def load_data(task_name=None):
"""Loads data associated with given task
Args:
task_name: str, the name of the task to load.
Returns:
Tuple of numpy arrays (inputs, data_to_generate) normalized between 0 and 1.
"""
assert task_name is not None
if task_name == 'mnist':
with open(MNIST_IMGS_PATH, 'rb') as imgs_file:
data = extract_images(imgs_file)
inputs = None
elif task_name == 'cmnist':
with open(MNIST_IMGS_PATH, 'rb') as imgs_file:
data = extract_images(imgs_file)
with open(MNIST_LABELS_PATH, 'rb') as labels_file:
inputs = extract_labels(labels_file)
inputs = np.eye(10)[inputs]
else:
raise ValueError('Unknown task: {}'.format(task_name))
data = (data - data.min()) / (data.max() - data.min()) # Normalize
data = (data * 2) - 1 # Move to range [-1, 1]
if inputs is not None:
inputs = (inputs - inputs.min()) / (inputs.max() - inputs.min()
) # Normalize
inputs = (inputs * 2) - 1 # Move to range [-1, 1]
return inputs, data
def maybe_download_minst(train_dir, SOURCE_URL, train=True, one_hot=True):
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'
local_file = base.maybe_download(TRAIN_IMAGES, train_dir,
SOURCE_URL + TRAIN_IMAGES)
with open(local_file, 'rb') as f:
train_images = extract_images(f)
local_file = base.maybe_download(TRAIN_LABELS, train_dir,
SOURCE_URL + TRAIN_LABELS)
with open(local_file, 'rb') as f:
train_labels = extract_labels(f, one_hot=one_hot)
local_file = base.maybe_download(TEST_IMAGES, train_dir,
SOURCE_URL + TEST_IMAGES)
with open(local_file, 'rb') as f:
test_images = extract_images(f)
local_file = base.maybe_download(TEST_LABELS, train_dir,
SOURCE_URL + TEST_LABELS)
with open(local_file, 'rb') as f:
test_labels = extract_labels(f, one_hot=one_hot)
if train:
return train_images, train_labels
else:
return test_images, test_labels
def import_mnist():
"""
This import mnist and saves the data as an object of our DataSet class
:return:
"""
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 = 0
ONE_HOT = True
TRAIN_DIR = 'MNIST_data'
local_file = base.maybe_download(TRAIN_IMAGES, TRAIN_DIR,
SOURCE_URL + TRAIN_IMAGES)
train_images = extract_images(open(local_file))
local_file = base.maybe_download(TRAIN_LABELS, TRAIN_DIR,
SOURCE_URL + TRAIN_LABELS)
train_labels = extract_labels(open(local_file), one_hot=ONE_HOT)
local_file = base.maybe_download(TEST_IMAGES, TRAIN_DIR,
SOURCE_URL + TEST_IMAGES)
test_images = extract_images(open(local_file))
local_file = base.maybe_download(TEST_LABELS, TRAIN_DIR,
SOURCE_URL + TEST_LABELS)
test_labels = extract_labels(open(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:]
## Process images
train_images = process_mnist(train_images)
validation_images = process_mnist(validation_images)
test_images = process_mnist(test_images)
## Standardize data
train_mean, train_std = get_data_info(train_images)
# train_images = standardize_data(train_images, train_mean, train_std)
# validation_images = standardize_data(validation_images, train_mean, train_std)
# test_images = standardize_data(test_images, train_mean, train_std)
# data = DataSet(train_images, train_labels)
# test = DataSet(test_images, test_labels)
# val = DataSet(validation_images, validation_labels)
data = DataSet(train_images, train_images)
test = DataSet(test_images, test_images)
val = DataSet(validation_images, validation_images)
return data, test, val
def import_mnist(validation_size=0):
"""
This import mnist and saves the data as an object of our DataSet class
:param concat_val: Concatenate training and validation
:return:
"""
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'
ONE_HOT = True
TRAIN_DIR = 'experiments/data/MNIST_data'
local_file = base.maybe_download(TRAIN_IMAGES, TRAIN_DIR,
SOURCE_URL + TRAIN_IMAGES)
with open(local_file) as f:
train_images = extract_images(f)
local_file = base.maybe_download(TRAIN_LABELS, TRAIN_DIR,
SOURCE_URL + TRAIN_LABELS)
with open(local_file) as f:
train_labels = extract_labels(f, one_hot=ONE_HOT)
local_file = base.maybe_download(TEST_IMAGES, TRAIN_DIR,
SOURCE_URL + TEST_IMAGES)
with open(local_file) as f:
test_images = extract_images(f)
local_file = base.maybe_download(TEST_LABELS, TRAIN_DIR,
SOURCE_URL + TEST_LABELS)
with open(local_file) as f:
test_labels = extract_labels(f, 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:]
# process images
train_images = process_mnist(train_images)
validation_images = process_mnist(validation_images)
test_images = process_mnist(test_images)
# standardize data
train_mean, train_std = get_data_info(train_images)
train_images = standardize_data(train_images, train_mean, train_std)
validation_images = standardize_data(validation_images, train_mean,
train_std)
test_images = standardize_data(test_images, train_mean, train_std)
data = DataSet(train_images, train_labels)
test = DataSet(test_images, test_labels)
