Related
First of all, I would like to say that this is my first question in stackOverflow, so I hope that the question as a whole respects the rules. I realize that the question is a bit long, but I would like to provide as much background and detail as possible .
I am currently developing a real-time image binary classification system based on Tensorflow 2.8.0 and I am quite new at it. Here are some of the peculiarities of the data that I have for the mentioned project:
Too big to fit in memory: I have more than 200 GB of data. Keep in mind that I have labeled only a small portion of it, but I want to write code that could manage the whole dataset in the future.
Some files are not directly compatible with Tensorflow: I have .FITS and .FIT files that cannot be opened directly with Tensorflow. Due to this issue, I use a library called Astropy to open these files.
The classes are very unbalanced.
After reading the official documentation and tutorials, I thought that, in order to load, preprocess and feed data to my CNN, the best option was to build an input pipeline using the tf.data.Dataset class due to the ease of opening FITS files. My general procedure follows this idea:
Get a list of file paths and split it into train, val and test partitions if desired.
Create a tf.data.Dataset with the from_tensor_slices() method
Shuffle the data (before the heavier reading and image processing operations)
Read and process every path with map()
Batch and prefetch
Here are some code fragments in case they help to understand my goal:
(...)
import config as cfg # Custom .py file
import tensorflow as tf
# x_train, x_val and x_test are previously split file paths lists
train_ds = tf.data.Dataset.from_tensor_slices([str(p) for p in x_train])
val_ds = tf.data.Dataset.from_tensor_slices([str(p) for p in x_val])
test_ds = tf.data.Dataset.from_tensor_slices([str(p) for p in x_test])
train_ds = configure_tf_ds(train_ds)
val_ds = configure_tf_ds(val_ds)
test_ds = configure_tf_ds(test_ds)
def configure_tf_ds(self, tf_ds, buf_size):
# reshuffle_each_iteration=True ensures that data is shuffled each time it is iterated
tf_ds = tf_ds.shuffle(buffer_size=cfg.SHUFFLE_BUF_SIZE, seed=cfg.seed, reshuffle_each_iteration=True)
tf_ds = tf_ds.map(lambda x: tf.py_function(self.process_path, [x], [self.img_dtpye, self.label_dtype]))
tf_ds = tf_ds.batch(self.batch_size)
tf_ds = tf_ds.prefetch(buffer_size=tf.data.AUTOTUNE)
return tf_ds
def process_path(self, file_path):
# Labels are extracted from the file path, not relevant for my problem
label = get_label(file_path)
path = bytes.decode(file_path.numpy()).lower()
img = None
# Open and process images depending on their file paths' extension: FITS, FIT, JPG
if "fit" in path:
img = tf.py_function(func=self.decode_fits, inp=[file_path], Tout=self.img_dtpye)
else:
img = tf.py_function(func=self.decode_img, inp=[file_path], Tout=self.img_dtpye)
return img, label
model.fit(train_ds, epochs=50, validation_data=val_ds)
# Then, I would like to obtain predictions, plot results, and so on but knowing which file paths I am working with
(...)
Following the previous idea, I have successfully created and tested different types of pipelines for different types of partitions of my dataset: unlabeled (remember that only a portion of the data is labeled), labeled and weighted labeled (I wanted to see if my models improve by specifying class weights when training).
However, in order to monitor results and make proper adjustments to my model, I would like to retrieve the usual predictions, real labels and images next to the file paths preserving the ability to shuffle the data after every epoch.
I have managed to solve my question if I do not shuffle data with .shuffle(reshuffle_each_iteration=True), but models' performance is supposed to increase if data is shuffled after each epoch, according to several sources.
I have read different posts in stackOverflow related to my question. I will list those posts next to the problems that I have found for my particular use case:
Solution 1: My dataset cannot be fed to the model as X, y because it is a tf.data.Dataset
Solution 2: I want to obtain the image and the label too.
