def define_imagenet_flags(dynamic_loss_scale=False, fp16_implementation=False): resnet_run_loop.define_resnet_flags( resnet_size_choices=['18', '34', '50', '101', '152', '200'], dynamic_loss_scale=dynamic_loss_scale, fp16_implementation=fp16_implementation) flags.adopt_module_key_flags(resnet_run_loop) flags_core.set_defaults(train_epochs=90)
def define_imagenet_flags(): resnet_run_loop.define_resnet_flags() flags.DEFINE_integer( name='train_data_size', short_name='tds', help=flags_core.help_wrap('The number of training images')) # flags.DEFINE_integer( # name='validation_data_size', short_name='vds', # help=flags_core.help_wrap('The number of validation images') # ) flags.DEFINE_integer(name='image_size', short_name='is', help=flags_core.help_wrap('size of the square image')) flags.DEFINE_integer(name='num_classes', short_name='nc', help=flags_core.help_wrap('number of classes')) flags.DEFINE_integer(name='kernel_size', short_name='ks', help=flags_core.help_wrap('convolution kernel size')) flags.DEFINE_integer(name='num_filters', short_name='is', help=flags_core.help_wrap('size of the square image')) flags.DEFINE_float(name='learning_rate', short_name='lr', help=flags_core.help_wrap('learning_rate')) flags.adopt_module_key_flags(resnet_run_loop)
def define_imagenet_flags(): resnet_run_loop.define_resnet_flags( resnet_size_choices=['18', '34', '50', '101', '152', '200']) flags.adopt_module_key_flags(resnet_run_loop) flags_core.set_defaults(train_epochs=90) flags.DEFINE_string('tfrecords_directory', None, 'should point to tfrecords part of our bucket')
def define_zj_flags(): resnet_run_loop.define_resnet_flags() flags.adopt_module_key_flags(resnet_run_loop) flags_core.set_defaults(model_dir='zj_model_3', resnet_size='32', train_epochs=250, epochs_between_evals=1, batch_size=128)
def define_cifar_flags(): resnet_run_loop.define_resnet_flags() flags.adopt_module_key_flags(resnet_run_loop) flags_core.set_defaults(data_dir='/tmp/cifar10_data', model_dir='/tmp/cifar10_model', resnet_size='32', train_epochs=250, epochs_between_evals=1, batch_size=128)
def define_cifar_flags(): resnet_run_loop.define_resnet_flags() flags.adopt_module_key_flags(resnet_run_loop) flags_core.set_defaults(data_dir='/tmp/cifar10_data', model_dir='/tmp/cifar10_model', resnet_size='32', train_epochs=250, epochs_between_evals=10, batch_size=128)
def define_cifar_flags(): resnet_run_loop.define_resnet_flags() flags.adopt_module_key_flags(resnet_run_loop) flags_core.set_defaults(data_dir='./cifar10_data', model_dir=sys.argv[1], resnet_size='32', train_epochs=250, epochs_between_evals=10, batch_size=BATCH_SIZE)
def define_cifar_flags(): resnet_run_loop.define_resnet_flags() flags.adopt_module_key_flags(resnet_run_loop) flags_core.set_defaults(data_dir='/tmp/cifar10_data/cifar-10-batches-bin', model_dir='/tmp/cifar10_model', resnet_size='56', train_epochs=182, epochs_between_evals=10, batch_size=128, image_bytes_as_serving_input=False)
def define_cifar_flags(): resnet_run_loop.define_resnet_flags() flags.adopt_module_key_flags(resnet_run_loop) flags_core.set_defaults(data_dir='/content/my_drive/tmp/TIMBERLAND_data', model_dir='/content/my_drive/tmp/TIMBERLAND_model', resnet_size='56', train_epochs=182, epochs_between_evals=10, batch_size=3, image_bytes_as_serving_input=False)
def define_imagenet_flags(): resnet_run_loop.define_resnet_flags( resnet_size_choices=['18', '34', '50', '101', '152', '200']) flags.adopt_module_key_flags(resnet_run_loop) flags_core.set_defaults(model_dir=path_to_resnet_model, resnet_size='50', train_epochs=100, epochs_between_evals=20, batch_size=64, use_synthetic_data=True)
def define_mydata_flags(): resnet_run_loop.define_resnet_flags( resnet_size_choices=['18', '34', '50', '101', '152', '200'], dynamic_loss_scale=True, fp16_implementation=True) flags.adopt_module_key_flags(resnet_run_loop) flags_core.set_defaults( train_epochs=20, use_train_and_evaluate=False, data_format='channels_last', ) flags_core.set_defaults(data_dir='_path/record/')
def define_cifar_flags(): ## input flags flags.DEFINE_string("job_name", "", "Either 'ps' or 'worker'") flags.DEFINE_integer("task_index", 0, "Index of task within the job") FLAGS = flags.FLAGS # try make all flags as tf.Variable - not here resnet_run_loop.define_resnet_flags() flags.adopt_module_key_flags(resnet_run_loop) flags_core.set_defaults(data_dir='/tmp/cifar10_data', model_dir='/tmp/cifar10_model', resnet_size='8', train_epochs=1, epochs_between_evals=1, batch_size=1, image_bytes_as_serving_input=False)
def define_imagenet_flags(): resnet_run_loop.define_resnet_flags( resnet_size_choices=['18', '34', '50', '101', '152', '200']) flags.adopt_module_key_flags(resnet_run_loop) flags_core.set_defaults(train_epochs=90)
def define_imagenet_flags(): resnet_run_loop.define_resnet_flags( resnet_size_choices=['18', '34', '50', '101', '152', '200']) flags.adopt_module_key_flags(resnet_run_loop) flags_core.set_defaults(train_epochs=100)
def define_imagenet_flags(dynamic_loss_scale=False): resnet_run_loop.define_resnet_flags( resnet_size_choices=['18', '34', '50', '101', '152', '200'], dynamic_loss_scale=dynamic_loss_scale) flags.adopt_module_key_flags(resnet_run_loop) flags_core.set_defaults(train_epochs=90)