Per iniziare: addestramento e previsione con Keras

BUCKET_NAME="your-bucket-name"

REGION="us-central1"

gsutil mb -l $REGION gs://$BUCKET_NAME

# Clone the repository of AI Platform samples
git clone --depth 1 https://github.com/GoogleCloudPlatform/cloudml-samples

# Set the working directory to the sample code directory
cd cloudml-samples/census/tf-keras

# `ls` shows the working directory's contents. The `p` flag adds trailing
# slashes to subdirectory names. The `R` flag lists subdirectories recursively.
ls -pR

.:
README.md  requirements.txt  trainer/

./trainer:
__init__.py  model.py  task.py  util.py

pip install -r requirements.txt

# This is similar to `python -m trainer.task --job-dir local-training-output`
# but it better replicates the AI Platform environment, especially
# for distributed training (not applicable here).
gcloud ai-platform local train \
  --package-path trainer \
  --module-name trainer.task \
  --job-dir local-training-output

Model exported to:  local-training-output/keras_export/1553709223

JOB_NAME="my_first_keras_job"
JOB_DIR="gs://$BUCKET_NAME/keras-job-dir"

gcloud ai-platform jobs submit training $JOB_NAME \
  --package-path trainer/ \
  --module-name trainer.task \
  --region $REGION \
  --python-version 3.7 \
  --runtime-version 1.15 \
  --job-dir $JOB_DIR \
  --stream-logs

INFO    2019-03-27 17:57:11 +0000   master-replica-0        Model exported to:  gs://your-bucket-name/keras-job-dir/keras_export/1553709421
INFO    2019-03-27 17:57:11 +0000   master-replica-0        Module completed; cleaning up.
INFO    2019-03-27 17:57:11 +0000   master-replica-0        Clean up finished.
INFO    2019-03-27 17:57:11 +0000   master-replica-0        Task completed successfully.

gcloud ai-platform jobs submit training ${JOB_NAME}_hpt \
  --config hptuning_config.yaml \
  --package-path trainer/ \
  --module-name trainer.task \
  --region $REGION \
  --python-version 3.7 \
  --runtime-version 1.15 \
  --job-dir $JOB_DIR \
  --stream-logs

MODEL_NAME="my_first_keras_model"

gcloud ai-platform models create $MODEL_NAME \
  --regions $REGION

Created ml engine model [projects/your-project-id/models/my_first_keras_model].

Model exported to:  gs://your-bucket-name/keras-job-dir/keras_export/1545439782

MODEL_VERSION="v1"

# Get a list of directories in the `keras_export` parent directory. Then pick
# the directory with the latest timestamp, in case you've trained multiple
# times.
SAVED_MODEL_PATH=$(gsutil ls $JOB_DIR/keras_export | head -n 1)

# Create model version based on that SavedModel directory
gcloud ai-platform versions create $MODEL_VERSION \
  --model $MODEL_NAME \
  --region $REGION \
  --runtime-version 1.15 \
  --python-version 3.7 \
  --framework tensorflow \
  --origin $SAVED_MODEL_PATH

from trainer import util

_, _, eval_x, eval_y = util.load_data()

prediction_input = eval_x.sample(20)
prediction_targets = eval_y[prediction_input.index]

prediction_input

import pandas as pd

_, eval_file_path = util.download(util.DATA_DIR)
raw_eval_data = pd.read_csv(eval_file_path,
                            names=util._CSV_COLUMNS,
                            na_values='?')

raw_eval_data.iloc[prediction_input.index]

import json

with open('prediction_input.json', 'w') as json_file:
  for row in prediction_input.values.tolist():
    json.dump(row, json_file)
    json_file.write('\n')

