markdown stringlengths 0 37k | code stringlengths 1 33.3k | path stringlengths 8 215 | repo_name stringlengths 6 77 | license stringclasses 15
values |
|---|---|---|---|---|
Creating a table
From here we need to create a first table. Let's recreate the Person table from the SW Carpentry db lesson, topic 1. | %%sql
CREATE TABLE Person
(ident CHAR(10),
personal CHAR(25),
family CHAR(25));
%sql SHOW TABLES;
%sql DESCRIBE Person; | lectures/week-03/sql-demo.ipynb | dchud/warehousing-course | cc0-1.0 |
Inserting data
Okay then, let's insert the sample data: | %%sql
INSERT INTO Person VALUES
("dyer", "William", "Dyer"),
("pb", "Frank", "Pabodie"),
("lake", "Anderson", "Lake"),
("roe", "Valentina", "Roerich"),
("danforth", "Frank", "Danforth")
; | lectures/week-03/sql-demo.ipynb | dchud/warehousing-course | cc0-1.0 |
Selecting data
Okay, now we're cooking. There's data in the Person table, so we can start to SELECT it. | %sql SELECT * FROM Person;
%sql SELECT * FROM Person WHERE personal = "Frank"; | lectures/week-03/sql-demo.ipynb | dchud/warehousing-course | cc0-1.0 |
Accessing data from Python
One of the great things about ipython-sql is it marshalls all the data into Python objects for you. For example, to get the result data into a Python object, grab it from _: | result = _
print result | lectures/week-03/sql-demo.ipynb | dchud/warehousing-course | cc0-1.0 |
You can even assign it to a Pandas dataframe: | df = result.DataFrame()
df | lectures/week-03/sql-demo.ipynb | dchud/warehousing-course | cc0-1.0 |
Cleaning up
If you were just doing a little exploring and wish to clean up, it's easy to get rid of tables and databases.
NOTE: these are permanent actions. Only do them if you know you don't need them any longer.
To get rid of a table, use DROP TABLE: | %sql DROP TABLE Person;
%sql SHOW TABLES; | lectures/week-03/sql-demo.ipynb | dchud/warehousing-course | cc0-1.0 |
And to get rid of a whole database, use DROP DATABASE: | %sql DROP DATABASE week3demo;
%sql SHOW DATABASES; | lectures/week-03/sql-demo.ipynb | dchud/warehousing-course | cc0-1.0 |
We can use gradient descent to minimize a cost function, thereby optimizing our weights.
ANNs in Sklearn
Multi-layer Perceptron (MLP) models in sklearn
The advantages of MLP are:
- Capability to learn non-linear models.
- Capability to learn models in real-time (on-line learning) using partial_fit.
The disadvantages of... | # build simple neural net with sklearn: An "OR" gate
from sklearn.neural_network import MLPClassifier
X = [[0., 0.], [1., 1.], [1., 0.], [0., 1.]]
y = [0, 1, 1, 1]
clf = MLPClassifier(hidden_layer_sizes=(5,2),
solver='lbfgs',
random_state=42)
clf.fit(X,y)
# predict new observa... | Projects/Project5/NeuralNetSum.ipynb | ptpro3/ptpro3.github.io | mit |
Question 4
Write a function to update the dataframe to include a new column called "Points" which is a weighted value where each gold medal counts for 3 points, silver medals for 2 points, and bronze mdeals for 1 point. The function should return only the column (a Series object) which you created.
This function should... | def answer_four():
return "YOUR ANSWER HERE" | intro-python-data-science/course1_downloads/Assignment 2.ipynb | joaoandre/algorithms | mit |
Question 6
Only looking at the three most populous counties for each state, what are the three most populous states (in order of highest population to lowest population)?
