single_dialog.py 文件源码

def train(self):
        trainS, trainQ, trainA = vectorize_data(self.trainData, self.word_idx, self.sentence_size, self.batch_size, self.n_cand, self.memory_size)
        valS, valQ, valA = vectorize_data(self.valData, self.word_idx, self.sentence_size, self.batch_size, self.n_cand, self.memory_size)
        n_train = len(trainS)
        n_val = len(valS)
        print("Training Size",n_train)
        print("Validation Size", n_val)
        tf.set_random_seed(self.random_state)
        batches = zip(range(0, n_train-self.batch_size, self.batch_size), range(self.batch_size, n_train, self.batch_size))
        batches = [(start, end) for start, end in batches]
        best_validation_accuracy=0

        for t in range(1, self.epochs+1):
            print('Epoch', t)
            np.random.shuffle(batches)
            total_cost = 0.0
            for start, end in batches:
                s = trainS[start:end]
                q = trainQ[start:end]
                a = trainA[start:end]
                cost_t = self.model.batch_fit(s, q, a)
                total_cost += cost_t
            if t % self.evaluation_interval == 0:
                train_preds=self.batch_predict(trainS,trainQ,n_train)
                val_preds=self.batch_predict(valS,valQ,n_val)
                train_acc = metrics.accuracy_score(np.array(train_preds), trainA)
                val_acc = metrics.accuracy_score(val_preds, valA)
                print('-----------------------')
                print('Epoch', t)
                print('Total Cost:', total_cost)
                print('Training Accuracy:', train_acc)
                print('Validation Accuracy:', val_acc)
                print('-----------------------')

                # write summary
                # train_acc_summary = tf.scalar_summary('task_' + str(self.task_id) + '/' + 'train_acc', tf.constant((train_acc), dtype=tf.float32))
                # val_acc_summary = tf.scalar_summary('task_' + str(self.task_id) + '/' + 'val_acc', tf.constant((val_acc), dtype=tf.float32))
                # merged_summary = tf.merge_summary([train_acc_summary, val_acc_summary])
                train_acc_summary = tf.summary.scalar('task_' + str(self.task_id) + '/' + 'train_acc', tf.constant((train_acc), dtype=tf.float32))
                val_acc_summary = tf.summary.scalar('task_' + str(self.task_id) + '/' + 'val_acc', tf.constant((val_acc), dtype=tf.float32))
                merged_summary = tf.summary.merge([train_acc_summary, val_acc_summary])
                summary_str = self.sess.run(merged_summary)
                self.summary_writer.add_summary(summary_str, t)
                self.summary_writer.flush()

                if val_acc > best_validation_accuracy:
                    best_validation_accuracy=val_acc
                    self.saver.save(self.sess,self.model_dir+'model.ckpt',global_step=t)