cat prediction_input.json

[0.9012127751273994, 1.0, 1.525541514460902, 2.0, 9.0, 0.0, 4.0, -0.14479173735784842, -0.21713186390175285, -0.43754385253479555, 38.0]
[-0.9221541171760282, 3.0, -0.4192650914017433, 4.0, 2.0, 3.0, 4.0, -0.14479173735784842, -0.21713186390175285, -0.03403923708700391, 38.0]
[-1.2138928199445767, 3.0, -0.030303770229214273, 4.0, 10.0, 1.0, 4.0, -0.14479173735784842, -0.21713186390175285, 1.5799792247041626, 38.0]
[-0.6304154144074798, 3.0, 0.35865755094331475, 4.0, 0.0, 3.0, 4.0, -0.14479173735784842, -0.21713186390175285, -0.6796466218034705, 38.0]
[-1.5056315227131252, 3.0, -1.5861490549193304, 4.0, 7.0, 3.0, 0.0, -0.14479173735784842, -0.21713186390175285, -0.03403923708700391, 38.0]
[-0.11987268456252011, 5.0, 0.35865755094331475, 2.0, 2.0, 0.0, 4.0, -0.14479173735784842, -0.21713186390175285, -0.8410484679825871, 38.0]
[0.24480069389816542, 3.0, 1.525541514460902, 2.0, 9.0, 0.0, 4.0, -0.14479173735784842, -0.21713186390175285, 1.176474609256371, 6.0]
[0.0989313425138912, 1.0, 1.525541514460902, 2.0, 9.0, 0.0, 4.0, 0.8868473744801746, -0.21713186390175285, -0.03403923708700391, 38.0]
[0.39067004528243965, 5.0, -0.030303770229214273, 2.0, 4.0, 0.0, 4.0, -0.14479173735784842, -0.21713186390175285, 4.7273152251969375, 38.0]
[1.1200168022038106, 3.0, 1.1365801932883728, 2.0, 12.0, 0.0, 4.0, -0.14479173735784842, -0.21713186390175285, -0.03403923708700391, 38.0]
[-1.2868274956367138, 3.0, -0.030303770229214273, 4.0, 11.0, 3.0, 4.0, -0.14479173735784842, -0.21713186390175285, -1.6480576988781703, 38.0]
[-0.7033500900996169, 3.0, -0.4192650914017433, 2.0, 7.0, 5.0, 4.0, -0.14479173735784842, 4.5022796885373735, -0.43754385253479555, 38.0]
[0.5365393966667138, 3.0, 1.525541514460902, 4.0, 3.0, 4.0, 4.0, -0.14479173735784842, -0.21713186390175285, -0.03403923708700391, 38.0]
[-1.3597621713288508, 3.0, -0.4192650914017433, 4.0, 6.0, 3.0, 2.0, -0.14479173735784842, -0.21713186390175285, -0.03403923708700391, 38.0]
[0.5365393966667138, 3.0, 1.1365801932883728, 0.0, 11.0, 2.0, 2.0, -0.14479173735784842, -0.21713186390175285, -0.03403923708700391, 38.0]
[1.338820829280222, 3.0, -0.4192650914017433, 2.0, 2.0, 0.0, 4.0, -0.14479173735784842, -0.21713186390175285, -0.03403923708700391, 38.0]
[0.6824087480509881, 3.0, 1.1365801932883728, 0.0, 12.0, 3.0, 4.0, -0.14479173735784842, -0.21713186390175285, -0.03403923708700391, 38.0]
[0.0259966668217541, 3.0, 1.1365801932883728, 2.0, 11.0, 0.0, 4.0, -0.14479173735784842, -0.21713186390175285, 0.3694653783607877, 38.0]
[-0.4116113873310685, 3.0, -0.4192650914017433, 2.0, 11.0, 0.0, 4.0, -0.14479173735784842, -0.21713186390175285, 1.176474609256371, 38.0]
[0.39067004528243965, 6.0, 1.1365801932883728, 4.0, 4.0, 1.0, 4.0, -0.14479173735784842, -0.21713186390175285, -0.03403923708700391, 38.0]

gcloud ai-platform predict \
  --model $MODEL_NAME \
  --region $REGION \
  --version $MODEL_VERSION \
  --json-instances prediction_input.json

