modelarts-umn/umn/source/examples_of_custom_scripts/tensorflow.rst

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modelarts_23_0173.html

TensorFlow

TensorFlow has two types of APIs: Keras and tf. Keras and tf use different code for training and saving models, but the same code for inference.

Training a Model (Keras API)

from keras.models import Sequential
model = Sequential()
from keras.layers import Dense
import tensorflow as tf

# Import a training dataset.
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

print(x_train.shape)

from keras.layers import Dense
from keras.models import Sequential
import keras
from keras.layers import Dense, Activation, Flatten, Dropout

# Define a model network.
model = Sequential()
model.add(Flatten(input_shape=(28,28)))
model.add(Dense(units=5120,activation='relu'))
model.add(Dropout(0.2))

model.add(Dense(units=10, activation='softmax'))

# Define an optimizer and loss functions.
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.summary()
# Train the model.
model.fit(x_train, y_train, epochs=2)
# Evaluate the model.
model.evaluate(x_test, y_test)

Saving a Model (Keras API)

from keras import backend as K

# K.get_session().run(tf.global_variables_initializer())

# Define the inputs and outputs of the prediction API.
# The key values of the inputs and outputs dictionaries are used as the index keys for the input and output tensors of the model.
 # The input and output definitions of the model must match the custom inference script.
predict_signature = tf.saved_model.signature_def_utils.predict_signature_def(
    inputs={"images" : model.input},
    outputs={"scores" : model.output}
)

# Define a save path.
builder = tf.saved_model.builder.SavedModelBuilder('./mnist_keras/')

builder.add_meta_graph_and_variables(

    sess = K.get_session(),
    # The tf.saved_model.tag_constants.SERVING tag needs to be defined for inference and deployment.
    tags=[tf.saved_model.tag_constants.SERVING],
    """
    signature_def_map: Only single items can exist, or the corresponding key needs to be defined as follows:
    tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
    """
    signature_def_map={
        tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
            predict_signature
    }

)
builder.save()

Training a Model (tf API)

from __future__ import print_function

import gzip
import os
import urllib

import numpy
import tensorflow as tf
from six.moves import urllib

# Training data is obtained from the Yann LeCun official website http://yann.lecun.com/exdb/mnist/.
SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'
TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'
TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'
TEST_IMAGES = 't10k-images-idx3-ubyte.gz'
TEST_LABELS = 't10k-labels-idx1-ubyte.gz'
VALIDATION_SIZE = 5000


def maybe_download(filename, work_directory):
    """Download the data from Yann's website, unless it's already here."""
    if not os.path.exists(work_directory):
        os.mkdir(work_directory)
    filepath = os.path.join(work_directory, filename)
    if not os.path.exists(filepath):
        filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)
        statinfo = os.stat(filepath)
        print('Successfully downloaded %s %d bytes.' % (filename, statinfo.st_size))
    return filepath


def _read32(bytestream):
    dt = numpy.dtype(numpy.uint32).newbyteorder('>')
    return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]


def extract_images(filename):
    """Extract the images into a 4D uint8 numpy array [index, y, x, depth]."""
    print('Extracting %s' % filename)
    with gzip.open(filename) as bytestream:
        magic = _read32(bytestream)
        if magic != 2051:
            raise ValueError(
                'Invalid magic number %d in MNIST image file: %s' %
                (magic, filename))
        num_images = _read32(bytestream)
        rows = _read32(bytestream)
        cols = _read32(bytestream)
        buf = bytestream.read(rows * cols * num_images)
        data = numpy.frombuffer(buf, dtype=numpy.uint8)
        data = data.reshape(num_images, rows, cols, 1)
        return data


def dense_to_one_hot(labels_dense, num_classes=10):
    """Convert class labels from scalars to one-hot vectors."""
    num_labels = labels_dense.shape[0]
    index_offset = numpy.arange(num_labels) * num_classes
    labels_one_hot = numpy.zeros((num_labels, num_classes))
    labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
    return labels_one_hot


