def rename_layers(model, scope=None):
if not scope:
scope = model.scope
for layer in model.layers:
if is_input_layer(layer) or layer.name.startswith(scope):
continue
layer.name = '%s/%s' % (scope, layer.name)
def rename_layers(model, scope=None):
if not scope:
scope = model.scope
for layer in model.layers:
if is_input_layer(layer) or layer.name.startswith(scope):
continue
layer.name = '%s/%s' % (scope, layer.name)
def set_trainability(self, model):
opts = self.opts
trainable = []
not_trainable = []
if opts.fine_tune:
not_trainable.append('.*')
elif opts.train_models:
not_trainable.append('.*')
for name in opts.train_models:
trainable.append('%s/' % name)
if opts.freeze_filter:
not_trainable.append(mod.get_first_conv_layer(model.layers).name)
if not trainable and opts.trainable:
trainable = opts.trainable
if not not_trainable and opts.not_trainable:
not_trainable = opts.not_trainable
if not trainable and not not_trainable:
return
table = OrderedDict()
table['layer'] = []
table['trainable'] = []
for layer in model.layers:
if is_input_layer(layer) or is_output_layer(layer, model):
continue
if not hasattr(layer, 'trainable'):
continue
for regex in not_trainable:
if re.match(regex, layer.name):
layer.trainable = False
for regex in trainable:
if re.match(regex, layer.name):
layer.trainable = True
table['layer'].append(layer.name)
table['trainable'].append(layer.trainable)
print('Layer trainability:')
print(format_table(table))
print()
def set_trainability(self, model):
opts = self.opts
trainable = []
not_trainable = []
if opts.fine_tune:
not_trainable.append('.*')
elif opts.train_models:
not_trainable.append('.*')
for name in opts.train_models:
trainable.append('%s/' % name)
if opts.freeze_filter:
not_trainable.append(mod.get_first_conv_layer(model.layers).name)
if not trainable and opts.trainable:
trainable = opts.trainable
if not not_trainable and opts.not_trainable:
not_trainable = opts.not_trainable
if not trainable and not not_trainable:
return
table = OrderedDict()
table['layer'] = []
table['trainable'] = []
for layer in model.layers:
if is_input_layer(layer) or is_output_layer(layer, model):
continue
if not hasattr(layer, 'trainable'):
continue
for regex in not_trainable:
if re.match(regex, layer.name):
layer.trainable = False
for regex in trainable:
if re.match(regex, layer.name):
layer.trainable = True
table['layer'].append(layer.name)
table['trainable'].append(layer.trainable)
print('Layer trainability:')
print(format_table(table))
print()