def test_estimator_keras_save_load(self):
import zoo.orca.data.pandas
tf.reset_default_graph()
model = self.create_model()
file_path = os.path.join(self.resource_path, "orca/learn/ncf.csv")
data_shard = zoo.orca.data.pandas.read_csv(file_path)
def transform(df):
result = {
"x": (df['user'].to_numpy().reshape([-1, 1]),
df['item'].to_numpy().reshape([-1, 1])),
"y": df['label'].to_numpy()
}
return result
data_shard = data_shard.transform_shard(transform)
est = Estimator.from_keras(keras_model=model)
est.fit(data=data_shard,
batch_size=8,
epochs=10,
validation_data=data_shard)
eval_result = est.evaluate(data_shard)
print(eval_result)
temp = tempfile.mkdtemp()
model_path = os.path.join(temp, 'test.h5')
est.save_keras_model(model_path)
tf.reset_default_graph()
from tensorflow.python.keras import models
from zoo.common.utils import load_from_file
def load_func(file_path):
return models.load_model(file_path)
model = load_from_file(load_func, model_path)
est = Estimator.from_keras(keras_model=model)
data_shard = zoo.orca.data.pandas.read_csv(file_path)
def transform(df):
result = {
"x": (df['user'].to_numpy().reshape([-1, 1]),
df['item'].to_numpy().reshape([-1, 1])),
}
return result
data_shard = data_shard.transform_shard(transform)
predictions = est.predict(data_shard).collect()
assert predictions[0]['prediction'].shape[1] == 2
shutil.rmtree(temp)
def load_keras_model(path):
"""
Create Estimator by loading an existing keras model (with weights) from HDF5 file.
:param path: String. The path to the pre-defined model.
:return: Orca TF Estimator.
"""
from tensorflow.python.keras import models
def load_func(file_path):
return models.load_model(file_path)
model = load_from_file(load_func, path)
return Estimator.from_keras(keras_model=model)