train_cnn.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import matplotlib

import matplotlib.pyplot as plt
import math

import time

from datasets import dataset_utils

import numpy as np
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf

try:
    import urllib2 as urllib
except ImportError:
    import urllib.request as urllib

from datasets import imagenet
from nets import inception
from preprocessing import inception_preprocessing

from tensorflow.contrib import slim

import input_loader as ipl

image_size = inception.inception_v1.default_image_size
checkpoints_dir = '/tmp/checkpoints'

def get_init_fn():
    """Returns a function run by the chief worker to warm-start the training."""
    checkpoint_exclude_scopes=["InceptionV1/Logits", "InceptionV1/AuxLogits"]

    exclusions = [scope.strip() for scope in checkpoint_exclude_scopes]

    variables_to_restore = []
    for var in slim.get_model_variables():
        excluded = False
        for exclusion in exclusions:
            if var.op.name.startswith(exclusion):
                excluded = True
                break
        if not excluded:
            variables_to_restore.append(var)

    return slim.assign_from_checkpoint_fn(
      os.path.join(checkpoints_dir, 'inception_v1.ckpt'),
      variables_to_restore)


train_dir = '/tmp/inception_finetuned/'

with tf.Graph().as_default():
    tf.logging.set_verbosity(tf.logging.INFO)

    # dataset = flowers.get_split('train', flowers_data_dir)
    # images, _, labels = load_batch(dataset, height=image_size, width=image_size)
    dataset = ipl.get_split('train','./images')
    images, _,labels = ipl.load_batch(dataset,3,height=image_size, width=image_size)

    # Create the model, use the default arg scope to configure the batch norm parameters.
    with slim.arg_scope(inception.inception_v1_arg_scope()):
        logits, _ = inception.inception_v1(images, num_classes=dataset.num_classes, is_training=True)

    # Specify the loss function:
    one_hot_labels = slim.one_hot_encoding(labels, dataset.num_classes)
    slim.losses.softmax_cross_entropy(logits, one_hot_labels)
    total_loss = slim.losses.get_total_loss()

    # Create some summaries to visualize the training process:
    tf.summary.scalar('losses/Total Loss', total_loss)

    # Specify the optimizer and create the train op:
    optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
    train_op = slim.learning.create_train_op(total_loss, optimizer)

    # Run the training:
    final_loss = slim.learning.train(
        train_op,
        logdir=train_dir,
        init_fn=get_init_fn(),
        number_of_steps=2)


print('Finished training. Last batch loss %f' % final_loss)