我就廢話不多說了，大家還是直接看程式碼吧~

# -*- coding: utf-8 -*-
#keras==2.0.5
#tensorflow==1.1.0

import os,sys,string
import sys
import logging
import multiprocessing
import time
import json
import cv2
import numpy as np
from sklearn.model_selection import train_test_split

import keras
import keras.backend as K
from keras.datasets import mnist
from keras.models import *
from keras.layers import *
from keras.optimizers import *
from keras.callbacks import *
from keras import backend as K
# from keras.utils.visualize_util import plot
from visual_callbacks import AccLossPlotter
plotter = AccLossPlotter(graphs=['acc','loss'],save_graph=True,save_graph_path=sys.path[0])

#識別字符集
char_ocr='0123456789' #string.digits
#定義識別字符串的最大長度
seq_len=8
#識別結果集合個數 0-9
label_count=len(char_ocr)+1

def get_label(filepath):
 # print(str(os.path.split(filepath)[-1]).split('.')[0].split('_')[-1])
 lab=[]
 for num in str(os.path.split(filepath)[-1]).split('.')[0].split('_')[-1]:
 lab.append(int(char_ocr.find(num)))
 if len(lab) < seq_len:
 cur_seq_len = len(lab)
 for i in range(seq_len - cur_seq_len):
  lab.append(label_count) #
 return lab

def gen_image_data(dir=r'data\train',file_list=[]):
 dir_path = dir
 for rt,dirs,files in os.walk(dir_path): # =pathDir
 for filename in files:
  # print (filename)
  if filename.find('.') >= 0:
  (shotname,extension) = os.path.splitext(filename)
  # print shotname,extension
  if extension == '.tif': # extension == '.png' or
   file_list.append(os.path.join('%s\\%s' % (rt,filename)))
   # print (filename)

 print(len(file_list))
 index = 0
 X = []
 Y = []
 for file in file_list:

 index += 1
 # if index>1000:
 # break
 # print(file)
 img = cv2.imread(file,0)
 # print(np.shape(img))
 # cv2.namedWindow("the window")
 # cv2.imshow("the window",img)
 img = cv2.resize(img,(150,50),interpolation=cv2.INTER_CUBIC)
 img = cv2.transpose(img,(50,150))
 img =cv2.flip(img,1)
 # cv2.namedWindow("the window")
 # cv2.imshow("the window",img)
 # cv2.waitKey()
 img = (255 - img) / 256 # 反色處理
 X.append([img])
 Y.append(get_label(file))
 # print(get_label(file))
 # print(np.shape(X))
 # print(np.shape(X))

 # print(np.shape(X))
 X = np.transpose(X,(0,2,3,1))
 X = np.array(X)
 Y = np.array(Y)
 return X,Y

# the actual loss calc occurs here despite it not being
# an internal Keras loss function

def ctc_lambda_func(args):
 y_pred,labels,input_length,label_length = args
 # the 2 is critical here since the first couple outputs of the RNN
 # tend to be garbage:
 # y_pred = y_pred[:,2:,:] 測試感覺沒影響
 y_pred = y_pred[:,:,:]
 return K.ctc_batch_cost(labels,y_pred,label_length)

if __name__ == '__main__':
 height=150
 width=50
 input_tensor = Input((height,width,1))
 x = input_tensor
 for i in range(3):
 x = Convolution2D(32*2**i,(3,3),activation='relu',padding='same')(x)
 # x = Convolution2D(32*2**i,activation='relu')(x)
 x = MaxPooling2D(pool_size=(2,2))(x)

 conv_shape = x.get_shape()
 # print(conv_shape)
 x = Reshape(target_shape=(int(conv_shape[1]),int(conv_shape[2] * conv_shape[3])))(x)

 x = Dense(32,activation='relu')(x)

 gru_1 = GRU(32,return_sequences=True,kernel_initializer='he_normal',name='gru1')(x)
 gru_1b = GRU(32,go_backwards=True,name='gru1_b')(x)
 gru1_merged = add([gru_1,gru_1b]) ###################

 gru_2 = GRU(32,name='gru2')(gru1_merged)
 gru_2b = GRU(32,name='gru2_b')(
 gru1_merged)
 x = concatenate([gru_2,gru_2b]) ######################
 x = Dropout(0.25)(x)
 x = Dense(label_count,activation='softmax')(x)
 base_model = Model(inputs=input_tensor,outputs=x)

 labels = Input(name='the_labels',shape=[seq_len],dtype='float32')
 input_length = Input(name='input_length',shape=[1],dtype='int64')
 label_length = Input(name='label_length',dtype='int64')
 loss_out = Lambda(ctc_lambda_func,output_shape=(1,),name='ctc')([x,label_length])

 model = Model(inputs=[input_tensor,label_length],outputs=[loss_out])
 model.compile(loss={'ctc': lambda y_true,y_pred: y_pred},optimizer='adadelta')
 model.summary()

