Wie machen Sie TensorFlow + Keras schnell mit einem TFRecord dataset?

Update 2018-08-29 dieser wird jetzt direkt unterstützt in keras, siehe Folgendes Beispiel:

https://github.com/keras-team/keras/blob/master/examples/mnist_tfrecord.py

Original Antwort:

TFRecords werden unterstützt durch den Einsatz einer externen Verlust. Hier sind die wichtigsten Linien konstruieren einer externen Verlust:

# tf yield ops that supply dataset images and labels
x_train_batch, y_train_batch = read_and_decode_recordinput(...)

# create a basic cnn
x_train_input = Input(tensor=x_train_batch)
x_train_out = cnn_layers(x_train_input)

model = Model(inputs=x_train_input, outputs=x_train_out)
loss = keras.losses.categorical_crossentropy(y_train_batch, x_train_out)
model.add_loss(loss)

model.compile(optimizer='rmsprop', loss=None)

Hier ist ein Beispiel für Keras 2. Es funktioniert nach dem aufbringen der kleine patch #7060:

'''MNIST dataset with TensorFlow TFRecords.

Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
'''
import os
import copy
import time

import numpy as np

import tensorflow as tf
from tensorflow.python.ops import data_flow_ops
from keras import backend as K
from keras.models import Model
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Flatten
from keras.layers import Input
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.callbacks import EarlyStopping
from keras.callbacks import TensorBoard
from keras.objectives import categorical_crossentropy
from keras.utils import np_utils
from keras.utils.generic_utils import Progbar
from keras import callbacks as cbks
from keras import optimizers, objectives
from keras import metrics as metrics_module

from keras.datasets import mnist

if K.backend() != 'tensorflow':
    raise RuntimeError('This example can only run with the '
                       'TensorFlow backend for the time being, '
                       'because it requires TFRecords, which '
                       'are not supported on other platforms.')


def images_to_tfrecord(images, labels, filename):
    def _int64_feature(value):
        return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))

    def _bytes_feature(value):
        return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))

    """ Save data into TFRecord """
    if not os.path.isfile(filename):
        num_examples = images.shape[0]

        rows = images.shape[1]
        cols = images.shape[2]
        depth = images.shape[3]

        print('Writing', filename)
        writer = tf.python_io.TFRecordWriter(filename)
        for index in range(num_examples):
            image_raw = images[index].tostring()
            example = tf.train.Example(features=tf.train.Features(feature={
                'height': _int64_feature(rows),
                'width': _int64_feature(cols),
                'depth': _int64_feature(depth),
                'label': _int64_feature(int(labels[index])),
                'image_raw': _bytes_feature(image_raw)}))
            writer.write(example.SerializeToString())
        writer.close()
    else:
        print('tfrecord %s already exists' % filename)


def read_and_decode_recordinput(tf_glob, one_hot=True, classes=None, is_train=None,
                                batch_shape=[1000, 28, 28, 1], parallelism=1):
    """ Return tensor to read from TFRecord """
    print 'Creating graph for loading %s TFRecords...' % tf_glob
    with tf.variable_scope("TFRecords"):
        record_input = data_flow_ops.RecordInput(
            tf_glob, batch_size=batch_shape[0], parallelism=parallelism)
        records_op = record_input.get_yield_op()
        records_op = tf.split(records_op, batch_shape[0], 0)
        records_op = [tf.reshape(record, []) for record in records_op]
        progbar = Progbar(len(records_op))

        images = []
        labels = []
        for i, serialized_example in enumerate(records_op):
            progbar.update(i)
            with tf.variable_scope("parse_images", reuse=True):
                features = tf.parse_single_example(
                    serialized_example,
                    features={
                        'label': tf.FixedLenFeature([], tf.int64),
                        'image_raw': tf.FixedLenFeature([], tf.string),
                    })
                img = tf.decode_raw(features['image_raw'], tf.uint8)
                img.set_shape(batch_shape[1] * batch_shape[2])
                img = tf.reshape(img, [1] + batch_shape[1:])

                img = tf.cast(img, tf.float32) * (1. /255) - 0.5

                label = tf.cast(features['label'], tf.int32)
                if one_hot and classes:
                    label = tf.one_hot(label, classes)

                images.append(img)
                labels.append(label)

        images = tf.parallel_stack(images, 0)
        labels = tf.parallel_stack(labels, 0)
        images = tf.cast(images, tf.float32)

        images = tf.reshape(images, shape=batch_shape)

