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Use datasets in different formats, including json and csv

TensorFlow peut lire des fichiers de données au format json et csv.

Que ce soit json ou csv le principe est le même.

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow import keras
import pandas as pd
titanic_file = tf.keras.utils.get_file("train.csv", "https://storage.googleapis.com/tf-datasets/titanic/train.csv")
Downloading data from https://storage.googleapis.com/tf-datasets/titanic/train.csv
32768/30874 [===============================] - 0s 0us/step

Rappel sur tensor_slices

var_x = tf.Variable(tf.constant([1, 2, 3]))
print(var_x)
<tf.Variable 'Variable:0' shape=(3,) dtype=int32, numpy=array([1, 2, 3], dtype=int32)>
print(var_x.shape)
(3,)
dataset = tf.data.Dataset.from_tensor_slices(var_x) 
list(dataset.as_numpy_iterator()) 
[1, 2, 3]
dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3]) 
list(dataset.as_numpy_iterator()) 
[1, 2, 3]
dataset = tf.data.Dataset.from_tensor_slices([[1, 2], [3, 4]]) 
list(dataset.as_numpy_iterator()) 
[array([1, 2], dtype=int32), array([3, 4], dtype=int32)]
dataset = tf.data.Dataset.from_tensor_slices(([1, 2], [3, 4], [5, 6])) 
list(dataset.as_numpy_iterator()) 
[(1, 3, 5), (2, 4, 6)]
dataset = tf.data.Dataset.from_tensor_slices(([1, 2], [3, 4], [5, 6], [7, 8])) 
list(dataset.as_numpy_iterator()) 
[(1, 3, 5, 7), (2, 4, 6, 8)]
dataset = tf.data.Dataset.from_tensor_slices({"a": [1, 2], "b": [3, 4]}) 
list(dataset.as_numpy_iterator())
[{'a': 1, 'b': 3}, {'a': 2, 'b': 4}]
features = tf.Variable([[1, 2], [3, 4], [5, 6]]) # ==> 3x2 tensor 
dataset = tf.data.Dataset.from_tensor_slices((features)) 
list(dataset.as_numpy_iterator())
[array([1, 2], dtype=int32),
 array([3, 4], dtype=int32),
 array([5, 6], dtype=int32)]
features = tf.constant([[1, 2], [3, 4], [5, 6]]) # ==> 3x2 tensor 
labels = tf.constant(['A', 'B', 'A']) # ==> 3x1 tensor 
dataset = tf.data.Dataset.from_tensor_slices((features, labels)) 
list(dataset.as_numpy_iterator())
[(array([1, 2], dtype=int32), b'A'),
 (array([3, 4], dtype=int32), b'B'),
 (array([5, 6], dtype=int32), b'A')]
features_dataset = tf.data.Dataset.from_tensor_slices(features) 
labels_dataset = tf.data.Dataset.from_tensor_slices(labels) 
dataset = tf.data.Dataset.zip((features_dataset, labels_dataset)) 
list(dataset.as_numpy_iterator())
[(array([1, 2], dtype=int32), b'A'),
 (array([3, 4], dtype=int32), b'B'),
 (array([5, 6], dtype=int32), b'A')]
list(features_dataset.as_numpy_iterator())
[array([1, 2], dtype=int32),
 array([3, 4], dtype=int32),
 array([5, 6], dtype=int32)]

Le rappel étant terminé.

df = pd.read_csv(titanic_file, index_col=None)
df.head()

survived	sex	age	n_siblings_spouses	parch	fare	class	deck	embark_town	alone
0	0	male	22.0	1	0	7.2500	Third	unknown	Southampton	n
1	1	female	38.0	1	0	71.2833	First	C	Cherbourg	n
2	1	female	26.0	0	0	7.9250	Third	unknown	Southampton	y
3	1	female	35.0	1	0	53.1000	First	C	Southampton	n
4	0	male	28.0	0	0	8.4583	Third	unknown	Queenstown	y
titanic_slices = tf.data.Dataset.from_tensor_slices(dict(df))

for feature_batch in titanic_slices.take(1):
  for key, value in feature_batch.items():
    print("  {!r:20s}: {}".format(key, value))
  'survived'          : 0
  'sex'               : b'male'
  'age'               : 22.0
  'n_siblings_spouses': 1
  'parch'             : 0
  'fare'              : 7.25
  'class'             : b'Third'
  'deck'              : b'unknown'
  'embark_town'       : b'Southampton'
  'alone'             : b'n'
titanic_batches = tf.data.experimental.make_csv_dataset(
    titanic_file, batch_size=4,
    label_name="survived")
titanic_batches.take(1)
<TakeDataset shapes: (OrderedDict([(sex, (4,)), (age, (4,)), (n_siblings_spouses, (4,)), (parch, (4,)), (fare, (4,)), (class, (4,)), (deck, (4,)), (embark_town, (4,)), (alone, (4,))]), (4,)), types: (OrderedDict([(sex, tf.string), (age, tf.float32), (n_siblings_spouses, tf.int32), (parch, tf.int32), (fare, tf.float32), (class, tf.string), (deck, tf.string), (embark_town, tf.string), (alone, tf.string)]), tf.int32)>
for feature_batch, label_batch in titanic_batches.take(1):
  print("'survived': {}".format(label_batch))
  print("features:")
  for key, value in feature_batch.items():
    print("  {!r:20s}: {}".format(key, value))
'survived': [1 0 1 1]
features:
  'sex'               : [b'male' b'male' b'female' b'male']
  'age'               : [28. 28. 52. 44.]
  'n_siblings_spouses': [0 0 1 0]
  'parch'             : [0 0 1 0]
  'fare'              : [26.55  26.55  93.5    7.925]
  'class'             : [b'First' b'First' b'First' b'Third']
  'deck'              : [b'C' b'C' b'B' b'unknown']
  'embark_town'       : [b'Southampton' b'Southampton' b'Southampton' b'Southampton']
  'alone'             : [b'y' b'y' b'n' b'y']