WindowGRU training error
Closed this issue · 4 comments
Hi,
I tried to run the following code:
import warnings
warnings.filterwarnings("ignore")
from nilmtk.api import API
from nilmtk.disaggregate import Mean
from nilmtk_contrib.disaggregate import DAE,Seq2Point, Seq2Seq, RNN, WindowGRU
redd = {
'power': {
'mains': ['apparent','active'],
'appliance': ['apparent','active']
},
'sample_rate': 60,
'appliances': ['fridge'],
'methods': {
'WindowGRU':WindowGRU({'n_epochs':50,'batch_size':32}),
'RNN':RNN({'n_epochs':50,'batch_size':32}),
'DAE':DAE({'n_epochs':50,'batch_size':32}),
'Seq2Point':Seq2Point({'n_epochs':50,'batch_size':32}),
'Seq2Seq':Seq2Seq({'n_epochs':50,'batch_size':32}),
'Mean': Mean({}),
},
'train': {
'datasets': {
'REDD': {
'path': 'C:/git/DNN-NILM/data/redd.hdf5',
'buildings': {
1: {
'start_time': '2011-04-17',
'end_time': '2011-04-27'
},
# 56: {
# 'start_time': '2015-01-28',
# 'end_time': '2015-01-30'
# },
}
}
}
},
'test': {
'datasets': {
'REDD': {
'path': 'C:/git/DNN-NILM/data/redd.hdf5',
'buildings': {
1: {
'start_time': '2013-01-05',
'end_time': '2013-01-08'
},
}
}
},
'metrics':['mae']
}
}
Then, I got the error after the training was done:
api_res = API(redd)
Started training for WindowGRU
Joint training for WindowGRU
............... Loading Data for training ...................
Loading data for REDD dataset
Loading building ... 1
Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')
Done loading data all meters for this chunk.
Dropping missing values
Training processing
First model training for fridge
WARNING:tensorflow:From C:\Users\WuTeng\anaconda3\envs\nilm\lib\site-packages\keras\backend\tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.
Train on 10206 samples, validate on 1802 samples
Epoch 1/50
10206/10206 [==============================] - 70s 7ms/step - loss: 0.0104 - val_loss: 0.0071
Epoch 00001: val_loss improved from inf to 0.00709, saving model to windowgru-temp-weights-74894.h5
Epoch 2/50
10206/10206 [==============================] - 75s 7ms/step - loss: 0.0067 - val_loss: 0.0043
...
Epoch 00048: val_loss did not improve from 0.00116
Epoch 49/50
10206/10206 [==============================] - 67s 7ms/step - loss: 0.0020 - val_loss: 0.0018
Epoch 00049: val_loss did not improve from 0.00116
Epoch 50/50
10206/10206 [==============================] - 68s 7ms/step - loss: 0.0017 - val_loss: 0.0012
Epoch 00050: val_loss did not improve from 0.00116
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-15-6b963021b56a> in <module>
----> 1 api_res = API(redd)
~\anaconda3\envs\nilm\lib\site-packages\nilmtk\api.py in __init__(self, params)
44 self.DROP_ALL_NANS = params.get("DROP_ALL_NANS", True)
45 self.site_only = params.get('site_only',False)
---> 46 self.experiment()
47
48
~\anaconda3\envs\nilm\lib\site-packages\nilmtk\api.py in experiment(self)
89 else:
90 print ("Joint training for ",clf.MODEL_NAME)
---> 91 self.train_jointly(clf,d)
92
93 print ("Finished training for ",clf.MODEL_NAME)
~\anaconda3\envs\nilm\lib\site-packages\nilmtk\api.py in train_jointly(self, clf, d)
238 self.train_submeters = appliance_readings
239
--> 240 clf.partial_fit(self.train_mains,self.train_submeters)
241
...
~\anaconda3\envs\nilm\lib\site-packages\keras\engine\network.py in load_weights(self, filepath, by_name, skip_mismatch, reshape)
1228 else:
1229 saving.load_weights_from_hdf5_group(
-> 1230 f, self.layers, reshape=reshape)
1231 if hasattr(f, 'close'):
1232 f.close()
~\anaconda3\envs\nilm\lib\site-packages\keras\engine\saving.py in load_weights_from_hdf5_group(f, layers, reshape)
1181 """
1182 if 'keras_version' in f.attrs:
-> 1183 original_keras_version = f.attrs['keras_version'].decode('utf8')
1184 else:
1185 original_keras_version = '1'
AttributeError: 'str' object has no attribute 'decode'
Any idea what's the issue? Keras version is wrong?
