def _finish_init(self): self.final_num_epoch = self.num_epoch self.curr_model = [] self.divide_layers_to_stack() self.conv_stack = net.FastNet.split_conv_to_stack(self.conv_params) self.fc_stack = net.FastNet.split_fc_to_stack(self.fc_params) self.fc_tmp = [self.fc_stack['fc8'][0], self.softmax_param] del self.fc_stack['fc8'] self.stack = self.fc_stack self.initialize_model() pprint.pprint(self.stack) self.num_epoch = self.frag_epoch net = parser.load_from_checkpoint(param_file, checkpoint_dumper.get_checkpoint(), image_shape)
train_range = range(101, 1301) #1,2,3,....,40 test_range = range(1, 101) #41, 42, ..., 48 data_provider = 'imagenet' train_dp = data.get_by_name(data_provider)(data_dir,train_range) test_dp = data.get_by_name(data_provider)(data_dir, test_range) checkpoint_dumper = trainer.CheckpointDumper(checkpoint_dir, test_id) save_freq = 100 test_freq = 100 adjust_freq = 100 factor = 1 num_epoch = 5 learning_rate = 0.1 batch_size = 128 image_color = 3 image_size = 224 image_shape = (image_color, image_size, image_size, batch_size) net = parser.load_from_checkpoint(param_file, checkpoint_dumper.get_checkpoint(), image_shape) param_dict = globals() print type(param_dict) t = trainer.Trainer(**param_dict) t.train()
output_method = 'disk' train_range = range(101, 1301) #1,2,3,....,40 test_range = range(1, 101) #41, 42, ..., 48 data_provider = 'imagenet' train_dp = data.get_by_name(data_provider)(data_dir, train_range) test_dp = data.get_by_name(data_provider)(data_dir, test_range) checkpoint_dumper = trainer.CheckpointDumper(checkpoint_dir, test_id) save_freq = 100 test_freq = 100 adjust_freq = 100 factor = 1 num_epoch = 5 learning_rate = 0.1 batch_size = 128 image_color = 3 image_size = 224 image_shape = (image_color, image_size, image_size, batch_size) net = parser.load_from_checkpoint(param_file, checkpoint_dumper.get_checkpoint(), image_shape) param_dict = globals() print type(param_dict) t = trainer.Trainer(**param_dict) t.train()