29 lines
1.3 KiB
Python
29 lines
1.3 KiB
Python
import tensorflow as tf
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def pywatts_input_fn(X, y=None, num_epochs=None, shuffle=True, batch_size=400):
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return tf.estimator.inputs.pandas_input_fn(x=X,
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y=y,
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num_epochs=num_epochs,
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shuffle=shuffle,
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batch_size=batch_size)
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class Net:
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__regressor = None
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__feature_cols = [tf.feature_column.numeric_column(col) for col in ['dc', 'temp', 'wind']]
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def __init__(self, feature_cols=__feature_cols):
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self.__regressor = tf.estimator.DNNRegressor(feature_columns=feature_cols,
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hidden_units=[50, 50],
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model_dir='tf_pywatts_model')
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def train(self, training_data, training_results, steps):
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self.__regressor.train(input_fn=pywatts_input_fn(training_data, y=training_results, num_epochs=None, shuffle=True), steps=steps)
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def evaluate(self, eval_data, eval_results):
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return self.__regressor.evaluate(input_fn=pywatts_input_fn(eval_data, y=eval_results, num_epochs=1, shuffle=False), steps=1)
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def predict1h(self, predict_data):
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return self.__regressor.predict(input_fn=pywatts_input_fn(predict_data, num_epochs=1, shuffle=False))
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