Solution 3: This works, but it would not respect the expected tf.data.Dataset format in the future .fit() call as stated here:
A tf.data dataset. Should return a tuple of either (inputs, targets)
or (inputs, targets, sample_weights)
I have also tried to keep a separate tf.data.Dataset with only the file paths but if I call the shuffle method with the reshuffle_each_iteration=True option in both tf.data.Dataset instances, the order of their elements does not match even if I set the same seed.
In short, is it possible to achieve what I want? If so, how should I proceed?
Thank you very much in advance.
Preprocess your data into three TFRecord files, one each for training, testing, and validation. Then you can shuffle and never cross records between the sets. This also speeds up data loading and can be done once and reused many times while playing with hyperparameters.
Here is an example of how you can preprocess and split your data. Your actual dataset data will have a different structure, this example has "encdata", a 2048-wide vector of vggface2 face encoding data. This assumes you have a single directory of data, with subdirectories named for a class and containing all the files for that class.
import tensorflow as tf
import numpy as np
import pickle
import sys
import os
# 80% to training, 10% to testing, 10% to validation
validation_portion = 1
testing_portion = 1
training_portion = 8
file_cycle_total = validation_portion + testing_portion + training_portion
# Where to store the TFRecord files
training_tfrecord_path = '/var/tmp/xtraining_tfrecords.tfr'
testing_tfrecord_path = '/var/tmp/xtesting_tfrecords.tfr'
validation_tfrecord_path = '/var/tmp/xvalidation_tfrecords.tfr'
# Where we keep the encodings
FACELIB_DIR='/aimiassd/Datasets/LabeledAstroFaces'
# Get list of all classes from all facelib dirs
classNames = sorted([x for x in os.listdir(FACELIB_DIR) if os.path.isdir(os.path.join(FACELIB_DIR,x)) and not x.startswith('.')])
classStrToInt = dict([(x,i) for i,x in enumerate(classNames)])
print('Found %d different classNames for labels\n' % len(classNames))
# Create our record writers
train_file_writer = tf.io.TFRecordWriter(training_tfrecord_path)
test_file_writer = tf.io.TFRecordWriter(testing_tfrecord_path)
val_file_writer = tf.io.TFRecordWriter(validation_tfrecord_path)
# Create a dataset of filenames of every enc2048 file in the facelibraries
cnt_records_written = [0,0,0]
for CN in classNames:
class_int = classStrToInt[CN]
# Get a list of all the encoding files
encfiles = sorted(filter((lambda x: x.endswith('.enc2048')), os.listdir(os.path.join(FACELIB_DIR, CN))))
# For each encoding file, read the encoding data and write it to the various tfrecords
for i, F in enumerate(encfiles):
file_path = os.path.join(FACELIB_DIR,CN,F)
with open(file_path,'rb') as fin:
encdata,_ = pickle.loads(fin.read()) # encodings, source_image_name
# Turn encdata into a tf.train.Example and serialize it for writing
record_bytes = tf.train.Example(features=tf.train.Features(feature={
"x": tf.train.Feature(float_list=tf.train.FloatList(value=encdata)),
"y": tf.train.Feature(int64_list=tf.train.Int64List(value=[class_int])),
})).SerializeToString()
# Write it out with the appropriate record writer
remainder = i % file_cycle_total
if remainder < validation_portion:
val_file_writer.write(record_bytes)
cnt_records_written[2] += 1
elif remainder < validation_portion + testing_portion:
test_file_writer.write(record_bytes)
cnt_records_written[1] += 1
else:
train_file_writer.write(record_bytes)
cnt_records_written[0] += 1
print('Writing records done.')
print('Wrote %d training, %d testing, %d validation records' %
(cnt_records_written[0], cnt_records_written[1], cnt_records_written[2]) )
train_file_writer.close()
test_file_writer.close()
val_file_writer.close()
print('Reading data back out...')