DENSE_4
[0.6854287385940552]
[0.011786997318267822]
[0.037236183881759644]
[0.016223609447479248]
[0.0012015104293823242]
[0.23621389269828796]
[0.6174039244651794]
[0.9822691679000854]
[0.3815768361091614]
[0.6715215444564819]
[0.001094043254852295]
[0.43077391386032104]
[0.22132840752601624]
[0.004075437784194946]
[0.22736871242523193]
[0.4111979305744171]
[0.27328649163246155]
[0.6981356143951416]
[0.3309604525566101]
[0.20807647705078125]

import os
from six.moves import urllib
import tempfile

import numpy as np
import pandas as pd
import tensorflow as tf

# Examine software versions
print(__import__('sys').version)
print(tf.__version__)
print(tf.keras.__version__)

### For downloading data ###

# Storage directory
DATA_DIR = os.path.join(tempfile.gettempdir(), 'census_data')

# Download options.
DATA_URL = 'https://storage.googleapis.com/cloud-samples-data/ai-platform' \
           '/census/data'
TRAINING_FILE = 'adult.data.csv'
EVAL_FILE = 'adult.test.csv'
TRAINING_URL = '%s/%s' % (DATA_URL, TRAINING_FILE)
EVAL_URL = '%s/%s' % (DATA_URL, EVAL_FILE)

### For interpreting data ###

# These are the features in the dataset.
# Dataset information: https://archive.ics.uci.edu/ml/datasets/census+income
_CSV_COLUMNS = [
    'age', 'workclass', 'fnlwgt', 'education', 'education_num',
    'marital_status', 'occupation', 'relationship', 'race', 'gender',
    'capital_gain', 'capital_loss', 'hours_per_week', 'native_country',
    'income_bracket'
]

_CATEGORICAL_TYPES = {
  'workclass': pd.api.types.CategoricalDtype(categories=[
    'Federal-gov', 'Local-gov', 'Never-worked', 'Private', 'Self-emp-inc',
    'Self-emp-not-inc', 'State-gov', 'Without-pay'
  ]),
  'marital_status': pd.api.types.CategoricalDtype(categories=[
    'Divorced', 'Married-AF-spouse', 'Married-civ-spouse',
    'Married-spouse-absent', 'Never-married', 'Separated', 'Widowed'
  ]),
  'occupation': pd.api.types.CategoricalDtype([
    'Adm-clerical', 'Armed-Forces', 'Craft-repair', 'Exec-managerial',
    'Farming-fishing', 'Handlers-cleaners', 'Machine-op-inspct',
    'Other-service', 'Priv-house-serv', 'Prof-specialty', 'Protective-serv',
    'Sales', 'Tech-support', 'Transport-moving'
  ]),
  'relationship': pd.api.types.CategoricalDtype(categories=[
    'Husband', 'Not-in-family', 'Other-relative', 'Own-child', 'Unmarried',
    'Wife'
  ]),
  'race': pd.api.types.CategoricalDtype(categories=[
    'Amer-Indian-Eskimo', 'Asian-Pac-Islander', 'Black', 'Other', 'White'
  ]),
  'native_country': pd.api.types.CategoricalDtype(categories=[
    'Cambodia', 'Canada', 'China', 'Columbia', 'Cuba', 'Dominican-Republic',
    'Ecuador', 'El-Salvador', 'England', 'France', 'Germany', 'Greece',
    'Guatemala', 'Haiti', 'Holand-Netherlands', 'Honduras', 'Hong', 'Hungary',
    'India', 'Iran', 'Ireland', 'Italy', 'Jamaica', 'Japan', 'Laos', 'Mexico',
    'Nicaragua', 'Outlying-US(Guam-USVI-etc)', 'Peru', 'Philippines', 'Poland',
    'Portugal', 'Puerto-Rico', 'Scotland', 'South', 'Taiwan', 'Thailand',
    'Trinadad&Tobago', 'United-States', 'Vietnam', 'Yugoslavia'
  ]),
  'income_bracket': pd.api.types.CategoricalDtype(categories=[
    '<=50K', '>50K'
  ])
}

# This is the label (target) we want to predict.
_LABEL_COLUMN = 'income_bracket'

### Hyperparameters for training ###

# This the training batch size
BATCH_SIZE = 128

# This is the number of epochs (passes over the full training data)
NUM_EPOCHS = 20

# Define learning rate.
LEARNING_RATE = .01

def _download_and_clean_file(filename, url):
  """Downloads data from url, and makes changes to match the CSV format.

  The CSVs may use spaces after the comma delimters (non-standard) or include
  rows which do not represent well-formed examples. This function strips out
  some of these problems.