val = DataSet(validation_images, validation_labels)
return data, test, val
def load_data():
with open('../../data/mnist/train-images-idx3-ubyte.gz', 'rb') as f:
train_images = np.squeeze(extract_images(f))
with open('../../data/mnist/train-labels-idx1-ubyte.gz', 'rb') as f:
train_labels = extract_labels(f)
with open('../../data/mnist/t10k-images-idx3-ubyte.gz', 'rb') as f:
test_images = np.squeeze(extract_images(f))
with open('../../data/mnist/t10k-labels-idx1-ubyte.gz', 'rb') as f:
test_labels = extract_labels(f)
return train_images, train_labels, test_images, test_labels
def read_data_sets(
fake_data=False,
one_hot=False,
dtype=dtypes.float32,
reshape=True,
validation_size=5000,
seed=None,
):
if fake_data:
def fake():
return DataSet([], [],
fake_data=True,
one_hot=one_hot,
dtype=dtype,
seed=seed)
train = fake()
validation = fake()
test = fake()
return base.Datasets(train=train, validation=validation, test=test)
with gfile.Open(train_data_dir, 'rb') as f:
train_images = extract_images(f)
with gfile.Open(train_labels_dir, 'rb') as f:
train_labels = extract_labels(f, one_hot=one_hot)
with gfile.Open(eval_data_dir, 'rb') as f:
test_images = extract_images(f)
with gfile.Open(eval_labels_dir, 'rb') as f:
test_labels = extract_labels(f, one_hot=one_hot)
if not 0 <= validation_size <= len(train_images):
raise ValueError(
'Validation size should be between 0 and {}. Received: {}.'.format(
len(train_images), validation_size))
validation_images = train_images[:validation_size]
validation_labels = train_labels[:validation_size]
train_images = train_images[validation_size:]
train_labels = train_labels[validation_size:]
options = dict(dtype=dtype, reshape=reshape, seed=seed)
train = DataSet(train_images, train_labels, **options)
validation = DataSet(validation_images, validation_labels, **options)
test = DataSet(test_images, test_labels, **options)
return base.Datasets(train=train, validation=validation, test=test)
def loadModelData():
# load EMNIST data
with open(TRAIN_IMAGES_PATH, 'rb') as f:
train_images = extract_images(f)
with open(TRAIN_LABELS_PATH, 'rb') as f:
train_labels = extract_labels(f)
with open(TEST_IMAGES_PATH, 'rb') as f:
test_images = extract_images(f)
with open(TEST_LABELS_PATH, 'rb') as f:
test_labels = extract_labels(f)
# "rename" to make it similar to the tutorial
# https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_mnist.py
X, Y, testX, testY = train_images, train_labels, test_images, test_labels
# data preprocessing
X = X.reshape([-1, 28, 28, 1])
testX = testX.reshape([-1, 28, 28, 1])
Y = to_categorical(Y, nb_classes=62)
testY = to_categorical(testY, nb_classes=62)
# Building convolutional network
# the input is a 28x28 image with 1 channel
network = input_data(shape=[None, 28, 28, 1], name='input')
# 3 x convolution + max pooling
network = conv_2d(network, 32, 3, activation='relu', regularizer="L2")
network = conv_2d(network, 32, 3, activation='relu', regularizer="L2")
network = conv_2d(network, 32, 3, activation='relu', regularizer="L2")
network = max_pool_2d(network, 2)
# fully connected with 512 nodes + some dropout
network = fully_connected(network, 512, activation='relu')
network = dropout(network, 0.5)
# fully connected with 62 nodes which are the outputs
network = fully_connected(network, 62, activation='softmax')
# train the network with regression
network = regression(network,
optimizer='adam',
loss='categorical_crossentropy',
name='target')
# Training
model = tflearn.DNN(network,
tensorboard_verbose=0,
checkpoint_path='classifier.tfl.ckpt')
return model, X, Y, testX, testY
def read_data_sets(train_dir,
one_hot=False,
dtype=dtypes.float32,
reshape=True,
validation_size=5000,
seed=None,
source_url=None): # omit url since we are using our own dataset
TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'
TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'
TEST_IMAGES = 'test-images-idx3-ubyte.gz'
TEST_LABELS = 'test-labels-idx1-ubyte.gz'
local_file = base.maybe_download(TRAIN_IMAGES, train_dir, None) # omit url, local file will be a path
# type: DataSets
with gfile.Open(local_file, 'rb') as f:
train_images = mnist_module.extract_images(f)
print (train_images.shape)
local_file = base.maybe_download(TRAIN_LABELS, train_dir, None) # omit url
with gfile.Open(local_file, 'rb') as f:
train_labels = mnist_module.extract_labels(f, one_hot=one_hot)
local_file = base.maybe_download(TEST_IMAGES, train_dir, None) # omit url
with gfile.Open(local_file, 'rb') as f:
test_images = mnist_module.extract_images(f)
local_file = base.maybe_download(TEST_LABELS, train_dir, None) # omit url
with gfile.Open(local_file, 'rb') as f:
test_labels = mnist_module.extract_labels(f, one_hot=one_hot)
if not 0 <= validation_size <= len(train_images):
raise ValueError(
'Validation size should be between 0 and {}. Received: {}.'
