def run_census(flags_obj):
"""Construct all necessary functions and call run_loop.
Args:
flags_obj: Object containing user specified flags.
"""
if flags_obj.download_if_missing:
census_dataset.download(flags_obj.data_dir)
train_file = os.path.join(flags_obj.data_dir, census_dataset.TRAINING_FILE)
test_file = os.path.join(flags_obj.data_dir, census_dataset.EVAL_FILE)
# Train and evaluate the model every `flags.epochs_between_evals` epochs.
def train_input_fn():
return census_dataset.input_fn(train_file,
flags_obj.epochs_between_evals, True,
flags_obj.batch_size)
def eval_input_fn():
return census_dataset.input_fn(test_file, 1, False,
flags_obj.batch_size)
tensors_to_log = {
'average_loss': '{loss_prefix}head/truediv',
'loss': '{loss_prefix}head/weighted_loss/Sum'
}
wide_deep_run_loop.run_loop(
name="Census Income",
train_input_fn=train_input_fn,
eval_input_fn=eval_input_fn,
model_column_fn=census_dataset.build_model_columns,
build_estimator_fn=build_estimator,
flags_obj=flags_obj,
tensors_to_log=tensors_to_log,
early_stop=True)
return child
def easy_input_function(df, label_key, num_epochs, shuffle, batch_size):
label = df[label_key]
ds = tf.data.Dataset.from_tensor_slices((dict(df), label))
if shuffle:
ds = ds.shuffle(10000)
ds = ds.batch(batch_size).repeat(num_epochs)
return ds
census_dataset.download("/tmp/census_data/")
if "PYTHONPATH" in os.environ:
os.environ['PYTHONPATH'] += os.pathsep + models_path
else:
os.environ['PYTHONPATH'] = models_path
train_file = "/tmp/census_data/adult.data"
test_file = "/tmp/census_data/adult.test"
train_df = pandas.read_csv(train_file,
header=None,
names=census_dataset._CSV_COLUMNS)
test_df = pandas.read_csv(test_file,
header=None,
names=census_dataset._CSV_COLUMNS)