This function should return a list of string values. | def answer_six():
return "YOUR ANSWER HERE" | intro-python-data-science/course1_downloads/Assignment 2.ipynb | joaoandre/algorithms | mit |
Construction de la dataframe et rΓ©alisation des graphiques | simulated_variables = ['coicop12_{}'.format(coicop12_index) for coicop12_index in range(1, 13)]
for year in [2000, 2005, 2011]:
survey_scenario = SurveyScenario.create(year = year)
pivot_table = pandas.DataFrame()
for values in simulated_variables:
pivot_table = pandas.concat... | openfisca_france_indirect_taxation/examples/notebooks/consommations_coicop_par_decile.ipynb | openfisca/openfisca-france-indirect-taxation | agpl-3.0 |
νμ₯ μ ν
<table class="tfo-notebook-buttons" align="left">
<td><a target="_blank" href="https://www.tensorflow.org/guide/extension_type"><img src="https://www.tensorflow.org/images/tf_logo_32px.png">TensorFlow.orgμμ 보기</a></td>
<td><a target="_blank" href="https://colab.research.google.com/github/tensorflow/docs-l10n/... | !pip install -q tf_nightly
import tensorflow as tf
import numpy as np
from typing import Tuple, List, Mapping, Union, Optional
import tempfile | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
νμ₯ μ ν
μ¬μ©μ μ μ μ νμ μ¬μ©νλ©΄ νλ‘μ νΈλ₯Ό λ μ½κΈ° μ½κ³ λͺ¨λμμΌλ‘ μ μ§ κ΄λ¦¬ν μ μμ΅λλ€. κ·Έλ¬λ λλΆλΆμ TensorFlow APIλ μ¬μ©μ μ μ Python μ νμ λν μ§μμ΄ λ§€μ° μ νμ μ
λλ€. μ΄κ²μ (μ λͺ¨λ λμ μμ€μ APIλ₯Ό ν¬ν¨ Keras , tf.function , tf.SavedModel (μλ‘μ νμ λ 벨μ API) tf.while_loop λ° tf.concat ). TensorFlow νμ₯ μ ν μ μ¬μ©νμ¬ TensorFlowμ APIμ μννκ² μλνλ μ¬μ©μ μ μ κ°μ²΄ μ§ν₯ μ νμ μμ±ν μ μμ΅λλ€. tf.experimental... | class TensorGraph(tf.experimental.ExtensionType):
"""A collection of labeled nodes connected by weighted edges."""
edge_weights: tf.Tensor # shape=[num_nodes, num_nodes]
node_labels: Mapping[str, tf.Tensor] # shape=[num_nodes]; dtype=any
class MaskedTensor(tf.experimental.ExtensionType):
"""A te... | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
tf.experimental.ExtensionType κΈ°λ³Έ ν΄λμ€λ νμ€ Python λΌμ΄λΈλ¬λ¦¬μ typing.NamedTuple λ° @dataclasses.dataclass μ μ μ¬νκ² μλν©λλ€. νΉν νλ μ ν μ£Όμμ κΈ°λ°μΌλ‘ μμ±μμ νΉμ λ©μλ(μ: __repr__ λ° __eq__
μΌλ°μ μΌλ‘ νμ₯ μ νμ λ€μ λ κ°μ§ λ²μ£Ό μ€ νλλ‘ λΆλ₯λλ κ²½ν₯μ΄ μμ΅λλ€.
κ΄λ ¨ κ°μ 컬λ μ
μ κ·Έλ£Ήννκ³ ν΄λΉ κ°μ κΈ°λ°μΌλ‘ μ μ©ν μμ
μ μ 곡ν μ μλ λ°μ΄ν° ꡬ쑰. λ°μ΄ν° ꡬ쑰λ μλΉν μΌλ°μ μΌ μ μμ΅λλ€(μ TensorGraph μ). λλ νΉμ λͺ¨λΈμ κ³ λλ‘ λ§... | class MaskedTensor(tf.experimental.ExtensionType):
values: tf.Tensor
mask: tf.Tensor
def replace_mask(self, new_mask):
self.values.shape.assert_is_compatible_with(new_mask.shape)
return MaskedTensor(self.values, new_mask) | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
ExtensionType μΆκ°ν κΈ°λ₯
ExtensionType κΈ°λ³Έ ν΄λμ€λ λ€μ κΈ°λ₯μ μ 곡ν©λλ€.
μμ±μ( __init__ ).
μΈμ κ°λ₯ν νν λ°©λ²( __repr__ ).
λ±μ λ° λΆλ±μ μ°μ°μ( __eq__ ).
μ ν¨μ± κ²μ¬ λ°©λ²( __validate__ ).