# Function to turn a serialized TFRecord back into a tf.train.Example
def decode_fn(record_bytes):
return tf.io.parse_single_example(
# Data
record_bytes,
# Schema
{"x": tf.io.FixedLenFeature([2048], dtype=tf.float32),
"y": tf.io.FixedLenFeature([], dtype=tf.int64)}
)
# Read and deserialize the datasets
train_ds = tf.data.TFRecordDataset([training_tfrecord_path]).map(decode_fn)
test_ds = tf.data.TFRecordDataset([ testing_tfrecord_path]).map(decode_fn)
validation_ds = tf.data.TFRecordDataset([validation_tfrecord_path]).map(decode_fn)
# Use a dataset
count = 0
for batch in tf.data.TFRecordDataset([training_tfrecord_path]).map(decode_fn):
print(batch)
count +=1
if count > 4:
sys.exit(0)
print('Done.')
Note how as the data is being process into TFRecords, it is alternately being written into the three datasets. Verify and Testing entries are written first, to ensure classes with very small amounts of samples still get something into the verify and testing datasets. This is controlled by the variables at the top, validation_portion, testing_portion, and training_portion, adjust per your preferences.
Finally, at the end, the TFRecords are re-read and used to build three new tf.data.Dataset, which can be fed to model.fit() and friends. The example code just prints four records to show the data is of the correct, original shape.
I am training Face-recognition model, So for Triplet Loss, I have to generate the batch such that it contains fixed amount of images from each label. For eg. I am saying that take 8 images from 3 random labels each time it generates batch for training, As suggested in this Github Issue.
In my dataset folder I have subfolder which is renamed as a label and contains the images of that folder.
In the given issue, solution is presented,
import numpy as np
import cv2
num_labels = len(path_list)
num_classes_per_batch = 3
num_images_per_class = 8
image_dirs = ["/content/drive/My Drive/smalld_processed/train/{:d}".format(i) for i in
range(num_labels)]
## Create the list of datasets creating filenames
#datasets = [tf.data.Dataset.list_files(f"{image_dir}/*.jpg" for image_dir in image_dirs)]
datasets = [tf.data.Dataset.list_files(f"{image_dir}/*.jpg") for image_dir in image_dirs]
adk = ["{}/*.jpg".format(image_dir) for image_dir in image_dirs]
print(adk)
def generator():
while True:
# Sample the labels that will compose the batch
labels = np.random.choice(range(num_labels),
num_classes_per_batch,
replace=False)
for label in labels:
for _ in range(num_images_per_class):
yield label
choice_dataset = tf.data.Dataset.from_generator(generator, tf.int64)
dataset = tf.data.experimental.choose_from_datasets(datasets, choice_dataset)
## Now you read the image content
def load_image(filename):
image = cv2.imread(filename,1)
image = dataset.map(image, num_parallel_calls=tf.data.experimental.AUTOTUNE)
image = image[...,::-1]
label = int(os.path.split(os.path.dirname(filename))[1])
image=dataset1.append()
label=dataset2.append
return image, label
dataset = dataset.map(load_image, num_parallel_calls=tf.data.experimental.AUTOTUNE)
batch_size = num_classes_per_batch * num_images_per_class
dataset = dataset.batch(batch_size)
dataset = dataset.prefetch(None)
With this I am not able to load the images and it's showing me this error.
SystemError: <built-in function imread> returned NULL without setting an error
Could you help me to fix the error or any other suggestion on how to load images.
Thanks in advance!!
I think that in this case your cv2.imread is acting up. I would first build a simple program that does not do the reading "on the fly", but instead pre-loads images to train on a small dataset.
It also feels like you are misusing the dataset.map function. I would recommend this tutorial on the tf.data.Dataset function: http://tensorexamples.com/2020/07/27/Using-the-tf.data.Dataset.html, and maybe this one on augmentation so you can see how you should use the map function properly: http://tensorexamples.com/2020/07/28/Augmentation.html.
Good luck!
