What is an example of how to use a TensorFlow TFRecord with a Keras Model and tf.session.run() while keeping the dataset in tensors w/ queue runners?
Below is a snippet that works but it needs the following improvements:
Here is the snippet, there are several TODO lines indicating what is needed:
from keras.models import Model
import tensorflow as tf
from keras import backend as K
from keras.layers import Dense, Input
from keras.objectives import categorical_crossentropy
from tensorflow.examples.tutorials.mnist import input_data
sess = tf.Session()
K.set_session(sess)
# Can this be done more efficiently than placeholders w/ TFRecords?
img = tf.placeholder(tf.float32, shape=(None, 784))
labels = tf.placeholder(tf.float32, shape=(None, 10))
# TODO: Use Input()
x = Dense(128, activation='relu')(img)
x = Dense(128, activation='relu')(x)
preds = Dense(10, activation='softmax')(x)
# TODO: Construct model = Model(input=inputs, output=preds)
loss = tf.reduce_mean(categorical_crossentropy(labels, preds))
# TODO: handle TFRecord data, is it the same?
mnist_data = input_data.read_data_sets('MNIST_data', one_hot=True)
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(loss)
sess.run(tf.global_variables_initializer())
# TODO remove default, add queuerunner
with sess.as_default():
for i in range(1000):
batch = mnist_data.train.next_batch(50)
train_step.run(feed_dict={img: batch[0],
labels: batch[1]})
print(loss.eval(feed_dict={img: mnist_data.test.images,
labels: mnist_data.test.labels}))
Why is this question relevant?
Here is some starter information for a semantic segmentation problem example:
Update 2018-08-29 this is now directly supported in keras, see the following example:
https://github.com/keras-team/keras/blob/master/examples/mnist_tfrecord.py
Original Answer:
TFRecords are supported by using an external loss. Here are the key lines constructing an external loss:
# 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)
Here is an example for Keras 2. It works after applying the small 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))
I've also been working to improve the support for TFRecords in the following issue and pull request:
Finally, it is possible to use tf.contrib.learn.Experiment to train Keras models in TensorFlow.