36 lines
956 B
Python
36 lines
956 B
Python
import tensorflow as tf
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import matplotlib.pyplot as pp
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import pywatts.neural
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from sklearn.model_selection import train_test_split
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df = pywatts.db.rows_to_df(list(range(1, 50)))
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X = df[[col for col in df.columns if col != 'dc']]
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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=23)
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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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X_train.shape, X_test.shape, X_val.shape
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feature_cols = [tf.feature_column.numeric_column(col) for col in X.columns]
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n = pywatts.neural.Net(feature_cols=feature_cols)
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def train(steps=100):
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evaluation = []
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for i in range(steps):
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n.train(X_train, y_train, steps=400)
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evaluation.append(n.evaluate(X_val, y_val))
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print("Training %s of %s" % (i, steps))
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return evaluation
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def plot_training(evaluation):
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loss = []
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for e in evaluation:
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loss.append(e['loss'])
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pp.plot(loss)
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