def train_model(args):
train_features, test_features, train_labels, test_labels = \
data_utils.load_data(args)
sonar_model = model.sonar_model()
sonar_model.fit(train_features, train_labels, epochs=args.epochs,
batch_size=args.batch_size)
score = sonar_model.evaluate(test_features, test_labels,
batch_size=args.batch_size)
print(score)
# Export the trained model
sonar_model.save(args.model_name)
if args.artifacts_dir:
# Save the model to GCS
data_utils.save_artifacts(args.artifacts_dir, RUN_ID, args.model_name)
def train_model(args):
train_features, test_features, train_labels, test_labels = \
data_utils.load_data(args)
sonar_model = model.sonar_model()
keras_weight_file = load_weight_from_gcs()
sonar_model.load_weights(keras_weight_file)
sonar_model.fit(train_features,
train_labels,
epochs=args.epochs,
batch_size=args.batch_size)
score = sonar_model.evaluate(test_features,
test_labels,
batch_size=args.batch_size)
print(score)
# Export the trained model
sonar_model.save(args.model_name)
if args.model_dir:
# Save the model to GCS
data_utils.save_model(args.model_dir, args.model_name)
import datetime from google.cloud import storage import tempfile import os import numpy as np import tempfile import model client = storage.Client() bucket_name = 'output-aiplatform' folder_name = 'sonar_20210323_084454' file_name = 'sonar_model.h5' blobs = list(client.list_blobs(bucket_name, prefix=folder_name)) blob = blobs[0] _, _ext = os.path.splitext(blob.name) _, temp_local_filename = tempfile.mkstemp(suffix=_ext) blob.download_to_filename(temp_local_filename) print(temp_local_filename) sonar_model = model.sonar_model() sonar_model.load_weights(temp_local_filename) sonar_model.summary()