I want to know if anyone has created their own custom input function for tensorflow's estimator ? like in (link) this image:
where the say it is recommended to use tf.data.dataset. But I do not want to use that one, as I want to write my own iterator which yields data in batches and shuffles it as well.
def data_in(train_data):
data = next(train_data)
ff = list(data)
tf.enable_eager_execution()
imgs = tf.stack([tf.convert_to_tensor(np.reshape(f[0], [img_size[0], img_size[1], img_size[2]])) for f
in ff])
lbls = tf.stack([f[1] for f in ff])
print('TRAIN data: %s %s ' % (imgs.get_shape(), lbls.get_shape()))
return imgs, lbls
output: TRAIN data: (10, 32, 32, 3) (10,)
where train_data is a generator object which iterates through my dataset using iter and np.reshape(f[0], [img_size[0], img_size2, img_size2] basically reshapes the data I extract to the required dimensions and it is a batch of an entire dataset. I use stack to convert the list of tensors to convert to stacked tensors. But when I use this with estimators I get an error for the features provided to the model saying the features do not have get_shape(). When I test it without an estimator it works well and it get_shape() also works well.
Hey kvish I figured it out how to do it. I just had to add these lines
experiment = tf.contrib.learn.Experiment(
cifar_classifier,
train_input_fn=lambda: data_in(),
eval_input_fn=lambda: data_in_eval(),
train_steps=train_steps)
I know experiment is deprecated, I will also do it with estimator now :)
Following this tutorial: https://www.tensorflow.org/versions/r1.3/get_started/mnist/pros
I wanted to solve a classification problem with labeled images by myself. Since I'm not using the MNIST database, I spent days creating my own dataset inside tensorflow. It looks like this:
#variables
batch_size = 50
dimension = 784
stages = 10
#step 1 read Dataset
filenames = tf.constant(filenamesList)
labels = tf.constant(labelsList)
#step 2 create Dataset
dataset = tf.data.Dataset.from_tensor_slices((filenames, labels))
#step 3: parse every image in the dataset using `map`
def _parse_function(filename, label):
#convert label to one-hot encoding
one_hot = tf.one_hot(label, stages)
#read image file
image_string = tf.read_file(filename)
image_decoded = tf.image.decode_image(image_string, channels=3)
image = tf.cast(image_decoded, tf.float32)
return image, one_hot
#step 4 final input tensor
dataset = dataset.map(_parse_function)
dataset = dataset.batch(batch_size) #batch_size = 100
iterator = dataset.make_one_shot_iterator()
images, labels = iterator.get_next()
images = tf.reshape(images, [batch_size,dimension]).eval()
labels = tf.reshape(labels, [batch_size,stages]).eval()
for _ in range(10):
dataset = dataset.shuffle(buffer_size = 100)
dataset = dataset.batch(batch_size)
iterator = dataset.make_one_shot_iterator()
images, labels = iterator.get_next()
images = tf.reshape(images, [batch_size,dimension]).eval()
labels = tf.reshape(labels, [batch_size,stages]).eval()
train_step.run(feed_dict={x: images, y_:labels})
Somehow using a higher batch_sizes will break python. What I'm trying to do is to train my neural network with new batches on each iteration. That's why Im also using dataset.shuffle(...). Using dataset.shuffle also breaks my Python.
What I wanted to do (because shuffle breaks) is to batch the whole dataset. By evaluating ('.eval()') I will get a numpy array. I will then shuffle the array with numpy.random.shuffle(images) and then pick up some the first elements to train it.
e.g.
for _ in range(1000):
images = tf.reshape(images, [batch_size,dimension]).eval()
labels = tf.reshape(labels, [batch_size,stages]).eval()
#shuffle
np.random.shuffle(images)
np.random.shuffle(labels)
train_step.run(feed_dict={x: images[0:train_size], y_:labels[0:train_size]})
But then here comes the problem that I can't batch the my whole dataset. It looks like that the data is too big for python to work with.
How should I solve this differently?
Since I'm not using the MNIST database there isn't a function like mnist.train.next_batch(100) which comes handy for me.
Notice how you call shuffle and batch inside your for loop? This is wrong. Datasets in TF work in the style of functional programming, so you are actually defining a pipeline for preprocessing the data to feed into your model. In a way, you give a recipe that answers the question "given this raw data, which operations (map, etc.) should I do to get batches that I can feed into my neural network?"
Now you are modifying that pipeline for every batch! What happens is that the first iteration, the batch size is, say [32 3600]. The next iteration, the elements of this shape are batched again, to [32 32 3600], and so on.