And here is my package list:
absl-py 0.12.0 pyhd8ed1ab_0 conda-forge
anyio 3.0.1 py37h03978a9_0 conda-forge
argon2-cffi 20.1.0 py37hcc03f2d_2 conda-forge
astor 0.8.1 pyh9f0ad1d_0 conda-forge
async_generator 1.10 py_0 conda-forge
attrs 21.2.0 pyhd8ed1ab_0 conda-forge
babel 2.9.1 pyh44b312d_0 conda-forge
backcall 0.2.0 pyh9f0ad1d_0 conda-forge
backports 1.0 py_2 conda-forge
backports.functools_lru_cache 1.6.4 pyhd8ed1ab_0 conda-forge
bleach 3.3.0 pyh44b312d_0 conda-forge
blosc 1.21.0 h0e60522_0 conda-forge
brotlipy 0.7.0 py37hcc03f2d_1001 conda-forge
bzip2 1.0.8 h8ffe710_4 conda-forge
ca-certificates 2020.12.5 h5b45459_0 conda-forge
cached-property 1.5.2 hd8ed1ab_1 conda-forge
cached_property 1.5.2 pyha770c72_1 conda-forge
certifi 2020.12.5 py37h03978a9_1 conda-forge
cffi 1.14.5 py37hd8e9650_0 conda-forge
chardet 4.0.0 py37h03978a9_1 conda-forge
colorama 0.4.4 pyh9f0ad1d_0 conda-forge
cryptography 3.4.7 py37h20c650d_0 conda-forge
cvxpy 1.1.12 py37h03978a9_0 conda-forge
cvxpy-base 1.1.12 py37h90003fb_0 conda-forge
cycler 0.10.0 py_2 conda-forge
dataclasses 0.8 pyhc8e2a94_1 conda-forge
decorator 5.0.9 pyhd8ed1ab_0 conda-forge
defusedxml 0.7.1 pyhd8ed1ab_0 conda-forge
ecos 2.0.8 py37hebb4d16_0 conda-forge
entrypoints 0.3 pyhd8ed1ab_1003 conda-forge
freetype 2.10.4 h546665d_1 conda-forge
gast 0.4.0 pyh9f0ad1d_0 conda-forge
google-pasta 0.2.0 pyh8c360ce_0 conda-forge
grpcio 1.37.1 py37h04d2302_0 conda-forge
h5py 3.2.1 nompi_py37he280515_100 conda-forge
hdf5 1.10.6 nompi_h5268f04_1114 conda-forge
hmmlearn 0.2.5 py37hda49f71_0 conda-forge
idna 2.10 pyh9f0ad1d_0 conda-forge
importlib-metadata 4.0.1 py37h03978a9_0 conda-forge
intel-openmp 2021.2.0 h57928b3_616 conda-forge
ipykernel 5.5.5 py37h7813e69_0 conda-forge
ipython 7.23.1 py37h7813e69_0 conda-forge
ipython_genutils 0.2.0 py_1 conda-forge
jedi 0.18.0 py37h03978a9_2 conda-forge
jinja2 3.0.0 pyhd8ed1ab_0 conda-forge
joblib 1.0.1 pyhd8ed1ab_0 conda-forge
json5 0.9.5 pyh9f0ad1d_0 conda-forge
jsonschema 3.2.0 pyhd8ed1ab_3 conda-forge
jupyter_client 6.1.12 pyhd8ed1ab_0 conda-forge
jupyter_core 4.7.1 py37h03978a9_0 conda-forge
jupyter_server 1.7.0 py37h03978a9_1 conda-forge
jupyterlab 3.0.15 pyhd8ed1ab_0 conda-forge
jupyterlab_pygments 0.1.2 pyh9f0ad1d_0 conda-forge
jupyterlab_server 2.5.1 pyhd8ed1ab_0 conda-forge
keras 2.3.1 py37h21ff451_0 conda-forge
keras-applications 1.0.8 py_1 conda-forge
keras-preprocessing 1.1.2 pyhd8ed1ab_0 conda-forge
kiwisolver 1.3.1 py37h8c56517_1 conda-forge
krb5 1.19.1 hbae68bd_0 conda-forge
libblas 3.9.0 9_mkl conda-forge
libcblas 3.9.0 9_mkl conda-forge
libcurl 7.76.1 h789b8ee_2 conda-forge
libgpuarray 0.7.6 h8ffe710_1003 conda-forge
