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Currently, learning is done with the existing data, but you can't continue to improve the AI later.
I think we should change that.
After training a prediction for testing
Since it is easier for humans to compare values than an interpretation of the Loss value, a single prediction should be started after training and saved to a CSV. The output dir is already created in 4e99c46
ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type datetime.datetime)
when running measurements_model.fit(train_measurements, epochs=100, batch_size=1, verbose=2, validation_split=0.2, callbacks=[measurements_model_checkpoint_callback, measurements_model_earlystopping_callback]) we get this error
% python ./solarKI-train.py
Measurements:
count mean std min 25% 50% 75% max
production 1662.0 799.126955 1182.978117 0.0 0.0 128.5 1113.75 4472.0
consumption 1662.0 458.116727 552.151204 96.0 240.0 358.0 504.75 7705.0
battery_charge 1662.0 169.667268 383.778746 0.0 0.0 5.0 53.00 2071.0
battery_discharge 1662.0 130.580024 175.052748 0.0 0.0 56.0 200.00 1717.0
grid_feedin 1662.0 432.141998 817.662773 0.0 0.0 0.0 443.00 3657.0
grid_consumption 1662.0 130.219013 411.663270 0.0 0.0 31.0 84.00 7227.0
battery_state_of_charge 1662.0 43.147413 34.329361 0.0 4.0 44.0 70.00 100.0
direct_consumption 1662.0 228.770156 336.913024 0.0 0.0 126.5 379.00 4068.0
Statistics:
count mean std min 25% 50% 75% max
produced_energy 208.0 19178.134615 11107.263080 649.0 7814.5 21813.5 29311.25 35171.0
consumed_energy 208.0 10987.937500 5427.975153 5669.0 8656.5 9616.0 10806.75 37079.0
battery_charged_energy 208.0 4070.625000 2030.822482 363.0 2837.5 4185.0 5563.00 9969.0
battery_discharged_energy 208.0 3129.774038 1613.599089 0.0 2332.5 3161.5 4166.50 9060.0
grid_feedin_energy 208.0 10529.706731 9016.965193 0.0 198.5 11014.0 18887.75 27601.0
grid_purchase_energy 208.0 3280.375000 5045.327483 305.0 414.0 559.0 4120.50 33850.0
Measurements:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1662 entries, 0 to 1661
Data columns (total 9 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 timestamp 1662 non-null object
1 production 1662 non-null int64
2 consumption 1662 non-null int64
3 battery_charge 1662 non-null int64
4 battery_discharge 1662 non-null int64
5 grid_feedin 1662 non-null float64
6 grid_consumption 1662 non-null float64
7 battery_state_of_charge 1662 non-null int64
8 direct_consumption 1662 non-null int64
dtypes: float64(2), int64(6), object(1)
memory usage: 117.0+ KB
None
Statistics:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 208 entries, 0 to 207
Data columns (total 7 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 timestamp 208 non-null object
1 produced_energy 208 non-null int64
2 consumed_energy 208 non-null int64
3 battery_charged_energy 208 non-null int64
4 battery_discharged_energy 208 non-null int64
5 grid_feedin_energy 208 non-null int64
6 grid_purchase_energy 208 non-null int64
dtypes: int64(6), object(1)
memory usage: 11.5+ KB
None
Train measurements:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1329 entries, 0 to 1328
Data columns (total 9 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 timestamp 1329 non-null object
1 production 1329 non-null int64
2 consumption 1329 non-null int64
3 battery_charge 1329 non-null int64
4 battery_discharge 1329 non-null int64
5 grid_feedin 1329 non-null float64
6 grid_consumption 1329 non-null float64
7 battery_state_of_charge 1329 non-null int64
8 direct_consumption 1329 non-null int64
dtypes: float64(2), int64(6), object(1)
memory usage: 93.6+ KB
None
Test measurements:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 333 entries, 1329 to 1661
Data columns (total 9 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 timestamp 333 non-null object
1 production 333 non-null int64
2 consumption 333 non-null int64