.format(len(train_images), validation_size))
validation_images = train_images[:validation_size]
validation_labels = train_labels[:validation_size]
train_images = train_images[validation_size:]
train_labels = train_labels[validation_size:]
options = dict(dtype=dtype, reshape=reshape, seed=seed)
train = mnist_module.DataSet(train_images, train_labels, **options)
validation = mnist_module.DataSet(validation_images, validation_labels, **options)
test = mnist_module.DataSet(test_images, test_labels, **options)
return base.Datasets(train=train, validation=validation, test=test)
def read_data_sets(train_dir,
one_hot=False,
dtype=dtypes.float32,
reshape=True,
seed=None,
source_url=DEFAULT_SOURCE_URL):
if not source_url: # empty string check
source_url = DEFAULT_SOURCE_URL
TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'
TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'
local_file = base.maybe_download(TRAIN_IMAGES, train_dir,
source_url + TRAIN_IMAGES)
with gfile.Open(local_file, 'rb') as f:
train_images = extract_images(f)
local_file = base.maybe_download(TRAIN_LABELS, train_dir,
source_url + TRAIN_LABELS)
with gfile.Open(local_file, 'rb') as f:
train_labels = extract_labels(f, one_hot=one_hot)
options = dict(dtype=dtype, reshape=reshape, seed=seed)
train = DataSet(train_images, train_labels, **options)
return base.Datasets(train=train, validation=None, test=None)
def write_mnist_data(input_images, input_labels, output, partitions):
with open(input_images, 'rb') as f:
images = numpy.array(mnist.extract_images(f))
with open(input_labels, 'rb') as f:
labels = numpy.array(mnist.extract_labels(f, one_hot=True))
shape = images.shape
print("images.shape: {0}".format(shape))
print("labels.shape: {0}".format(labels.shape))
images = images.reshape(shape[0], shape[1], shape[2])
num_per_part = int(math.ceil(float(shape[0]) / partitions))
seq = 0
filename = output + "/" + str(seq) + ".tfrecords"
writer = tf.python_io.TFRecordWriter(filename)
for i in range(shape[0]):
if i != 0 and i % num_per_part == 0:
writer.close()
seq += 1
filename = output + "/" + str(seq) + ".tfrecords"
writer = tf.python_io.TFRecordWriter(filename)
image_raw = images[i].tostring()
example = tf.train.Example(features=tf.train.Features(
feature={
'image_raw': _bytes_feature(image_raw),
'label': _int64_feature(labels[i].astype(int))
}))
writer.write(example.SerializeToString())
writer.close()
def load_data_fashion_mnist(data_dir, one_hot=False, num_classes=10):
train_image_file = os.path.join(data_dir, 'train-images-idx3-ubyte.gz')
train_labels_file = os.path.join(data_dir, 'train-labels-idx1-ubyte.gz')
test_image_file = os.path.join(data_dir, 't10k-images-idx3-ubyte.gz')
test_labels_file = os.path.join(data_dir, 't10k-labels-idx1-ubyte.gz')
with gfile.Open(train_image_file, 'rb') as f:
train_images = extract_images(f)
with gfile.Open(train_labels_file, 'rb') as f:
train_labels = extract_labels(f, one_hot=True, num_classes=10)
with gfile.Open(test_image_file, 'rb') as f:
test_images = extract_images(f)
with gfile.Open(test_labels_file, 'rb') as f:
test_labels = extract_labels(f, one_hot=True, num_classes=10)
return train_images, train_labels, test_images, test_labels
def load(data_dir, subset):
SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'
TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'
TEST_IMAGES = 't10k-images-idx3-ubyte.gz'
if subset == 'train' or subset == 'validate':
local_file = base.maybe_download(TRAIN_IMAGES, data_dir,
SOURCE_URL + TRAIN_IMAGES)
with open(local_file, 'rb') as f:
images = extract_images(f)
elif subset == 'test':
local_file = base.maybe_download(TEST_IMAGES, data_dir,
SOURCE_URL + TEST_IMAGES)
with open(local_file, 'rb') as f:
images = extract_images(f)
else:
raise Exception('subset must be train or test')
return images
def load_data():
cwd = os.getcwd()
# training images
with open(os.path.join(cwd, "train-images-idx3-ubyte.gz"), "rb") as f:
train_images = extract_images(f)
# training labels
with open(os.path.join(cwd, "train-labels-idx1-ubyte.gz"), "rb") as f:
train_labels = extract_labels(f)
# testing images
with open(os.path.join(cwd, "t10k-images-idx3-ubyte.gz"), "rb") as f:
test_images = extract_images(f)
