tensorflow创建cnn网络进行中文手写文字识别
数据集下载地址：http://www.nlpr.ia.ac.cn/databases/handwriting/download.html
chinese_write_detection.py
# -*- coding: utf-8 -*-
import tensorflow as tf
import os
import random
import tensorflow.contrib.slim as slim
import time
import numpy as np
import pickle
from PIL import Image
from log_utils import get_logger
logger = get_logger("HandWritten Practice")
root_path = 'D:/eclipse-workspace/sxzsb'
tf.app.flags.DEFINE_boolean('random_flip_up_down', False, "Whether to random flip up down")
tf.app.flags.DEFINE_boolean('random_brightness', True, "whether to adjust brightness")
tf.app.flags.DEFINE_boolean('random_contrast', True, "whether to random constrast")
tf.app.flags.DEFINE_integer('charset_size', 3755, "Choose the first `charset_size` character to conduct our experiment.")
tf.app.flags.DEFINE_integer('image_size', 64, "Needs to provide same value as in training.")
tf.app.flags.DEFINE_boolean('gray', True, "whether to change the rbg to gray")
tf.app.flags.DEFINE_integer('max_steps', 12002, 'the max training steps ')
tf.app.flags.DEFINE_integer('eval_steps', 50, "the step num to eval")
tf.app.flags.DEFINE_integer('save_steps', 2000, "the steps to save")
tf.app.flags.DEFINE_string('checkpoint_dir', 'D:/eclipse-workspace/sxzsb/checkpoint', 'the checkpoint dir')
tf.app.flags.DEFINE_string('train_data_dir', 'D:/eclipse-workspace/sxzsb/data/train', 'the train dataset dir(containing png files)')
tf.app.flags.DEFINE_string('test_data_dir', 'D:/eclipse-workspace/sxzsb/data/test', 'the test dataset dir(containing png files)')
tf.app.flags.DEFINE_string('log_dir', 'D:/eclipse-workspace/sxzsb/log', 'the logging path)')
tf.app.flags.DEFINE_boolean('restore', False, 'whether to restore from checkpoint')
tf.app.flags.DEFINE_integer('epoch', 1, 'Number of epoches')
tf.app.flags.DEFINE_integer('batch_size', 128, 'Validation batch size')
tf.app.flags.DEFINE_string('mode', 'train', 'Running mode. One of {"train", "valid", "test"}')
FLAGS = tf.app.flags.FLAGS
class DataIterator:
def __init__(self, data_dir):
# Set FLAGS.charset_size to a small value if available computation power is limited.
truncate_path = data_dir + ('%05d' % FLAGS.charset_size)
print(truncate_path)
self.image_names = []
for root, sub_folder, file_list in os.walk(data_dir):
if root < truncate_path: # some problem here ,because the first root is contain inside ,and there is no file_list
self.image_names += [os.path.join(root, file_path) for file_path in file_list]
random.shuffle(self.image_names)
self.labels = [int(file_name[len(data_dir):].split(os.sep)[0]) for file_name in self.image_names] # int("00020") output:20
@property
def size(self): # @property,负责把一个方法变成属性调用的,还可以定义只读属性，只定义getter方法，不定义setter方法就是一个只读属性
return len(self.labels)
@staticmethod
def data_augmentation(images):
if FLAGS.random_flip_up_down:
images = tf.image.random_flip_up_down(images)
if FLAGS.random_brightness:
images = tf.image.random_brightness(images, max_delta=0.3)
if FLAGS.random_contrast:
images = tf.image.random_contrast(images, 0.8, 1.2)
return images
def input_pipeline(self, batch_size, num_epochs=None, aug=False):
# 1、convert images to a tensor 构造数据queue
images_tensor = tf.convert_to_tensor(self.image_names, dtype=tf.string)
# 执行tf.convert_to_tensor()的时候，在图上生成了一个Op，Op中保存了传入参数的数据。op经过计算产生tensor
labels_tensor = tf.convert_to_tensor(self.labels, dtype=tf.int64)
input_queue = tf.train.slice_input_producer([images_tensor, labels_tensor], num_epochs=num_epochs)
# 2、 ## queue输出数据
labels = input_queue[1]
