def training_model(model_name='mobilenet'):
train_gen, valid_gen, tconfig = get_gen_tconfig()
callbacks = get_callbacks('mobilenet05_short_adam03_dr35_v3', patience=4)
if model_name == 'mobilenet':
print('MobileNet')
model = MobileNet(config=tconfig, alpha=1.0)
model.summary()
elif model_name == 'mobilenet_dih':
print('MobileNetDih')
model = MobileNetDih4(config=tconfig, alpha=1)
model.summary()
elif model_name == 'mobilenet_dih_r':
print('MobileNetDihR')
model = MobileNetDR(config=tconfig, alpha=0.5)
model.summary()
opt = Adam(lr=1e-3, beta_1=0.9, beta_2=0.999)
#opt = Adadelta(lr=1e-1, rho=0.95, decay=0.1)
#opt = SGD(lr=1e-7, momentum=0.9, decay=0., nesterov=True)
model.compile(optimizer=opt, loss='mse', metrics=['mae', 'mse'])
#model.load_weights('mobilenet_05shortd01_catcros_resize_b16.hdf5')
model.fit_generator(generator=train_gen,
steps_per_epoch=1000,
epochs=40,
validation_data=valid_gen,
verbose=2,
validation_steps=500,
callbacks=callbacks)
#opt = Adam(lr=1e-3, beta_1=0.9, beta_2=0.999)
#opt = Adadelta(lr=1e-1, rho=0.95, decay=0.1)
"""
def training_model(model_name='mobilenet'):
train_img, valid_img, train_y, valid_y = get_data()
callbacks = get_callbacks('mobilenet_10fulld01_b16', patience=2)
if model_name == 'mobilenet':
print('MobileNet')
model = MobileNet(alpha=1.)
model.summary()
elif model_name == 'mobilenet_dih':
print('MobileNetDih')
model = MobileNetDih4(alpha=1.)
model.summary()
elif model_name == 'mobilenet_dih_r':
print('MobileNetDihR')
model = MobileNetDR(alpha=1.)
model.summary()
opt = Adam(lr=1e-3, beta_1=0.9, beta_2=0.999)
#opt = Adadelta(lr=1e-1, rho=0.95, decay=0.1)
#opt = SGD(lr=1e-7, momentum=0.9, decay=0., nesterov=True)
model.compile(optimizer=opt,
loss='binary_crossentropy',
metrics=['accuracy'])
#model.load_weights('mobilenet_05shortd01_catcros_resize_b16.hdf5')
gen = ImageDataGenerator(rotation_range=359,
zoom_range=[0.5, 2],
width_shift_range=0.1,
height_shift_range=0.1,
vertical_flip=True,
horizontal_flip=True)
model.fit_generator(
gen.flow(np.array(train_img), np.array(train_y),
batch_size=BATCH_SIZE),
steps_per_epoch=16 * len(train_y) // BATCH_SIZE,
epochs=40,
validation_data=[np.array(valid_img),
np.array(valid_y)],
verbose=1,
callbacks=callbacks)
# """
#opt = Adam(lr=1e-3, beta_1=0.9, beta_2=0.999)
#opt = Adadelta(lr=1e-1, rho=0.95, decay=0.1)
opt = SGD(lr=0.05, momentum=0.9, decay=0., nesterov=True)
model.load_weights('mobilenet_10shortd01_b16_sgd')
model.fit_generator(
gen.flow(np.array(train_img), np.array(train_y),
batch_size=BATCH_SIZE),
steps_per_epoch=16 * len(train_y) // BATCH_SIZE,
epochs=10,
validation_data=[np.array(valid_img),
np.array(valid_y)],
verbose=1,
callbacks=callbacks)
def train():
tr_config = {
'flag': True,
'rg': 25, # 7, 5
'wrg': 0.25, # 1, 3
'hrg': 0.25, # 1, 3
'zoom': 0.25 # 1, 1
}
callbacks = get_callbacks('mynet_v4_bias', patience=30)
paths, y = search_file('set1/segmented_set1')
paths, y = search_file('set2/segmented_set2', paths=paths, y=y)
ds = DataSet(nframe=30,
fstride=6,
name='UT interaction',
size=[224, 224, 3],
filepaths=paths,
y=y,
kernel_size=4)
ds.make_set(op='msqr', name='train')
ds.make_set(op='msqr', name='valid')
#opt = Adam(lr=1e-4, beta_1=0.9, beta_2=0.999, decay=0.1)
#opt = SGD(lr=2*1e-1, momentum=0.9, nesterov=True, decay=0.2)
opt = RMSprop(lr=0.001, rho=0.9, decay=0.01)
model = MobileNet(alpha=1.0, shape=[29, 56, 56, 1], nframe=29)
model.compile(optimizer=opt,
loss='categorical_crossentropy',
metrics=['accuracy'])
model.summary()
#model.load_weights('mynet_v4.h5')
model.fit_generator(generator=ds.train_gen(batch_size=5,
aug_config=tr_config),
steps_per_epoch=100,
epochs=300,
validation_data=ds.valid_gen(),
verbose=1,
validation_steps=ds.getVlen,
callbacks=callbacks)
# 神兽保佑
# BUG是不可能有BUG的!
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
from mobilenet import MobileNet
import numpy as np
np.random.seed(10)
from keras.datasets import cifar10
from keras.utils import np_utils
(x_img_train, y_label_train),(x_img_test, y_label_test) = cifar10.load_data()
x_img_train = x_img_train.astype('float')/255.0
x_img_test = x_img_test.astype('float')/255.0
y_label_train = np_utils.to_categorical(y_label_train)
y_label_test = np_utils.to_categorical(y_label_test)
model = MobileNet()
try:
model.load_weights("mobileV1-lite.h5")
print("模型加载成功！继续训练")
except:
print("模型加载失败！从头开始训练")
model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
train_history = model.fit(x_img_train, y_label_train, validation_split=0.2, epochs=10, batch_size=128, verbose=2)
model.save_weights("mobileV1-lite.h5")
print("保存模型成功！")
import matplotlib.patches
np.set_printoptions(precision=2)
os.chdir("./handwritten_digit_recognition/")
# from wide_resnet_28_10 import WideResNet28_10
from mobilenet import MobileNet
from utils import load_mnist
os.chdir("../")
PATH = './handwritten_digit_recognition/models/'
#model_name = "WideResNet28_10"
#model=WideResNet28_10()
model_name = "MobileNet"
model = MobileNet()
model.compile()
model2 = MobileNet()
model2.compile()
print('Loading pretrained weights for ', model_name, '...', sep='')
model.load_weights(PATH + model_name + "_ajustado_1" + '.h5')
model2.load_weights(PATH + model_name + '.h5')
def mesaImagen(mesa, boleta=1):
fname = 'actas/{0:06d}'.format(mesa * 10 + boleta) + '.jpg'
data_name = "mesas_rv/" + '{}'.format(mesa) + '.json'
path = "./"
out_path = './results/'
