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手寫漢字識別及其可視化(代碼),學習了很長時間的圖像處理,選定了一個手寫漢字識別的課題。我個人感覺相對于其他任務的識別(比如MNIST,CIFAR-10)難點在于識別種類繁多,在如此繁多的種類中要達到一個很高的識別率確實不容易。

代碼片段和文件信息
#?encoding:?utf-8
‘‘‘
@author:?真夢行路
@file:?cnn.py
@time:?18-8-30?下午10:02
‘‘‘
#########################導入第三方庫###################################
import?time
import?math
import?random
import?os
import?pandas?as?pd
import?numpy?as?np
import?matplotlib.pyplot?as?plt
import?tensorflow?as?tf
import?dataset
import?cv2
from?sklearn.metrics?import?confusion_matrix
from?datetime?import?timedelta
###################################加載數據路徑########################################
train_path?=?‘/home/wcy/圖片/python/data/train‘
test_path?=?‘/home/wcy/圖片/python/data0/test‘
checkpoint_dir?=?‘/home/wcy/圖片/00/models‘
###################################加載數據路徑########################################
############################設定模型參數###########################################
#?Convolutional?layer?1.
filter_size1?=?3
num_filters1?=?36
#?Convolutional?layer?2.
filter_size2?=?3
num_filters2?=?36
#?Convolutional?layer?3.
filter_size3?=?3
num_filters3?=?64
#?Fully-connected?layer.
fc_size?=?256?????????????#?Number?of?neurons?in?fully-connected?layer.
#?Number?of?color?channels?for?the?images:?1?channel?for?gray-scale.
num_channels?=?3
#?image?dimensions?(only?squares?for?now)
img_size?=?70
#?Size?of?image?when?flattened?to?a?single?dimension
img_size_flat?=?img_size?*?img_size?*?num_channels
#?Tuple?with?height?and?width?of?images?used?to?reshape?arrays.
img_shape?=?(img_size?img_size)
#?class?info
#?classes?=?[‘dog‘?‘cat‘]
classes?=?os.listdir(train_path)
num_classes?=?len(classes)
#?batch?size
batch_size?=?1
#?validation?split
validation_size?=?.16
#?how?long?to?wait?after?validation?loss?stops?improving?before?terminating?training
early_stopping?=?None??#?use?None?if?you?don‘t?want?to?implement?early?stoping
############################設定模型參數###########################################
############################讀取數據##############################################
data?=?dataset.read_train_sets(train_path?img_size?classes?validation_size=validation_size)
test_images?test_ids?=?dataset.read_test_set(test_path?img_size)
print(data.train.labels.shape)
print(“Size?of:“)
print(“-?Training-set:\t\t{}“.format(len(data.train.labels)))
print(“-?Test-set:\t\t{}“.format(len(test_images)))
print(“-?Validation-set:\t{}“.format(len(data.valid.labels)))
############################讀取數據##############################################
######函數##################隨機取出9副圖顯示######################################
def?plot_images(images?cls_true?cls_pred=None):
????if?len(images)?==?0:
????????print(“no?images?to?show“)
????????return
????else:
????????random_indices?=?random.sample(range(len(images))?min(len(images)?9))
????images?cls_true?=?zip(*[(images[i]?cls_true[i])?for?i?in?random_indices])
????#?print(‘1111111111111111111‘len(images))
????#?Create?figure?with?3x3?sub-plots.
????fig?axes?=?plt.subplots(3?3)
????fig.subplots_adjust(hspace=0.3?wspace=0.3)
????for?i?ax?in?enumerate(axes.flat):
????????#?Plot?image.
????????ax.imshow(images[i].reshape(img_size
?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????文件??????25442??2019-04-11?13:59??dome\cnn.py
?????文件????????155??2019-04-11?14:08??dome\readme
?????文件???????4695??2018-11-21?16:34??dome\dataset.py
?????文件????????569??2018-11-21?19:04??dome\test.py
?????文件???????4554??2018-11-21?16:34??dome\__pycache__\dataset.cpython-35.pyc
?????文件????1046164??2018-08-31?14:50??dome\.ipynb_checkpoints\cnn-checkpoint.ipynb
?????目錄??????????0??2019-04-11?14:02??dome\__pycache__
?????目錄??????????0??2019-04-11?14:02??dome\.ipynb_checkpoints
?????目錄??????????0??2019-04-11?14:08??dome
-----------?---------??----------?-----??----
??????????????1081579????????????????????9
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