images_content = tf.read_file(input_queue[0]) # read images from the queue,refer to input_queue
images = tf.image.convert_image_dtype(tf.image.decode_png(images_content, channels=1), tf.float32)
if aug:
images = self.data_augmentation(images)
new_size = tf.constant([FLAGS.image_size, FLAGS.image_size], dtype=tf.int32)
images = tf.image.resize_images(images, new_size)
# collect batches of images before processing
# 3、shuffle_batch批量从queu批量读取数据
image_batch, label_batch = tf.train.shuffle_batch([images, labels], batch_size=batch_size, capacity=50000,
min_after_dequeue=10000) # produce shunffled batch
return image_batch, label_batch
def build_graph(top_k):
# with tf.device('/cpu:0'):
keep_prob = tf.placeholder(dtype=tf.float32, shape=[], name='keep_prob')
images = tf.placeholder(dtype=tf.float32, shape=[None, 64, 64, 1], name='image_batch')
labels = tf.placeholder(dtype=tf.int64, shape=[None], name='label_batch')
conv_1 = slim.conv2d(images, 64, [3, 3], 1, padding='SAME', scope='conv1')
# (inputs,num_outputs,[卷积核个数] kernel_size,[卷积核的高度，卷积核的宽]stride=1,padding='SAME',)
max_pool_1 = slim.max_pool2d(conv_1, [2, 2], [2, 2], padding='SAME')
conv_2 = slim.conv2d(max_pool_1, 128, [3, 3], padding='SAME', scope='conv2')
max_pool_2 = slim.max_pool2d(conv_2, [2, 2], [2, 2], padding='SAME')
conv_3 = slim.conv2d(max_pool_2, 256, [3, 3], padding='SAME', scope='conv3')
max_pool_3 = slim.max_pool2d(conv_3, [2, 2], [2, 2], padding='SAME')
flatten = slim.flatten(max_pool_3)
fc1 = slim.fully_connected(tf.nn.dropout(flatten, keep_prob), 1024, activation_fn=tf.nn.tanh, scope='fc1')
logits = slim.fully_connected(tf.nn.dropout(fc1, keep_prob), FLAGS.charset_size, activation_fn=None, scope='fc2')
# logits = slim.fully_connected(flatten, FLAGS.charset_size, activation_fn=None, reuse=reuse, scope='fc')
loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels))
# y表示的是实际类别，y_表示预测结果，这实际上面是把原来的神经网络输出层的softmax和cross_entrop何在一起计算，为了追求速度
accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(logits, 1), labels), tf.float32))
global_step = tf.get_variable("step", [], initializer=tf.constant_initializer(0.0), trainable=False) # global_step interesting sharing varialbes
rate = tf.train.exponential_decay(2e-4, global_step, decay_steps=2000, decay_rate=0.97, staircase=True)
train_op = tf.train.AdamOptimizer(learning_rate=rate).minimize(loss, global_step=global_step) # train_op 包含了训练数据
probabilities = tf.nn.softmax(logits) # 上一个用logits是soft_max和cross_entropy一起算的，这次只是算了soft_max输出
tf.summary.scalar('loss', loss)
tf.summary.scalar('accuracy', accuracy)
merged_summary_op = tf.summary.merge_all()
predicted_val_top_k, predicted_index_top_k = tf.nn.top_k(probabilities, k=top_k)
accuracy_in_top_k = tf.reduce_mean(tf.cast(tf.nn.in_top_k(probabilities, labels, top_k), tf.float32)) # 这个思路真是清奇！！！看来我回答对了
# return the operator
return {'images': images,
'labels': labels,
'keep_prob': keep_prob,
'top_k': top_k,
'global_step': global_step,
'train_op': train_op,
'loss': loss,
'accuracy': accuracy,
'accuracy_top_k': accuracy_in_top_k,
'merged_summary_op': merged_summary_op,
'predicted_distribution': probabilities,
'predicted_index_top_k': predicted_index_top_k,
'predicted_val_top_k': predicted_val_top_k}
def train():
print('Begin training')
train_feeder = DataIterator(data_dir='../data/train/')
test_feeder = DataIterator(data_dir='../data/test/')
with tf.Session() as sess:
# session操作之前启动队列runners才能激活pipelines/input pipeline 并载入数据
train_images, train_labels = train_feeder.input_pipeline(batch_size=FLAGS.batch_size, aug=True) # num_epochs what's refer to ?
