def train_model():
# calc num_classes
key_f = open(params.char_path, 'r', encoding='utf-8')
chars = key_f.read()
key_f.close()
params.num_classes = len(chars) + 1
print('params.num_classes: ', params.num_classes)
template_model = crnn_model.get_Model(training=True)
try:
latest_weights = params.load_weights_path
print("find latest_weights exists.", latest_weights)
if latest_weights != None:
template_model.load_weights(latest_weights)
print("...load exist weights: ", latest_weights)
else:
print("history weights file not exist, train a new one.")
except Exception as e:
print('warn: ', str(e))
print("historical weights data can not be used, train a new one...")
pass
model = multi_gpu_model(template_model, gpus=params.gpu_nums_in_multi_model)
model.layers[-2].set_weights(template_model.get_weights())
ada = Adadelta()
# the loss calc occurs elsewhere, so use a dummy lambda func for the loss
model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=ada)
early_stop = EarlyStopping(monitor='val_loss',
min_delta=0.001,
patience=6,
mode='min',
verbose=1)
my_checkpoint = MyCheckPoint(template_model)
tensor_board = TensorBoard(log_dir='/data/output')
train_data_gen, val_data_gen, train_sample_num, val_sample_num = load_train_and_val_data()
batch_size = params.batch_size
epoch_num = params.epoch_num
val_batch_size = params.val_batch_size
for ep_i in range(epoch_num):
print("epoch: ", ep_i+1)
model.fit_generator(generator=train_data_gen,
steps_per_epoch=train_sample_num // batch_size,
epochs=ep_i + 1,
callbacks=[],
verbose=2,
initial_epoch=ep_i,
validation_data=val_data_gen,
validation_steps=val_sample_num // val_batch_size)
curr_weights_path = "/data/output/crnn_weights_20190106/crnn_weights_v1.13_ep_%d.h5" % (ep_i + 1)
template_model.save_weights(curr_weights_path)
train_data_acc = epoch_eval.eval_on_generating_data(curr_weights_path)
print(" -- train_data_acc: ", train_data_acc)
real_data_acc, detail_info = epoch_eval.eval_on_real_data(curr_weights_path)
print(" -- real_data_acc: ", real_data_acc, detail_info)
def train_model():
# calc num_classes
key_f = open(params.char_path, 'r', encoding='utf-8')
chars = key_f.read()
key_f.close()
params.num_classes = len(chars) + 1
print('params.num_classes: ', params.num_classes)
model = crnn_model.get_Model(training=True)
try:
latest_weights = params.load_weights_path
print("find latest_weights exists.", latest_weights)
if latest_weights != None:
model.load_weights(latest_weights)
print("...load exist weights: ", latest_weights)
else:
print("history weights file not exist, train a new one.")
except Exception as e:
print('warn: ', str(e))
print("historical weights data can not be used, train a new one...")
pass
train_data_gen, val_data_gen, train_sample_num, val_sample_num = load_train_and_val_data(
)
ada = Adadelta()
early_stop = EarlyStopping(monitor='val_loss',
min_delta=0.001,
patience=8,
mode='min',
verbose=1)
checkpoint = ModelCheckpoint(
filepath='/data/output/CRNN--{epoch:02d}--{val_loss:.3f}.h5',
monitor='val_loss',
save_best_only=False,
save_weights_only=True,
verbose=1,
mode='min',
period=1)
tensor_board = TensorBoard(log_dir='/data/output')
# the loss calc occurs elsewhere, so use a dummy lambda func for the loss
model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=ada)
# captures output of softmax so we can decode the output during visualization
batch_size = params.batch_size
epoch_num = params.epoch_num
val_batch_size = params.val_batch_size
for ep_i in range(epoch_num):
print("epoch: ", ep_i + 1)
model.fit_generator(generator=train_data_gen,
steps_per_epoch=train_sample_num // batch_size,
epochs=ep_i + 1,
callbacks=[],
verbose=1,
initial_epoch=ep_i,
validation_data=val_data_gen,
validation_steps=val_sample_num // val_batch_size)
curr_weights_path = "/data/output/crnn_ticket_20190327/crnn_weights_d10w_ticket_id_date_20190327_ep_%d.h5" % (
ep_i + 1)
model.save_weights(curr_weights_path)
train_data_acc = epoch_eval.eval_on_generating_data(curr_weights_path)
print(" -- train_data_acc: ", train_data_acc)
real_data_acc, detail_info = epoch_eval.eval_on_real_data(
curr_weights_path)
print(" -- real_data_acc: ", real_data_acc, detail_info)
from batch_test import epoch_eval
import os
if __name__ == '__main__':
print('start doing batch_testing...')
weights_dir = '/data/output/crnn_ticket_20190327'
#weights_fn_list = ['crnn_weights_d10w_20190325_effects_ep_3.h5']
weights_fn_list = os.listdir(weights_dir)
weights_fn_list = sorted(weights_fn_list)
# weights_fn_list = ['crnn_weights_d10w_ep_%d.h5'%i for i in range(1,20)]
#weights_fn_list = weights_fn_list[:1]
for idx, weights_fn in enumerate(weights_fn_list):
print('--------------------------------------')
print(idx, weights_fn)
weights_path = os.path.join(weights_dir, weights_fn)
#acc = epoch_eval.eval_on_generating_data(weights_path)
#print('acc', acc)
acc, test_res = epoch_eval.eval_on_real_data(weights_path)
print('test_res: ', test_res)