There's a great tutorial on the TF website where you can find out more how Datasets work, but here are a few suggestions how you can resolve your problem.
Move the shuffling to right after "Step 2" in your code. Then you are shuffling the whole dataset so your batches will have a good mixture of examples. Also increase the buffer_size argument, this works in a different way than you probably assume. It's usually a good idea to shuffle as early as possible, as it can be a slow operation if you have a large dataset -- the shuffled part of dataset will have to be read into memory. Here it does not really matter whether you shuffle the filenames and labels, or the read images and labels -- but the latter will have more work to do since the dataset is larger by that time.
Move batching and the iterator generator to be the last steps, just before starting your training loop.
Don't use feed_dict with Dataset iterators to input data into your model. Instead, define your model in terms of the outputs of iterator.get_next() and omit the feed_dict argument. See more details from this Q&A: Tensorflow: create minibatch from numpy array > 2 GB
Ive been getting through a lot of problems with creating tensorflow datasets. So I decided to use OpenCV to import images.
import opencv as cv
imgDataset = []
for i in range(len(files)):
imgDataset.append(cv2.imread(files[i]))
imgDataset = np.asarray(imgDataset)
the shape of imgDataset is (num_img, height, width, col_channels). Getting the i-th image should be imgDataset[i].
shuffling the dataset and getting only batches of it can be done like this:
from sklearn.utils import shuffle
X,y = shuffle(X, y)
X_feed = X[batch_size]
y_feed = y[batch_size]
Then you feed X_feed and y_feed into your model
I have a huge training CSV file (709M) and a large testing CSV file (125M) that I want to send into a DNNClassifier in the context of using the high-level Tensorflow API.
It appears that the input_fn param accepted by fit and evaluate must hold all feature and label data in memory, but I currently would like to run this on my local machine, and thus expect it to run out of memory rather quickly if I read these files into memory and then process them.
I skimmed the doc on streamed-reading of data, but the sample code for reading CSVs appears to be for the low-level Tensorflow API.
And - if you'll forgive a bit of whining - it seems overly-complex for the trivial use case of sending well-prepared files of training and test data into an Estimator ... although, perhaps that level of complexity is actually required for training and testing large volumes of data in Tensorflow?
In any case, I'd really appreciate an example of using that approach with the high-level API, if it's even possible, which I'm beginning to doubt.
After poking around, I did manage to find DNNClassifier#partial_fit, and will attempt to use it for training.
Examples of how to use this method would save me some time, though hopefully I'll stumble into the correct usage in the next few hours.
However, there doesn't seem to be a corresponding DNNClassifier#partial_evaluate ... though I suspect that I could break-up the testing data into smaller pieces and run DNNClassifier#evaluate successively on each batch, which might actually be a great way to do it since I could segment the testing data into cohorts, and thereby obtain per-cohort accuracy.
==== Update ====
Short version:
DomJack's recommendation should be the accepted answer.
However, my Mac's 16GB of RAM enough for it to hold the entire 709Mb training data set in memory without crashing. So, while I will use the DataSets feature when I eventually deploy the app, I'm not using it yet for local dev work.
Longer version:
I started by using the partial_fit API as described above, but upon every use it emitted a warning.
So, I went to look at the source for the method here, and discovered that its complete implementation looks like this:
logging.warning('The current implementation of partial_fit is not optimized'
' for use in a loop. Consider using fit() instead.')
return self.fit(x=x, y=y, input_fn=input_fn, steps=steps,
batch_size=batch_size, monitors=monitors)
... which reminds me of this scene from Hitchhiker's Guide:
Arthur Dent: What happens if I press this button?
Ford Prefect: I wouldn't-
Arthur Dent: Oh.
Ford Prefect: What happened?
Arthur Dent: A sign lit up, saying 'Please do not press this button again'.
Which is to say: partial_fit seems to exist for the sole purpose of telling you not to use it.
Furthermore, the model generated by using partial_fit iteratively on training file chunks was much smaller than the one generated by using fit on the whole training file, which strongly suggests that only the last partial_fit training chunk actually "took".