3 battery_charge 333 non-null int64
4 battery_discharge 333 non-null int64
5 grid_feedin 333 non-null float64
6 grid_consumption 333 non-null float64
7 battery_state_of_charge 333 non-null int64
8 direct_consumption 333 non-null int64
dtypes: float64(2), int64(6), object(1)
memory usage: 23.5+ KB
None
Train statistics:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 166 entries, 0 to 165
Data columns (total 7 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 timestamp 166 non-null object
1 produced_energy 166 non-null int64
2 consumed_energy 166 non-null int64
3 battery_charged_energy 166 non-null int64
4 battery_discharged_energy 166 non-null int64
5 grid_feedin_energy 166 non-null int64
6 grid_purchase_energy 166 non-null int64
dtypes: int64(6), object(1)
memory usage: 9.2+ KB
None
Test statistics:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 42 entries, 166 to 207
Data columns (total 7 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 timestamp 42 non-null object
1 produced_energy 42 non-null int64
2 consumed_energy 42 non-null int64
3 battery_charged_energy 42 non-null int64
4 battery_discharged_energy 42 non-null int64
5 grid_feedin_energy 42 non-null int64
6 grid_purchase_energy 42 non-null int64
dtypes: int64(6), object(1)
memory usage: 2.4+ KB
None
2022-07-29 23:56:49.969541: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1126 MB memory: -> device: 0, name: NVIDIA GeForce GTX 960, pci bus id: 0000:03:00.0, compute capability: 5.2
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
Input (LSTM) (None, 1, 128) 70144
_________________________________________________________________
lstm (LSTM) (None, 1, 128) 131584
_________________________________________________________________
lstm_1 (LSTM) (None, 1, 64) 49408
_________________________________________________________________
lstm_2 (LSTM) (None, 1, 64) 33024
_________________________________________________________________
Output (Dense) (None, 1, 8) 520
=================================================================
Total params: 284,680
Trainable params: 284,680
Non-trainable params: 0
_________________________________________________________________
None
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
Input (LSTM) (None, 1, 128) 69120
_________________________________________________________________
lstm_3 (LSTM) (None, 1, 128) 131584
_________________________________________________________________
lstm_4 (LSTM) (None, 1, 64) 49408
_________________________________________________________________
lstm_5 (LSTM) (None, 1, 64) 33024
_________________________________________________________________
Output (Dense) (None, 1, 6) 390
=================================================================
Total params: 283,526
Trainable params: 283,526
Non-trainable params: 0
_________________________________________________________________
None
Traceback (most recent call last):
File "/GitHub/User/GitFolder/ProjectFolder/KiPython/lib/python3.10/site-packages/tensorflow/python/data/util/structure.py", line 102, in normalize_element
spec = type_spec_from_value(t, use_fallback=False)
File "/GitHub/User/GitFolder/ProjectFolder/KiPython/lib/python3.10/site-packages/tensorflow/python/data/util/structure.py", line 485, in type_spec_from_value
raise TypeError("Could not build a `TypeSpec` for {} with type {}".format(
TypeError: Could not build a `TypeSpec` for timestamp production consumption battery_charge battery_discharge grid_feedin grid_consumption battery_state_of_charge direct_consumption
0 2022-01-01 00:00:00+01:00 0 749 5 0 0.0 754.0 0 0
1 2022-01-01 03:00:00+01:00 0 200 26 0 0.0 226.0 0 0
2 2022-01-01 06:00:00+01:00 3 225 5 0 0.0 227.0 0 3
3 2022-01-01 09:00:00+01:00 129 582 23 0 0.0 476.0 0 129
4 2022-01-01 12:00:00+01:00 178 757 26 0 0.0 605.0 0 178
... ... ... ... ... ... ... ... ... ...
