[FIXED] How to load pre-trained tensorflow model named inception by Google?

Issue

I have downloaded a tensorflow checkpoint model named inception_resnet_v2_2016_08_30.ckpt.

Do I need to create a graph (with all the variables) that were used when this checkpoint was created?

How do I make use of this model?

Solution

First of you have get the network architecture in memory. You can get the network architecture from here

Once you have this program with you, use the following approach to use the model:

from inception_resnet_v2 import inception_resnet_v2, inception_resnet_v2_arg_scope

height = 299
width = 299
channels = 3

X = tf.placeholder(tf.float32, shape=[None, height, width, channels])
with slim.arg_scope(inception_resnet_v2_arg_scope()):
     logits, end_points = inception_resnet_v2(X, num_classes=1001,is_training=False)

With this you have all the network in memory, Now you can initialize the network with checkpoint file(ckpt) by using tf.train.saver:

saver = tf.train.Saver()
sess = tf.Session()
saver.restore(sess, "/home/pramod/Downloads/inception_resnet_v2_2016_08_30.ckpt")

If you want to do bottle feature extraction, its simple like lets say you want to get features from last layer, then simply you have to declare predictions = end_points["Logits"] If you want to get it for other intermediate layer, you can get those names from the above program inception_resnet_v2.py

After that you can call: output = sess.run(predictions, feed_dict={X:batch_images})

Answered By - Pramod Patil