Tensorflow

def create_embed(sparse_col):
    dim = 10 # default
    if hasattr(sparse_col, 'bucket_size'):
       nbins = sparse_col.bucket_size
       if nbins is not None:
          dim = 1 + int(round(np.log2(nbins)))
    return tflayers.embedding_column(sparse_col, dimension=dim)

def dnn_model(output_dir):
    real, sparse = get_features()
    all = {}
    all.update(real)

    # create embeddings of the sparse columns
    embed = {
       colname : create_embed(col) \
          for colname, col in sparse.items()
    }
    all.update(embed)

    estimator = tflearn.DNNClassifier(
         model_dir=output_dir,
         feature_columns=all.values(),
         hidden_units=[64, 16, 4])
    estimator = tf.contrib.estimator.add_metrics(estimator, my_rmse)
    return estimator

## Logs

2021-02-25 11:57:47.592 IST
ps-replica-1
"Cancellation requested for RunGraph."
Info
2021-02-25 11:57:47.630 IST
worker-replica-2
"loss = 169.66252, step = 1002"
Info
2021-02-25 11:57:47.674 IST
worker-replica-2
"Loss for final step: 169.66252."
Info
2021-02-25 11:57:47.837 IST
worker-replica-1
"loss = 169.63147, step = 1002"
Info
2021-02-25 11:57:47.868 IST
worker-replica-2
"model dir gs://qwiklabs-gcp-00-dfadb4cd0b7b/flights/chapter9/output"
Info
2021-02-25 11:57:47.881 IST
worker-replica-1
"Loss for final step: 169.63147."
Info
2021-02-25 11:57:47.898 IST
worker-replica-2
"Module completed; cleaning up."
Info
2021-02-25 11:57:47.898 IST
worker-replica-2
"Clean up finished."
Info
2021-02-25 11:57:47.899 IST
worker-replica-2
"Task completed successfully."
Warning
2021-02-25 11:57:47.948 IST
worker-replica-0
"Training with estimator made no steps. Perhaps input is empty or misspecified."
Info
2021-02-25 11:57:47.949 IST
worker-replica-0
"Loss for final step: None."
Info
2021-02-25 11:57:48.080 IST
worker-replica-1
"model dir gs://qwiklabs-gcp-00-dfadb4cd0b7b/flights/chapter9/output"

def parse_hidden_units(s):
    return [int(item) for item in s.split(',')]

def wide_and_deep_model(output_dir,nbuckets=5,
                        hidden_units='64,32', learning_rate=0.01):
    real, sparse = get_features()
    # lat/lon cols can be discretized to "air traffic corridors"
    latbuckets = np.linspace(20.0, 50.0, nbuckets).tolist()
    lonbuckets = np.linspace(-120.0, -70.0, nbuckets).tolist()
    disc = {}
    disc.update({
       'd_{}'.format(key) : \
           tflayers.bucketized_column(real[key], latbuckets) \
           for key in ['dep_lat', 'arr_lat']
    })
    disc.update({
       'd_{}'.format(key) : \
           tflayers.bucketized_column(real[key], lonbuckets) \
           for key in ['dep_lon', 'arr_lon']
    })
    # cross columns that make sense in combination
    sparse['dep_loc'] = tflayers.crossed_column( \
           [disc['d_dep_lat'], disc['d_dep_lon']],\
           nbuckets*nbuckets)
    sparse['arr_loc'] = tflayers.crossed_column( \
           [disc['d_arr_lat'], disc['d_arr_lon']],\
           nbuckets*nbuckets)
    sparse['dep_arr'] = tflayers.crossed_column( \
           [sparse['dep_loc'], sparse['arr_loc']],\
           nbuckets ** 4)
    sparse['ori_dest'] = tflayers.crossed_column( \
           [sparse['origin'], sparse['dest']], \
           hash_bucket_size=1000)

    # create embeddings of all the sparse columns
    embed = {
       colname : create_embed(col) \
          for colname, col in sparse.items()
    }
    real.update(embed)

    #lin_opt=tf.train.FtrlOptimizer(learning_rate=learning_rate)
    #l_rate=learning_rate*0.25
    #dnn_opt=tf.train.AdagradOptimizer(learning_rate=l_rate)
    estimator = tflearn.DNNLinearCombinedClassifier(
         model_dir=output_dir,
         linear_feature_columns=sparse.values(),
         dnn_feature_columns=real.values(),
         dnn_hidden_units=parse_hidden_units(hidden_units))
         #linear_optimizer=lin_opt,
         #dnn_optimizer=dnn_opt)
    estimator = tf.contrib.estimator.add_metrics(estimator, my_rmse)
    return estimator

$ export JOBNAME=learn_rate_flights_$(date -u +%y%m%d_%H%M%S)
$ gcloud ai-platform jobs submit training $JOBNAME   \
  --module-name=trainer.task \
  --package-path=$(pwd)/flights/trainer \
  --job-dir=$OUTPUT_DIR \
  --staging-bucket=gs://$BUCKET \
  --region=$REGION \
  --scale-tier=STANDARD_1 \
  --runtime-version=1.15 \
  -- \
  --output_dir=$OUTPUT_DIR \
  --traindata $DATA_DIR/train* \
  --evaldata $DATA_DIR/test*

$ gcloud ai-platform versions create v1 --model flights \
>                                     --origin ${MODEL_LOCATION} \
>                                     --runtime-version 1.15 \
>                                     --region global

# pip install --upgrade google-api-python-client
# pip install --upgrade oauth2client
>>> from oauth2client.client import GoogleCredentials
>>> import os
>>> import json
>>> credentials = GoogleCredentials.get_application_default()
>>> api = discovery.build('ml', 'v1', credentials=credentials,
...       discoveryServiceUrl=
...      'https://storage.googleapis.com/cloud-ml/discovery/ml_v1_discovery.json')

>>> PROJECT = 'qwiklabs-gcp-00-dfadb4cd0b7b'
>>> parent = 'projects/%s/models/%s/versions/%s' % (PROJECT, 'flights', 'v1')
>>> request_data = {'instances':
...   [
...       {
...         'dep_delay': 16.0,
...         'taxiout': 13.0,
...         'distance': 160.0,
...         'avg_dep_delay': 13.34,
...         'avg_arr_delay': 67.0,
...         'carrier': 'AS',
...         'dep_lat': 61.17,
...         'dep_lon': -150.00,
...         'arr_lat': 60.49,
...         'arr_lon': -145.48,
...         'origin': 'ANC',
...         'dest': 'CDV'
...       }
...   ]
... }
>>> response = api.projects().predict(body=request_data, name=parent).execute()
>>> print ("response={0}".format(response))
response={u'predictions': [{u'all_class_ids': [0, 1], u'all_classes': [u'0', u'1'], u'probabilities': [0.9874195456504822, 0.01258043386042118], u'classes': [u'0'], u'logistic': [0.012580434791743755], u'logits': [-4.36295223236084], u'class_ids': [0]}]}

python -m scripts.retrain \
  --bottleneck_dir=tf_files/bottlenecks \
  --how_many_training_steps=500 \
  --model_dir=tf_files/models/ \
  --summaries_dir=tf_files/training_summaries/"${ARCHITECTURE}" \
  --output_graph=tf_files/retrained_graph.pb \
  --output_labels=tf_files/retrained_labels.txt \
  --architecture="${ARCHITECTURE}" \
  --image_dir=tf_files/flower_photos