Check out the tf.data.Dataset API. There are a number of ways to create a dataset. I'll outline four - but you'll only have to implement one.
I assume each row of your csv files is n_features float values followed by a single int value.
Creating a tf.data.Dataset
Wrap a python generator with Dataset.from_generator
The easiest way to get started is to wrap a native python generator. This can have performance issues, but may be fine for your purposes.
def read_csv(filename):
with open(filename, 'r') as f:
for line in f.readlines():
record = line.rstrip().split(',')
features = [float(n) for n in record[:-1]]
label = int(record[-1])
yield features, label
def get_dataset():
filename = 'my_train_dataset.csv'
generator = lambda: read_csv(filename)
return tf.data.Dataset.from_generator(
generator, (tf.float32, tf.int32), ((n_features,), ()))
This approach is highly versatile and allows you to test your generator function (read_csv) independently of TensorFlow.
Use Tensorflow Datasets API
Supporting tensorflow versions 1.12+, tensorflow datasets is my new favourite way of creating datasets. It automatically serializes your data, collects statistics and makes other meta-data available to you via info and builder objects. It can also handle automatic downloading and extracting making collaboration simple.
import tensorflow_datasets as tfds
class MyCsvDatasetBuilder(tfds.core.GeneratorBasedBuilder):
VERSION = tfds.core.Version("0.0.1")
def _info(self):
return tfds.core.DatasetInfo(
builder=self,
description=(
"My dataset"),
features=tfds.features.FeaturesDict({
"features": tfds.features.Tensor(
shape=(FEATURE_SIZE,), dtype=tf.float32),
"label": tfds.features.ClassLabel(
names=CLASS_NAMES),
"index": tfds.features.Tensor(shape=(), dtype=tf.float32)
}),
supervised_keys=("features", "label"),
)
def _split_generators(self, dl_manager):
paths = dict(
train='/path/to/train.csv',
test='/path/to/test.csv',
)
# better yet, if the csv files were originally downloaded, use
# urls = dict(train=train_url, test=test_url)
# paths = dl_manager.download(urls)
return [
tfds.core.SplitGenerator(
name=tfds.Split.TRAIN,
num_shards=10,
gen_kwargs=dict(path=paths['train'])),
tfds.core.SplitGenerator(
name=tfds.Split.TEST,
num_shards=2,
gen_kwargs=dict(cvs_path=paths['test']))
]
def _generate_examples(self, csv_path):
with open(csv_path, 'r') as f:
for i, line in enumerate(f.readlines()):
record = line.rstrip().split(',')
features = [float(n) for n in record[:-1]]
label = int(record[-1])
yield dict(features=features, label=label, index=i)
Usage:
builder = MyCsvDatasetBuilder()
builder.download_and_prepare() # will only take time to run first time
# as_supervised makes output (features, label) - good for model.fit
datasets = builder.as_dataset(as_supervised=True)
train_ds = datasets['train']
test_ds = datasets['test']
Wrap an index-based python function
One of the downsides of the above is shuffling the resulting dataset with a shuffle buffer of size n requires n examples to be loaded. This will either create periodic pauses in your pipeline (large n) or result in potentially poor shuffling (small n).
def get_record(i):
# load the ith record using standard python, return numpy arrays
return features, labels
def get_inputs(batch_size, is_training):
def tf_map_fn(index):
features, labels = tf.py_func(
get_record, (index,), (tf.float32, tf.int32), stateful=False)
features.set_shape((n_features,))
labels.set_shape(())
# do data augmentation here
return features, labels
epoch_size = get_epoch_size()
dataset = tf.data.Dataset.from_tensor_slices((tf.range(epoch_size,))
if is_training:
dataset = dataset.repeat().shuffle(epoch_size)
dataset = dataset.map(tf_map_fn, (tf.float32, tf.int32), num_parallel_calls=8)
dataset = dataset.batch(batch_size)
# prefetch data to CPU while GPU processes previous batch
dataset = dataset.prefetch(1)
# Also possible
# dataset = dataset.apply(
# tf.contrib.data.prefetch_to_device('/gpu:0'))
features, labels = dataset.make_one_shot_iterator().get_next()
return features, labels
In short, we create a dataset just of the record indices (or any small record ID which we can load entirely into memory). We then do shuffling/repeating operations on this minimal dataset, then map the index to the actual data via tf.data.Dataset.map and tf.py_func. See the Using with Estimators and Testing in isolation sections below for usage. Note this requires your data to be accessible by row, so you may need to convert from csv to some other format.
