tflearn 中文汉字识别，训练后模型存为pb给TensorFlow使用——模型层次太深，或者太复杂训练时候都不会收敛
tflearn 中文汉字识别，训练后模型存为pb给TensorFlow使用。
数据目录在data，data下放了汉字识别图片：
data$ ls
0 1 10 11 12 13 14 15 16 2 3 4 5 6 7 8 9
datag$ ls 0
xxx.png yyy.png ....
代码：
如果将get model里的模型层数加非常深，训练时候很可能不会收敛，精度一直停留下1%以内。
# -*- coding: utf-8 -*- from __future__ import division, print_function, absolute_import import os
import numpy as np
import pickle
import tflearn from PIL import Image
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import conv_2d, max_pool_2d, avg_pool_2d
from tflearn.layers.merge_ops import merge
from tflearn.layers.estimator import regression
from tflearn.data_utils import to_categorical, shuffle
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, confusion_matrix
from tflearn.layers.conv import highway_conv_2d, max_pool_2d
from tflearn.layers.normalization import local_response_normalization, batch_normalization def resize_image(in_image, new_width, new_height, out_image=None,
resize_mode=Image.ANTIALIAS):
""" Resize an image.
Arguments:
in_image: `PIL.Image`. The image to resize.
new_width: `int`. The image new width.
new_height: `int`. The image new height.
out_image: `str`. If specified, save the image to the given path.
resize_mode: `PIL.Image.mode`. The resizing mode. Returns:
`PIL.Image`. The resize image.
"""
img = in_image.resize((new_width, new_height), resize_mode)
if out_image:
img.save(out_image)
return img def convert_color(in_image, mode):
""" Convert image color with provided `mode`. """
return in_image.convert(mode) def pil_to_nparray(pil_image):
""" Convert a PIL.Image to numpy array. """
pil_image.load()
return np.asarray(pil_image, dtype="float32") def iterbrowse(path):
for home, dirs, files in os.walk(path):
for filename in files:
yield os.path.join(home, filename) def directory_to_samples(directory, flags):
""" Read a directory, and list all subdirectories files as class sample """
samples = []
targets = []
# label class is from 0 !!!
label = 0
try: # Python 2
classes = sorted(os.walk(directory).next()[1])
except Exception: # Python 3
classes = sorted(os.walk(directory).__next__()[1])
for c in classes:
c_dir = os.path.join(directory, c)
try: # Python 2
walk = os.walk(c_dir).next()
except Exception: # Python 3
walk = os.walk(c_dir).__next__()
for sample in walk[2]:
if any(flag in sample for flag in flags):
samples.append(os.path.join(c_dir, sample))
targets.append(label)
label += 1
return samples, targets # Get the pixel from the given image
def get_pixel(image, i, j):
# Inside image bounds?
width, height = image.size
if i > width or j > height:
return None # Get Pixel
pixel = image.getpixel((i, j))
return pixel # Create a Grayscale version of the image
def convert_to_one_channel(image):
# !!! I assume that the png file is grayscale. And R == G == B !!!! So I check it...
"""
for i in range(len(image)):
for j in range(len(image[i])):
pixel = image[i][j]
# Get R, G, B values (This are int from 0 to 255)
assert len(pixel) == 3
red = pixel[0]
green = pixel[1]
blue = pixel[2]
assert red == green == blue
assert 0 <= red <= 1
"""
# Just extract 1 channel data
return image[:, :, [0]] def image_dirs_to_samples(directory, resize=None, convert_gray=False,
filetypes=None):
print("Starting to parse images...")
samples, targets = directory_to_samples(directory, flags=filetypes)
for i, s in enumerate(samples):
print("Process %d th file %s" % (i+1, s))
samples[i] = Image.open(s) # Load an image, returns PIL.Image.
if resize:
######################## TODO #######################
samples[i] = resize_image(samples[i], resize[0],
resize[1])
######################### TODO ####################### convert to more data
# if convert_gray:
# samples[i] = convert_color(samples[i], 'L')
samples[i] = pil_to_nparray(samples[i])
samples[i] /= 255. # ormalize a list of sample image data in the range of 0 to 1
samples[i] = convert_to_one_channel(samples[i]) # just want 1 channel data
print("Parsing Done!")
