TensorFlow I/O

TensorFlow 14 Mayıs'ta Google I/O'ya geri döndü! Şimdi üye Ol

Bu sayfa, Cloud Translation API ile çevrilmiştir.

TensorFlow.org'da görüntüleyin

Google Colab'da çalıştırın

Kaynağı GitHub'da görüntüleyin

Not defterini indir

genel bakış

Bu örnek, gösterir tfio.genome yani çeşitli genomik dosya biçimleri okuma ve verilerin hazırlanması için bir ortak bir işlem sunan (örneğin - - bir sıcak kodlama veya olasılık içine Phred kalite ayrıştırma) genel olarak kullanılan genomik ES işlevselliği sağlar paketi.

Bu paket kullanan Google Nucleus temel işlevlerinin bir kısmını sağlamak üzere kütüphane.

Kurmak

try:
  %tensorflow_version 2.x
except Exception:
  pass
!pip install -q tensorflow-io

import tensorflow_io as tfio
import tensorflow as tf

FASTQ Verileri

FASTQ, temel kalite bilgisine ek olarak her iki dizi bilgisini de depolayan yaygın bir genomik dosya formatıdır.

İlk olarak, örnek bir indirmesine olanak fastq dosyasını.

# Download some sample data:
curl -OL https://raw.githubusercontent.com/tensorflow/io/master/tests/test_genome/test.fastq

% Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100   407  100   407    0     0   2035      0 --:--:-- --:--:-- --:--:--  2035

FASTQ Verilerini Oku

Şimdi, diyelim kullanım tfio.genome.read_fastq bu dosyayı okumak için (bir not tf.data API yakında).

fastq_data = tfio.genome.read_fastq(filename="test.fastq")
print(fastq_data.sequences)
print(fastq_data.raw_quality)

tf.Tensor(
[b'GATTACA'
 b'CGTTAGCGCAGGGGGCATCTTCACACTGGTGACAGGTAACCGCCGTAGTAAAGGTTCCGCCTTTCACT'
 b'CGGCTGGTCAGGCTGACATCGCCGCCGGCCTGCAGCGAGCCGCTGC' b'CGG'], shape=(4,), dtype=string)
tf.Tensor(
[b'BB>B@FA'
 b'AAAAABF@BBBDGGGG?FFGFGHBFBFBFABBBHGGGFHHCEFGGGGG?FGFFHEDG3EFGGGHEGHG'
 b'FAFAF;F/9;.:/;999B/9A.DFFF;-->.AAB/FC;9-@-=;=.' b'FAD'], shape=(4,), dtype=string)

Gördüğünüz gibi, dönen fastq_data sahiptir fastq_data.sequences birlikte (her biri farklı boyutta olabilir) fastq dosyasındaki tüm dizilerin bir dize tensörünü olan fastq_data.raw_quality Phred her temel okuma kalitesi hakkında kalitede bilgi kodlanmış içerir sırayla.

Kalite

Eğer ilgileniyorsanız, bu kaliteli bilgiyi olasılıklara dönüştürmek için bir yardımcı op kullanabilirsiniz.

quality = tfio.genome.phred_sequences_to_probability(fastq_data.raw_quality)
print(quality.shape)
print(quality.row_lengths().numpy())
print(quality)

WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:574: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Use fn_output_signature instead
(4, None, 1)
[ 7 68 46  3]
<tf.RaggedTensor [[[0.0005011872854083776], [0.0005011872854083776], [0.0012589251855388284], [0.0005011872854083776], [0.0007943279924802482], [0.00019952621369156986], [0.0006309572490863502]], [[0.0006309572490863502], [0.0006309572490863502], [0.0006309572490863502], [0.0006309572490863502], [0.0006309572490863502], [0.0005011872854083776], [0.00019952621369156986], [0.0007943279924802482], [0.0005011872854083776], [0.0005011872854083776], [0.0005011872854083776], [0.0003162277571391314], [0.0001584893325343728], [0.0001584893325343728], [0.0001584893325343728], [0.0001584893325343728], [0.0010000000474974513], [0.00019952621369156986], [0.00019952621369156986], [0.0001584893325343728], [0.00019952621369156986], [0.0001584893325343728], [0.00012589251855388284], [0.0005011872854083776], [0.00019952621369156986], [0.0005011872854083776], [0.00019952621369156986], [0.0005011872854083776], [0.00019952621369156986], [0.0006309572490863502], [0.0005011872854083776], [0.0005011872854083776], [0.0005011872854083776], [0.00012589251855388284], [0.0001584893325343728], [0.0001584893325343728], [0.0001584893325343728], [0.00019952621369156986], [0.00012589251855388284], [0.00012589251855388284], [0.0003981070767622441], [0.0002511885541025549], [0.00019952621369156986], [0.0001584893325343728], [0.0001584893325343728], [0.0001584893325343728], [0.0001584893325343728], [0.0001584893325343728], [0.0010000000474974513], [0.00019952621369156986], [0.0001584893325343728], [0.00019952621369156986], [0.00019952621369156986], [0.00012589251855388284], [0.0002511885541025549], [0.0003162277571391314], [0.0001584893325343728], [0.015848929062485695], [0.0002511885541025549], [0.00019952621369156986], [0.0001584893325343728], [0.0001584893325343728], [0.0001584893325343728], [0.00012589251855388284], [0.0002511885541025549], [0.0001584893325343728], [0.00012589251855388284], [0.0001584893325343728]], [[0.00019952621369156986], [0.0006309572490863502], [0.00019952621369156986], [0.0006309572490863502], [0.00019952621369156986], [0.002511885715648532], [0.00019952621369156986], [0.03981072083115578], [0.003981071058660746], [0.002511885715648532], [0.050118714570999146], [0.003162277629598975], [0.03981072083115578], [0.002511885715648532], [0.003981071058660746], [0.003981071058660746], [0.003981071058660746], [0.0005011872854083776], [0.03981072083115578], [0.003981071058660746], [0.0006309572490863502], [0.050118714570999146], [0.0003162277571391314], [0.00019952621369156986], [0.00019952621369156986], [0.00019952621369156986], [0.002511885715648532], [0.06309572607278824], [0.06309572607278824], [0.0012589251855388284], [0.050118714570999146], [0.0006309572490863502], [0.0006309572490863502], [0.0005011872854083776], [0.03981072083115578], [0.00019952621369156986], [0.0003981070767622441], [0.002511885715648532], [0.003981071058660746], [0.06309572607278824], [0.0007943279924802482], [0.06309572607278824], [0.001584893325343728], [0.002511885715648532], [0.001584893325343728], [0.050118714570999146]], [[0.00019952621369156986], [0.0006309572490863502], [0.0003162277571391314]]]>

Bir sıcak kodlama

Ayrıca, (oluşur genom sekansı veri kodlamak isteyebilir A T C G bir sıcak kodlayıcı kullanılarak bazlar). Bu konuda yardımcı olabilecek yerleşik bir işlem var.

one_hot = tfio.genome.sequences_to_onehot(fastq_data.sequences)
print(one_hot)
print(one_hot.shape)

<tf.RaggedTensor [[[0, 0, 1, 0], [1, 0, 0, 0], [0, 0, 0, 1], [0, 0, 0, 1], [1, 0, 0, 0], [0, 1, 0, 0], [1, 0, 0, 0]], [[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 1, 0], [0, 0, 1, 0], [0, 0, 1, 0], [0, 0, 1, 0], [0, 0, 1, 0], [0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 0, 1], [0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 0, 1], [0, 1, 0, 0], [1, 0, 0, 0], [0, 1, 0, 0], [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0], [0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 1, 0], [1, 0, 0, 0], [0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 1, 0], [0, 0, 1, 0], [0, 0, 0, 1], [1, 0, 0, 0], [1, 0, 0, 0], [0, 1, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1], [1, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0], [0, 0, 1, 0], [0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 0, 1], [0, 1, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 0, 1], [0, 0, 0, 1], [0, 1, 0, 0], [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1]], [[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0], [0, 0, 1, 0], [0, 0, 0, 1], [0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 1, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0], [1, 0, 0, 0], [0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 0, 1], [0, 1, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0], [0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 1, 0], [1, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0], [0, 1, 0, 0]], [[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 1, 0]]]>
(4, None, 4)

print(tfio.genome.sequences_to_onehot.__doc__)

Convert DNA sequences into a one hot nucleotide encoding.

    Each nucleotide in each sequence is mapped as follows:
    A -> [1, 0, 0, 0]
    C -> [0, 1, 0, 0]
    G -> [0 ,0 ,1, 0]
    T -> [0, 0, 0, 1]

    If for some reason a non (A, T, C, G) character exists in the string, it is
    currently mapped to a error one hot encoding [1, 1, 1, 1].

    Args:
        sequences: A tf.string tensor where each string represents a DNA sequence

    Returns:
        tf.RaggedTensor: The output sequences with nucleotides one hot encoded.