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updating documentation in dataset.md
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@cyfra @copybara-github
cyfra authored and copybara-github committed Feb 22, 2019
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Expand Up @@ -37,6 +37,8 @@ np_datasets = tfds.as_numpy(datasets)
* [`"cifar10"`](#cifar10)
* [`"cifar100"`](#cifar100)
* [`"coco2014"`](#coco2014)
* [`"colorectal_histology"`](#colorectal_histology)
* [`"colorectal_histology_large"`](#colorectal_histology_large)
* [`"diabetic_retinopathy_detection"`](#diabetic_retinopathy_detection)
* [`"fashion_mnist"`](#fashion_mnist)
* [`"image_label_folder"`](#image_label_folder)
Expand All @@ -49,6 +51,8 @@ np_datasets = tfds.as_numpy(datasets)
* [`"svhn_cropped"`](#svhn_cropped)
* [`"tf_flowers"`](#tf_flowers)

* [`structured`](#structured)
* [`"titanic"`](#titanic)
* [`text`](#text)
* [`"imdb_reviews"`](#imdb_reviews)
* [`"lm1b"`](#lm1b)
Expand Down Expand Up @@ -124,7 +128,7 @@ TEST | 4,096
* [https://g.co/magenta/nsynth-dataset](https://g.co/magenta/nsynth-dataset)

### Supervised keys (for `as_supervised=True`)
`(u'', u'')`
`None`

### Citation
```
Expand Down Expand Up @@ -284,7 +288,7 @@ VALIDATION | 19,867
* [http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html)

### Supervised keys (for `as_supervised=True`)
`(u'', u'')`
`None`

### Citation
```
Expand Down Expand Up @@ -480,7 +484,7 @@ ALL | 30,000
* [https://github.com/tkarras/progressive_growing_of_gans](https://github.com/tkarras/progressive_growing_of_gans)

### Supervised keys (for `as_supervised=True`)
`(u'', u'')`
`None`

### Citation
```
Expand Down Expand Up @@ -632,7 +636,7 @@ VALIDATION | 40,504
* [http://cocodataset.org/#home](http://cocodataset.org/#home)

### Supervised keys (for `as_supervised=True`)
`(u'', u'')`
`None`

### Citation
```
Expand Down Expand Up @@ -662,6 +666,97 @@ VALIDATION | 40,504

---

## `"colorectal_histology"`

Classification of textures in colorectal cancer histology. Each example is a 150 x 150 x 3 RGB image of one of 8 classes.

* URL: [https://zenodo.org/record/53169#.XGZemKwzbmG](https://zenodo.org/record/53169#.XGZemKwzbmG)
* `DatasetBuilder`: [`tfds.image.colorectal_histology.ColorectalHistology`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/image/colorectal_histology.py)
* Version: `v0.0.1`

### Features
```python
FeaturesDict({
'filename': Text(shape=(), dtype=tf.string, encoder=None),
'image': Image(shape=(150, 150, 3), dtype=tf.uint8),
'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=8),
})
```


### Statistics
Split | Examples
:----- | ---:
TRAIN | 5,000
ALL | 5,000


### Urls
* [https://zenodo.org/record/53169#.XGZemKwzbmG](https://zenodo.org/record/53169#.XGZemKwzbmG)

### Supervised keys (for `as_supervised=True`)
`(u'image', u'label')`

### Citation
```
@article{kather2016multi,
title={Multi-class texture analysis in colorectal cancer histology},
author={Kather, Jakob Nikolas and Weis, Cleo-Aron and Bianconi, Francesco and Melchers, Susanne M and Schad, Lothar R and Gaiser, Timo and Marx, Alexander and Z{"o}llner, Frank Gerrit},
journal={Scientific reports},
volume={6},
pages={27988},
year={2016},
publisher={Nature Publishing Group}
}
```

---

## `"colorectal_histology_large"`

10 large 5000 x 5000 textured colorectal cancer histology images

* URL: [https://zenodo.org/record/53169#.XGZemKwzbmG](https://zenodo.org/record/53169#.XGZemKwzbmG)
* `DatasetBuilder`: [`tfds.image.colorectal_histology.ColorectalHistologyLarge`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/image/colorectal_histology.py)
* Version: `v0.0.1`

### Features
```python
FeaturesDict({
'filename': Text(shape=(), dtype=tf.string, encoder=None),
'image': Image(shape=(5000, 5000, 3), dtype=tf.uint8),
})
```


