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mnist_tfrecord.py added (keras-team#7061, keras-team#7072, keras-team…
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ahundt committed Jun 21, 2017
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'''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.layers 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]):
""" Return tensor to read from TFRecord """
print 'Creating graph for loading TFRecords...'
with tf.variable_scope("TFRecords"):
record_input = data_flow_ops.RecordInput(tf_glob, batch_size=batch_shape[0])
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 = 1
batch_shape = [batch_size, 28, 28, 1]
epochs = 60000
classes = 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)

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)


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'])

tensorboard = TensorBoard(write_graph=True)

train_model.summary()
train_model.fit(batch_size=batch_size,
epochs=epochs)
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))

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