DL之CNN：基于CRNN_OCR算法(keras,CNN+RNN)利用数据集(torch,mdb格式)训练来实现新图片上不定长度字符串进行识别—训练过程
目录
输出结果
设计思路
核心代码
输出结果
设计思路
核心代码
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateaunclass = len(characters) + 1 model, basemodel = get_model_train(height=imgH, nclass=nclass) import osif os.path.exists('./models/pretrain_models/keras.hdf5'):basemodel.load_weights('./models/pretrain_models/keras.hdf5')checkpointer = ModelCheckpoint(filepath="./models/ intermediate_model/model{epoch:02d}-{val_loss:.4f}.hdf5", monitor='val_loss',verbose=0, save_weights_only=False, save_best_only=True)rlu = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=1, verbose=0, mode='auto', epsilon=0.0001,cooldown=0, min_lr=0)model.fit_generator(gen(train_loader, flag='train'), steps_per_epoch=1024,epochs=10000,validation_data=gen(test_loader, flag='test'),callbacks=[checkpointer, rlu],validation_steps=1024)#保存两个h5文件model.save_weights('./models/final_model/final_model_weights.h5') model.save('./models/final_model/final_model.h5')
DL之CNN：基于CRNN_OCR算法(keras CNN+RNN)利用数据集(torch mdb格式)训练来实现新图片上不定长度字符串进行识别—训练过程
