Membuat skrip pelatihan

%%writefile task.py  import argparse import numpy as np import os  import pandas as pd import tensorflow as tf  from google.cloud import bigquery from google.cloud import storage  # Read environmental variables training_data_uri = os.getenv("AIP_TRAINING_DATA_URI") validation_data_uri = os.getenv("AIP_VALIDATION_DATA_URI") test_data_uri = os.getenv("AIP_TEST_DATA_URI")  # Read args parser = argparse.ArgumentParser() parser.add_argument('--label_column', required=True, type=str) parser.add_argument('--epochs', default=10, type=int) parser.add_argument('--batch_size', default=10, type=int) args = parser.parse_args()  # Set up training variables LABEL_COLUMN = args.label_column  # See https://cloud.google.com/vertex-ai/docs/workbench/managed/executor#explicit-project-selection for issues regarding permissions. PROJECT_NUMBER = os.environ["CLOUD_ML_PROJECT_ID"] bq_client = bigquery.Client(project=PROJECT_NUMBER)   # Download a table def download_table(bq_table_uri: str):     # Remove bq:// prefix if present     prefix = "bq://"     if bq_table_uri.startswith(prefix):         bq_table_uri = bq_table_uri[len(prefix) :]      # Download the BigQuery table as a dataframe     # This requires the "BigQuery Read Session User" role on the custom training service account.     table = bq_client.get_table(bq_table_uri)     return bq_client.list_rows(table).to_dataframe()  # Download dataset splits df_train = download_table(training_data_uri) df_validation = download_table(validation_data_uri) df_test = download_table(test_data_uri)  def convert_dataframe_to_dataset(     df_train: pd.DataFrame,     df_validation: pd.DataFrame, ):     df_train_x, df_train_y = df_train, df_train.pop(LABEL_COLUMN)     df_validation_x, df_validation_y = df_validation, df_validation.pop(LABEL_COLUMN)      y_train = tf.convert_to_tensor(np.asarray(df_train_y).astype("float32"))     y_validation = tf.convert_to_tensor(np.asarray(df_validation_y).astype("float32"))      # Convert to numpy representation     x_train = tf.convert_to_tensor(np.asarray(df_train_x).astype("float32"))     x_test = tf.convert_to_tensor(np.asarray(df_validation_x).astype("float32"))      # Convert to one-hot representation     num_species = len(df_train_y.unique())     y_train = tf.keras.utils.to_categorical(y_train, num_classes=num_species)     y_validation = tf.keras.utils.to_categorical(y_validation, num_classes=num_species)      dataset_train = tf.data.Dataset.from_tensor_slices((x_train, y_train))     dataset_validation = tf.data.Dataset.from_tensor_slices((x_test, y_validation))     return (dataset_train, dataset_validation)  # Create datasets dataset_train, dataset_validation = convert_dataframe_to_dataset(df_train, df_validation)  # Shuffle train set dataset_train = dataset_train.shuffle(len(df_train))  def create_model(num_features):     # Create model     Dense = tf.keras.layers.Dense     model = tf.keras.Sequential(         [             Dense(                 100,                 activation=tf.nn.relu,                 kernel_initializer="uniform",                 input_dim=num_features,             ),             Dense(75, activation=tf.nn.relu),             Dense(50, activation=tf.nn.relu),             Dense(25, activation=tf.nn.relu),             Dense(3, activation=tf.nn.softmax),         ]     )      # Compile Keras model     optimizer = tf.keras.optimizers.RMSprop(lr=0.001)     model.compile(         loss="categorical_crossentropy", metrics=["accuracy"], optimizer=optimizer     )      return model  # Create the model model = create_model(num_features=dataset_train._flat_shapes[0].dims[0].value)  # Set up datasets dataset_train = dataset_train.batch(args.batch_size) dataset_validation = dataset_validation.batch(args.batch_size)  # Train the model model.fit(dataset_train, epochs=args.epochs, validation_data=dataset_validation)  tf.saved_model.save(model, os.getenv("AIP_MODEL_DIR")) 

JOB_NAME = "custom_job_unique"  EPOCHS = 20 BATCH_SIZE = 10  CMDARGS = [     "--label_column=" + LABEL_COLUMN,     "--epochs=" + str(EPOCHS),     "--batch_size=" + str(BATCH_SIZE), ]