κ°μ λΆλ³μ±.
μ€μ²©λ TypeSpec .
ν
μ API λμ€ν¨μΉ μ§μ.
μ΄ κΈ°λ₯μ μ¬μ©μ μ μνλ λ°©λ²μ λν μμΈν λ΄μ©μ μλμ "ExtensionType μ¬μ©μ μ μ" μΉμ
μ μ°Έμ‘°νμμμ€.
건μ€μ
ExtensionType μ μν΄ μΆκ°λ μμ±μλ κ° νλλ₯Ό λͺ
λͺ
λ μΈμλ‘ μ¬μ©ν©λλ€(ν΄λμ€ μ μμ... | class MaskedTensor(tf.experimental.ExtensionType):
values: tf.Tensor
mask: tf.Tensor
# Constructor takes one parameter for each field.
mt = MaskedTensor(values=[[1, 2, 3], [4, 5, 6]],
mask=[[True, True, False], [True, False, True]])
# Fields are type-checked and converted to the declared types.
... | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
νλ κ°μ μ μΈλ μ νμΌλ‘ λ³νν μ μλ κ²½μ° μμ±μλ TypeError | try:
MaskedTensor([1, 2, 3], None)
except TypeError as e:
print(f"Got expected TypeError: {e}") | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
νλμ κΈ°λ³Έκ°μ ν΄λμ€ μμ€μμ κ°μ μ€μ νμ¬ μ§μ ν μ μμ΅λλ€. | class Pencil(tf.experimental.ExtensionType):
color: str = "black"
has_erasor: bool = True
length: tf.Tensor = 1.0
Pencil()
Pencil(length=0.5, color="blue") | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
μΈμ κ°λ₯ν νν
ExtensionType μ ν΄λμ€ μ΄λ¦κ³Ό κ° νλμ κ°μ ν¬ν¨νλ κΈ°λ³Έ μΈμ κ°λ₯ν νν λ°©λ²( __repr__ | print(MaskedTensor(values=[1, 2, 3], mask=[True, True, False])) | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
λ±νΈ μ°μ°μ
ExtensionType μ μ νμ΄ λμΌνκ³ λͺ¨λ νλκ° λμΌν κ²½μ° λ κ°μ λμΌνκ² κ°μ£Όνλ κΈ°λ³Έ λλ± μ°μ°μ( __eq__ λ° __ne__ ν
μ νλλ λͺ¨μμ΄ λμΌνκ³ λͺ¨λ μμμ λν΄ μμλ³λ‘ λμΌν κ²½μ° λμΌν κ²μΌλ‘ κ°μ£Όλ©λλ€. | a = MaskedTensor([1, 2], [True, False])
b = MaskedTensor([[3, 4], [5, 6]], [[False, True], [True, True]])
print(f"a == a: {a==a}")
print(f"a == b: {a==b}")
print(f"a == a.values: {a==a.values}") | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
μ°Έκ³ : Tensor κ° ν¬ν¨λ κ²½μ° __eq__ λ (Python λΆμΈ κ° λμ ) Tensor λ°νν μ μμ΅λλ€.
κ²μ¦ λ°©λ²
ExtensionType μ νλμ λν μ ν¨μ± κ²μ¬λ₯Ό μννκΈ° μν΄ μ¬μ μν μ μλ __validate__ μμ±μκ° νΈμΆλκ³ νλκ° μ ν κ²μ¬λκ³ μ μΈλ μ νμΌλ‘ λ³νλ νμ μ€νλλ―λ‘ λͺ¨λ νλμ μ μΈλ μ νμ΄ μλ€κ³ κ°μ ν μ μμ΅λλ€.
