def run(name, epochs=100, verbose=1, limit_data=False, dataset_type='cifar10'):
try:
import keras
import tensorflow as tf
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
from Config import MyConfig
from Model import MyModel
from keras.utils.generic_utils import get_custom_objects
from Model import IdentityConv, GroupIdentityConv
get_custom_objects()['IdentityConv'] = IdentityConv
get_custom_objects()['GroupIdentityConv'] = GroupIdentityConv
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
with sess.as_default():
config = MyConfig(name=name,
epochs=epochs,
verbose=verbose,
clean=False,
limit_data=limit_data,
dataset_type=dataset_type)
# device = 0
# with tf.device(device):
model = MyModel(config=config,
model=keras.models.load_model(config.model_path))
logger.debug('model {} start fit epochs {}'.format(name, epochs))
score = model.comp_fit_eval()
keras.models.save_model(model.model, model.config.model_path)
return name, score
except Exception as inst:
print 'INST is'
print str(inst)
errors = [
'ResourceExhaustedError', 'Resource exhausted: OOM', 'OOM',
'Failed to create session', 'CUDNN_STATUS_INTERNAL_ERROR', 'Chunk',
'CUDA_ERROR_OUT_OF_MEMORY'
]
for error in errors:
if error in str(inst):
return NOMEM, NOMEM
return None, None
'filters': 64
}], ["MaxPooling2D", 'maxpooling2d1', {}],
["Group", 'Group1', {
'filters': 120,
'group_num': 2
}], ["Conv2D_Pooling", 'Conv2D_Pooling_1', {
'filters': 139
}], ["Conv2D", 'Conv2D3', {
'filters': 10
}], ['GlobalMaxPooling2D', 'GlobalMaxPooling2D1', {}],
['Activation', 'Activation1', {
'activation_type': 'softmax'
}]]
graph = MyGraph(model_l)
before_model = MyModel(config, graph)
before_model.comp_fit_eval()
net2net = Net2Net()
model = net2net.wider_group_conv2d(before_model,
layer_name='Group1',
new_width=174,
config=config.copy('wider_group'))
model.comp_fit_eval()
'''
model = net2net.deeper(before_model, config=config.copy('deeper1'))
model = net2net.deeper_conv2d(before_model, layer_name='Conv2D2', config=config.copy('deeper1'))
model = net2net.wider_conv2d(model, layer_name='Conv2D2', width_ratio=2, config=config.copy('wide'))
model.comp_fit_eval()
model = net2net.wider(model, config=config)
