TensorFlow: extract data with a given feature, from NSynth Dataset
I have a data set of TFRecord files of serialized TensorFlow Example protocol buffers with one Example proto per note, downloaded from https://magenta.tensorflow.org/datasets/nsynth. I am using the test set, which is approximately 1 Gb, in case someone wants to download it, to check the code below. Each Example contains many features: pitch, instrument ...
The code that reads in this data is:
import tensorflow as tf
import numpy as np
sess = tf.InteractiveSession()
# Reading input data
dataset = tf.data.TFRecordDataset('../data/nsynth-test.tfrecord')
# Convert features into tensors
features =
"pitch": tf.FixedLenFeature([1], dtype=tf.int64),
"audio": tf.FixedLenFeature([64000], dtype=tf.float32),
"instrument_family": tf.FixedLenFeature([1], dtype=tf.int64)
parse_function = lambda example_proto: tf.parse_single_example(example_proto,features)
dataset = dataset.map(parse_function)
# Consuming TFRecord data.
dataset = dataset.shuffle(buffer_size=10000)
dataset = dataset.batch(batch_size=3)
dataset = dataset.repeat()
iterator = dataset.make_one_shot_iterator()
batch = iterator.get_next()
sess.run(batch)
Now, the pitch ranges from 21 to 108. But I want to consider data of a given pitch only, e.g. pitch = 51. How do I extract this "pitch=51" subset from the whole dataset? Or alternatively, what do I do to make my iterator go through this subset only?
python-3.x tensorflow magenta
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I have a data set of TFRecord files of serialized TensorFlow Example protocol buffers with one Example proto per note, downloaded from https://magenta.tensorflow.org/datasets/nsynth. I am using the test set, which is approximately 1 Gb, in case someone wants to download it, to check the code below. Each Example contains many features: pitch, instrument ...
The code that reads in this data is:
import tensorflow as tf
import numpy as np
sess = tf.InteractiveSession()
# Reading input data
dataset = tf.data.TFRecordDataset('../data/nsynth-test.tfrecord')
# Convert features into tensors
features =
"pitch": tf.FixedLenFeature([1], dtype=tf.int64),
"audio": tf.FixedLenFeature([64000], dtype=tf.float32),
"instrument_family": tf.FixedLenFeature([1], dtype=tf.int64)
parse_function = lambda example_proto: tf.parse_single_example(example_proto,features)
dataset = dataset.map(parse_function)
# Consuming TFRecord data.
dataset = dataset.shuffle(buffer_size=10000)
dataset = dataset.batch(batch_size=3)
dataset = dataset.repeat()
iterator = dataset.make_one_shot_iterator()
batch = iterator.get_next()
sess.run(batch)
Now, the pitch ranges from 21 to 108. But I want to consider data of a given pitch only, e.g. pitch = 51. How do I extract this "pitch=51" subset from the whole dataset? Or alternatively, what do I do to make my iterator go through this subset only?
python-3.x tensorflow magenta
add a comment |
I have a data set of TFRecord files of serialized TensorFlow Example protocol buffers with one Example proto per note, downloaded from https://magenta.tensorflow.org/datasets/nsynth. I am using the test set, which is approximately 1 Gb, in case someone wants to download it, to check the code below. Each Example contains many features: pitch, instrument ...
The code that reads in this data is:
import tensorflow as tf
import numpy as np
sess = tf.InteractiveSession()
# Reading input data
dataset = tf.data.TFRecordDataset('../data/nsynth-test.tfrecord')
# Convert features into tensors
features =
"pitch": tf.FixedLenFeature([1], dtype=tf.int64),
"audio": tf.FixedLenFeature([64000], dtype=tf.float32),
"instrument_family": tf.FixedLenFeature([1], dtype=tf.int64)
parse_function = lambda example_proto: tf.parse_single_example(example_proto,features)
dataset = dataset.map(parse_function)
# Consuming TFRecord data.
dataset = dataset.shuffle(buffer_size=10000)
dataset = dataset.batch(batch_size=3)
dataset = dataset.repeat()
iterator = dataset.make_one_shot_iterator()
batch = iterator.get_next()
sess.run(batch)
Now, the pitch ranges from 21 to 108. But I want to consider data of a given pitch only, e.g. pitch = 51. How do I extract this "pitch=51" subset from the whole dataset? Or alternatively, what do I do to make my iterator go through this subset only?
