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)
validation_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(64, 64),
batch_size=batch_size,
class_mode='categorical')
validation_generator = validation_datagen.flow_from_directory(
validation_data_dir,
target_size=(64, 64),
batch_size=batch_size,
class_mode='categorical',
shuffle = False)
class_weights = class_weight.compute_class_weight(
'balanced',
np.unique(train_generator.classes),
train_generator.classes)
print(class_weights)
print(train_generator.class_indices)
step_per_epoch = len(glob.glob(os.path.join(train_data_dir, '*/*'))) // batch_size
validation_steps = len(glob.glob(os.path.join(validation_data_dir, '*/*'))) // batch_size
print(step_per_epoch)
print(validation_steps)
model.fit_generator(generator=train_generator, epochs=50, verbose=1, class_weight=class_weights, steps_per_epoch=step_per_epoch,\
callbacks=[earlyStopping, mcp_save, reduce_lr], validation_data=validation_generator, validation_steps=validation_steps)
