記錄準確率(Accuracy)、精確率(Precision)、召回率(Recall)和F1值(F-Measure)計算公式，和如何使用TensorFlow實現

一、計算公式

二、TensorFlow實現

        # Accuracy
        with tf.name_scope("accuracy"):
            correct_predictions = tf.equal(self.predictions, tf.argmax(self.input_y, 1))
            self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, "float"), name="accuracy")

        # TODO: Reconsider the metrics calculation
        # Number of correct predictions
        with tf.name_scope("num_correct"):
            correct = tf.equal(self.predictions, tf.argmax(self.input_y, 1))
            self.num_correct = tf.reduce_sum(tf.cast(correct, "float"), name="num_correct")

        # Calculate Fp
        with tf.name_scope("fp"):
            fp = tf.metrics.false_positives(labels=tf.argmax(self.input_y, 1), predictions=self.predictions)
            self.fp = tf.reduce_sum(tf.cast(fp, "float"), name="fp")

        # Calculate Fn
        with tf.name_scope("fn"):
            fn = tf.metrics.false_negatives(labels=tf.argmax(self.input_y, 1), predictions=self.predictions)
            self.fn = tf.reduce_sum(tf.cast(fn, "float"), name="fn")

        # Calculate Recall
        with tf.name_scope("recall"):
            self.recall = self.num_correct / (self.num_correct + self.fn)

        # Calculate Precision
        with tf.name_scope("precision"):
            self.precision = self.num_correct / (self.num_correct + self.fp)

        # Calculate F1
        with tf.name_scope("F1"):
            self.F1 = (2 * self.precision * self.recall) / (self.precision + self.recall)

        # Calculate AUC
        with tf.name_scope("AUC"):
            self.AUC = tf.metrics.auc(self.softmax_scores, self.input_y, name="AUC")