return samples, targets def load_data(dirname, resize_pics=(128, 128), shuffle_data=True):
dataset_file = os.path.join(dirname, 'data.pkl')
try:
X, Y, org_labels = pickle.load(open(dataset_file, 'rb'))
except Exception:
# X, Y = image_dirs_to_samples(os.path.join(dirname, 'train/'), resize_pics, False, ['.jpg', '.png'])
X, Y = image_dirs_to_samples(dirname, resize_pics, False,
['.jpg', '.png']) # TODO, memory is too small to load all data
org_labels = Y
Y = to_categorical(Y, np.max(Y) + 1) # First class is '0', Convert class vector (integers from 0 to nb_classes)
if shuffle_data:
X, Y, org_labels = shuffle(X, Y, org_labels)
pickle.dump((X, Y, org_labels), open(dataset_file, 'wb'))
return X, Y, org_labels class EarlyStoppingCallback(tflearn.callbacks.Callback):
def __init__(self, val_acc_thresh):
# Store a validation accuracy threshold, which we can compare against
# the current validation accuracy at, say, each epoch, each batch step, etc.
self.val_acc_thresh = val_acc_thresh def on_epoch_end(self, training_state):
"""
This is the final method called in trainer.py in the epoch loop.
We can stop training and leave without losing any information with a simple exception.
"""
# print dir(training_state)
print("Terminating training at the end of epoch", training_state.epoch)
if training_state.val_acc >= self.val_acc_thresh and training_state.acc_value >= self.val_acc_thresh:
raise StopIteration def on_train_end(self, training_state):
"""
Furthermore, tflearn will then immediately call this method after we terminate training,
(or when training ends regardless). This would be a good time to store any additional
information that tflearn doesn't store already.
"""
print("Successfully left training! Final model accuracy:", training_state.acc_value) def get_model(width, height, classes=40):
# TODO, modify model
# Real-time data preprocessing
img_prep = tflearn.ImagePreprocessing()
# Real-time data preprocessing
img_prep = tflearn.ImagePreprocessing()
img_prep.add_featurewise_zero_center(per_channel=True)
img_prep.add_featurewise_stdnorm()
network = input_data(shape=[None, width, height, 1], data_preprocessing=img_prep) # if RGB, 224,224,3
network = conv_2d(network, 32, 3, activation='relu')
network = max_pool_2d(network, 2)
network = conv_2d(network, 64, 3, activation='relu')
network = conv_2d(network, 64, 3, activation='relu')
network = max_pool_2d(network, 2)
network = fully_connected(network, 512, activation='relu')
network = dropout(network, 0.5)
network = fully_connected(network, classes, activation='softmax')
network = regression(network, optimizer='adam',
loss='categorical_crossentropy',
learning_rate=0.001)
model = tflearn.DNN(network, tensorboard_verbose=0)
return model if __name__ == "__main__":
width, height = 32, 32
X, Y, org_labels = load_data(dirname="data", resize_pics=(width, height))
trainX, testX, trainY, testY = train_test_split(X, Y, test_size=0.2, random_state=666)
print("sample data:")
print(trainX[0])
print(trainY[0])
print(testX[-1])
print(testY[-1]) model = get_model(width, height, classes=100) filename = 'cnn_handwrite-acc0.8.tflearn'
# try to load model and resume training
#try:
# model.load(filename)
# print("Model loaded OK. Resume training!")
#except:
# pass # Initialize our callback with desired accuracy threshold.
early_stopping_cb = EarlyStoppingCallback(val_acc_thresh=0.9)
try:
model.fit(trainX, trainY, validation_set=(testX, testY), n_epoch=500, shuffle=True,
snapshot_epoch=True, # Snapshot (save & evaluate) model every epoch.
show_metric=True, batch_size=32, callbacks=early_stopping_cb, run_id='cnn_handwrite')
except StopIteration as e:
print("OK, stop iterate!Good!") model.save(filename) # predict all data and calculate confusion_matrix
model.load(filename) pro_arr =model.predict(X)
predict_labels = np.argmax(pro_arr, axis=1)
print(classification_report(org_labels, predict_labels))
print(confusion_matrix(org_labels, predict_labels))
运行效果：100个汉字2分钟就可以达到95%精度。
---------------------------------
Run id: cnn_handwrite
Log directory: /tmp/tflearn_logs/
---------------------------------
Preprocessing... Calculating mean over all dataset (this may take long)...
