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)
# 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 keras
from keras.models import load_model
from keras.preprocessing import image
from keras.preprocessing.image import load_img
from mobilenet import MobileNet
from keras.applications.imagenet_utils import decode_predictions
img_file = 'demo.png'
img = load_img(img_file, target_size=(32, 32))
image = image.img_to_array(img)
image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))
model = MobileNet()
model.load_weights('mobileV1-lite.h5')
# model.summary()
result = model.predict(image)
print(result)
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/'
exists = os.path.isfile(path + fname)
if not exists:
print("No se encontró el scan de la mesa ", mesa, ", boleta ", boleta)
return None