# testing labels
with open(os.path.join(cwd, "t10k-labels-idx1-ubyte.gz"), "rb") as f:
test_labels = extract_labels(f)
return (train_images, train_labels), (test_images, test_labels)
def load_minst(src=None, path=None, one_hot=False):
mnist = DataSets()
if src:
mnist = input_data.read_data_sets("MNIST_data/", one_hot=one_hot)
if path:
if path[-1] != '/':
path += '/'
train_images = extract_images(path + TRAIN_IMAGES)
train_labels = extract_labels(path + TRAIN_LABELS, one_hot=one_hot)
test_images = extract_images(path + TEST_IMAGES)
test_labels = extract_labels(path + TEST_LABELS, 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:]
mnist.train = DataSet(train_images, train_labels)
mnist.validation = DataSet(validation_images, validation_labels)
mnist.test = DataSet(test_images, test_labels)
return mnist
def convert_to_data_sets(data_gzs,
one_hot=False,
dtype=dtypes.float32,
reshape=True,
validation_size=5000,
seed=None):
""" Modified version of tensorflow/tensorflow/contrib/learn/python/learn/datasets/mnist.py """
with gfile.Open(data_gzs['train-images'][0], 'rb') as f:
train_images = tf_mnist.extract_images(f)
with gfile.Open(data_gzs['train-labels'][0], 'rb') as f:
train_labels = tf_mnist.extract_labels(f, one_hot=one_hot)
with gfile.Open(data_gzs['t10k-images'][0], 'rb') as f:
test_images = tf_mnist.extract_images(f)
with gfile.Open(data_gzs['t10k-labels'][0], 'rb') as f:
test_labels = tf_mnist.extract_labels(f, one_hot=one_hot)
if not 0 <= validation_size <= len(train_images):
raise ValueError(
'Validation size should be between 0 and {}. Received: {}.'.format(
len(train_images), validation_size))
validation_images = train_images[:validation_size]
validation_labels = train_labels[:validation_size]
train_images = train_images[validation_size:]
train_labels = train_labels[validation_size:]
options = dict(dtype=dtype, reshape=reshape, seed=seed)
train = tf_mnist.DataSet(train_images, train_labels, **options)
validation = tf_mnist.DataSet(validation_images, validation_labels,
**options)
test = tf_mnist.DataSet(test_images, test_labels, **options)
return base.Datasets(train=train, validation=validation, test=test)
def mnist_data_loader(one_hot=False, reshape=True):
"""Load MNIST dataset."""
# Download the dataset if not exist.
# CVDF mirror of http://yann.lecun.com/exdb/mnist/
DATA_URL = 'https://storage.googleapis.com/cvdf-datasets/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'
file_list = [TRAIN_IMAGES, TRAIN_LABELS, TEST_IMAGES, TEST_LABELS]
file_list = [
maybe_download(MNIST_DIR, DATA_URL + fil) for fil in file_list
]
with gfile.Open(file_list[0], 'rb') as f:
train_data = mnist.extract_images(f) / 255
with gfile.Open(file_list[1], 'rb') as f:
train_labels = mnist.extract_labels(f, one_hot)
with gfile.Open(file_list[2], 'rb') as f:
test_data = mnist.extract_images(f) / 255
with gfile.Open(file_list[3], 'rb') as f:
test_labels = mnist.extract_labels(f, one_hot)
# Convert the shape of image, if reshape
# [n_samples, width, length, 1] ==> [n_samples, n_features]
if reshape:
assert train_data.shape[1:] == test_data.shape[1:]
n_train, width, length, _ = train_data.shape
n_test = test_data.shape[0]
train_data = train_data.reshape(n_train, width * length)
test_data = test_data.reshape(n_test, width * length)
return train_data, train_labels, test_data, test_labels
def load_mnist_data():
path = '/content/gdrive/My Drive/542HW4/'
with open((path + "train-images-idx3-ubyte.gz"), "rb") as f:
train_allimages = extract_images(f)
with open((path + "train-labels-idx1-ubyte.gz"), "rb") as f:
train_alllabels = extract_labels(f)
valid_set_size = 10000
split = len(train_allimages) - valid_set_size
valid_images = train_allimages[split:]
valid_labels = train_alllabels[split:]
train_images = train_allimages[:split]
train_labels = train_alllabels[:split]
with open((path + "t10k-images-idx3-ubyte.gz"), "rb") as f:
test_images = extract_images(f)
with open((path + "t10k-labels-idx1-ubyte.gz"), "rb") as f:
test_labels = extract_labels(f)
return (train_images, train_labels), (valid_images,
valid_labels), (test_images,
test_labels)
def load_mnist_data(train_dir, one_hot=True):
"""Returns all 'train' data --- images and labels."""
TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'
TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'
SOURCE_URL = mnist.SOURCE_URL
local_file = base.maybe_download(TRAIN_IMAGES, train_dir,
SOURCE_URL + TRAIN_IMAGES)
with open(local_file, 'rb') as f:
train_images = mnist.extract_images(f)
local_file = base.maybe_download(TRAIN_LABELS, train_dir,
SOURCE_URL + TRAIN_LABELS)
with open(local_file, 'rb') as f:
train_labels = mnist.extract_labels(f, one_hot=one_hot)
return train_images, train_labels
def load_gz(self):
result = []
data = {
'train':
['train-images-idx3-ubyte.gz', 'train-labels-idx1-ubyte.gz'],
'test': ['t10k-images-idx3-ubyte.gz', 't10k-labels-idx1-ubyte.gz']
}
image_path = os.path.join(self.data_dir, data[self.__mode][0])
label_path = os.path.join(self.data_dir, data[self.__mode][1])
with open(image_path, 'rb') as f:
result.append(extract_images(f))
with open(label_path, 'rb') as f:
result.append(extract_labels(f, self.one_hot))
return self.sc.parallelize(zip(*result))
def writeMNIST(sc, input_images, input_labels, output, format, num_partitions):
"""Writes MNIST image/label vectors into parallelized files on HDFS"""
# load MNIST gzip into memory
with open(input_images, 'rb') as f:
images = numpy.array(mnist.extract_images(f))
with open(input_labels, 'rb') as f:
if format == "csv2":
labels = numpy.array(mnist.extract_labels(f, one_hot=False))
else:
labels = numpy.array(mnist.extract_labels(f, one_hot=True))
shape = images.shape
print("images.shape: {0}".format(shape)) # 60000 x 28 x 28
print("labels.shape: {0}".format(labels.shape)) # 60000 x 10
# create RDDs of vectors
imageRDD = sc.parallelize(images.reshape(shape[0], shape[1] * shape[2]),
num_partitions)
labelRDD = sc.parallelize(labels, num_partitions)
output_images = output + "/images"
output_labels = output + "/labels"
# save RDDs as specific format
if format == "pickle":
imageRDD.saveAsPickleFile(output_images)
labelRDD.saveAsPickleFile(output_labels)
elif format == "csv":
imageRDD.map(toCSV).saveAsTextFile(output_images)
labelRDD.map(toCSV).saveAsTextFile(output_labels)
elif format == "csv2":
imageRDD.map(toCSV).zip(labelRDD).map(
lambda x: str(x[1]) + "|" + x[0]).saveAsTextFile(output)
else: # format == "tfr":
tfRDD = imageRDD.zip(labelRDD).map(
lambda x: (bytearray(toTFExample(x[0], x[1])), None))
# requires: --jars tensorflow-hadoop-1.0-SNAPSHOT.jar
tfRDD.saveAsNewAPIHadoopFile(
output,
"org.tensorflow.hadoop.io.TFRecordFileOutputFormat",
keyClass="org.apache.hadoop.io.BytesWritable",
valueClass="org.apache.hadoop.io.NullWritable")
def import_mnist():
"""
This import mnist and saves the data as an object of our DataSet class
:return:
"""
VALIDATION_SIZE = 0
ONE_HOT = True
TRAIN_DIR = 'INFMNIST_data/'
train_images = extract_images_2(
open(TRAIN_DIR + 'mnist8m-patterns-idx3-ubyte.gz'))
train_labels = extract_labels(open(TRAIN_DIR +
'mnist8m-labels-idx1-ubyte.gz'),
one_hot=ONE_HOT)
test_images = extract_images(open(TRAIN_DIR + 'test10k-patterns.gz'))
test_labels = extract_labels(open(TRAIN_DIR + 'test10k-labels.gz'),
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:]
## Process images
train_images = process_mnist(train_images)
validation_images = process_mnist(validation_images)
test_images = process_mnist(test_images)
## Standardize data
train_mean, train_std = get_data_info(train_images)
# train_images = standardize_data(train_images, train_mean, train_std)
# validation_images = standardize_data(validation_images, train_mean, train_std)
# test_images = standardize_data(test_images, train_mean, train_std)
data = DataSet(train_images, train_labels)
test = DataSet(test_images, test_labels)
val = DataSet(validation_images, validation_labels)
return data, test, val
def _get_data(self):
from tensorflow.contrib.learn.python.learn.datasets.base \
import maybe_download
from tensorflow.contrib.learn.python.learn.datasets.mnist \
import extract_images, extract_labels
if self.is_train:
IMAGES = 'train-images-idx3-ubyte.gz'
LABELS = 'train-labels-idx1-ubyte.gz'
else:
print('using test dataset..')
IMAGES = 't10k-images-idx3-ubyte.gz'
LABELS = 't10k-labels-idx1-ubyte.gz'
SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'
local_file = maybe_download(IMAGES, self.path, SOURCE_URL)
with open(local_file, 'rb') as f:
images = extract_images(f)
local_file = maybe_download(LABELS, self.path, SOURCE_URL)
with open(local_file, 'rb') as f:
labels = extract_labels(f, one_hot=False)
values, counts = np.unique(labels, return_counts=True)
data = []
for i in range(10):
label = values[i]
count = counts[i]
arr = np.empty([count, 1, 28, 28], dtype=np.float32)
data.append(arr)
l_iter = [0] * 10
for i in range(labels.shape[0]):
label = labels[i]
data[label][l_iter[label]] = np.reshape(images[i],
[1, 28, 28]) / 255.
