When restoring a network, an operation cannot be found in the restored graph - tensorflow

Question

Using TensorFlow 1.9, I want to train a neural network in one Python file, and then restore the network using a different Python file. I have tried to do this using a simple example, but when I try to load my "prediction" operation, I receive an error. Specifically, the error is: KeyError: "The name 'prediction' refers to an Operation not in the graph.".
Below is my Python file to train and save the network. It generates some example data and trains a simple neural network, then saves the network every epoch.
import numpy as np
import tensorflow as tf
input_data = np.zeros([100, 10])
label_data = np.zeros([100, 1])
for i in range(100):
for j in range(10):
input_data[i, j] = i * j / 1000
label_data[i] = 2 * input_data[i, 0] + np.random.uniform(0.01)
input_placeholder = tf.placeholder(tf.float32, shape=[None, 10], name='input_placeholder')
label_placeholder = tf.placeholder(tf.float32, shape=[None, 1], name='label_placeholder')
x = tf.layers.dense(inputs=input_placeholder, units=10, activation=tf.nn.relu)
x = tf.layers.dense(inputs=x, units=10, activation=tf.nn.relu)
prediction = tf.layers.dense(inputs=x, units=1, name='prediction')
loss_op = tf.reduce_mean(tf.square(prediction - label_placeholder))
train_op = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss_op)
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_num in range(100):
_, loss = sess.run([train_op, loss_op], feed_dict={input_placeholder: input_data, label_placeholder: label_data})
print('epoch ' + str(epoch_num) + ', loss = ' + str(loss))
saver.save(sess, '../Models/model', global_step=epoch_num + 1)
And below is my Python file to restore the network. It loads the input and output placeholders, together with the operation required for making predictions. However, even though I have named an operation as prediction in the training code above, the code below cannot seem to find this operation in the loaded graph.
import tensorflow as tf
import numpy as np
input_data = np.zeros([100, 10])
for i in range(100):
for j in range(10):
input_data[i, j] = i * j / 1000
with tf.Session() as sess:
saver = tf.train.import_meta_graph('../Models/model-99.meta')
saver.restore(sess, '../Models/model-99')
graph = tf.get_default_graph()
input_placeholder = graph.get_tensor_by_name('input_placeholder:0')
label_placeholder = graph.get_tensor_by_name('label_placeholder:0')
prediction = graph.get_operation_by_name('prediction')
pred = sess.run([prediction], feed_dict={input_placeholder: input_data})
Why can this code not find this operation, and what should I do to correct my code?

Answer 1

You have to modify a single line in your loading script (tested with tf 1.8):
prediction = graph.get_tensor_by_name('prediction/BiasAdd:0')
You have to specify which tensor you want to access, as prediction is only the namespace for the dense layer. You can check the exact name during saving with prediction.name. And when restoring, use tf.get_tensor_by_name as you are interested in the value, not the operation producing it.