Tensorflow:如何在python中获取图形输入和输出？

import tensorflow as tf

input_placeholder = tf.placeholder(dtype=tf.float32, shape=[None, input_size], name='input')
output_placeholder = tf.placeholder(dtype=tf.float32, shape=[None, output_size], name='output')

# 定义模型的网络结构
# ...

# 使用输入占位符作为模型的输入
model_output = model(input_placeholder)

loss = tf.losses.mean_squared_error(labels=output_placeholder, predictions=model_output)

optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
train_op = optimizer.minimize(loss)

with tf.Session() as sess:
    # 初始化变量
    sess.run(tf.global_variables_initializer())

    # 训练模型
    for epoch in range(num_epochs):
        # 获取输入和输出数据
        input_data, output_data = get_data()

        # 执行训练操作
        _, current_loss = sess.run([train_op, loss], feed_dict={input_placeholder: input_data, output_placeholder: output_data})

        # 打印当前损失
        print("Epoch {}: Loss = {}".format(epoch, current_loss))