lstm.py
def predict(self, x: np.ndarray) -> Tuple[int,float]:
        """ Generate predictions for one single input sample.
            Return result and precision.
        """
        prediction = self.model.predict(x, batch_size=1)

        probability_0 = prediction[0,0]
        probability_1 = prediction[0,1]

        if probability_0 >= probability_1:
            prediction_result = int(0)
            prediction_precision = probability_0
        else:
            prediction_result = int(1)
            prediction_precision = probability_1

        return prediction_result, prediction_precision
 Generate predictions for one single input sample.
            Return result and precision.
        
Generate predictions for one single input sample.
Return result and precision.
Generate predictions for one single input sample . Return result and precision .
def predict(self, x: np.ndarray) -> Tuple[int,float]:
        prediction = self.model.predict(x, batch_size=1)
        probability_0 = prediction[0,0]
        probability_1 = prediction[0,1]
        if probability_0 >= probability_1:
            prediction_result = int(0)
            prediction_precision = probability_0
        else:
            prediction_result = int(1)
            prediction_precision = probability_1
        return prediction_result, prediction_precision
def predict ( self , x : np . ndarray ) -> Tuple [ int , float ] : prediction = self . model . predict ( x , batch_size = 1 ) probability_0 = prediction [ 0 , 0 ] probability_1 = prediction [ 0 , 1 ] if probability_0 >= probability_1 : prediction_result = int ( 0 ) prediction_precision = probability_0 else : prediction_result = int ( 1 ) prediction_precision = probability_1 return prediction_result , prediction_precision
Generate predictions for one single input sample.
Generate predictions for one single input sample .
""" Generate predictions for one single input sample.
            Return result and precision.
        """
self
x
np.ndarray