def load_estimator(self): """ Returns ------- estimator A tf.estimator.DNNClassifier """ # Feature columns describe how to use the input. my_feature_columns = [] for key in self.train_x.keys(): my_feature_columns.append( tf.feature_column.numeric_column(key=key)) run_config = RunConfig() run_config = run_config.replace(model_dir=self.model_path) return tf.estimator.DNNClassifier( feature_columns=my_feature_columns, # Two hidden layers of 10 nodes each. hidden_units=[10, 10], # The model must choose between 3 classes. n_classes=3, # Use runconfig to load model, config=run_config, model_dir=self.model_path)
def main(): tf.logging.set_verbosity(tf.logging.DEBUG) parsed_args = get_parser().parse_args() session_config = tf.ConfigProto(allow_soft_placement=True) session_config.gpu_options.allow_growth = True run_config = RunConfig(session_config=session_config) run_config = run_config.replace(model_dir=get_model_dir(parsed_args)) params = HParams(learning_rate=parsed_args.lr, train_steps=parsed_args.train_steps, steps_per_eval=parsed_args.steps_per_eval, batch_size=parsed_args.batch_size, vgg_model_path=parsed_args.vgg_model_path, selector=parsed_args.selector, dropout=parsed_args.dropout, ctx2out=parsed_args.ctx2out, prev2out=parsed_args.prev2out, dataset=parsed_args.dataset, eval_steps=parsed_args.eval_steps, hard_attention=parsed_args.hard_attention, use_sampler=parsed_args.use_sampler, bin_size=14) learn_runner.run(experiment_fn=experiment_fn_inner, run_config=run_config, schedule="continuous_train_and_eval", hparams=params)
args = parser.parse_args() # Input pipe settings input_param = { 'data_dir': args.data_dir, 'batch_size': args.batch_size, 'buffer_size': args.buffer_size, 'epochs': args.train_epochs, 'num_parallel_calls': args.num_parallel_calls, 'img_sizes': input_pipe.get_tf_record_image_size(args.data_dir), 'padding': args.unet_padding } # Create run configuration default run_config = RunConfig() run_config = run_config.replace(model_dir=os.path.join(args.output_dir, args.model_type)) run_config = run_config.replace(save_summary_steps=args.save_summary_steps) run_config = run_config.replace(save_checkpoints_steps=args.save_checkpoints_steps) # Define model and input parameters hparams = HParams( learning_rate=args.learning_rate, l2_gain=args.l2_gain, model_type=args.model_type, rmsprop_momentum=args.rmsprop_momentum, opt_epsilon=args.opt_epsilon, rmsprop_decay=args.rmsprop_decay, padding=args.unet_padding, optimizer=args.optimizer, model_dir=run_config.model_dir )