l_iter[label] += 1
self.data = data
self.l_iter = l_iter
return data
def generate_mnist_jpg(subdatadir, source_image_path, source_label_path):
create_folder(subdatadir)
local_file = base.maybe_download(source_image_path, train_dir,
SOURCE_URL + source_image_path)
with open(local_file, 'rb') as f:
images = mnist.extract_images(f)
local_file = base.maybe_download(source_label_path, train_dir,
SOURCE_URL + source_label_path)
with open(local_file, 'rb') as f:
labels = mnist.extract_labels(f, one_hot=False)
for img in range(labels.size):
subdirpath = subdatadir + str(labels[img])
create_folder(subdirpath)
filepath = (subdirpath + '/' + str(labels[img]) + '_' + str(img) + '.jpg')
im = Image.fromarray(images[img,:,:,0])
im.save(filepath)
def _get_data(self):
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.contrib.learn.python.learn.datasets.base \
import maybe_download
from tensorflow.contrib.learn.python.learn.datasets.mnist \
import extract_images, extract_labels
if self.is_train:
IMAGES = 'train-images-idx3-ubyte.gz'
LABELS = 'train-labels-idx1-ubyte.gz'
else:
IMAGES = 't10k-images-idx3-ubyte.gz'
LABELS = 't10k-labels-idx1-ubyte.gz'
SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'
# local_file = maybe_download(IMAGES, self.path, SOURCE_URL)
with open("MNIST_data/" + IMAGES, 'rb') as f:
images = extract_images(f)
# local_file = maybe_download(LABELS, self.path, SOURCE_URL)
with open("MNIST_data/" + LABELS, 'rb') as f:
labels = extract_labels(f, one_hot=False)
values, counts = np.unique(labels, return_counts=True)
data = []
for i in range(10):
label = values[i]
count = counts[i]
arr = np.empty([count, 28, 28, 1], dtype=np.float32)
data.append(arr)
l_iter = [0] * 10
for i in range(labels.shape[0]):
label = labels[i]
data[label][l_iter[label]] = images[i] / 255.
l_iter[label] += 1
return data
def writeMNIST(sc, input_images, input_labels, output, format, num_partitions):
"""Writes MNIST image/label vectors into parallelized files on HDFS"""
# load MNIST gzip into memory
with open(input_images, 'rb') as f:
images = numpy.array(mnist.extract_images(f))
with open(input_labels, 'rb') as f:
if format == "csv2":
labels = numpy.array(mnist.extract_labels(f, one_hot=False))
else:
labels = numpy.array(mnist.extract_labels(f, one_hot=True))
shape = images.shape
print("images.shape: {0}".format(shape)) # 60000 x 28 x 28
print("labels.shape: {0}".format(labels.shape)) # 60000 x 10
# create RDDs of vectors
imageRDD = sc.parallelize(images.reshape(shape[0], shape[1] * shape[2]), num_partitions)
labelRDD = sc.parallelize(labels, num_partitions)
output_images = output + "/images"
output_labels = output + "/labels"
# save RDDs as specific format
if format == "pickle":
imageRDD.saveAsPickleFile(output_images)
labelRDD.saveAsPickleFile(output_labels)
elif format == "csv":
imageRDD.map(toCSV).saveAsTextFile(output_images)
labelRDD.map(toCSV).saveAsTextFile(output_labels)
elif format == "csv2":
imageRDD.map(toCSV).zip(labelRDD).map(lambda x: str(x[1]) + "|" + x[0]).saveAsTextFile(output)
else: # format == "tfr":
tfRDD = imageRDD.zip(labelRDD).map(lambda x: (bytearray(toTFExample(x[0], x[1])), None))
# requires: --jars tensorflow-hadoop-1.0-SNAPSHOT.jar
tfRDD.saveAsNewAPIHadoopFile(output, "org.tensorflow.hadoop.io.TFRecordFileOutputFormat",
keyClass="org.apache.hadoop.io.BytesWritable",
valueClass="org.apache.hadoop.io.NullWritable")
print("Completed processing 10000 images")
if i == 20000:
print("Completed processing 20000 images")
if i == 30000:
print("Completed processing 30000 images")
if i == 40000:
print("Completed processing 40000 images")
if i == 50000:
print("Completed processing 50000 images")
if i == 59999:
print("Completed processing 60000 images")
return w, bias
with open('train-images-idx3-ubyte.gz', 'rb') as f:
X_train = extract_images(f)
with open('train-labels-idx1-ubyte.gz', 'rb') as f:
Y_train = extract_labels(f)
with open('t10k-images-idx3-ubyte.gz', 'rb') as f:
x_test = extract_images(f)
with open('t10k-labels-idx1-ubyte.gz', 'rb') as f:
y_test = extract_labels(f)
num_pixels = X_train.shape[1] * X_train.shape[2]
X_train = X_train.reshape((X_train.shape[0], num_pixels)).astype('float32')
x_test = x_test.reshape((x_test.shape[0], num_pixels)).astype('float32')
Y_train_new = convert(Y_train)
y_test_new = convert(y_test)
# normalize inputs from 0-255 to 0-1
X_train = X_train / 255
"""
Created on Wed May 16 00:35:00 2018
@author: ram
"""
import numpy as np
""" Reading MNIST data """
'''
==============================================================================================================
'''
from tensorflow.contrib.learn.python.learn.datasets.mnist import extract_images, extract_labels
with open('train-images-idx3-ubyte.gz', 'rb') as f:
train_images = extract_images(f)
with open('train-labels-idx1-ubyte.gz', 'rb') as f:
train_labels = extract_labels(f)