test_images, test_labels = test_feeder.input_pipeline(batch_size=FLAGS.batch_size)
graph = build_graph(top_k=1) # very important
sess.run(tf.global_variables_initializer())
# 4、 ## 启动queue线程
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
saver = tf.train.Saver()
train_writer = tf.summary.FileWriter(FLAGS.log_dir + '/train', sess.graph)
test_writer = tf.summary.FileWriter(FLAGS.log_dir + '/val')
start_step = 0
if FLAGS.restore: # 这里是加载保存好的模型，的到step继续训练
ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
if ckpt:
saver.restore(sess, ckpt)
print("restore from the checkpoint {0}".format(ckpt))
start_step += int(ckpt.split('-')[-1])
logger.info(':::Training Start:::')
try:
while not coord.should_stop(): ###----
start_time = time.time()
print(start_time)
train_images_batch, train_labels_batch = sess.run([train_images, train_labels])
print(len(train_images_batch))
feed_dict = {graph['images']: train_images_batch,
graph['labels']: train_labels_batch,
graph['keep_prob']: 0.8} # keep 80% connection
_, loss_val, train_summary, step = sess.run(
[graph['train_op'], graph['loss'], graph['merged_summary_op'], graph['global_step']],
feed_dict=feed_dict)
train_writer.add_summary(train_summary, step)
end_time = time.time()
logger.info("the step {0} takes {1} loss {2}".format(step, end_time - start_time, loss_val))
if step > FLAGS.max_steps:
break
if step % FLAGS.eval_steps == 1:
test_images_batch, test_labels_batch = sess.run([test_images, test_labels])
feed_dict = {graph['images']: test_images_batch,
graph['labels']: test_labels_batch,
graph['keep_prob']: 1.0}
accuracy_test, test_summary = sess.run(
[graph['accuracy'], graph['merged_summary_op']],
feed_dict=feed_dict) # 这里的多层括号问题
test_writer.add_summary(test_summary, step)
logger.info('===============Eval a batch=======================')
logger.info('the step {0} test accuracy: {1}'
.format(step, accuracy_test))
logger.info('===============Eval a batch=======================')
if step % FLAGS.save_steps == 1:
logger.info('Save the ckpt of {0}'.format(step))
saver.save(sess, os.path.join(FLAGS.checkpoint_dir, 'my-model'),
global_step=graph['global_step'])
except tf.errors.OutOfRangeError:
logger.info('==================Train Finished================')
saver.save(sess, os.path.join(FLAGS.checkpoint_dir, 'my-model'), global_step=graph['global_step'])
finally:
coord.request_stop() # 任何一个线程请求停止，则coord.should_stop()就会返回True ,然后都停下来
coord.join(threads)
def validation():
print('validation')
test_feeder = DataIterator(data_dir='../data/test/')
final_predict_val = []
final_predict_index = []
groundtruth = []
with tf.Session() as sess:
test_images, test_labels = test_feeder.input_pipeline(batch_size=FLAGS.batch_size, num_epochs=1)
graph = build_graph(3)
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer()) # initialize test_feeder's inside state
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
saver = tf.train.Saver()
ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
if ckpt is not None:
saver.restore(sess, ckpt)
print("restore from the checkpoint {0}".format(ckpt))
logger.info(':::Start validation:::')
try:
i = 0
acc_top_1, acc_top_k = 0.0, 0.0
while not coord.should_stop():
i += 1
start_time = time.time()
test_images_batch, test_labels_batch = sess.run([test_images, test_labels])
feed_dict = {graph['images']: test_images_batch,
graph['labels']: test_labels_batch,
graph['keep_prob']: 1.0}
batch_labels, probs, indices, acc_1, acc_k = sess.run([graph['labels'],
graph['predicted_val_top_k'],
graph['predicted_index_top_k'],
graph['accuracy'],
graph['accuracy_top_k']], feed_dict=feed_dict)
final_predict_val += probs.tolist()
final_predict_index += indices.tolist()
groundtruth += batch_labels.tolist()