python-3.x tensorflow magenta
I have a data set of TFRecord files of serialized TensorFlow Example protocol buffers with one Example proto per note, downloaded from https://magenta.tensorflow.org/datasets/nsynth. I am using the test set, which is approximately 1 Gb, in case someone wants to download it, to check the code below. Each Example contains many features: pitch, instrument ...
The code that reads in this data is:
import tensorflow as tf
import numpy as np
sess = tf.InteractiveSession()
# Reading input data
dataset = tf.data.TFRecordDataset('../data/nsynth-test.tfrecord')
# Convert features into tensors
features =
"pitch": tf.FixedLenFeature([1], dtype=tf.int64),
"audio": tf.FixedLenFeature([64000], dtype=tf.float32),
"instrument_family": tf.FixedLenFeature([1], dtype=tf.int64)
parse_function = lambda example_proto: tf.parse_single_example(example_proto,features)
dataset = dataset.map(parse_function)
# Consuming TFRecord data.
dataset = dataset.shuffle(buffer_size=10000)
dataset = dataset.batch(batch_size=3)
dataset = dataset.repeat()
iterator = dataset.make_one_shot_iterator()
batch = iterator.get_next()
sess.run(batch)
Now, the pitch ranges from 21 to 108. But I want to consider data of a given pitch only, e.g. pitch = 51. How do I extract this "pitch=51" subset from the whole dataset? Or alternatively, what do I do to make my iterator go through this subset only?
python-3.x tensorflow magenta
python-3.x tensorflow magenta
asked Nov 14 '18 at 16:58
MenciaMencia
2371316
2371316
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1 Answer
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What you have looks pretty good, all you're missing is a filter function.
For example if you only wanted to extract pitch=51, you should add after your map function
dataset = dataset.filter(lambda example: tf.equal(example["pitch"][0], 51))
Thank you so much, this is exactly what I needed!
– Mencia
Nov 28 '18 at 11:41
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
What you have looks pretty good, all you're missing is a filter function.
For example if you only wanted to extract pitch=51, you should add after your map function
dataset = dataset.filter(lambda example: tf.equal(example["pitch"][0], 51))
Thank you so much, this is exactly what I needed!
– Mencia
Nov 28 '18 at 11:41
add a comment |
What you have looks pretty good, all you're missing is a filter function.
For example if you only wanted to extract pitch=51, you should add after your map function
dataset = dataset.filter(lambda example: tf.equal(example["pitch"][0], 51))
Thank you so much, this is exactly what I needed!
– Mencia
Nov 28 '18 at 11:41
add a comment |
What you have looks pretty good, all you're missing is a filter function.
For example if you only wanted to extract pitch=51, you should add after your map function
dataset = dataset.filter(lambda example: tf.equal(example["pitch"][0], 51))
What you have looks pretty good, all you're missing is a filter function.
For example if you only wanted to extract pitch=51, you should add after your map function
dataset = dataset.filter(lambda example: tf.equal(example["pitch"][0], 51))
answered Nov 27 '18 at 18:36
jengeljengel
44938
44938
Thank you so much, this is exactly what I needed!
– Mencia
Nov 28 '18 at 11:41
add a comment |
Thank you so much, this is exactly what I needed!
– Mencia
Nov 28 '18 at 11:41
Thank you so much, this is exactly what I needed!
– Mencia
Nov 28 '18 at 11:41
Thank you so much, this is exactly what I needed!
– Mencia
Nov 28 '18 at 11:41
add a comment |
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