training.py

# How to run?
# 1. run the training.py once
# 2. call the run_training() in the console to train the model.

# Note: 
# it is suggested to restart your kenel to train the model multiple times 
#(in order to clear all the variables in the memory)
# Otherwise errors may occur: conv1/weights/biases already exist......


#%%

import os
import numpy as np
import tensorflow as tf
import input_data
import model

#%%

N_CLASSES = 5
IMG_W = 208  # resize the image, if the input image is too large, training will be very slow.
IMG_H = 208
BATCH_SIZE = 16
CAPACITY = 2000
MAX_STEP = 50000 # with current parameters, it is suggested to use MAX_STEP>10k
learning_rate = 0.0001 # with current parameters, it is suggested to use learning rate<0.0001
path='/home/user/Desktop/flower-tensorflow/train/'
logs_train_dir = '/home/user/Desktop/flower-tensorflow/train_logits/'

#%%
def run_training():
    
    # you need to change the directories to yours.
    path='/home/user/Desktop/flower-tensorflow/train/'
    logs_train_dir = '/home/user/Desktop/flower-tensorflow/train_logits/'
    
    train, train_label = input_data.get_files(path)
    
    train_batch, train_label_batch = input_data.get_batch(train,
                                                          train_label,
                                                          IMG_W,
                                                          IMG_H,
                                                          BATCH_SIZE, 
                                                          CAPACITY)      
    train_logits = model.inference(train_batch, BATCH_SIZE, N_CLASSES)
    print train_logits
    train_loss = model.losses(train_logits, train_label_batch)        
    train_op = model.trainning(train_loss, learning_rate)
    train__acc = model.evaluation(train_logits, train_label_batch)
       
    #summary_op = tf.summary.merge_all()
    sess = tf.Session()
    #train_writer = tf.summary.FileWriter(train_logits, sess.graph)
    saver = tf.train.Saver()
    
    sess.run(tf.global_variables_initializer())
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(sess=sess, coord=coord)
    
    try:
        for step in np.arange(MAX_STEP):
            if coord.should_stop():
                    break
            _, tra_loss, tra_acc = sess.run([train_op, train_loss, train__acc])
               
            if step % 50 == 0:
                print('Step %d, train loss = %.2f, train accuracy = %.2f%%' %(step, tra_loss, tra_acc*100.0))
                         
            if step % 2000 == 0 or (step + 1) == MAX_STEP:
                checkpoint_path = os.path.join(logs_train_dir, 'model.ckpt')
                saver.save(sess, checkpoint_path, global_step=step)
                
    except tf.errors.OutOfRangeError:
        print('Done training -- epoch limit reached')
    finally:
        coord.request_stop()
        
    coord.join(threads)
    sess.close()