λ€μ μμ λ MaskedTensor λ₯Ό μ
λ°μ΄νΈνμ¬ ν΄λΉ νλμ shape s λ° dtype μ νμΈν©λλ€. | class MaskedTensor(tf.experimental.ExtensionType):
"""A tensor paired with a boolean mask, indicating which values are valid."""
values: tf.Tensor
mask: tf.Tensor
def __validate__(self):
self.values.shape.assert_is_compatible_with(self.mask.shape)
assert self.mask.dtype.is_bool, 'mask.dtype must be bool... | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
κ°μ λΆλ³μ±
ExtensionType __setattr__ λ° __delattr__ λ©μλλ₯Ό μ¬μ μνμ¬ λ³νμ λ°©μ§νμ¬ νμ₯ μ ν κ°μ λ³κ²½ν μ μλλ‘ ν©λλ€. | mt = MaskedTensor([1, 2, 3], [True, False, True])
try:
mt.mask = [True, True, True]
except AttributeError as e:
print(f"Got expected AttributeError: {e}")
try:
mt.mask[0] = False
except TypeError as e:
print(f"Got expected TypeError: {e}")
try:
del mt.mask
except AttributeError as e:
print(f"Got expected... | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
μ€μ²©λ μ ν μ¬μ
κ° ExtensionType ν΄λμ€μλ μλμΌλ‘ μμ±λκ³ <extension_type_name>.Spec TypeSpec ν΄λμ€κ° μμ΅λλ€.
μ΄ ν΄λμ€λ μ€μ²©λ ν
μμ κ°μ μ μΈν κ°μμ λͺ¨λ μ 보λ₯Ό μΊ‘μ²ν©λλ€. νΉν TypeSpec μ μ€μ²©λ Tensor, ExtensionType λλ CompositeTensorλ₯Ό TypeSpec μΌλ‘ λ체νμ¬ μμ±λ©λλ€. | class Player(tf.experimental.ExtensionType):
name: tf.Tensor
attributes: Mapping[str, tf.Tensor]
anne = Player("Anne", {"height": 8.3, "speed": 28.1})
anne_spec = tf.type_spec_from_value(anne)
print(anne_spec.name) # Records dtype and shape, but not the string value.
print(anne_spec.attributes) # Records keys an... | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
TypeSpec κ°μ λͺ
μμ μΌλ‘ ꡬμ±νκ±°λ tf.type_spec_from_value μ¬μ©νμ¬ ExtensionType κ°μμ λΉλν μ μμ΅λλ€. | spec1 = Player.Spec(name=tf.TensorSpec([], tf.float32), attributes={})
spec2 = tf.type_spec_from_value(anne) | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
TypeSpec μ TensorFlowμμ κ°μ μ μ κ΅¬μ± μμ μ λμ κ΅¬μ± μμλ‘ λλλ λ° μ¬μ©λ©λλ€.
κ·Έλν μμ± μ κ³ μ λλ μ μ κ΅¬μ± μμ tf.TypeSpec μΈμ½λ©λ©λλ€.
κ·Έλνκ° μ€νλ λλ§λ€ λ€λ₯Ό μ μλ λμ κ΅¬μ± μμ tf.Tensor λͺ©λ‘μΌλ‘ μΈμ½λ©λ©λλ€.
μλ₯Ό λ€μ΄, tf.functionμ μΈμμ μ΄μ μ λ³Ό μ μλ TypeSpec tf.function . | @tf.function
def anonymize_player(player):
print("<<TRACING>>")
return Player("<anonymous>", player.attributes)
# Function gets traced (first time the function has been called):
anonymize_player(Player("Anne", {"height": 8.3, "speed": 28.1}))
# Function does NOT get traced (same TypeSpec: just tensor values chang... | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
μμΈν λ΄μ©μ tf.function κ°μ΄λλ₯Ό μ°Έμ‘°νμμμ€.
ExtensionType μ¬μ©μ μ μ
λ¨μν νλμ ν΄λΉ μ νμ μ μΈνλ κ² μΈμλ νμ₯ μ νμ λ€μμ μνν μ μμ΅λλ€.
κΈ°λ³Έ μΈμ κ°λ₯ν νν( __repr__ )μ μ¬μ μν©λλ€.
λ°©λ²μ μ μν©λλ€.
ν΄λμ€ λ©μλμ μ μ λ©μλλ₯Ό μ μν©λλ€.
μμ±μ μ μν©λλ€.
κΈ°λ³Έ μμ±μ( __init__ )λ₯Ό μ¬μ μν©λλ€.
κΈ°λ³Έ νλ± μ°μ°μ( __eq__ )λ₯Ό μ¬μ μν©λλ€.
μ°μ°μλ₯Ό μ μν©λλ€(μ: __add__ λ° __lt__ ).