Mean: [ 0.89235026] (To avoid repetitive computation, add it to argument 'mean' of `add_featurewise_zero_center`)
---------------------------------
Preprocessing... Calculating std over all dataset (this may take long)...
STD: 0.192279 (To avoid repetitive computation, add it to argument 'std' of `add_featurewise_stdnorm`)
---------------------------------
Training samples: 19094
Validation samples: 4774
--
Training Step: 597 | total loss: 0.70288 | time: 40.959ss
| Adam | epoch: 001 | loss: 0.70288 - acc: 0.7922 | val_loss: 0.54380 - val_acc: 0.8460 -- iter: 19094/19094
--
Terminating training at the end of epoch 1
Training Step: 1194 | total loss: 0.48860 | time: 40.245s
| Adam | epoch: 002 | loss: 0.48860 - acc: 0.8783 | val_loss: 0.37020 - val_acc: 0.8923 -- iter: 19094/19094
--
Terminating training at the end of epoch 2
Training Step: 1791 | total loss: 0.35790 | time: 41.315ss
| Adam | epoch: 003 | loss: 0.35790 - acc: 0.9090 | val_loss: 0.34719 - val_acc: 0.9049 -- iter: 19094/19094
--
Terminating training at the end of epoch 3
Successfully left training! Final model accuracy: 0.908959209919
OK, stop iterate!Good!
precision recall f1-score support 0 1.00 0.99 0.99 239
1 0.95 0.96 0.96 237
2 0.91 0.98 0.94 240
3 0.90 0.98 0.94 239
4 0.96 0.98 0.97 239
5 0.94 0.97 0.96 239
6 0.98 0.98 0.98 239
7 0.84 0.99 0.91 240
8 0.99 0.87 0.93 239
9 0.95 0.98 0.96 239
10 0.97 0.94 0.96 240
11 0.95 0.98 0.97 240
12 0.92 0.99 0.95 240
13 0.95 0.96 0.96 239
14 0.94 0.94 0.94 236
15 0.94 0.97 0.96 240
16 0.94 0.98 0.96 240
17 0.97 0.99 0.98 240
18 0.94 0.93 0.94 240
19 1.00 0.95 0.98 239
20 0.96 0.98 0.97 240
21 0.98 0.91 0.95 239
22 0.97 0.95 0.96 239
23 1.00 0.97 0.98 239
24 0.94 0.98 0.96 240
25 0.98 0.98 0.98 237
26 0.91 1.00 0.95 239
27 0.91 0.96 0.93 239
28 0.97 0.88 0.92 239
29 1.00 0.98 0.99 240
30 0.99 0.94 0.96 239
31 0.97 0.97 0.97 237
32 0.94 0.98 0.96 236
33 0.94 0.96 0.95 239
34 0.98 0.99 0.98 239
35 0.99 0.98 0.99 240
36 0.96 0.92 0.94 239
37 1.00 0.93 0.96 240
38 0.96 0.99 0.98 238
39 0.98 0.97 0.97 238
40 0.92 0.90 0.91 240
41 0.96 0.97 0.96 237
42 0.98 0.97 0.97 240
43 0.95 0.96 0.95 239
44 0.97 0.96 0.97 239
45 0.95 0.94 0.95 239
