def main(model_id):
import tensorflow as tf
from tf_template.model import get_builder
tf.logging.set_verbosity(tf.logging.INFO)
builder = get_builder('example')
evaluation = builder.eval()
print(evaluation)
def main(model_id):
import tensorflow as tf
import tf_toolbox.testing
from tf_template.model import get_builder
builder = get_builder(model_id)
def get_train_op():
features, labels = builder.get_train_inputs()
return builder.get_estimator_spec(features, labels,
tf.estimator.ModeKeys.TRAIN).train_op
update_ops_run = tf_toolbox.testing.do_update_ops_run(get_train_op)
tf_toolbox.testing.report_train_val_changes(get_train_op)
if update_ops_run:
print('Update ops run :)')
else:
print('Update ops not run :(')
def main(model_id, skip_runs=10):
import os
import tensorflow as tf
from tf_template.model import get_builder
from tf_toolbox.profile import create_profile
builder = get_builder(model_id)
def graph_fn():
mode = tf.estimator.ModeKeys.TRAIN
features, labels = builder.get_inputs(mode)
spec = builder.get_estimator_spec(features, labels, mode)
return spec.train_op
folder = os.path.join(os.path.realpath(os.path.dirname(__file__)),
'_profiles')
if not os.path.isdir(folder):
os.makedirs(folder)
filename = os.path.join(folder, '%s.json' % model_id)
create_profile(graph_fn, filename, skip_runs)
def main(model_id, mode):
from tf_template.model import get_builder
builder = get_builder(model_id)
builder.vis_inputs()
def main(model_id):
import tensorflow as tf
from tf_template.model import get_builder
tf.logging.set_verbosity(tf.logging.INFO)
builder = get_builder('example')
builder.vis_predictions()
def main(model_id, max_steps):
import tensorflow as tf
from tf_template.model import get_builder
tf.logging.set_verbosity(tf.logging.INFO)
builder = get_builder('example')
builder.train(max_steps=max_steps)