νλμ κΈ°λ³Έκ°μ μ μΈν©λλ€.
νμ ν΄λμ€λ₯Ό μ μν©λλ€.
κΈ°λ³Έ μΈμ κ°λ₯ν νν ... | class MaskedTensor(tf.experimental.ExtensionType):
"""A tensor paired with a boolean mask, indicating which values are valid."""
values: tf.Tensor
mask: tf.Tensor # shape=values.shape; false for invalid values.
def __repr__(self):
return masked_tensor_str(self.values, self.mask)
def masked_tensor_st... | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
λ©μλ μ μ
νμ₯ μ νμ μΌλ° Python ν΄λμ€μ λ§μ°¬κ°μ§λ‘ λ©μλλ₯Ό μ μν μ μμ΅λλ€. μλ₯Ό λ€μ΄ MaskedTensor default λ체λ λ§μ€νΉλ κ° self μ 볡μ¬λ³Έμ λ°ννλ with_default λ©μλλ₯Ό μ μν μ μμ΅λλ€. @tf.function λ°μ½λ μ΄ν°λ‘ μ£Όμμ λ¬ μ μμ΅λλ€. | class MaskedTensor(tf.experimental.ExtensionType):
values: tf.Tensor
mask: tf.Tensor
def with_default(self, default):
return tf.where(self.mask, self.values, default)
MaskedTensor([1, 2, 3], [True, False, True]).with_default(0) | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
ν΄λμ€ λ©μλ λ° μ μ λ©μλ μ μ
@classmethod λ° @staticmethod λ°μ½λ μ΄ν°λ₯Ό μ¬μ©νμ¬ λ©μλλ₯Ό μ μν μ μμ΅λλ€. μλ₯Ό λ€μ΄ MaskedTensor μ νμ μ£Όμ΄μ§ κ°μΌλ‘ λͺ¨λ μμλ₯Ό λ§μ€νΉνλ ν©ν 리 λ©μλλ₯Ό μ μν μ μμ΅λλ€. | class MaskedTensor(tf.experimental.ExtensionType):
values: tf.Tensor
mask: tf.Tensor
def __repr__(self):
return masked_tensor_str(self.values, self.mask)
@staticmethod
def from_tensor_and_value_to_mask(values, value_to_mask):
return MaskedTensor(values, values == value_to_mask)
x = tf.constant([[1,... | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
μμ± μ μ
νμ₯ μ νμ μΌλ° Python ν΄λμ€μ λ§μ°¬κ°μ§λ‘ @property λ°μ½λ μ΄ν°λ₯Ό μ¬μ©νμ¬ μμ±μ μ μν μ μμ΅λλ€. μλ₯Ό λ€μ΄ MaskedTensor μ νμ κ°μ dtypeμ λν μ½μΉμΈ dtype μμ±μ μ μν μ μμ΅λλ€. | class MaskedTensor(tf.experimental.ExtensionType):
values: tf.Tensor
mask: tf.Tensor
@property
def dtype(self):
return self.values.dtype
MaskedTensor([1, 2, 3], [True, False, True]).dtype | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
κΈ°λ³Έ μμ±μ μ¬μ μ
νμ₯ μ νμ λν κΈ°λ³Έ μμ±μλ₯Ό μ¬μ μν μ μμ΅λλ€. μ¬μ©μ μ μ μμ±μλ μ μΈλ λͺ¨λ νλμ λν΄ κ°μ μ€μ ν΄μΌ ν©λλ€. μ¬μ©μ μ μ μμ±μκ° λ°νλ ν λͺ¨λ νλκ° μ ν κ²μ¬λκ³ μμμ μ€λͺ
ν λλ‘ κ°μ΄ λ³νλ©λλ€. | class Toy(tf.experimental.ExtensionType):
name: str
price: tf.Tensor
def __init__(self, name, price, discount=0):
self.name = name
self.price = price * (1 - discount)