with open('t10k-images-idx3-ubyte.gz', 'rb') as f:
test_images = extract_images(f)
with open('t10k-labels-idx1-ubyte.gz', 'rb') as f:
test_labels = extract_labels(f)
'''
==============================================================================================================
'''
#printing shapes of all train and test data
"""JYI, 11/13/2018 """
# load data set, data exploration
from tensorflow.contrib.learn.python.learn.datasets.mnist import extract_images, extract_labels
with open('train-images-idx3-ubyte.gz', 'rb') as f:
train_x = extract_images(f)
with open('train-labels-idx1-ubyte.gz', 'rb') as f:
train_y = extract_labels(f)
with open('t10k-images-idx3-ubyte.gz', 'rb') as f:
test_x = extract_images(f)
with open('t10k-labels-idx1-ubyte.gz', 'rb') as f:
test_y = extract_labels(f)
import matplotlib.pyplot as plt
fig1 = plt.figure(1, figsize=(9, 6))
plt.imshow(train_x[0].reshape((28, 28)))
fig1.suptitle('Training data sample', fontsize=10)
fig2 = plt.figure(2, figsize=(9, 6))
plt.imshow(test_x[0].reshape((28, 28)))
fig2.suptitle('Testing data sample', fontsize=10)
plt.show()
print('train_y[0]:{}'.format(train_y[0])) # 5
print('train_x.shape:{}'.format(train_x.shape)) # (60000, 28, 28, 1)
print('train_y.shape:{}'.format(train_y.shape)) # (60000,)
print('test_x.shape:{}'.format(test_x.shape)) # (10000, 28, 28, 1)
print('test_y.shape:{}'.format(test_y.shape)) # (10000,)
# data set pre-processing
import numpy as np
num_class = 10
num_feature = 784
def load_data():
return extract_images(open('data/emnist-bymerge-train-images-idx3-ubyte.gz', 'rb')), \
extract_labels(open('data/emnist-bymerge-train-labels-idx1-ubyte.gz', 'rb')), \
extract_images(open('data/emnist-bymerge-test-images-idx3-ubyte.gz', 'rb')), \
extract_labels(open('data/emnist-bymerge-test-labels-idx1-ubyte.gz', 'rb'))
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
import random
import matplotlib.pyplot as plt
import Dataset_main
tf.set_random_seed(777) # for reproducibility
from tensorflow.contrib.learn.python.learn.datasets.mnist import extract_images, extract_labels
_TEST_DATA_FILENAME = 'D:\DataSet\EMNIST_MNISTFORMAT\gzip\emnist-balanced-test-images-idx3-ubyte.gz'
_TEST_LABELS_FILENAME = 'D:\DataSet\EMNIST_MNISTFORMAT\gzip\emnist-balanced-test-labels-idx1-ubyte.gz'
_TRAIN_DATA_FILENAME = 'D:\DataSet\EMNIST_MNISTFORMAT\gzip\emnist-balanced-train-images-idx3-ubyte.gz'
_TRAIN_LABELS_FILENAME = 'D:\DataSet\EMNIST_MNISTFORMAT\gzip\emnist-balanced-train-labels-idx1-ubyte.gz'
with open('my/directory/train-images-idx3-ubyte.gz', 'rb') as f:
train_images = extract_images(f)
with open('my/directory/train-labels-idx1-ubyte.gz', 'rb') as f:
train_labels = extract_images(f)
with open('my/directory/t10k-images-idx3-ubyte.gz', 'rb') as f:
test_images = extract_labels(f)
with open('my/directory/t10k-labels-idx1-ubyte.gz', 'rb') as f:
test_labels = extract_labels(f)
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
nb_classes = 10
# MNIST data image of shape 28 * 28 = 784
X = tf.placeholder(tf.float32, [None, 784])
def read_data_sets(train_dir, fake_data=False, one_hot=False,
shuffle=False, validation_percentage=0.1):
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
class DataSets(object):
pass
data_sets = DataSets()
if fake_data:
def fake():
return DataSet([], [], fake_data=True, one_hot=one_hot)
data_sets.train = fake()
data_sets.validation = fake()
data_sets.test = fake()
return data_sets
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'
if 0 == rank:
local_file = maybe_download(TRAIN_IMAGES, train_dir, "http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz")
train_images = extract_images(local_file)
if shuffle:
# shuffle the data
perm = np.arange(train_images.shape[0])
np.random.shuffle(perm)
train_images = train_images[perm]
# bcast the data
shape = train_images.shape
shape = comm.bcast(shape, root=0)
comm.Bcast(train_images, root=0)
local_file = maybe_download(TRAIN_LABELS, train_dir, "http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz")
train_labels = extract_labels(local_file, one_hot=one_hot)
if shuffle:
# shuffle the data, using same indices as images above
train_labels = train_labels[perm]
# bcast the data
shape = train_labels.shape
shape = comm.bcast(shape, root=0)
comm.Bcast(train_labels, root=0)
local_file = maybe_download(TEST_IMAGES, train_dir, "http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz")
test_images = extract_images(local_file)
shape = test_images.shape
shape = comm.bcast(shape, root=0)
comm.Bcast(test_images, root=0)
local_file = maybe_download(TEST_LABELS, train_dir, "http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz")
test_labels = extract_labels(local_file, one_hot=one_hot)
shape = test_labels.shape
shape = comm.bcast(shape, root=0)
comm.Bcast(test_labels, root=0)
else:
shape = None
shape = comm.bcast(shape, root=0)