46 0.93 0.96 0.94 232
47 0.98 0.91 0.94 237
48 0.95 0.97 0.96 239
49 0.97 0.80 0.88 226
50 0.90 0.95 0.92 240
51 0.98 0.99 0.99 236
52 0.96 0.90 0.93 240
53 0.99 0.96 0.97 235
54 0.97 0.93 0.95 240
55 0.99 0.98 0.99 240
56 0.97 0.92 0.95 239
57 0.97 0.97 0.97 239
58 1.00 0.98 0.99 238
59 0.92 0.98 0.95 240
60 0.99 0.90 0.94 240
61 1.00 0.99 0.99 238
62 0.92 0.95 0.94 239
63 0.92 0.98 0.95 238
64 0.98 0.92 0.95 240
65 0.99 0.92 0.95 239
66 0.98 0.99 0.99 240
67 0.95 0.95 0.95 240
68 0.96 0.98 0.97 239
69 0.97 0.97 0.97 239
70 0.98 0.94 0.96 240
71 0.91 0.96 0.93 239
72 0.98 0.97 0.97 239
73 0.99 0.89 0.94 240
74 0.97 0.99 0.98 237
75 0.89 0.97 0.92 240
76 0.97 0.96 0.97 241
77 0.89 0.91 0.90 240
78 1.00 0.89 0.94 239
79 0.90 0.98 0.94 239
80 0.89 0.96 0.92 240
81 0.96 0.71 0.81 225
82 0.95 1.00 0.97 238
83 0.67 0.96 0.79 239
84 0.97 0.85 0.91 240
85 0.95 0.98 0.96 239
86 0.99 0.93 0.96 240
87 0.98 0.91 0.94 239
88 0.97 0.97 0.97 240
89 0.97 0.94 0.95 239
90 0.97 0.96 0.96 236
91 0.91 0.97 0.94 239
92 0.98 0.95 0.96 240
93 0.98 0.97 0.98 239
94 0.98 0.95 0.97 240
95 0.98 0.99 0.99 239
96 0.95 0.97 0.96 240
97 0.98 0.97 0.98 239
98 0.95 0.98 0.97 237
99 0.97 0.96 0.97 239 avg / total 0.96 0.95 0.95 23868 [[237 0 0 ..., 0 0 0]
[ 0 228 0 ..., 0 0 0]
[ 0 0 235 ..., 0 0 0]
...,
[ 0 0 0 ..., 233 0 0]
[ 0 0 0 ..., 0 233 0]
[ 0 0 0 ..., 0 0 230]]
更多模型见：http://www.cnblogs.com/bonelee/p/8978060.html
将上述模型保存并给TensorFlow使用，仅仅在保存模型前加del tf.get_collection_ref(tf.GraphKeys.TRAIN_OPS)[:]，仅仅保留inference时候的OP（如果需要retrain注意），如下：
model = get_model(width, height, classes=100)
filename = 'cnn_handwrite-acc0.8.tflearn'
# try to load model and resume training
#try:
# model.load(filename)
# print("Model loaded OK. Resume training!")
#except:
# pass
# Initialize our callback with desired accuracy threshold.
early_stopping_cb = EarlyStoppingCallback(val_acc_thresh=0.8)
try:
model.fit(trainX, trainY, validation_set=(testX, testY), n_epoch=500, shuffle=True,
snapshot_epoch=True, # Snapshot (save & evaluate) model every epoch.
show_metric=True, batch_size=32, callbacks=early_stopping_cb, run_id='cnn_handwrite')
except StopIteration as e:
print("OK, stop iterate!Good!")
