Create predictions with custom CNN using tfrecord input - tensorflow

Question

My aim is to classify images into ten categories. I have a tfrecord file as input. You can download it here (30 MB). My modified the code according to the answer:
import tensorflow as tf
import tensorflow.contrib.slim as slim
import numpy as np
def my_cnn(images, num_classes, is_training): # is_training is not used...
with slim.arg_scope([slim.max_pool2d], kernel_size=[3, 3], stride=2):
net = slim.conv2d(images, 64, [5, 5])
net = slim.max_pool2d(net)
net = slim.conv2d(net, 64, [5, 5])
net = slim.max_pool2d(net)
net = slim.flatten(net)
net = slim.fully_connected(net, 192)
net = slim.fully_connected(net, num_classes, activation_fn=None)
return net
data_path = 'train-some.tfrecords'
with tf.Graph().as_default():
batch_size, height, width, channels = 10, 224, 224, 3
feature = {'train/image': tf.FixedLenFeature([], tf.string),
'train/label': tf.FixedLenFeature([], tf.int64)}
filename_queue = tf.train.string_input_producer([data_path], num_epochs=1)
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(serialized_example, features=feature)
image = tf.decode_raw(features['train/image'], tf.float32)
label = tf.cast(features['train/label'], tf.int32)
image = tf.reshape(image, [224, 224, 3])
images, labels = tf.train.shuffle_batch([image, label], batch_size, capacity=30, num_threads=1, min_after_dequeue=10)
num_classes = 10
logits = my_cnn(images, num_classes, is_training=True)
probabilities = tf.nn.softmax(logits)
with tf.Session() as sess:
init_op = [tf.global_variables_initializer(), tf.local_variables_initializer()]
# Run the init_op, evaluate the model outputs and print the results:
sess.run(init_op)
#probabilities = sess.run(probabilities)
# Create a coordinator, launch the queue runner threads.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
while not coord.should_stop():
while True:
prob = sess.run(probabilities)
print('Probabilities Shape:')
print(prob.shape)
except tf.errors.OutOfRangeError:
# When done, ask the threads to stop.
print('Done training -- epoch limit reached')
finally:
coord.request_stop()
# Wait for threads to finish.
coord.join(threads)
# Save the model
saver = tf.train.Saver()
saver.save(sess, './slim_model/custom_model')
Unfortunately, I still have error messages:
ValueError: Tensor Tensor("Softmax:0", shape=(10, 10), dtype=float32) is not an element of this graph.
ValueError: Fetch argument cannot be interpreted as a Tensor. (Tensor Tensor("Softmax:0", shape=(10, 10), dtype=float32) is not an element of this graph.)

Answer 1

The issue is with your training. You need to start the queues using tf.train.start_queue_runners that will run a few threads to process and enqueue examples. Create a Coordinator and ask the queue runner to start its threads with the coordinator.
Check the code changes:
with tf.Session() as sess:
init_op = [tf.global_variables_initializer(), tf.local_variables_initializer()]
# Run the init_op, evaluate the model outputs and print the results:
sess.run(init_op)
#probabilities = sess.run(probabilities)
# Create a coordinator, launch the queue runner threads.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
while not coord.should_stop():
while True:
prob = sess.run(probabilities)
print('Probabilities Shape:')
print(prob.shape)
except tf.errors.OutOfRangeError:
# When done, ask the threads to stop.
print('Done training -- epoch limit reached')
finally:
coord.request_stop()
# Wait for threads to finish.
coord.join(threads)
# Save the model
saver = tf.train.Saver()
saver.save(sess, './slim_model/custom_model'
Output:
Probabilities Shape:
(10, 10)
Probabilities Shape:
(10, 10)
Probabilities Shape:
(10, 10)
Probabilities Shape:
(10, 10)
Probabilities Shape:
(10, 10)
Done training -- epoch limit reached
Code with the above fixes along with saving and restoring the model can be downloaded from here.