我就廢話不多說了，大家還是直接看代碼吧~
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# -*- 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])#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 0-9label_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 rain', 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 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:, :] 測試感覺沒影響 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 est', 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() |
補充知識：日常填坑之keras.backend.ctc_batch_cost參數問題
InvalidArgumentError sequence_length(0) <=30錯誤
下面的代碼是在網上絕大多數文章給出的關于k.ctc_batch_cost()函數的使用代碼
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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:, :]，這里把y_pred 的第二維數據去掉了兩列，說人話：把送進lstm序列的step減了2步。后來偶然在一篇文章中有提到說這里之所以減2是因為在將feature送入keras的lstm時自動少了2維，所以這里就寫成這樣了。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
'<='后面的數值 = 你cnn最后的輸出維度 - 2。這個錯誤我找了很久，一直不明白30哪里來的，后來一行行的檢查代碼是發現了這里很可疑，于是改成如下形式錯誤解決。
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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) |
訓練時出現ctc_loss_calculator.cc:144] No valid path found或loss: inf錯誤
熟悉CTC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，那么肯定就是label的問題了。再看ctc_batch_cost的四個參數，labels和label_length這兩個地方有可疑。對于ctc_batch_cost()的參數，labels需要one-hot編碼，形狀：[batch, max_labelLength]，其中max_labelLength指預測的最大字符長度；label_length就是每個label中的字符長度了，受之前tf.ctc_loss的影響把這里都設置成了最大長度，所以報錯。
對于參數labels而言，max_labelLength是能預測的最大字符長度。這個值與送lstm的featue的第二維，即特征序列的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。至于到底要小多少，還得從ctc算法里找，由于ctc算法在標簽中的每個字符后都加了一個空格，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里ctc_batch_cost()函數的實現細節，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！
錯誤代碼：
batch_label_length = np.ones(batch_size) * max_labelLength
正確打開方式：
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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識別不定長字符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，也希望大家多多支持服務器之家。
原文鏈接：https://blog.csdn.net/xinfeng2005/article/details/78278832
