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Train.py
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import time
import os
from input_data import *
from model import *
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.Session(config=config)
N_CLASSES = 2
IMG_SIZE = 208
BATCH_SIZE = 20
CAPACITY = 2000
MAX_STEP = 15000
LEARNING_RATE = 1e-4
# start train
def run_training():
# load pic
train_dir = os.getcwd() + '/data/train'
logs_train_dir = os.getcwd() + '/logs/logs_1' # dir of checkpoint
train_list = get_all_files(train_dir, True)
image_train_batch, label_train_batch = get_batch(train_list, IMG_SIZE, BATCH_SIZE, CAPACITY, True)
train_logits = inference(image_train_batch, BATCH_SIZE, N_CLASSES)
train_loss = losses(train_logits, label_train_batch)
train_acc = evaluation(train_logits, label_train_batch)
train_op = tf.train.AdamOptimizer(LEARNING_RATE).minimize(train_loss)
summary_op = tf.summary.merge_all()
sess = tf.Session()
train_writer = tf.summary.FileWriter(logs_train_dir, 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)
var_list = tf.trainable_variables()
paras_count = tf.reduce_sum([tf.reduce_prod(v.shape) for v in var_list])
print('Arguments:%d' % sess.run(paras_count), end='\n\n')
try:
for step in range(MAX_STEP):
if coord.should_stop():
break
_, loss, acc = sess.run([train_op, train_loss, train_acc])
if step % 100 == 0: # show procedure
print('Step: %d, loss: %.2f, accuracy: %.2f%%'
% (step, loss, acc * 100))
summary_str = sess.run(summary_op)
train_writer.add_summary(summary_str, step)
if step % 1000 == 0 or step == MAX_STEP - 1: # save checkpoint
checkpoint_path = os.path.join(logs_train_dir, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)
except tf.errors.OutOfRangeError:
print('Done.')
finally:
coord.request_stop()
coord.join(threads=threads)
sess.close()
# # 测试检查点
# def eval():
# N_CLASSES = 2
# IMG_SIZE = 208
# BATCH_SIZE = 1
# CAPACITY = 200
# MAX_STEP = 10
#
# image_dir = os.getcwd() + '/data/train'
# logs_dir = os.getcwd() +'/logs/logs_1' # 检查点目录
#
# sess = tf.Session()
#
# train_list = get_all_files(image_dir, True)
# image_train_batch, label_train_batch = get_batch(train_list, IMG_SIZE, BATCH_SIZE, CAPACITY, True)
# train_logits = inference(image_train_batch, 8, N_CLASSES)
# train_logits = tf.nn.softmax(train_logits) # 用softmax转化为百分比数值
#
# # 载入检查点
# saver = tf.train.Saver()
# print('\n载入检查点...')
# ckpt = tf.train.get_checkpoint_state(logs_dir)
# if ckpt and ckpt.model_checkpoint_path:
# global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
# saver.restore(sess, ckpt.model_checkpoint_path)
# print('载入成功，global_step = %s\n' % global_step)
# else:
# print('没有找到检查点')
# coord = tf.train.Coordinator()
# threads = tf.train.start_queue_runners(sess=sess, coord=coord)
#
# try:
# for step in range(MAX_STEP):
# if coord.should_stop():
# break
#
# image, prediction = sess.run([image_train_batch, train_logits])
# max_index = np.argmax(prediction)
# if max_index == 0:
# label = '%.2f%% is a cat.' % (prediction[0][0] * 100)
# else:
# label = '%.2f%% is a dog.' % (prediction[0][1] * 100)
#
# plt.imshow(image[0])
# plt.title(label)
# plt.show()
#
# except tf.errors.OutOfRangeError:
# print('Done.')
# finally:
# coord.request_stop()
#
# coord.join(threads=threads)
# sess.close()
#
#
# if __name__ == '__main__':
# # training()
run_training()