我就废话不多说了，大家还是直接看代码吧~

# -*- coding: utf-8 -*-#keras==2.0.5#tensorflow==1.1.0import os,sys,stringimport sysimport loggingimport multiprocessingimport timeimport jsonimport cv2import numpy as npfrom sklearn.model_selection import train_test_splitimport kerasimport keras.backend as Kfrom keras.datasets import mnistfrom 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 plotfrom visual_callbacks import AccLossPlotterplotter = AccLossPlotter(graphs=['acc', 'loss'], save_graph=True, save_graph_path=sys.path[0])#识别字符集char_ocr='0123456789' #string.digits#定义识别字符串的最大长度seq_len=8#识别结果集合个数 0-9label_count=len(char_ocr)+1def 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 labdef 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 # 反色处理 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 functiondef 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:, :] 测试感觉没影响 y_pred = y_pred[:, :, :] return K.ctc_batch_cost(labels, y_pred, input_length, 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, (3, 3), 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, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru1_b')(x) gru1_merged = add([gru_1, gru_1b]) ################### gru_2 = GRU(32, return_sequences=True, kernel_initializer='he_normal', name='gru2')(gru1_merged) gru_2b = GRU(32, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru2_b')( gru1_merged) x = concatenate([gru_2, gru_2b]) ###################### x = Dropout(0.25)(x) x = Dense(label_count, kernel_initializer='he_normal', 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', shape=[1], dtype='int64') loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([x, labels, input_length, label_length]) model = Model(inputs=[input_tensor, labels, input_length, 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]], Y[: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()

补充知识：日常填坑之keras.backend.ctc_batch_cost参数问题

InvalidArgumentError sequence_length(0) <=30错误

下面的代码是在网上绝大多数文章给出的关于k.ctc_batch_cost()函数的使用代码

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:, :] return K.ctc_batch_cost(labels, y_pred, input_length, label_length)

可以注意到有一句：y_pred = y_pred[:, 2:, :]，这里把y_pred 的第二维数据去掉了两列，说人话：把送进lstm序列的step减了2步。后来偶然在一篇文章中有提到说这里之所以减2是因为在将feature送入keras的lstm时自动少了2维，所以这里就写成这样了。估计是之前老版本的bug，现在的新版本已经修复了。如果依然按照上面的写法，会得到如下错误：

InvalidArgumentError sequence_length(0) <=30

'<='后面的数值 = 你cnn最后的输出维度 - 2。这个错误我找了很久，一直不明白30哪里来的，后来一行行的检查代码是发现了这里很可疑，于是改成如下形式错误解决。

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

训练时出现ctc_loss_calculator.cc:144] No valid path found或loss: inf错误

熟悉CTC算法的话，这个提示应该是ctc没找到有效路径。既然是没找到有效路径，那肯定是label和input之间哪个地方又出问题了！和input相关的错误已经解决了，那么肯定就是label的问题了。再看ctc_batch_cost的四个参数，labels和label_length这两个地方有可疑。对于ctc_batch_cost()的参数，labels需要one-hot编码，形状：[batch, max_labelLength]，其中max_labelLength指预测的最大字符长度；label_length就是每个label中的字符长度了，受之前tf.ctc_loss的影响把这里都设置成了最大长度，所以报错。

对于参数labels而言，max_labelLength是能预测的最大字符长度。这个值与送lstm的featue的第二维，即特征序列的max_step有关，表面上看只要max_labelLength<max_step即可，但是如果小的不多依然会出现上述错误。至于到底要小多少，还得从ctc算法里找，由于ctc算法在标签中的每个字符后都加了一个空格，所以应该把这个长度考虑进去，所以有 max_labelLength < max_step//2。没仔细研究keras里ctc_batch_cost()函数的实现细节，上面是我的猜测。如果有很明确的答案，还请麻烦告诉我一声，谢了先！

错误代码：

batch_label_length = np.ones(batch_size) * max_labelLength

正确打开方式：

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])

最后附一张我的crnn的模型图：

以上这篇使用keras框架cnn+ctc_loss识别不定长字符图片操作就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。