def model_init(input_shape, **kwargs):
from keras.applications import Xception
from keras import backend
assert (backend.backend() == 'tensorflow')
assert (len(input_shape) == 3 and input_shape[2] == 3)
backend.set_image_data_format('channels_last')
fix_base = False
if not fix_base:
warn('%s model fix_base=False, training may take a long time' %
_model_id)
max_block = kwargs.pop('max_block', MAX_BLOCK)
base_model = Xception(input_shape=input_shape,
weights="imagenet",
include_top=False,
pooling="avg")
n_layers = len(base_model.layers)
max_layer = kwargs.pop('max_layer', n_layers)
max_layer = min(n_layers,
max_layer) if max_layer > 0 else n_layers + max_layer
# pop any unwanted top layers
for i in range(n_layers - max_layer):
base_model.pop()
if fix_base:
trainable_layers, fixed_layers = [], []
print('Fixing %s base_model layers' % _model_id)
# first: train only the top layers (which were randomly initialized)
trainable = False # fix blocks until we hit max_block
for i, layer in enumerate(base_model.layers):
lname = layer.name
if not trainable and max_block < MAX_BLOCK:
if lname.startswith('block'):
spl = lname.split('_')
lid = int(spl[0].replace('block', ''))
if lid > max_block:
trainable = True
print(
'All layers starting at layer %d ("%s") trainable'
% (i, lname))
layer.trainable = trainable
if trainable:
trainable_layers.append((i, lname))
else:
fixed_layers.append((i, lname))
print('Trainable layers:', trainable_layers)
print('Fixed layers:', fixed_layers)
return dict(model=base_model, lr_mult=0.25)
def model_init(input_shape, **kwargs):
from keras.applications import Xception
from keras import backend
assert (backend.backend() == 'tensorflow')
assert (len(input_shape) == 3 and input_shape[2] == 3)
backend.set_image_data_format('channels_last')
fix_base = kwargs.pop('fix_base', True)
if not fix_base:
warn('%s model fix_base=False, training may take a long time' %
_model_id)
max_block = kwargs.pop('max_block', MAX_BLOCK)
print(_model_id + ' input_shape: "%s"' % str((input_shape)))
base_model = Xception(input_shape=input_shape,
weights="imagenet",
include_top=False,
pooling="avg")
n_layers = len(base_model.layers)
max_layer = kwargs.pop('max_layer', n_layers)
max_layer = min(n_layers,
max_layer) if max_layer > 0 else n_layers + max_layer
# pop any unwanted top layers
n_pop = n_layers - max_layer
if n_pop != 0:
print('Popping %d layers' % n_pop)
for i in range(n_pop):
base_model.pop()
if fix_base:
print('Fixing %s base_model layers' % _model_id)
# first: train only the top layers (which were randomly initialized)
trainable = False # fix blocks until we hit max_block
for i, layer in enumerate(base_model.layers):
lname = layer.name
if not trainable and max_block <= MAX_BLOCK:
if lname.startswith('block'):
lid = int(lname.split('_')[0].replace('block', ''))
if lid > max_block:
trainable = True
base_model.layers[i - 1].trainable = trainable
layer.trainable = trainable
return dict(model=base_model, lr_mult=1.0)