  Args:
    filename: filename to save url to
    url: URL of resource to download
  """
  temp_file, _ = urllib.request.urlretrieve(url)
  with tf.gfile.Open(temp_file, 'r') as temp_file_object:
    with tf.gfile.Open(filename, 'w') as file_object:
      for line in temp_file_object:
        line = line.strip()
        line = line.replace(', ', ',')
        if not line or ',' not in line:
          continue
        if line[-1] == '.':
          line = line[:-1]
        line += '\n'
        file_object.write(line)
  tf.gfile.Remove(temp_file)


def download(data_dir):
  """Downloads census data if it is not already present.

  Args:
    data_dir: directory where we will access/save the census data
  """
  tf.gfile.MakeDirs(data_dir)

  training_file_path = os.path.join(data_dir, TRAINING_FILE)
  if not tf.gfile.Exists(training_file_path):
    _download_and_clean_file(training_file_path, TRAINING_URL)

  eval_file_path = os.path.join(data_dir, EVAL_FILE)
  if not tf.gfile.Exists(eval_file_path):
    _download_and_clean_file(eval_file_path, EVAL_URL)

  return training_file_path, eval_file_path

training_file_path, eval_file_path = download(DATA_DIR)

# This census data uses the value '?' for fields (column) that are missing data.
# We use na_values to find ? and set it to NaN values.
# https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html

train_df = pd.read_csv(training_file_path, names=_CSV_COLUMNS, na_values='?')
eval_df = pd.read_csv(eval_file_path, names=_CSV_COLUMNS, na_values='?')

UNUSED_COLUMNS = ['fnlwgt', 'education', 'gender']


def preprocess(dataframe):
  """Converts categorical features to numeric. Removes unused columns.

  Args:
    dataframe: Pandas dataframe with raw data

  Returns:
    Dataframe with preprocessed data
  """
  dataframe = dataframe.drop(columns=UNUSED_COLUMNS)

  # Convert integer valued (numeric) columns to floating point
  numeric_columns = dataframe.select_dtypes(['int64']).columns
  dataframe[numeric_columns] = dataframe[numeric_columns].astype('float32')

  # Convert categorical columns to numeric
  cat_columns = dataframe.select_dtypes(['object']).columns
  dataframe[cat_columns] = dataframe[cat_columns].apply(lambda x: x.astype(
    _CATEGORICAL_TYPES[x.name]))
  dataframe[cat_columns] = dataframe[cat_columns].apply(lambda x: x.cat.codes)
  return dataframe

prepped_train_df = preprocess(train_df)
prepped_eval_df = preprocess(eval_df)

# Split train and test data with labels.
# The pop() method will extract (copy) and remove the label column from the dataframe
train_x, train_y = prepped_train_df, prepped_train_df.pop(_LABEL_COLUMN)
eval_x, eval_y = prepped_eval_df, prepped_eval_df.pop(_LABEL_COLUMN)

# Reshape label columns for use with tf.data.Dataset
train_y = np.asarray(train_y).astype('float32').reshape((-1, 1))
eval_y = np.asarray(eval_y).astype('float32').reshape((-1, 1))

def standardize(dataframe):
  """Scales numerical columns using their means and standard deviation to get
  z-scores: the mean of each numerical column becomes 0, and the standard
  deviation becomes 1. This can help the model converge during training.

  Args:
    dataframe: Pandas dataframe

  Returns:
    Input dataframe with the numerical columns scaled to z-scores
  """
  dtypes = list(zip(dataframe.dtypes.index, map(str, dataframe.dtypes)))
  # Normalize numeric columns.
  for column, dtype in dtypes:
      if dtype == 'float32':
          dataframe[column] -= dataframe[column].mean()
          dataframe[column] /= dataframe[column].std()
  return dataframe


# Join train_x and eval_x to normalize on overall means and standard
# deviations. Then separate them again.
all_x = pd.concat([train_x, eval_x], keys=['train', 'eval'])
all_x = standardize(all_x)
train_x, eval_x = all_x.xs('train'), all_x.xs('eval')

def input_fn(features, labels, shuffle, num_epochs, batch_size):
  """Generates an input function to be used for model training.