liblapack 3.9.0 9_mkl conda-forge
libpng 1.6.37 h1d00b33_2 conda-forge
libprotobuf 3.17.0 h7755175_0 conda-forge
libsodium 1.0.18 h8d14728_1 conda-forge
libssh2 1.9.0 h680486a_6 conda-forge
m2w64-gcc-libgfortran 5.3.0 6 conda-forge
m2w64-gcc-libs 5.3.0 7 conda-forge
m2w64-gcc-libs-core 5.3.0 7 conda-forge
m2w64-gmp 6.1.0 2 conda-forge
m2w64-libwinpthread-git 5.0.0.4634.697f757 2 conda-forge
mako 1.1.4 pyh44b312d_0 conda-forge
markdown 3.3.4 pyhd8ed1ab_0 conda-forge
markupsafe 2.0.0 py37hcc03f2d_0 conda-forge
matplotlib-base 3.1.3 py37h2981e6d_0 conda-forge
matplotlib-inline 0.1.2 pyhd8ed1ab_2 conda-forge
mistune 0.8.4 py37hcc03f2d_1003 conda-forge
mkl 2021.2.0 hb70f87d_389 conda-forge
mock 4.0.3 py37h03978a9_1 conda-forge
msys2-conda-epoch 20160418 1 conda-forge
nbclassic 0.2.8 pyhd8ed1ab_0 conda-forge
nbclient 0.5.3 pyhd8ed1ab_0 conda-forge
nbconvert 6.0.7 py37h03978a9_3 conda-forge
nbformat 5.1.3 pyhd8ed1ab_0 conda-forge
nest-asyncio 1.5.1 pyhd8ed1ab_0 conda-forge
networkx 2.1 py_1 conda-forge
nilm_metadata 0.2.4 0 nilmtk
nilmtk 0.4.3 py_0 nilmtk
nilmtk-contrib 0.1.1 py_0 nilmtk
nose 1.3.7 py_1006 conda-forge
notebook 6.3.0 pyha770c72_1 conda-forge
numexpr 2.7.3 py37h08fd248_0 conda-forge
numpy 1.19.5 py37hd20adf4_1 conda-forge
openssl 1.1.1k h8ffe710_0 conda-forge
osqp 0.6.2 py37h08fd248_1 conda-forge
packaging 20.9 pyh44b312d_0 conda-forge
pandas 0.25.3 py37he350917_0 conda-forge
pandoc 2.13 h8ffe710_0 conda-forge
pandocfilters 1.4.2 py_1 conda-forge
parso 0.8.2 pyhd8ed1ab_0 conda-forge
pickleshare 0.7.5 py_1003 conda-forge
pip 21.1.1 pyhd8ed1ab_0 conda-forge
prometheus_client 0.10.1 pyhd8ed1ab_0 conda-forge
prompt-toolkit 3.0.18 pyha770c72_0 conda-forge
protobuf 3.17.0 py37hf2a7229_0 conda-forge
pycparser 2.20 pyh9f0ad1d_2 conda-forge
pygments 2.9.0 pyhd8ed1ab_0 conda-forge
pygpu 0.7.6 py37hda49f71_1002 conda-forge
pyopenssl 20.0.1 pyhd8ed1ab_0 conda-forge
pyparsing 2.4.7 pyh9f0ad1d_0 conda-forge
pyreadline 2.1 py37h03978a9_1003 conda-forge
pyrsistent 0.17.3 py37hcc03f2d_2 conda-forge
pysocks 1.7.1 py37h03978a9_3 conda-forge
pytables 3.6.1 py37hdc91d43_3 conda-forge
python 3.7.10 h7840368_100_cpython conda-forge
python-dateutil 2.8.1 py_0 conda-forge
python_abi 3.7 1_cp37m conda-forge
pytz 2021.1 pyhd8ed1ab_0 conda-forge
pywin32 300 py37hcc03f2d_0 conda-forge
pywinpty 1.0.1 py37h7f67f24_0 conda-forge
pyyaml 5.4.1 py37hcc03f2d_0 conda-forge
pyzmq 22.0.3 py37hcce574b_1 conda-forge
qdldl-python 0.1.5 py37h08fd248_0 conda-forge
requests 2.25.1 pyhd3deb0d_0 conda-forge
scikit-learn 0.24.2 py37h8ded0a9_0 conda-forge
scipy 1.6.3 py37h924764e_0 conda-forge
scs 2.1.3 py37he58051b_0 conda-forge
send2trash 1.5.0 py_0 conda-forge
