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
)