train_images = np.ndarray(shape=shape, dtype=np.uint8)
comm.Bcast(train_images, root=0)
shape = None
shape = comm.bcast(shape, root=0)
train_labels = np.ndarray(shape=shape)
comm.Bcast(train_labels, root=0)
shape = None
shape = comm.bcast(shape, root=0)
test_images = np.ndarray(shape=shape, dtype=np.uint8)
comm.Bcast(test_images, root=0)
shape = None
shape = comm.bcast(shape, root=0)
test_labels = np.ndarray(shape=shape)
comm.Bcast(test_labels, root=0)
VALIDATION_SIZE = int(0)
total = train_images.shape[0] - VALIDATION_SIZE
count = total / size
remain = total % size
if 0 == rank:
print "total images", total
print "image subset (%d,%d)=%d" % (total,size,count)
print "image subset remainder", remain
start = rank * count
stop = rank * count + count
if rank < remain:
start += rank
stop += rank + 1
else :
start += remain
stop += remain
validation_images = train_images[:VALIDATION_SIZE]
validation_labels = train_labels[:VALIDATION_SIZE]
train_images = train_images[VALIDATION_SIZE:]
train_labels = train_labels[VALIDATION_SIZE:]
train_images = train_images[start:stop]
train_labels = train_labels[start:stop]
data_sets.train = DataSet(train_images, train_labels)
data_sets.validation = DataSet(validation_images, validation_labels)
data_sets.test = DataSet(test_images, test_labels)
if 0 == rank:
print "Rank Start Stop NumExamples"
sys.stdout.flush()
for i in xrange(size):
if rank == i:
print i,start,stop,data_sets.train.num_examples
sys.stdout.flush()
comm.Barrier()
return data_sets
def read_data_sets(train_dir,
fake_data=False,
one_hot=False,
shuffle=False,
validation_percentage=0.1):
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
class DataSets(object):
pass
data_sets = DataSets()
if fake_data:
def fake():
return DataSet([], [], fake_data=True, one_hot=one_hot)
data_sets.train = fake()
data_sets.validation = fake()
data_sets.test = fake()
return data_sets
SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'
WORK_DIRECTORY = 'data'
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'
if 0 == rank:
local_file = maybe_download(TRAIN_IMAGES)
temp_file = open(local_file, 'r')
train_images = extract_images(temp_file)
if shuffle:
# shuffle the data
perm = np.arange(train_images.shape[0])
np.random.shuffle(perm)
train_images = train_images[perm]
# bcast the data
shape = train_images.shape
shape = comm.bcast(shape, root=0)
comm.Bcast(train_images, root=0)
local_file = maybe_download(TRAIN_LABELS)
temp_file = open(local_file, 'r')
train_labels = extract_labels(temp_file, one_hot=one_hot)
if shuffle:
# shuffle the data, using same indices as images above
train_labels = train_labels[perm]
# bcast the data
shape = train_labels.shape
shape = comm.bcast(shape, root=0)
comm.Bcast(train_labels, root=0)
local_file = maybe_download(TEST_IMAGES)
temp_file = open(local_file, 'r')
test_images = extract_images(temp_file)
shape = test_images.shape
shape = comm.bcast(shape, root=0)
comm.Bcast(test_images, root=0)
local_file = maybe_download(TEST_LABELS)
temp_file = open(local_file, 'r')
test_labels = extract_labels(temp_file, one_hot=one_hot)
shape = test_labels.shape
shape = comm.bcast(shape, root=0)
comm.Bcast(test_labels, root=0)
else:
shape = None
shape = comm.bcast(shape, root=0)
train_images = np.ndarray(shape=shape, dtype=np.uint8)
comm.Bcast(train_images, root=0)
shape = None
shape = comm.bcast(shape, root=0)
train_labels = np.ndarray(shape=shape)
comm.Bcast(train_labels, root=0)
shape = None
shape = comm.bcast(shape, root=0)
test_images = np.ndarray(shape=shape, dtype=np.uint8)
comm.Bcast(test_images, root=0)
shape = None
shape = comm.bcast(shape, root=0)
test_labels = np.ndarray(shape=shape)
comm.Bcast(test_labels, root=0)
VALIDATION_SIZE = train_images.shape[0] * validation_percentage
total = train_images.shape[0] - VALIDATION_SIZE
count = total / size
remain = total % size
if 0 == rank:
print "total images", total
print "image subset (%d,%d)=%d" % (total, size, count)
print "image subset remainder", remain
start = rank * count
stop = rank * count + count
if rank < remain:
start += rank
stop += rank + 1
else:
start += remain
stop += remain
VALIDATION_SIZE = int(VALIDATION_SIZE)
start = int(start)
stop = int(stop)
validation_images = train_images[:VALIDATION_SIZE]
validation_labels = train_labels[:VALIDATION_SIZE]
train_images = train_images[VALIDATION_SIZE:]
train_labels = train_labels[VALIDATION_SIZE:]
train_images = train_images[start:stop]
train_labels = train_labels[start:stop]
data_sets.train = DataSet(train_images, train_labels)
data_sets.validation = DataSet(validation_images, validation_labels)
data_sets.test = DataSet(test_images, test_labels)
if 0 == rank:
print "Rank Start Stop NumExamples"
sys.stdout.flush()
for i in xrange(size):
if rank == i:
print i, start, stop, data_sets.train.num_examples
sys.stdout.flush()
comm.Barrier()
return data_sets