setuptools 49.6.0 py37h03978a9_3 conda-forge
six 1.16.0 pyh6c4a22f_0 conda-forge
sniffio 1.2.0 py37h03978a9_1 conda-forge
sqlite 3.35.5 h8ffe710_0 conda-forge
tbb 2021.2.0 h2d74725_0 conda-forge
tensorboard 1.14.0 py37_0 conda-forge
tensorflow 1.14.0 h1f41ff6_0 conda-forge
tensorflow-base 1.14.0 py37hc8dfbb8_0 conda-forge
tensorflow-estimator 1.14.0 py37h5ca1d4c_0 conda-forge
termcolor 1.1.0 py_2 conda-forge
terminado 0.9.4 py37h03978a9_0 conda-forge
testpath 0.4.4 py_0 conda-forge
theano 1.0.5 py37hf2a7229_1 conda-forge
threadpoolctl 2.1.0 pyh5ca1d4c_0 conda-forge
tk 8.6.10 h8ffe710_1 conda-forge
tornado 6.1 py37hcc03f2d_1 conda-forge
traitlets 5.0.5 py_0 conda-forge
typing_extensions 3.7.4.3 py_0 conda-forge
urllib3 1.26.4 pyhd8ed1ab_0 conda-forge
vc 14.2 hb210afc_4 conda-forge
vs2015_runtime 14.28.29325 h5e1d092_4 conda-forge
vs2017_win-64 19.16.27038 h2e3bad8_2 conda-forge
vswhere 2.8.4 h57928b3_0 conda-forge
wcwidth 0.2.5 pyh9f0ad1d_2 conda-forge
webencodings 0.5.1 py_1 conda-forge
websocket-client 0.57.0 py37h03978a9_4 conda-forge
werkzeug 2.0.0 pyhd8ed1ab_0 conda-forge
wheel 0.36.2 pyhd3deb0d_0 conda-forge
win_inet_pton 1.1.0 py37h03978a9_2 conda-forge
wincertstore 0.2 py37h03978a9_1006 conda-forge
winpty 0.4.3 4 conda-forge
wrapt 1.12.1 py37hcc03f2d_3 conda-forge
yaml 0.2.5 he774522_0 conda-forge
zeromq 4.3.4 h0e60522_0 conda-forge
zipp 3.4.1 pyhd8ed1ab_0 conda-forge
zlib 1.2.11 h62dcd97_1010 conda-forge
Just downgrade the h5py to version 2.10, it worked fine:
pip install h5py==2.10
I tried nilmtk-contrib with multiple homes in REDD dataset as following
redd = {
'power': {
'mains': ['apparent','active'],
'appliance': ['apparent','active']
},
'sample_rate': 60,
'appliances': ['fridge', 'light', 'dish washer', 'sockets', 'microwave'],
'methods': {
'DAE':DAE({'n_epochs':10,'batch_size':128}),
},
'train': {
'datasets': {
'REDD': {
'path': '/content/drive/MyDrive/datasets/low_redd.h5',
'buildings': {
2: {
'start_time': '2011-04-18',
'end_time': '2011-04-30'
},
3: {
'start_time': '2011-04-18',
'end_time': '2011-04-30'
},
4: {
'start_time': '2011-04-18',
'end_time': '2011-04-30'
},
}
}
}
},
'test': {
'datasets': {
'REDD': {
'path': '/content/drive/MyDrive/datasets/low_redd.h5',
'buildings': {
1: {
'start_time': '2011-04-30',
'end_time': '2011-05-24'
},
}
}
},
'metrics':['mae']
}
}
I got the following error could any one help me
Started training for DAE
Joint training for DAE
............... Loading Data for training ...................
Loading data for REDD dataset
Loading building ... 2
Loading data for meter ElecMeterID(instance=2, building=2, dataset='REDD')
Done loading data all meters for this chunk.