  Args:
    features: numpy array of features used for training or inference
    labels: numpy array of labels for each example
    shuffle: boolean for whether to shuffle the data or not (set True for
      training, False for evaluation)
    num_epochs: number of epochs to provide the data for
    batch_size: batch size for training

  Returns:
    A tf.data.Dataset that can provide data to the Keras model for training or
      evaluation
  """
  if labels is None:
    inputs = features
  else:
    inputs = (features, labels)
  dataset = tf.data.Dataset.from_tensor_slices(inputs)

  if shuffle:
    dataset = dataset.shuffle(buffer_size=len(features))

  # We call repeat after shuffling, rather than before, to prevent separate
  # epochs from blending together.
  dataset = dataset.repeat(num_epochs)
  dataset = dataset.batch(batch_size)
  return dataset

# Pass a numpy array by using DataFrame.values
training_dataset = input_fn(features=train_x.values,
                    labels=train_y,
                    shuffle=True,
                    num_epochs=NUM_EPOCHS,
                    batch_size=BATCH_SIZE)

num_eval_examples = eval_x.shape[0]

# Pass a numpy array by using DataFrame.values
validation_dataset = input_fn(features=eval_x.values,
                    labels=eval_y,
                    shuffle=False,
                    num_epochs=NUM_EPOCHS,
                    batch_size=num_eval_examples)

def create_keras_model(input_dim, learning_rate):
  """Creates Keras Model for Binary Classification.

  Args:
    input_dim: How many features the input has
    learning_rate: Learning rate for training

  Returns:
    The compiled Keras model (still needs to be trained)
  """
  Dense = tf.keras.layers.Dense
  model = tf.keras.Sequential(
    [
        Dense(100, activation=tf.nn.relu, kernel_initializer='uniform',
                input_shape=(input_dim,)),
        Dense(75, activation=tf.nn.relu),
        Dense(50, activation=tf.nn.relu),
        Dense(25, activation=tf.nn.relu),
        Dense(1, activation=tf.nn.sigmoid)
    ])

  # Custom Optimizer:
  # https://www.tensorflow.org/api_docs/python/tf/train/RMSPropOptimizer
  optimizer = tf.keras.optimizers.RMSprop(
      lr=learning_rate)

  # Compile Keras model
  model.compile(
      loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])
  return model

num_train_examples, input_dim = train_x.shape
print('Number of features: {}'.format(input_dim))
print('Number of examples: {}'.format(num_train_examples))

keras_model = create_keras_model(
    input_dim=input_dim,
    learning_rate=LEARNING_RATE)

Number of features: 11
Number of examples: 32561
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense (Dense)                (None, 100)               1200      
_________________________________________________________________
dense_1 (Dense)              (None, 75)                7575      
_________________________________________________________________
dense_2 (Dense)              (None, 50)                3800      
_________________________________________________________________
dense_3 (Dense)              (None, 25)                1275      
_________________________________________________________________
dense_4 (Dense)              (None, 1)                 26        
=================================================================
Total params: 13,876
Trainable params: 13,876
Non-trainable params: 0
_________________________________________________________________

# Setup Learning Rate decay.
lr_decay_cb = tf.keras.callbacks.LearningRateScheduler(
    lambda epoch: LEARNING_RATE + 0.02 * (0.5 ** (1 + epoch)),
    verbose=True)

# Setup TensorBoard callback.
JOB_DIR = os.getenv('JOB_DIR')
tensorboard_cb = tf.keras.callbacks.TensorBoard(
      os.path.join(JOB_DIR, 'keras_tensorboard'),
      histogram_freq=1)

history = keras_model.fit(training_dataset,
                          epochs=NUM_EPOCHS,
                          steps_per_epoch=int(num_train_examples/BATCH_SIZE),
                          validation_data=validation_dataset,
                          validation_steps=1,
                          callbacks=[lr_decay_cb, tensorboard_cb],
                          verbose=1)

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.

Epoch 00001: LearningRateScheduler reducing learning rate to 0.02.
Epoch 1/20
254/254 [==============================] - 1s 5ms/step - loss: 0.6986 - acc: 0.7893 - val_loss: 0.3894 - val_acc: 0.8329

Epoch 00002: LearningRateScheduler reducing learning rate to 0.015.
Epoch 2/20
254/254 [==============================] - 1s 4ms/step - loss: 0.3574 - acc: 0.8335 - val_loss: 0.3861 - val_acc: 0.8131

...

