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()