acc_top_1 += acc_1
acc_top_k += acc_k
end_time = time.time()
logger.info("the batch {0} takes {1} seconds, accuracy = {2}(top_1) {3}(top_k)"
.format(i, end_time - start_time, acc_1, acc_k))
except tf.errors.OutOfRangeError:
logger.info('==================Validation Finished================')
acc_top_1 = acc_top_1 * FLAGS.batch_size / test_feeder.size
acc_top_k = acc_top_k * FLAGS.batch_size / test_feeder.size
logger.info('top 1 accuracy {0} top k accuracy {1}'.format(acc_top_1, acc_top_k))
finally:
coord.request_stop()
coord.join(threads)
return {'prob': final_predict_val, 'indices': final_predict_index, 'groundtruth': groundtruth}
def inference(image):
print('inference')
temp_image = Image.open(image).convert('L')
temp_image = temp_image.resize((FLAGS.image_size, FLAGS.image_size), Image.ANTIALIAS)
temp_image = np.asarray(temp_image) / 255.0
temp_image = temp_image.reshape([-1, 64, 64, 1])
with tf.Session() as sess:
logger.info('========start inference============')
# images = tf.placeholder(dtype=tf.float32, shape=[None, 64, 64, 1])
# Pass a shadow label 0. This label will not affect the computation graph.
graph = build_graph(top_k=3)
saver = tf.train.Saver()
ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
if ckpt:
saver.restore(sess, ckpt)
predict_val, predict_index = sess.run([graph['predicted_val_top_k'], graph['predicted_index_top_k']],
feed_dict={graph['images']: temp_image, graph['keep_prob']: 1.0})
return predict_val, predict_index
def main(_):
print(FLAGS.mode)
if FLAGS.mode == "train":
train()
elif FLAGS.mode == 'validation':
dct = validation() # thinking what is "dct"
result_file = 'result.dict'
logger.info('Write result into {0}'.format(result_file))
with open(result_file, 'wb') as f:
pickle.dump(dct, f)
logger.info('Write file ends')
elif FLAGS.mode == 'inference':
image_path = '../data/test/00159/75700.png'
final_predict_val, final_predict_index = inference(image_path) # figure out what is inference
logger.info('the result info label {0} predict index {1} predict_val {2}'.format(190, final_predict_index,
final_predict_val))
if __name__ == "__main__":
tf.app.run() # It's just a very quick wrapper that handles flag parsing and then dispatches to your own main.log_utils.py
# -*- coding:utf-8 -*-
import os, os.path as osp
import time
def strftime(t=None):
return time.strftime("%Y%m%d-%H%M%S", time.localtime(t or time.time()))
#################
# Logging
#################
import logging
from logging.handlers import TimedRotatingFileHandler
logging.basicConfig(format="[ %(asctime)s][%(module)s.%(funcName)s] %(message)s")
DEFAULT_LEVEL = logging.INFO
DEFAULT_LOGGING_DIR = osp.join("logs", "gcforest")
fh = None
def init_fh():
global fh
if fh is not None:
return
if DEFAULT_LOGGING_DIR is None:
return
if not osp.exists(DEFAULT_LOGGING_DIR): os.makedirs(DEFAULT_LOGGING_DIR)
logging_path = osp.join(DEFAULT_LOGGING_DIR, strftime() + ".log")
fh = logging.FileHandler(logging_path)
fh.setFormatter(logging.Formatter("[ %(asctime)s][%(module)s.%(funcName)s] %(message)s"))
def update_default_level(defalut_level):
global DEFAULT_LEVEL
DEFAULT_LEVEL = defalut_level
def update_default_logging_dir(default_logging_dir):
global DEFAULT_LOGGING_DIR
DEFAULT_LOGGING_DIR = default_logging_dir
def get_logger(name="HandWrittenPractice", level=None):
level = level or DEFAULT_LEVEL
logger = logging.getLogger(name)
logger.setLevel(level)
init_fh()
if fh is not None:
logger.addHandler(fh)
return loggerTrain
python chinese_write_detection.py --mode=train --max_steps=200000 --eval_steps=1000 --save_steps=10000Validation
python chinese_write_detection.py --mode=validationInference
python chinese_write_detection.py --mode=inference