TextLineDataset
You can also read the csv file directly using a tf.data.TextLineDataset.
def get_record_defaults():
zf = tf.zeros(shape=(1,), dtype=tf.float32)
zi = tf.ones(shape=(1,), dtype=tf.int32)
return [zf]*n_features + [zi]
def parse_row(tf_string):
data = tf.decode_csv(
tf.expand_dims(tf_string, axis=0), get_record_defaults())
features = data[:-1]
features = tf.stack(features, axis=-1)
label = data[-1]
features = tf.squeeze(features, axis=0)
label = tf.squeeze(label, axis=0)
return features, label
def get_dataset():
dataset = tf.data.TextLineDataset(['data.csv'])
return dataset.map(parse_row, num_parallel_calls=8)
The parse_row function is a little convoluted since tf.decode_csv expects a batch. You can make it slightly simpler if you batch the dataset before parsing.
def parse_batch(tf_string):
data = tf.decode_csv(tf_string, get_record_defaults())
features = data[:-1]
labels = data[-1]
features = tf.stack(features, axis=-1)
return features, labels
def get_batched_dataset(batch_size):
dataset = tf.data.TextLineDataset(['data.csv'])
dataset = dataset.batch(batch_size)
dataset = dataset.map(parse_batch)
return dataset
TFRecordDataset
Alternatively you can convert the csv files to TFRecord files and use a TFRecordDataset. There's a thorough tutorial here.
Step 1: Convert the csv data to TFRecords data. Example code below (see read_csv from from_generator example above).
with tf.python_io.TFRecordWriter("my_train_dataset.tfrecords") as writer:
for features, labels in read_csv('my_train_dataset.csv'):
example = tf.train.Example()
example.features.feature[
"features"].float_list.value.extend(features)
example.features.feature[
"label"].int64_list.value.append(label)
writer.write(example.SerializeToString())
This only needs to be run once.
Step 2: Write a dataset that decodes these record files.
def parse_function(example_proto):
features = {
'features': tf.FixedLenFeature((n_features,), tf.float32),
'label': tf.FixedLenFeature((), tf.int64)
}
parsed_features = tf.parse_single_example(example_proto, features)
return parsed_features['features'], parsed_features['label']
def get_dataset():
dataset = tf.data.TFRecordDataset(['data.tfrecords'])
dataset = dataset.map(parse_function)
return dataset
Using the dataset with estimators
def get_inputs(batch_size, shuffle_size):
dataset = get_dataset() # one of the above implementations
dataset = dataset.shuffle(shuffle_size)
dataset = dataset.repeat() # repeat indefinitely
dataset = dataset.batch(batch_size)
# prefetch data to CPU while GPU processes previous batch
dataset = dataset.prefetch(1)
# Also possible
# dataset = dataset.apply(
# tf.contrib.data.prefetch_to_device('/gpu:0'))
features, label = dataset.make_one_shot_iterator().get_next()
estimator.train(lambda: get_inputs(32, 1000), max_steps=1e7)
Testing the dataset in isolation
I'd strongly encourage you to test your dataset independently of your estimator. Using the above get_inputs, it should be as simple as
batch_size = 4
shuffle_size = 100
features, labels = get_inputs(batch_size, shuffle_size)
with tf.Session() as sess:
f_data, l_data = sess.run([features, labels])
print(f_data, l_data) # or some better visualization function
Performance
Assuming your using a GPU to run your network, unless each row of your csv file is enormous and your network is tiny you probably won't notice a difference in performance. This is because the Estimator implementation forces data loading/preprocessing to be performed on the CPU, and prefetch means the next batch can be prepared on the CPU as the current batch is training on the GPU. The only exception to this is if you have a massive shuffle size on a dataset with a large amount of data per record, which will take some time to load in a number of examples initially before running anything through the GPU.
