def test_train_model_local(self):
with tempfile.TemporaryDirectory() as temp_dir_name:
num_records = 1000
columns = [
"sepal_length",
"sepal_width",
"petal_length",
"petal_width",
"class",
]
training_data = create_iris_csv_file(
size=num_records, columns=columns, temp_dir=temp_dir_name
)
validation_data = create_iris_csv_file(
size=num_records, columns=columns, temp_dir=temp_dir_name
)
data_reader_params = (
'columns=["sepal_length", "sepal_width", "petal_length",'
'"petal_width", "class"]; sep=","'
)
args = LocalExecutorArgs(
num_epochs=1,
minibatch_size=32,
training_data=training_data,
validation_data=validation_data,
evaluation_steps=10,
model_zoo=_model_zoo_path,
model_def=_iris_dnn_def,
dataset_fn="dataset_fn",
loss="loss",
optimizer="optimizer",
eval_metrics_fn="eval_metrics_fn",
model_params="",
prediction_outputs_processor="PredictionOutputsProcessor",
data_reader_params=data_reader_params,
num_minibatches_per_task=5,
callbacks="callbacks",
)
local_executor = LocalExecutor(args)
train_tasks = local_executor._gen_tasks(
local_executor.training_data
)
validation_tasks = local_executor._gen_tasks(
local_executor.validation_data
)
train_dataset = local_executor._get_dataset(train_tasks)
features, labels = next(iter(train_dataset))
loss = local_executor._train(features, labels)
self.assertEqual(type(loss.numpy()), np.float32)
validation_dataset = local_executor._get_dataset(validation_tasks)
metrics = local_executor._evaluate(validation_dataset)
self.assertEqual(list(metrics.keys()), ["accuracy"])
def test_csv_data_reader(self):
with tempfile.TemporaryDirectory() as temp_dir_name:
num_records = 128
columns = [
"sepal_length",
"sepal_width",
"petal_length",
"petal_width",
"class",
]
iris_file_name = create_iris_csv_file(
size=num_records, columns=columns, temp_dir=temp_dir_name
)
csv_data_reader = CSVDataReader(columns=columns, sep=",")
task = _MockedTask(
0, num_records, iris_file_name, elasticdl_pb2.TRAINING
)
def _gen():
for record in csv_data_reader.read_records(task):
yield record
def _dataset_fn(dataset, mode, metadata):
def _parse_data(record):
features = tf.strings.to_number(record[0:-1], tf.float32)
label = tf.strings.to_number(record[-1], tf.float32)
return features, label
dataset = dataset.map(_parse_data)
dataset = dataset.batch(10)
return dataset
dataset = tf.data.Dataset.from_generator(
_gen, csv_data_reader.records_output_types
)
dataset = _dataset_fn(dataset, None, None)
for features, labels in dataset:
self.assertEqual(features.shape.as_list(), [10, 4])
self.assertEqual(labels.shape.as_list(), [10])
break
def test_csv_data_reader(self):
with tempfile.TemporaryDirectory() as temp_dir_name:
num_records = 128
columns = [
"sepal_length",
"sepal_width",
"petal_length",
"petal_width",
"class",
]
iris_file_name = create_iris_csv_file(size=num_records,
columns=columns,
temp_dir=temp_dir_name)
csv_data_reader = TextDataReader(filename=iris_file_name,
records_per_task=20)
shards = csv_data_reader.create_shards()
self.assertEqual(len(shards), 7)
task = _Task(iris_file_name, 0, 20, elasticai_api_pb2.TRAINING)
record_count = 0
for record in csv_data_reader.read_records(task):
record_count += 1
self.assertEqual(csv_data_reader.get_size(), num_records)
self.assertEqual(record_count, 20)
