raise TypeError(
/usr/local/lib/python3.5/dist-packages/mxnet-0.11.0-py3.5.egg/mxnet/ndarray.py in _sync_copyfrom(self, source_array)
556 print(self.shape)
557 raise ValueError('Shape inconsistent: expected %s vs got %s'%(
--> 558 str(self.shape), str(source_array.shape)))
559 check_call(_LIB.MXNDArraySyncCopyFromCPU(
560 self.handle,
ValueError: Shape inconsistent: expected () vs got (1,)
我为什么要使用 MXNet？它似乎比 tensorflow 更快，我将不得不对许多具有不同输入和不同#s 节点和超参数的模型执行训练测试验证。通过使用多处理并行训练多个不同的模型，我已经能够显着提高带有 MXNet 后端的 keras 模型的速度。但是，使用 tensroflow 后端在尝试进行多处理时出现线程锁定错误。
就上下文而言，我在 p2.xlarge 实例上使用 Deep Learning AMI Ubuntu Linux - 2.3_Sep2017 (ami-d6ee1dae) 环境。
任何想法将不胜感激！
【问题讨论】：
-
嘿，你知道了吗？
-
不使用 MXNet，我放弃了它并切换到 tensorflow。我想出了如何使用 tensorflow 实现多处理以同时训练-测试-验证模型。
标签： python tensorflow keras mxnet