model.save(filename)
del tf.get_collection_ref(tf.GraphKeys.TRAIN_OPS)[:]
"""
# print op name
with tf.Session() as sess:
init_op = tf.initialize_all_variables()
sess.run(init_op)
for v in sess.graph.get_operations():
print(v.name)
"""
filename = 'cnn_handwrite-acc0.8.infer.tflearn'
model.save(filename)
参考：http://www.cnblogs.com/bonelee/p/8445261.html 里的脚本，修改：
output_node_names = "FullyConnected/Softmax"
通常为：
output_node_names = "FullyConnected/Softmax"
或者
output_node_names = "FullyConnected_1/Softmax"
output_node_names = "FullyConnected_2/Softmax"
就看你使用的全连接层数，上面分别是1，2，3层。
最后，tensorflow里的使用：
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, 32, 32, 1])
from tensorflow.python.platform import gfile
with tf.Graph().as_default():
output_graph_def = tf.GraphDef()
with open("frozen_model.pb", "rb") as f:
output_graph_def.ParseFromString(f.read())
tensors = tf.import_graph_def(output_graph_def, name="")
#print tensors
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
op = sess.graph.get_operations()
"""
for m in op:
print(m.values())
"""
op = sess.graph.get_tensor_by_name("FullyConnected_1/Softmax:0")
input_tensor = sess.graph.get_tensor_by_name('InputData/X:0')
probs = sess.run(op,feed_dict = {input_tensor:temp_image})
print probs result = []
for word in probs:
result.append(np.argsort(-word)[:3])
return result def main(_):
image_path = './data/test/00098/104405.png'
#image_path = '../data/00010/17724.png'
final_predict_val = inference(image_path)
logger.info('the result info label {0} predict index {1}'.format(98, final_predict_val))
一般，输入TensorFlow input name默认为InputData/X，但只是op，如果要tensor的话，加上数字0，也就是：InputData/X:0
同理，FullyConnected_1/Softmax:0。
最后预测效果：
[[ 8.42533936e-08 1.60850794e-11 2.60133332e-10 2.42555542e-14
4.96124599e-08 4.45251297e-15 3.98175590e-11 1.64476592e-11
7.03968351e-13 5.42319011e-12 8.55469237e-11 4.91866422e-13
1.77282828e-07 4.05237593e-10 3.13049003e-10 1.34780919e-11
2.05803235e-06 2.87827305e-07 1.47789994e-12 2.53391891e-11
3.77086790e-13 2.02639586e-10 9.03167027e-13 3.96698889e-11
1.30850096e-11 5.71980917e-12 3.03487374e-11 2.04132298e-14
6.25303683e-13 1.46122332e-07 2.17450633e-07 1.69623715e-09
6.80857757e-12 2.52643609e-13 6.56771096e-11 8.55152287e-16
1.34496514e-09 1.22644633e-06 1.12011307e-07 7.93476283e-05
8.24334611e-12 4.77531155e-14 9.39397757e-13 2.38438267e-14
2.11416329e-10 5.54395712e-08 2.30046147e-12 2.63584043e-10
4.70621564e-16 5.14432724e-12 6.42602327e-09 1.62485829e-13
7.39078274e-08 3.19146315e-12 5.25887156e-09 1.35877786e-13
1.39127886e-13 2.11998293e-13 9.09501097e-09 9.46486750e-07
2.47498733e-09 2.74523763e-12 1.02716433e-14 1.02069058e-17
3.09356682e-16 1.51022904e-15 9.34333665e-13 2.62195051e-14
3.38079781e-16 7.43019903e-13 1.92409091e-13 3.86611994e-13
2.61276265e-12 1.07969211e-09 1.30814548e-09 2.44038188e-14
9.79275905e-13 1.41007803e-10 6.15137758e-12 2.08893070e-10
1.34751668e-14 2.76824767e-15 7.84100464e-16 7.70873335e-15
5.45704757e-12 3.69386271e-18 2.06012223e-13 1.62567273e-14
1.54544960e-03 2.05292008e-06 1.31726174e-09 7.04993663e-09
4.11338266e-03 3.19344110e-07 3.96519717e-05 2.26919351e-12
2.39114349e-12 2.35558744e-07 9.94213998e-01 1.10125060e-11]]
the result info label 98 predict index [array([98, 92, 88])]
tflearn 中文汉字识别，训练后模型存为pb给TensorFlow使用——模型层次太深，或者太复杂训练时候都不会收敛的更多相关文章
- tflearn 中文汉字识别模型试验汇总
def get_model(width, height, classes=40): # TODO, modify model # Building 'VGG Network' network = in ...
- TensorFlow 模型优化工具包 — 训练后整型量化
模型优化工具包是一套先进的技术工具包,可协助新手和高级开发者优化待部署和执行的机器学习模型.自推出该工具包以来, 我们一直努力降低机器学习模型量化的复杂性 (https://www.tensorfl ...
