Using Tensorflow to train a image classifier using my own data using inception and TFrecords - image

Question

I follow the tutorial on how to train your own data from tensorflow at Github: https://github.com/tensorflow/models/tree/master/inception#how-to-construct-a-new-dataset-for-retraining.
I split my data (Training and validation), created labels suggested and managed to created the TFrecords using bazel-bin. Everything works and now I have my own data as TFrecords.
Now I want to train my image classifier using inception-v3 model from scratch and it seems I should use the script inception_train.py, but I am not sure. Is that right ? https://github.com/tensorflow/models/blob/master/inception/inception/inception_train.py.
If so, I have two questions:
1-) How can I train it using my TFrecords. If you can show me an example would be great.
2-) Can I run on CPU or is only possible on GPUs ?
Thank you very much.

Answer 1

Try the following sample code to read images and labels from your tfrecords,
import os
import glob
import tensorflow as tf
from matplotlib import pyplot as plt
def read_and_decode_file(filename_queue):
# Create an instance of tf record reader
reader = tf.TFRecordReader()
# Read the generated filename queue
_, serialized_reader = reader.read(filename_queue)
# extract the features you require from the tfrecord using their corresponding key
# In my example, all images were written with 'image' key
features = tf.parse_single_example(
serialized_reader, features={
'image': tf.FixedLenFeature([], tf.string),
'labels': tf.FixedLenFeature([], tf.int16)
})
# Extract the set of images as shown below
img = features['image']
img_out = tf.image.resize_image_with_crop_or_pad(img, target_height=128, target_width=128)
# Similarly extract the labels, be careful with the type
label = features['labels']
return img_out, label
if __name__ == "__main__":
tf.reset_default_graph()
# Path to your tfrecords
path_to_tf_records = os.getcwd() + '/*.tfrecords'
# Collect all tfrecords present in the records folder using glob
list_of_tfrecords = sorted(glob.glob(path_to_tf_records))
# Generate a tensorflow readable filename queue by supplying it with
# a list of tfrecords, optionally it is recommended to shuffle your data
# before feeding into the network
filename_queue = tf.train.string_input_producer(list_of_tfrecords, shuffle=False)
# Supply the tensorflow generated filename queue to the custom function above
image, label = read_and_decode_file(filename_queue)
# Create a new tf session to read the data
sess = tf.Session()
tf.train.start_queue_runners(sess=sess)
# Arbitrary number of iterations
for i in range(50):
img =sess.run(image)
# Show image
plt.imshow(img)
Now, you also have a function called tf.train.shuffle_batch to help you spawn multiple CPU threads that perform this function and return images and labels based on user specified batch size. You would need to create simultaneous data and training pipelines so that they work simultaneously.
To answer your second question, yes you can train your model using CPU alone but it would be slow and might take several hours or even days to achieve decent results. Remove the with tf.device('/gpu:{0}'): decorator before the creation of your inception model and tensorflow would create the model on your CPU.
Hope this explanation helps.