### Statistics
Split | Examples
:----- | ---:
TEST | 10
ALL | 10


### Urls
* [https://zenodo.org/record/53169#.XGZemKwzbmG](https://zenodo.org/record/53169#.XGZemKwzbmG)

### Supervised keys (for `as_supervised=True`)
`None`

### Citation
```
@article{kather2016multi,
title={Multi-class texture analysis in colorectal cancer histology},
author={Kather, Jakob Nikolas and Weis, Cleo-Aron and Bianconi, Francesco and Melchers, Susanne M and Schad, Lothar R and Gaiser, Timo and Marx, Alexander and Z{"o}llner, Frank Gerrit},
journal={Scientific reports},
volume={6},
pages={27988},
year={2016},
publisher={Nature Publishing Group}
}
```

---

## `"diabetic_retinopathy_detection"`

A large set of high-resolution retina images taken under a variety of imaging conditions.
Expand Down Expand Up @@ -693,7 +788,7 @@ SAMPLE | 10
* [https://www.kaggle.com/c/diabetic-retinopathy-detection/data](https://www.kaggle.com/c/diabetic-retinopathy-detection/data)

### Supervised keys (for `as_supervised=True`)
`(u'', u'')`
`None`

### Citation
```
Expand Down Expand Up @@ -894,7 +989,7 @@ VALIDATION | 300
* [https://www.yf.io/p/lsun](https://www.yf.io/p/lsun)

### Supervised keys (for `as_supervised=True`)
`(u'', u'')`
`None`

### Citation
```
Expand Down Expand Up @@ -1069,7 +1164,7 @@ VALIDATION | 41,620
* [https://storage.googleapis.com/openimages/web/index.html](https://storage.googleapis.com/openimages/web/index.html)

### Supervised keys (for `as_supervised=True`)
`(u'', u'')`
`None`

### Citation
```
Expand Down Expand Up @@ -1248,6 +1343,66 @@ url = "http://download.tensorflow.org/example_images/flower_photos.tgz" }



# [`structured`](#structured)

## `"titanic"`

Dataset describing the survival status of individual passengers on the Titanic. Missing values in the original dataset are represented using ?. Float and int missing values are replaced with -1, string missing values are replaced with 'Unknown'.

* URL: [https://www.openml.org/d/40945](https://www.openml.org/d/40945)
* `DatasetBuilder`: [`tfds.structured.titanic.Titanic`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/structured/titanic.py)
* Version: `v1.0.0`

### Features
```python
FeaturesDict({
'features': FeaturesDict({
'age': Tensor(shape=(), dtype=tf.float32),
'boat': Tensor(shape=(), dtype=tf.string),
'body': Tensor(shape=(), dtype=tf.int32),
'cabin': Tensor(shape=(), dtype=tf.string),
'embarked': ClassLabel(shape=(), dtype=tf.int64, num_classes=4),
'fare': Tensor(shape=(), dtype=tf.float32),
'home.dest': Tensor(shape=(), dtype=tf.string),
'name': Tensor(shape=(), dtype=tf.string),
'parch': Tensor(shape=(), dtype=tf.int32),
'pclass': ClassLabel(shape=(), dtype=tf.int64, num_classes=3),
'sex': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
'sibsp': Tensor(shape=(), dtype=tf.int32),
'ticket': Tensor(shape=(), dtype=tf.string),
}),
'survived': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
})
```


### Statistics
Split | Examples
:----- | ---:
TRAIN | 1,309
ALL | 1,309


### Urls
* [https://www.openml.org/d/40945](https://www.openml.org/d/40945)

### Supervised keys (for `as_supervised=True`)
`(u'features', u'survived')`

### Citation
```
@ONLINE {titanic,
author = "Frank E. Harrell Jr., Thomas Cason",
title = "Titanic dataset",
month = "oct",
year = "2017",
url = "https://www.openml.org/d/40945"
}
```

---


# [`text`](#text)

## `"imdb_reviews"`
Expand Down Expand Up @@ -1755,7 +1910,7 @@ TEST | 256
* [https://sites.google.com/site/brainrobotdata/home/push-dataset](https://sites.google.com/site/brainrobotdata/home/push-dataset)

### Supervised keys (for `as_supervised=True`)
`(u'', u'')`
`None`

### Citation
```
Expand Down Expand Up @@ -1811,7 +1966,7 @@ ALL | 10,000
* [http://www.cs.toronto.edu/~nitish/unsupervised_video/](http://www.cs.toronto.edu/~nitish/unsupervised_video/)

### Supervised keys (for `as_supervised=True`)
`(u'', u'')`
`None`

### Citation
```
Expand Down Expand Up @@ -1955,7 +2110,7 @@ TEST | 2,000
* [https://storage.googleapis.com/scv_dataset/README.html](https://storage.googleapis.com/scv_dataset/README.html)

### Supervised keys (for `as_supervised=True`)
`(u'', u'')`
`None`

### Citation
```
Expand Down

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