Dropping missing values
Loading building ... 3
Loading data for meter ElecMeterID(instance=2, building=3, dataset='REDD')
Done loading data all meters for this chunk.
Dropping missing values
Loading building ... 4
Loading data for meter ElecMeterID(instance=2, building=4, dataset='REDD')
Done loading data all meters for this chunk.
Traceback (most recent call last):
File "/content/drive/MyDrive/NILM/redd-test.py", line 75, in
api_res = API(redd)
File "/usr/local/lib/python3.7/site-packages/nilmtk/api.py", line 46, in init
self.experiment()
File "/usr/local/lib/python3.7/site-packages/nilmtk/api.py", line 91, in experiment
self.train_jointly(clf,d)
File "/usr/local/lib/python3.7/site-packages/nilmtk/api.py", line 217, in train_jointly
appliance_df = next(train.buildings[building].elec[appliance_name].load(physical_quantity='power', ac_type=self.power['appliance'], sample_period=self.sample_period))
File "/usr/local/lib/python3.7/site-packages/nilmtk/metergroup.py", line 208, in getitem
return self[(key, 1)]
File "/usr/local/lib/python3.7/site-packages/nilmtk/metergroup.py", line 252, in getitem
return self[{'type': key[0], 'instance': key[1]}]
File "/usr/local/lib/python3.7/site-packages/nilmtk/metergroup.py", line 271, in getitem
raise KeyError(key)
KeyError: {'type': 'fridge', 'instance': 1}
I tried nilmtk-contrib with multiple homes in REDD dataset as following
redd = { 'power': { 'mains': ['apparent','active'], 'appliance': ['apparent','active'] }, 'sample_rate': 60,
'appliances': ['fridge', 'light', 'dish washer', 'sockets', 'microwave'], 'methods': {
'DAE':DAE({'n_epochs':10,'batch_size':128}),}, 'train': { 'datasets': { 'REDD': { 'path': '/content/drive/MyDrive/datasets/low_redd.h5', 'buildings': { 2: { 'start_time': '2011-04-18', 'end_time': '2011-04-30' }, 3: { 'start_time': '2011-04-18', 'end_time': '2011-04-30' }, 4: { 'start_time': '2011-04-18', 'end_time': '2011-04-30' },
} } } }, 'test': { 'datasets': { 'REDD': { 'path': '/content/drive/MyDrive/datasets/low_redd.h5', 'buildings': { 1: { 'start_time': '2011-04-30', 'end_time': '2011-05-24' }, } }}, 'metrics':['mae'] } }
I got the following error could any one help me
Started training for DAE Joint training for DAE ............... Loading Data for training ................... Loading data for REDD dataset Loading building ... 2 Loading data for meter ElecMeterID(instance=2, building=2, dataset='REDD') Done loading data all meters for this chunk. Dropping missing values Loading building ... 3 Loading data for meter ElecMeterID(instance=2, building=3, dataset='REDD') Done loading data all meters for this chunk. Dropping missing values Loading building ... 4 Loading data for meter ElecMeterID(instance=2, building=4, dataset='REDD') Done loading data all meters for this chunk. Traceback (most recent call last): File "/content/drive/MyDrive/NILM/redd-test.py", line 75, in api_res = API(redd) File "/usr/local/lib/python3.7/site-packages/nilmtk/api.py", line 46, in init self.experiment() File "/usr/local/lib/python3.7/site-packages/nilmtk/api.py", line 91, in experiment self.train_jointly(clf,d) File "/usr/local/lib/python3.7/site-packages/nilmtk/api.py", line 217, in train_jointly appliance_df = next(train.buildings[building].elec[appliance_name].load(physical_quantity='power', ac_type=self.power['appliance'], sample_period=self.sample_period)) File "/usr/local/lib/python3.7/site-packages/nilmtk/metergroup.py", line 208, in getitem return self[(key, 1)] File "/usr/local/lib/python3.7/site-packages/nilmtk/metergroup.py", line 252, in getitem return self[{'type': key[0], 'instance': key[1]}] File "/usr/local/lib/python3.7/site-packages/nilmtk/metergroup.py", line 271, in getitem raise KeyError(key) KeyError: {'type': 'fridge', 'instance': 1}
Hello, I am getting the following key error did you encounter such an issue??