- Tika结合Tesseract-OCR 实现光学汉字识别（简体、宋体的识别率百分之百）—附Java源码、测试数据和训练集下载地址
OCR(Optical character recognition) —— 光学字符识别,是图像处理的一个重要分支,中文的识别具有一定挑战性,特别是手写体和草书的识别,是重要和热门的科学研究方向.可 ...
- python中文utf8编码后是占3个字符,unicode汉字为2字节
一个中文utf8编码后是占3个字符,所以求长度的函数可以这样写 def str_len(str): try: row_l=len(str) utf8_l=len(str.encode('utf-8') ...
- python 将中文转拼音后填充到url做参数并写入excel
闲着没事写了个小工具,将中文转拼音后填充到url做参数并写如excel 一.先看下演示,是个什么东西 二.代码 代码用到一个中文转拼音的库,库是网上下的,稍微做了下修改,已经找不原来下载的地址了,然后 ...
- Python之TensorFlow的模型训练保存与加载-3
一.TensorFlow的模型保存和加载,使我们在训练和使用时的一种常用方式.我们把训练好的模型通过二次加载训练,或者独立加载模型训练.这基本上都是比较常用的方式. 二.模型的保存与加载类型有2种 1 ...
- DL4NLP——词表示模型（二）基于神经网络的模型：NPLM；word2vec（CBOW/Skip-gram）
本文简述了以下内容: 神经概率语言模型NPLM,训练语言模型并同时得到词表示 word2vec:CBOW / Skip-gram,直接以得到词表示为目标的模型 (一)原始CBOW(Continuous ...
- 『高性能模型』Roofline Model与深度学习模型的性能分析
转载自知乎:Roofline Model与深度学习模型的性能分析 在真实世界中,任何模型(例如 VGG / MobileNet 等)都必须依赖于具体的计算平台(例如CPU / GPU / ASIC 等 ...
- 学习笔记CB014:TensorFlow seq2seq模型步步进阶
神经网络.<Make Your Own Neural Network>,用非常通俗易懂描述讲解人工神经网络原理用代码实现,试验效果非常好. 循环神经网络和LSTM.Christopher ...
随机推荐
- javascript好文 --- 深入理解可视区尺寸client
可视区大小 可视区大小client又称为可见大小或客户区大小,指的是元素内容及其内边距所占据的空间大小 clientHeight clientHeight属性返回元素节点的可见高度 clientHei ...
- 好用的Python IDLE Sublime Text 3推荐
Sublime Text 3 下载地址为 LINK, Sublime Text 3 is currently in beta. The latest build is 3114. 参考的激活方式为输入 ...
- 【IntelliJ idea/My/ecplise】启动项目前，修改配置JVM参数
My/ecplise下都是一样的: IDEA下:
- 26个高效工作的小技巧 z
1.时间常有,时间优先. 2.时间总会有的:每天只计划4-5 小时真正的工作. 3.当你在状态时,就多干点:不然就好好休息:有时候会连着几天不是工作状态,有时在工作状态时却又能天天忙活 12 小时,这 ...
- ubuntu 配置 django
安装 安装Apache sudo apt-get install apache2 安装Django 下载Django 安装mod_wsgi sudo apt-get install libapache ...
- win10 sublime license
—– BEGIN LICENSE —– TwitterInc 200 User License EA7E-890007 1D77F72E 390CDD93 4DCBA022 FAF60790 61AA ...
- utuntu16.04安装caffe+Matlab2017a+opencv3.1+CUDA8.0+cudnn6.0
上午把tensorflow安装好了,下午和晚上装caffe的确很费劲. 默认CUDA,cuDNN可以用了 caffe官方安装教程 有些安装顺序自己也不清楚,简直就是碰运气 1. 安装之前依赖项 Gen ...
- nload 命令
网卡 流量监控命令 // 安装 yum intall nload nload 上下page 键 切换网卡查看
- vue2.0 自定义过滤器
2.0中已经废弃了过滤器,需要我们自定义 <div id="app"> {{message|uppercase}} </div> //过滤器 Vue.fil ...
- ActiveX控件打包成Cab置于网页中自动下载安装
[背景] http://www.360doc.com/content/13/1120/20/10159093_330853247.shtml 做过ActiveX控件的朋友都知道,要想把自己做的Acti ...