Epoch 00019: LearningRateScheduler reducing learning rate to 0.010000038146972657.
Epoch 19/20
254/254 [==============================] - 1s 4ms/step - loss: 0.3239 - acc: 0.8512 - val_loss: 0.3334 - val_acc: 0.8496

Epoch 00020: LearningRateScheduler reducing learning rate to 0.010000019073486329.
Epoch 20/20
254/254 [==============================] - 1s 4ms/step - loss: 0.3279 - acc: 0.8504 - val_loss: 0.3174 - val_acc: 0.8523

from matplotlib import pyplot as plt

# Visualize History for Loss.
plt.title('Keras model loss')
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['training', 'validation'], loc='upper right')
plt.show()

# Visualize History for Accuracy.
plt.title('Keras model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.legend(['training', 'validation'], loc='lower right')
plt.show()

# Export the model to a local SavedModel directory
export_path = tf.contrib.saved_model.save_keras_model(keras_model, 'keras_export')
print("Model exported to: ", export_path)

WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
  * https://github.com/tensorflow/addons
If you depend on functionality not listed there, please file an issue.

WARNING:tensorflow:This model was compiled with a Keras optimizer (<tensorflow.python.keras.optimizers.RMSprop object at 0x7fc198c4e400>) but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.

Consider using a TensorFlow optimizer from `tf.train`.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/network.py:1436: update_checkpoint_state (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.train.CheckpointManager to manage checkpoints rather than manually editing the Checkpoint proto.
WARNING:tensorflow:Model was compiled with an optimizer, but the optimizer is not from `tf.train` (e.g. `tf.train.AdagradOptimizer`). Only the serving graph was exported. The train and evaluate graphs were not added to the SavedModel.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:205: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.
INFO:tensorflow:Signatures INCLUDED in export for Classify: None
INFO:tensorflow:Signatures INCLUDED in export for Regress: None
INFO:tensorflow:Signatures INCLUDED in export for Predict: ['serving_default']
INFO:tensorflow:Signatures INCLUDED in export for Train: None
INFO:tensorflow:Signatures INCLUDED in export for Eval: None
INFO:tensorflow:No assets to save.
INFO:tensorflow:No assets to write.
INFO:tensorflow:SavedModel written to: keras_export/1553710367/saved_model.pb
Model exported to:  b'keras_export/1553710367'

JOB_DIR = os.getenv('JOB_DIR')

# Export the model to a SavedModel directory in Cloud Storage
export_path = tf.contrib.saved_model.save_keras_model(keras_model, JOB_DIR + '/keras_export')
print("Model exported to: ", export_path)

WARNING:tensorflow:This model was compiled with a Keras optimizer (<tensorflow.python.keras.optimizers.RMSprop object at 0x7fc198c4e400>) but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.

Consider using a TensorFlow optimizer from `tf.train`.
WARNING:tensorflow:Model was compiled with an optimizer, but the optimizer is not from `tf.train` (e.g. `tf.train.AdagradOptimizer`). Only the serving graph was exported. The train and evaluate graphs were not added to the SavedModel.
INFO:tensorflow:Signatures INCLUDED in export for Classify: None
INFO:tensorflow:Signatures INCLUDED in export for Regress: None
INFO:tensorflow:Signatures INCLUDED in export for Predict: ['serving_default']
INFO:tensorflow:Signatures INCLUDED in export for Train: None
INFO:tensorflow:Signatures INCLUDED in export for Eval: None
INFO:tensorflow:No assets to save.
INFO:tensorflow:No assets to write.
INFO:tensorflow:SavedModel written to: gs://your-bucket-name/keras-job-dir/keras_export/1553710379/saved_model.pb
Model exported to:  b'gs://your-bucket-name/keras-job-dir/keras_export/1553710379'

# Delete model version resource
gcloud ai-platform versions delete $MODEL_VERSION --quiet --model $MODEL_NAME

# Delete model resource
gcloud ai-platform models delete $MODEL_NAME --quiet

# Delete Cloud Storage objects that were created
gsutil -m rm -r $JOB_DIR

# If training job is still running, cancel it
gcloud ai-platform jobs cancel $JOB_NAME --quiet