如何使用批量训练模型,对单个输入进行预测?

我有经过数据集培训的RNN模型:

train = tf.data.Dataset.from_tensor_slices((data_x[:train_size],
                          data_y[:train_size])).batch(batch_size).repeat()

模型:

    model = tf.keras.Sequential()
    model.add(tf.keras.layers.GRU(units=lstm_num_units,
                                   return_sequences=True,
                                   kernel_initializer='random_uniform',
                                   recurrent_initializer='random_uniform',
                                   bias_initializer='random_uniform',
                                   batch_size=batch_size,
                                   input_shape = [seq_len, num_features]))
    model.add(tf.keras.layers.LSTM(units=lstm_num_units,
                                   batch_size=batch_size,
                                   return_sequences=True,
                                   input_shape = [seq_len, num_features]))
    model.add(tf.keras.layers.Flatten())
    model.add(tf.keras.layers.Dense(units=dence_units))
    model.add(tf.keras.layers.Dropout(drop_flat))
    model.add(tf.keras.layers.Dense(units=out_units))
    model.add(tf.keras.layers.Softmax())   

    model.compile(loss="sparse_categorical_crossentropy",
            optimizer=tf.train.RMSPropOptimizer(opt),
            metrics=['accuracy'])

 model.fit(train, epochs=EPOCHS,
                        steps_per_epoch=repeat_size_train,
                        validation_data=validate,
                        validation_steps=repeat_size_validate,
                        verbose=1,
                        shuffle=True)
                        callbacks=[tensorboard, cp_callback])

我需要对seq-len的单个输入进行预测,但看起来我的输入必须是一个批大小:

ar = np.random.randint(98, size=[batch_size, seq_len])
ar = np.reshape(ar, [batch_size, seq_len, 1])
prediction = model.m.predict(ar)

有没有一种方法可以使它在形状[1,序列长度,1]的单个输入上工作?