def extract_labels(filename, one_hot=False):
    """Extract the labels into a 1D uint8 numpy array [index]."""
    print('Extracting %s' % filename)
    with gzip.open(filename) as bytestream:
        magic = _read32(bytestream)
        if magic != 2049:
            raise ValueError(
                'Invalid magic number %d in MNIST label file: %s' %
                (magic, filename))
        num_items = _read32(bytestream)
        buf = bytestream.read(num_items)
        labels = numpy.frombuffer(buf, dtype=numpy.uint8)
        if one_hot:
            return dense_to_one_hot(labels)
        return labels


class DataSet(object):
    """Class encompassing test, validation and training MNIST data set."""

    def __init__(self, images, labels, fake_data=False, one_hot=False):
        """Construct a DataSet. one_hot arg is used only if fake_data is true."""

        if fake_data:
            self._num_examples = 10000
            self.one_hot = one_hot
        else:
            assert images.shape[0] == labels.shape[0], (
                    'images.shape: %s labels.shape: %s' % (images.shape,
                                                           labels.shape))
            self._num_examples = images.shape[0]

            # Convert shape from [num examples, rows, columns, depth]
            # to [num examples, rows*columns] (assuming depth == 1)
            assert images.shape[3] == 1
            images = images.reshape(images.shape[0],
                                    images.shape[1] * images.shape[2])
            # Convert from [0, 255] -> [0.0, 1.0].
            images = images.astype(numpy.float32)
            images = numpy.multiply(images, 1.0 / 255.0)
        self._images = images
        self._labels = labels
        self._epochs_completed = 0
        self._index_in_epoch = 0

    @property
    def images(self):
        return self._images

    @property
    def labels(self):
        return self._labels

    @property
    def num_examples(self):
        return self._num_examples

    @property
    def epochs_completed(self):
        return self._epochs_completed

    def next_batch(self, batch_size, fake_data=False):
        """Return the next `batch_size` examples from this data set."""
        if fake_data:
            fake_image = [1] * 784
            if self.one_hot:
                fake_label = [1] + [0] * 9
            else:
                fake_label = 0
            return [fake_image for _ in range(batch_size)], [
                fake_label for _ in range(batch_size)
            ]
        start = self._index_in_epoch
        self._index_in_epoch += batch_size
        if self._index_in_epoch > self._num_examples:
            # Finished epoch
            self._epochs_completed += 1
            # Shuffle the data
            perm = numpy.arange(self._num_examples)
            numpy.random.shuffle(perm)
            self._images = self._images[perm]
            self._labels = self._labels[perm]
            # Start next epoch
            start = 0
            self._index_in_epoch = batch_size
            assert batch_size <= self._num_examples
        end = self._index_in_epoch
        return self._images[start:end], self._labels[start:end]


def read_data_sets(train_dir, fake_data=False, one_hot=False):
    """Return training, validation and testing data sets."""

    class DataSets(object):
        pass

    data_sets = DataSets()

    if fake_data:
        data_sets.train = DataSet([], [], fake_data=True, one_hot=one_hot)
        data_sets.validation = DataSet([], [], fake_data=True, one_hot=one_hot)
        data_sets.test = DataSet([], [], fake_data=True, one_hot=one_hot)
        return data_sets

    local_file = maybe_download(TRAIN_IMAGES, train_dir)
    train_images = extract_images(local_file)

    local_file = maybe_download(TRAIN_LABELS, train_dir)
    train_labels = extract_labels(local_file, one_hot=one_hot)

    local_file = maybe_download(TEST_IMAGES, train_dir)
    test_images = extract_images(local_file)

    local_file = maybe_download(TEST_LABELS, train_dir)
    test_labels = extract_labels(local_file, one_hot=one_hot)

    validation_images = train_images[:VALIDATION_SIZE]
    validation_labels = train_labels[:VALIDATION_SIZE]
    train_images = train_images[VALIDATION_SIZE:]
    train_labels = train_labels[VALIDATION_SIZE:]