        # StagingArea will store tensors
        # across multiple steps to
        # speed up execution
        images_shape = images.get_shape()
        labels_shape = labels.get_shape()
        copy_stage = data_flow_ops.StagingArea(
            [tf.float32, tf.float32],
            shapes=[images_shape, labels_shape])
        copy_stage_op = copy_stage.put(
            [images, labels])
        staged_images, staged_labels = copy_stage.get()

        return images, labels


def save_mnist_as_tfrecord():
    (X_train, y_train), (X_test, y_test) = mnist.load_data()
    X_train = X_train[..., np.newaxis]
    X_test = X_test[..., np.newaxis]
    images_to_tfrecord(images=X_train, labels=y_train, filename='train.mnist.tfrecord')
    images_to_tfrecord(images=X_test, labels=y_test, filename='test.mnist.tfrecord')


def cnn_layers(x_train_input):
    x = Conv2D(32, (3, 3), activation='relu', padding='valid')(x_train_input)
    x = Conv2D(64, (3, 3), activation='relu')(x)
    x = MaxPooling2D(pool_size=(2, 2))(x)
    x = Dropout(0.25)(x)
    x = Flatten()(x)
    x = Dense(128, activation='relu')(x)
    x = Dropout(0.5)(x)
    x_train_out = Dense(classes,
                        activation='softmax',
                        name='x_train_out')(x)
    return x_train_out


sess = tf.Session()
K.set_session(sess)

save_mnist_as_tfrecord()

batch_size = 100
batch_shape = [batch_size, 28, 28, 1]
epochs = 3000
classes = 10
parallelism = 10

x_train_batch, y_train_batch = read_and_decode_recordinput(
    'train.mnist.tfrecord',
    one_hot=True,
    classes=classes,
    is_train=True,
    batch_shape=batch_shape,
    parallelism=parallelism)

x_test_batch, y_test_batch = read_and_decode_recordinput(
    'test.mnist.tfrecord',
    one_hot=True,
    classes=classes,
    is_train=True,
    batch_shape=batch_shape,
    parallelism=parallelism)


x_batch_shape = x_train_batch.get_shape().as_list()
y_batch_shape = y_train_batch.get_shape().as_list()

x_train_input = Input(tensor=x_train_batch, batch_shape=x_batch_shape)
x_train_out = cnn_layers(x_train_input)
y_train_in_out = Input(tensor=y_train_batch, batch_shape=y_batch_shape, name='y_labels')
cce = categorical_crossentropy(y_train_batch, x_train_out)
train_model = Model(inputs=[x_train_input], outputs=[x_train_out])
train_model.add_loss(cce)

train_model.compile(optimizer='rmsprop',
                    loss=None,
                    metrics=['accuracy'])
train_model.summary()

tensorboard = TensorBoard()

# tensorboard disabled due to Keras bug
train_model.fit(batch_size=batch_size,
                epochs=epochs)  # callbacks=[tensorboard])

train_model.save_weights('saved_wt.h5')

K.clear_session()

# Second Session, pure Keras
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train[..., np.newaxis]
X_test = X_test[..., np.newaxis]
x_test_inp = Input(batch_shape=(None,) + (X_test.shape[1:]))
test_out = cnn_layers(x_test_inp)
test_model = Model(inputs=x_test_inp, outputs=test_out)

test_model.load_weights('saved_wt.h5')
test_model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
test_model.summary()

loss, acc = test_model.evaluate(X_test, np_utils.to_categorical(y_test), classes)
print('\nTest accuracy: {0}'.format(acc))

Ich habe auch schon arbeiten an der Verbesserung der Unterstützung für TFRecords in der folgenden Ausgabe und pull-Anforderung:

• #6928 Yield-Op support: Hohe Performance Große Datenmengen über TFRecords, und RecordInput
• #7102 Keras Input-Tensor-API-Design-Vorschlag

Schließlich ist es möglich tf.contrib.learn.Experiment zu trainieren Keras Modelle in TensorFlow.

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