I agree with DomJack about using the Dataset API, except the need to read the whole csv file and then convert to TfRecord. I am hereby proposing to emply TextLineDataset - a sub-class of the Dataset API to directly load data into a TensorFlow program. An intuitive tutorial can be found here.
The code below is used for the MNIST classification problem for illustration and hopefully, answer the question of the OP. The csv file has 784 columns, and the number of classes is 10. The classifier I used in this example is a 1-hidden-layer neural network with 16 relu units.
Firstly, load libraries and define some constants:
# load libraries
import tensorflow as tf
import os
# some constants
n_x = 784
n_h = 16
n_y = 10
# path to the folder containing the train and test csv files
# You only need to change PATH, rest is platform independent
PATH = os.getcwd() + '/'
# create a list of feature names
feature_names = ['pixel' + str(i) for i in range(n_x)]
Secondly, we create an input function reading a file using the Dataset API, then provide the results to the Estimator API. The return value must be a two-element tuple organized as follows: the first element must be a dict in which each input feature is a key, and then a list of values for the training batch, and the second element is a list of labels for the training batch.
def my_input_fn(file_path, batch_size=32, buffer_size=256,\
perform_shuffle=False, repeat_count=1):
'''
Args:
- file_path: the path of the input file
- perform_shuffle: whether the data is shuffled or not
- repeat_count: The number of times to iterate over the records in the dataset.
For example, if we specify 1, then each record is read once.
If we specify None, iteration will continue forever.
Output is two-element tuple organized as follows:
- The first element must be a dict in which each input feature is a key,
and then a list of values for the training batch.
- The second element is a list of labels for the training batch.
'''
def decode_csv(line):
record_defaults = [[0.]]*n_x # n_x features
record_defaults.insert(0, [0]) # the first element is the label (int)
parsed_line = tf.decode_csv(records=line,\
record_defaults=record_defaults)
label = parsed_line[0] # First element is the label
del parsed_line[0] # Delete first element
features = parsed_line # Everything but first elements are the features
d = dict(zip(feature_names, features)), label
return d
dataset = (tf.data.TextLineDataset(file_path) # Read text file
.skip(1) # Skip header row
.map(decode_csv)) # Transform each elem by applying decode_csv fn
if perform_shuffle:
# Randomizes input using a window of 256 elements (read into memory)
dataset = dataset.shuffle(buffer_size=buffer_size)
dataset = dataset.repeat(repeat_count) # Repeats dataset this # times
dataset = dataset.batch(batch_size) # Batch size to use
iterator = dataset.make_one_shot_iterator()
batch_features, batch_labels = iterator.get_next()
return batch_features, batch_labels
Then, the mini-batch can be computed as
next_batch = my_input_fn(file_path=PATH+'train1.csv',\
batch_size=batch_size,\
perform_shuffle=True) # return 512 random elements
Next, we define the feature columns are numeric
feature_columns = [tf.feature_column.numeric_column(k) for k in feature_names]
Thirdly, we create an estimator DNNClassifier:
classifier = tf.estimator.DNNClassifier(
feature_columns=feature_columns, # The input features to our model
hidden_units=[n_h], # One layer
n_classes=n_y,
model_dir=None)
Finally, the DNN is trained using the test csv file, while the evaluation is performed on the test file. Please change the repeat_count and steps to ensure that the training meets the required number of epochs in your code.
# train the DNN
classifier.train(
input_fn=lambda: my_input_fn(file_path=PATH+'train1.csv',\
perform_shuffle=True,\
repeat_count=1),\
steps=None)
# evaluate using the test csv file
evaluate_result = classifier.evaluate(
input_fn=lambda: my_input_fn(file_path=PATH+'test1.csv',\
perform_shuffle=False))
print("Evaluation results")
for key in evaluate_result:
print(" {}, was: {}".format(key, evaluate_result[key]))