    data_sets.train = DataSet(train_images, train_labels)
    data_sets.validation = DataSet(validation_images, validation_labels)
    data_sets.test = DataSet(test_images, test_labels)
    return data_sets

training_iteration = 1000

modelarts_example_path =  './modelarts-mnist-train-save-deploy-example'

export_path = modelarts_example_path + '/model/'
data_path = './'

print('Training model...')
mnist = read_data_sets(data_path, one_hot=True)
sess = tf.InteractiveSession()
serialized_tf_example = tf.placeholder(tf.string, name='tf_example')
feature_configs = {'x': tf.FixedLenFeature(shape=[784], dtype=tf.float32), }
tf_example = tf.parse_example(serialized_tf_example, feature_configs)
x = tf.identity(tf_example['x'], name='x')  # use tf.identity() to assign name
y_ = tf.placeholder('float', shape=[None, 10])
w = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
sess.run(tf.global_variables_initializer())
y = tf.nn.softmax(tf.matmul(x, w) + b, name='y')
cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
values, indices = tf.nn.top_k(y, 10)
table = tf.contrib.lookup.index_to_string_table_from_tensor(
    tf.constant([str(i) for i in range(10)]))
prediction_classes = table.lookup(tf.to_int64(indices))
for _ in range(training_iteration):
    batch = mnist.train.next_batch(50)
    train_step.run(feed_dict={x: batch[0], y_: batch[1]})
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
print('training accuracy %g' % sess.run(
    accuracy, feed_dict={
        x: mnist.test.images,
        y_: mnist.test.labels
    }))
print('Done training!')

Saving a Model (tf API)

# Export the model.
# The model needs to be saved using the saved_model API.
print('Exporting trained model to', export_path)
builder = tf.saved_model.builder.SavedModelBuilder(export_path)

tensor_info_x = tf.saved_model.utils.build_tensor_info(x)
tensor_info_y = tf.saved_model.utils.build_tensor_info(y)

# Define the inputs and outputs of the prediction API.
# The key values of the inputs and outputs dictionaries are used as the index keys for the input and output tensors of the model.
 # The input and output definitions of the model must match the custom inference script.
prediction_signature = (
    tf.saved_model.signature_def_utils.build_signature_def(
        inputs={'images': tensor_info_x},
        outputs={'scores': tensor_info_y},
        method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME))

legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')
builder.add_meta_graph_and_variables(
    # Set tag to serve/tf.saved_model.tag_constants.SERVING.
    sess, [tf.saved_model.tag_constants.SERVING],
    signature_def_map={
        'predict_images':
            prediction_signature,
    },
    legacy_init_op=legacy_init_op)

builder.save()

print('Done exporting!')

Inference Code (Keras and tf APIs)

from PIL import Image
import numpy as np
from model_service.tfserving_model_service import TfServingBaseService


class mnist_service(TfServingBaseService):

    # Match the model input with the user's HTTPS API input during preprocessing.
    # The model input corresponding to the preceding training part is {"images":<array>}.
    def _preprocess(self, data):

        preprocessed_data = {}
        images = []
        # Iterate the input data.
        for k, v in data.items():
            for file_name, file_content in v.items():
                image1 = Image.open(file_content)
                image1 = np.array(image1, dtype=np.float32)
                image1.resize((1,784))
                images.append(image1)
        # Return the numpy array.
        images = np.array(images,dtype=np.float32)
        # Perform batch processing on multiple input samples and ensure that the shape is the same as that inputted during training.
        images.resize((len(data), 784))
        preprocessed_data['images'] = images
        return preprocessed_data

    # Processing logic of the inference for invoking the parent class.

    # The output corresponding to model saving in the preceding training part is {"scores":<array>}.
    # Postprocess the HTTPS output.
    def _postprocess(self, data):
        infer_output = {"mnist_result": []}
        # Iterate the model output.
        for output_name, results in data.items():
            for result in results:
                infer_output["mnist_result"].append(result.index(max(result)))
        return infer_output