I am able to train my model and use ML Engine for prediction but my results don't include any identifying information. This works fine when submitting one row at a time for prediction but when submitting multiple rows I have no way of connecting the prediction back to the original input data. The GCP documentation discusses using instance keys but I can't find any example code that trains and predicts using an instance key. Taking the GCP census example how would I update the input functions to pass a unique ID through the graph and ignore it during training yet return the unique ID with predictions? Or alternatively if anyone knows of a different example already using keys that would help as well.
def serving_input_fn():
feature_placeholders = {
column.name: tf.placeholder(column.dtype, [None])
for column in INPUT_COLUMNS
}
features = {
key: tf.expand_dims(tensor, -1)
for key, tensor in feature_placeholders.items()
}
return input_fn_utils.InputFnOps(
features,
None,
feature_placeholders
)
def generate_input_fn(filenames,
num_epochs=None,
shuffle=True,
skip_header_lines=0,
batch_size=40):
def _input_fn():
files = tf.concat([
tf.train.match_filenames_once(filename)
for filename in filenames
], axis=0)
filename_queue = tf.train.string_input_producer(
files, num_epochs=num_epochs, shuffle=shuffle)
reader = tf.TextLineReader(skip_header_lines=skip_header_lines)
_, rows = reader.read_up_to(filename_queue, num_records=batch_size)
row_columns = tf.expand_dims(rows, -1)
columns = tf.decode_csv(row_columns, record_defaults=CSV_COLUMN_DEFAULTS)
features = dict(zip(CSV_COLUMNS, columns))
# Remove unused columns
for col in UNUSED_COLUMNS:
features.pop(col)
if shuffle:
features = tf.train.shuffle_batch(
features,
batch_size,
capacity=batch_size * 10,
min_after_dequeue=batch_size*2 + 1,
num_threads=multiprocessing.cpu_count(),
enqueue_many=True,
allow_smaller_final_batch=True
)
label_tensor = parse_label_column(features.pop(LABEL_COLUMN))
return features, label_tensor
return _input_fn
Update: I was able to use the suggested code from this answer below I just needed to alter it slightly to update the output alternatives in the model_fn_ops instead of just the prediction dict. However, this only works if my serving input function is coded for json inputs similar to this. My serving input function was previously modeled after the CSV serving input function in the Census Core Sample.
I think my problem is coming from the build_standardized_signature_def function and even more so the is_classification_problem function that it calls. The input dict length using the csv serving function is 1 so this logic ends up using the classification_signature_def which only ends up displaying the scores (which turns out are actually the probabilities) whereas the input dict length is greater than 1 with the json serving input function and instead the predict_signature_def is used which includes all of the outputs.