1058 2022-05-13 06:00:00+02:00 806 242 4 11 571.0 0.0 31 242
1059 2022-05-13 09:00:00+02:00 2886 574 23 7 2296.0 0.0 29 574
1060 2022-05-13 12:00:00+02:00 3681 400 1486 0 1795.0 0.0 58 400
1061 2022-05-13 15:00:00+02:00 2397 347 18 14 2046.0 0.0 100 347
1062 2022-05-13 18:00:00+02:00 400 455 18 207 134.0 0.0 98 400
[1063 rows x 9 columns] with type DataFrame
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/GitHub/User/GitFolder/ProjectFolder/./solarKI-train.py", line 125, in <module>
main()
File "/GitHub/User/GitFolder/ProjectFolder/./solarKI-train.py", line 122, in main
measurements_model.fit(train_measurements, train_measurements, epochs=100, batch_size=1, verbose=2, validation_split=0.2, callbacks=[measurements_model_checkpoint_callback, measurements_model_earlystopping_callback])
File "/GitHub/User/GitFolder/ProjectFolder/KiPython/lib/python3.10/site-packages/tensorflow/python/keras/engine/training.py", line 1139, in fit
data_handler = data_adapter.get_data_handler(
File "/GitHub/User/GitFolder/ProjectFolder/KiPython/lib/python3.10/site-packages/tensorflow/python/keras/engine/data_adapter.py", line 1394, in get_data_handler
return DataHandler(*args, **kwargs)
File "/GitHub/User/GitFolder/ProjectFolder/KiPython/lib/python3.10/site-packages/tensorflow/python/keras/engine/data_adapter.py", line 1149, in __init__
self._adapter = adapter_cls(
File "/GitHub/User/GitFolder/ProjectFolder/KiPython/lib/python3.10/site-packages/tensorflow/python/keras/engine/data_adapter.py", line 335, in __init__
dataset = self.slice_inputs(indices_dataset, inputs)
File "/GitHub/User/GitFolder/ProjectFolder/KiPython/lib/python3.10/site-packages/tensorflow/python/keras/engine/data_adapter.py", line 361, in slice_inputs
dataset_ops.DatasetV2.from_tensors(inputs).repeat()
File "/GitHub/User/GitFolder/ProjectFolder/KiPython/lib/python3.10/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 729, in from_tensors
return TensorDataset(tensors, name=name)
File "/GitHub/User/GitFolder/ProjectFolder/KiPython/lib/python3.10/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 4531, in __init__
element = structure.normalize_element(element)
File "/GitHub/User/GitFolder/ProjectFolder/KiPython/lib/python3.10/site-packages/tensorflow/python/data/util/structure.py", line 107, in normalize_element
ops.convert_to_tensor(t, name="component_%d" % i))
File "/GitHub/User/GitFolder/ProjectFolder/KiPython/lib/python3.10/site-packages/tensorflow/python/profiler/trace.py", line 183, in wrapped
return func(*args, **kwargs)
File "/GitHub/User/GitFolder/ProjectFolder/KiPython/lib/python3.10/site-packages/tensorflow/python/framework/ops.py", line 1640, in convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/GitHub/User/GitFolder/ProjectFolder/KiPython/lib/python3.10/site-packages/tensorflow/python/framework/constant_op.py", line 343, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "/GitHub/User/GitFolder/ProjectFolder/KiPython/lib/python3.10/site-packages/tensorflow/python/framework/constant_op.py", line 267, in constant
return _constant_impl(value, dtype, shape, name, verify_shape=False,
File "/GitHub/User/GitFolder/ProjectFolder/KiPython/lib/python3.10/site-packages/tensorflow/python/framework/constant_op.py", line 279, in _constant_impl
return _constant_eager_impl(ctx, value, dtype, shape, verify_shape)
File "/GitHub/User/GitFolder/ProjectFolder/KiPython/lib/python3.10/site-packages/tensorflow/python/framework/constant_op.py", line 304, in _constant_eager_impl
t = convert_to_eager_tensor(value, ctx, dtype)
File "/GitHub/User/GitFolder/ProjectFolder/KiPython/lib/python3.10/site-packages/tensorflow/python/framework/constant_op.py", line 102, in convert_to_eager_tensor
return ops.EagerTensor(value, ctx.device_name, dtype)
ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type datetime.datetime).
python ./solarKI-train.py 6,96s user 4,56s system 173% cpu 6,625 total
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