Evaluating trained models

In this section, we will take the already trained model from the previous section and launch a batch inference job on the test data. The first step here will be to load our test data into a Jupyter Notebook:

from io import BytesIO
import numpy as np
from tensorflow.python.lib.io import file_io
dest = 'gs://data-bucket-417812395597/'
test_x = np.load(BytesIO(file_io.read_file_to_string(dest+'test_x',\
    binary_mode=True)))
test_y = np.load(BytesIO(file_io.read_file_to_string(dest+'test_y',\
    binary_mode=True)))
print(test_x.shape, test_y.shape)

The next step is to create a JSON payload of instances from our test data and save it in a cloud storage location. The batch inference module will be able to read these instances and perform inference:

import json
BATCH_PREDICTION_INSTANCES_FILE = "batch_prediction_instances.jsonl"
BATCH_PREDICTION_GCS_SOURCE = (
  ...