def main():
# img_width, img_height = 48, 48
img_width, img_height = 200, 60
img_channels = 1
# batch_size = 1024
batch_size = 32
nb_epoch = 1000
post_correction = False
save_dir = 'save_model/' + str(datetime.now()).split('.')[0].split()[0] + '/' # model is saved corresponding to the datetime
train_data_dir = 'train_data/ip_train/'
# train_data_dir = 'train_data/single_1000000/'
val_data_dir = 'train_data/ip_val/'
test_data_dir = 'test_data//'
weights_file_path = 'save_model/2016-10-27/weights.11-1.58.hdf5'
char_set, char2idx = get_char_set(train_data_dir)
nb_classes = len(char_set)
max_nb_char = get_maxnb_char(train_data_dir)
label_set = get_label_set(train_data_dir)
# val 'char_set:', char_set
print 'nb_classes:', nb_classes
print 'max_nb_char:', max_nb_char
print 'size_label_set:', len(label_set)
model = build_shallow(img_channels, img_width, img_height, max_nb_char, nb_classes) # build CNN architecture
# model.load_weights(weights_file_path) # load trained model
val_data = load_data(val_data_dir, max_nb_char, img_width, img_height, img_channels, char_set, char2idx)
# val_data = None
train_data = load_data(train_data_dir, max_nb_char, img_width, img_height, img_channels, char_set, char2idx)
train(model, batch_size, nb_epoch, save_dir, train_data, val_data, char_set)
def main():
# img_width, img_height = 48, 48
img_width, img_height = 200, 60
img_channels = 1
# batch_size = 1024
batch_size = 32
nb_epoch = 1000
post_correction = False
save_dir = 'save_model/' + str(datetime.now()).split('.')[0].split(
)[0] + '/' # model is saved corresponding to the datetime
train_data_dir = 'train_data/ip_train/'
# train_data_dir = 'train_data/single_1000000/'
val_data_dir = 'train_data/ip_val/'
test_data_dir = 'test_data//'
weights_file_path = 'save_model/2016-10-27/weights.11-1.58.hdf5'
char_set, char2idx = get_char_set(train_data_dir)
nb_classes = len(char_set)
max_nb_char = get_maxnb_char(train_data_dir)
label_set = get_label_set(train_data_dir)
# val 'char_set:', char_set
print 'nb_classes:', nb_classes
print 'max_nb_char:', max_nb_char
print 'size_label_set:', len(label_set)
model = build_shallow(img_channels, img_width, img_height, max_nb_char,
nb_classes) # build CNN architecture
# model.load_weights(weights_file_path) # load trained model
val_data = load_data(val_data_dir, max_nb_char, img_width, img_height,
img_channels, char_set, char2idx)
# val_data = None
train_data = load_data(train_data_dir, max_nb_char, img_width, img_height,
img_channels, char_set, char2idx)
train(model, batch_size, nb_epoch, save_dir, train_data, val_data,
char_set)
def main():
img_width, img_height = 64, 64
img_channels = 1
batch_size = 32
nb_epoch = 4
post_correction = False
save_dir = 'models/models/' + str(datetime.now()).split('.')[0].split(
)[0] + '/' # model is saved corresponding to the datetime
train_data_dir = './data/train/'
val_data_dir = './data/val/'
char_set, char2idx = get_char_set(
'./data/'
) #charset = ['empty',...,'鸵', '豸', '山',...] char2idx = { ...,'弗': 3290, '毓': 6488,...}
nb_classes = len(char_set)
print('nb_classes:', nb_classes) #607
print("===========Building Model:===============")
model = build_shallow(img_channels, img_width, img_height,
nb_classes) # build CNN architecture
print("=========Begin Loading Val Data:=============\n")
val_data = load_data(val_data_dir, img_width, img_height, img_channels,
char_set)
print("=========Begin Loading Train Data:=============\n")
train_data = load_data(train_data_dir, img_width, img_height, img_channels,
char_set)
print("===========Begin Training=============:\n")
train(model, batch_size, nb_epoch, save_dir, train_data, val_data,
char_set)
#-*-coding:utf8-*-
import os
import time
import numpy as np
from datetime import datetime
from keras.callbacks import ModelCheckpoint
from util import one_hot_decoder, plot_loss_figure, load_data, get_char_set,load_img
from util import list2str
from post_correction import correction
from models.shallow import build_shallow
import pdb
char_set, char2idx = get_char_set('./data/')
nb_classes = len(char_set)
print ('nb_classes:', nb_classes)#607
def pred(model, X, char_set, post_correction):
pred_res = model.predict(X)
pred_res = [one_hot_decoder(i, char_set) for i in pred_res]
pred_res = [list2str(i) for i in pred_res]
# post correction
if post_correction:
pred_res = correction(pred_res, char_set)
return pred_res
def infer(path):
img_width, img_height = 64, 64
img_channels = 1
def __init__(self):
self.char_set = get_char_set(self.train_data_dir)[0]
self.nb_classes = len(self.char_set)
self.max_nb_char = get_maxnb_char(self.train_data_dir)
self.label_set = get_label_set(self.train_data_dir)
self.pred_probs = None
def __init__(self):
self.char_set = get_char_set(self.train_data_dir)[0]
self.nb_classes = len(self.char_set)
self.max_nb_char = get_maxnb_char(self.train_data_dir)
self.label_set = get_label_set(self.train_data_dir)
self.pred_probs = None