 def test(base_model):
 file_list = []
 X,Y = gen_image_data(r'data\test',file_list)
 y_pred = base_model.predict(X)
 shape = y_pred[:,:].shape # 2:
 out = K.get_value(K.ctc_decode(y_pred[:,:],input_length=np.ones(shape[0]) * shape[1])[0][0])[:,:seq_len] # 2:
 print()
 error_count=0
 for i in range(len(X)):
  print(file_list[i])
  str_src = str(os.path.split(file_list[i])[-1]).split('.')[0].split('_')[-1]
  print(out[i])
  str_out = ''.join([str(x) for x in out[i] if x!=-1 ])
  print(str_src,str_out)
  if str_src!=str_out:
  error_count+=1
  print('################################',error_count)
  # img = cv2.imread(file_list[i])
  # cv2.imshow('image',img)
  # cv2.waitKey()

 class LossHistory(Callback):
 def on_train_begin(self,logs={}):
  self.losses = []

 def on_epoch_end(self,epoch,logs=None):
  model.save_weights('model_1018.w')
  base_model.save_weights('base_model_1018.w')
  test(base_model)

 def on_batch_end(self,batch,logs={}):
  self.losses.append(logs.get('loss'))


 # checkpointer = ModelCheckpoint(filepath="keras_seq2seq_1018.hdf5",verbose=1,save_best_only=True,)
 history = LossHistory()

 # base_model.load_weights('base_model_1018.w')
 # model.load_weights('model_1018.w')

 X,Y=gen_image_data()
 maxin=4900
 subseq_size = 100
 batch_size=10
 result=model.fit([X[:maxin],Y[:maxin],np.array(np.ones(len(X))*int(conv_shape[1]))[:maxin],np.array(np.ones(len(X))*seq_len)[:maxin]],batch_size=20,epochs=1000,callbacks=[history,plotter,EarlyStopping(patience=10)],#checkpointer,history,validation_data=([X[maxin:],Y[maxin:],np.array(np.ones(len(X))*int(conv_shape[1]))[maxin:],np.array(np.ones(len(X))*seq_len)[maxin:]],Y[maxin:]),)

 test(base_model)

 K.clear_session()

補充知識：日常填坑之keras.backend.ctc_batch_cost引數問題

InvalidArgumentError sequence_length(0) <=30錯誤

下面的程式碼是在網上絕大多數文章給出的關於k.ctc_batch_cost()函式的使用程式碼

def ctc_lambda_func(args):
 y_pred,label_length = args
 # the 2 is critical here since the first couple outputs of the RNN
 # tend to be garbage: 
 y_pred = y_pred[:,label_length)

可以注意到有一句：y_pred = y_pred[:,:]，這裡把y_pred 的第二維資料去掉了兩列，說人話：把送進lstm序列的step減了2步。後來偶然在一篇文章中有提到說這裡之所以減2是因為在將feature送入keras的lstm時自動少了2維，所以這裡就寫成這樣了。估計是之前老版本的bug，現在的新版本已經修復了。如果依然按照上面的寫法，會得到如下錯誤：

InvalidArgumentError sequence_length(0) <=30

'<='後面的數值 = 你cnn最後的輸出維度 - 2。這個錯誤我找了很久，一直不明白30哪裡來的，後來一行行的檢查程式碼是發現了這裡很可疑，於是改成如下形式錯誤解決。

def ctc_lambda_func(args):
 y_pred,label_length = args 
 return K.ctc_batch_cost(labels,label_length)

訓練時出現ctc_loss_calculator.cc:144] No valid path found或loss: inf錯誤

熟悉CTC演算法的話，這個提示應該是ctc沒找到有效路徑。既然是沒找到有效路徑，那肯定是label和input之間哪個地方又出問題了！和input相關的錯誤已經解決了，那麼肯定就是label的問題了。再看ctc_batch_cost的四個引數，labels和label_length這兩個地方有可疑。對於ctc_batch_cost()的引數，labels需要one-hot編碼，形狀：[batch,max_labelLength]，其中max_labelLength指預測的最大字元長度；label_length就是每個label中的字元長度了，受之前tf.ctc_loss的影響把這裡都設定成了最大長度，所以報錯。

對於引數labels而言，max_labelLength是能預測的最大字元長度。這個值與送lstm的featue的第二維，即特徵序列的max_step有關，表面上看只要max_labelLength<max_step即可，但是如果小的不多依然會出現上述錯誤。至於到底要小多少，還得從ctc演算法裡找，由於ctc演算法在標籤中的每個字元後都加了一個空格，所以應該把這個長度考慮進去，所以有 max_labelLength < max_step//2。沒仔細研究keras裡ctc_batch_cost()函式的實現細節，上面是我的猜測。如果有很明確的答案，還請麻煩告訴我一聲，謝了先！

錯誤程式碼：

batch_label_length = np.ones(batch_size) * max_labelLength

正確開啟方式：

batch_x,batch_y = [],[]
batch_input_length = np.ones(batch_size) * (max_img_weigth//8)
batch_label_length = []
for j in range(i,i + batch_size):
 x,y = self.get_img_data(index_all[j])
 batch_x.append(x)
 batch_y.append(y)
 batch_label_length.append(self.label_length[j])

最後附一張我的crnn的模型圖：

以上這篇使用keras框架cnn+ctc_loss識別不定長字元圖片操作就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支援我們。