从我的tf代码中我的.meta文件中的输出节点名称是什么

Question

我正在尝试将我的TF转换为.meta文件。 我使用了以下代码：

from __future__ import division, print_function, absolute_import

import tensorflow as tf

# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

counter=0
for i in range(1, 3):
    # Training Parameters
    learning_rate = 0.001
    num_steps = 200
    batch_size = 128
    display_step = 10

    print("******************************&&&&&&&&&&&&&&&&&&&&counter: ", counter)
    counter=counter+1
    # Network Parameters
    num_input = 784 # MNIST data input (img shape: 28*28)
    num_classes = 10 # MNIST total classes (0-9 digits)
    dropout = 0.75 # Dropout, probability to keep units

    # tf Graph input
    X = tf.placeholder(tf.float32, [None, num_input])
    Y = tf.placeholder(tf.float32, [None, num_classes])
    keep_prob = tf.placeholder(tf.float32) # dropout (keep probability)


    # Create some wrappers for simplicity
    def conv2d(x, W, b, strides=1):
        # Conv2D wrapper, with bias and relu activation
        x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')
        x = tf.nn.bias_add(x, b)
        return tf.nn.relu(x)


    def maxpool2d(x, k=2):
        # MaxPool2D wrapper
        return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],
                              padding='SAME')


    # Create model
    def conv_net(x, weights, biases, dropout):
        # MNIST data input is a 1-D vector of 784 features (28*28 pixels)
        # Reshape to match picture format [Height x Width x Channel]
        # Tensor input become 4-D: [Batch Size, Height, Width, Channel]
        x = tf.reshape(x, shape=[-1, 28, 28, 1])

        # Convolution Layer
        conv1 = conv2d(x, weights['wc1'], biases['bc1'])
        # Max Pooling (down-sampling)
        conv1 = maxpool2d(conv1, k=2)

        # Convolution Layer
        conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])
        # Max Pooling (down-sampling)
        conv2 = maxpool2d(conv2, k=2)

        # Fully connected layer
        # Reshape conv2 output to fit fully connected layer input
        fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])
        fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
        fc1 = tf.nn.relu(fc1)
        # Apply Dropout
        fc1 = tf.nn.dropout(fc1, dropout)

        # Output, class prediction
        out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])
        return out

    # Store layers weight & bias
    weights = {
        # 5x5 conv, 1 input, 32 outputs
        'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])),
        # 5x5 conv, 32 inputs, 64 outputs
        'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),
        # fully connected, 7*7*64 inputs, 1024 outputs
        'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])),
        # 1024 inputs, 10 outputs (class prediction)
        'out': tf.Variable(tf.random_normal([1024, num_classes]))
    }

    biases = {
        'bc1': tf.Variable(tf.random_normal([32])),
        'bc2': tf.Variable(tf.random_normal([64])),
        'bd1': tf.Variable(tf.random_normal([1024])),
        'out': tf.Variable(tf.random_normal([num_classes]))
    }

    # Construct model
    logits = conv_net(X, weights, biases, keep_prob)
    prediction = tf.nn.softmax(logits)

    # Define loss and optimizer
    loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
        logits=logits, labels=Y))
    optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
    train_op = optimizer.minimize(loss_op)


    # Evaluate model
    correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

    # Initialize the variables (i.e. assign their default value)
    **init = tf.global_variables_initializer()**

    **saver = tf.train.Saver()**
    # Start training
    with tf.Session() as sess:

        # Run the initializer
        sess.run(init)

        for step in range(0, num_steps+1):
            batch_x, batch_y = mnist.train.next_batch(batch_size)
            # Run optimization op (backprop)
            sess.run(train_op, feed_dict={X: batch_x, Y: batch_y, keep_prob: 0.8})
            if step % display_step == 0 or step == 1:
                # Calculate batch loss and accuracy
                loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x,
                                                                     Y: batch_y,
                                                                     keep_prob: 1.0})
                print("Step " + str(step) + ", Minibatch Loss= " + \
                      "{:.4f}".format(loss) + ", Training Accuracy= " + \
                      "{:.3f}".format(acc))

        print("Optimization Finished!")

        # Calculate accuracy for 256 MNIST test images
        print("Testing Accuracy:", \
            sess.run(accuracy, feed_dict={X: mnist.test.images[:256],
                                          Y: mnist.test.labels[:256],
                                          keep_prob: 1.0}))

        **saver.save(sess, 'my_test_model'+str(i))**
        print("*************************file name",'my_test_model'+str(i))

此代码生成“checkpoint”，“。index”，“。meta”和“.data-00000-of-00001”文件。 现在我不知道用于.pb转换的输出节点名称或检查点名称是什么，或者使用freeze_graph.py工具。 我在哪里可以找到输出节点名称？