rnn.py 文件源码

def __call__(self, sess, epoch, iteration, model, loss):
        if iteration == 0 and epoch % self.at_every_epoch == 0:
            total = 0
            correct = 0
            truth_all = []
            pred_all = []
            for values in self.batcher:
                total += len(values[-1])
                feed_dict = {}
                for i in range(0, len(self.placeholders)):
                    feed_dict[self.placeholders[i]] = values[i]
                truth = np.argmax(values[-1], 1)  # values[2], batch sampled from data[2], is a 3-legth one-hot vector containing the labels. this is to transform those back into integers
                predicted = sess.run(tf.arg_max(tf.nn.softmax(model), 1),
                                     feed_dict=feed_dict)
                correct += sum(truth == predicted)
                truth_all.extend(truth)
                pred_all.extend(predicted)
            print(classification_report(truth_all, pred_all, target_names=["NONE", "AGAINST", "FAVOR"], digits=4))