50 lines
1.0 KiB
Python
50 lines
1.0 KiB
Python
import tensorflow as tf
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import pywatts.db
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from pywatts.routines import *
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NUM_STATIONS_FROM_DB = 75
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NUM_TRAIN_STATIONS = 400
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NUM_EVAL_STATIONS = 40
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TRAIN = True
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PLOT = True
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TRAIN_STEPS = 50
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df = pywatts.db.rows_to_df(list(range(1, NUM_STATIONS_FROM_DB)))
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X = df
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y = df['dc']
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#X_train, X_tmp, y_train, y_tmp = train_test_split(X, y, test_size=0.2, random_state=34)
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#X_test, X_val, y_test, y_val = train_test_split(X_tmp, y_tmp, test_size=0.5, random_state=23)
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# Define feature columns and initialize Regressor
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feature_col = [tf.feature_column.numeric_column(str(idx)) for idx in range(336)]
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n = pywatts.neural.Net(feature_cols=feature_col)
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# Training data
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(X_train, y_train) = train_split(df, NUM_TRAIN_STATIONS)
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# Evaluation data
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(X_val, y_val) = train_split(df, NUM_EVAL_STATIONS)
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train_eval = {}
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if TRAIN:
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# Train the model with the steps given
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train_eval = train(n, X_train, y_train, X_val, y_val, TRAIN_STEPS)
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if PLOT:
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# Plot training success rate (with 'average loss')
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pywatts.routines.plot_training(train_eval)
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exit()
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