print(Toy("ball", 5.0, discount=0.2)) # On sale -- 20% off! | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
λλ κΈ°λ³Έ μμ±μλ₯Ό κ·Έλλ‘ λκ³ νλ μ΄μμ ν©ν 리 λ©μλλ₯Ό μΆκ°νλ κ²μ κ³ λ €ν μ μμ΅λλ€. μ: | class Toy(tf.experimental.ExtensionType):
name: str
price: tf.Tensor
@staticmethod
def new_toy_with_discount(name, price, discount):
return Toy(name, price * (1 - discount))
print(Toy.new_toy_with_discount("ball", 5.0, discount=0.2)) | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
κΈ°λ³Έ νλ± μ°μ°μ μ¬μ μ( __eq__ )
νμ₯ μ νμ λν __eq__ μ°μ°μλ₯Ό μ¬μ μν μ μμ΅λλ€. λ€μ μμ μμλ MaskedTensor λΉκ΅ν λ λ§μ€ν¬λ μμλ₯Ό 무μνλλ‘ MaskedTensorλ₯Ό μ
λ°μ΄νΈν©λλ€. | class MaskedTensor(tf.experimental.ExtensionType):
values: tf.Tensor
mask: tf.Tensor
def __repr__(self):
return masked_tensor_str(self.values, self.mask)
def __eq__(self, other):
result = tf.math.equal(self.values, other.values)
result = result | ~(self.mask & other.mask)
return tf.reduce_all(... | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
μ°Έκ³ : κΈ°λ³Έ ꡬνμ λ¨μν __eq__ λ₯Ό νΈμΆνκ³ κ²°κ³Όλ₯Ό 무ν¨ννκΈ° __ne__ λ₯Ό μ¬μ μν νμκ° μμ΅λλ€.
μ λ°©ν₯ μ°Έμ‘° μ¬μ©
νλ μ νμ΄ μμ§ μ μλμ§ μμ κ²½μ° μ ν μ΄λ¦μ΄ ν¬ν¨λ λ¬Έμμ΄μ λμ μ¬μ©ν μ μμ΅λλ€. λ€μ μμ μμλ Node μ νμ΄ μμ§ (μμ ν) μ μλμ§ μμκΈ° λλ¬Έμ "Node" children νλμ μ£Όμμ λ€λ λ° μ¬μ©λ©λλ€. | class Node(tf.experimental.ExtensionType):
value: tf.Tensor
children: Tuple["Node", ...] = ()
Node(3, [Node(5), Node(2)]) | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
μλΈν΄λμ€ μ μ
νμ₯ μ νμ νμ€ Python ꡬ문μ μ¬μ©νμ¬ νμ λΆλ₯λ μ μμ΅λλ€. νμ₯ μ ν νμ ν΄λμ€λ μ νλ, λ©μλ λ° μμ±μ μΆκ°ν μ μμ΅λλ€. μμ±μ, μΈμ κ°λ₯ν νν λ° λ±νΈ μ°μ°μλ₯Ό μ¬μ μν μ μμ΅λλ€. λ€μ μμ λ μΈ κ°μ Tensor νλλ₯Ό μ¬μ©νμ¬ λ
Έλ μ¬μ΄μ κ°μ₯μ리 μ§ν©μ μΈμ½λ©νλ TensorGraph κ·Έλ° λ€μ κ° λ
Έλμ λν "κΈ°λ₯ κ°"μ κΈ°λ‘νκΈ° μν΄ Tensor νλλ₯Ό μΆκ°νλ νμ ν΄λμ€λ₯Ό μ μν©λλ€. λν νμ ν΄λμ€λ κ°μ₯μ리λ₯Ό λ°λΌ νΉμ± κ°μ μ ννλ λ°©λ²μ μ μν©λλ€. | class TensorGraph(tf.experimental.ExtensionType):
num_nodes: tf.Tensor
edge_src: tf.Tensor # edge_src[e] = index of src node for edge e.
edge_dst: tf.Tensor # edge_dst[e] = index of dst node for edge e.
class TensorGraphWithNodeFeature(TensorGraph):
node_features: tf.Tensor # node_features[n] = feature va... | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
κ°μΈ νλ μ μ
νμ₯ μ νμ νλλ μ λμ¬μ λ°μ€μ λΆμ¬ λΉκ³΅κ°λ‘ νμν μ μμ΅λλ€(νμ€ Python κ·μΉμ λ°λΌ). μ΄κ²μ TensorFlowκ° μ΄λ€ μμΌλ‘λ νλλ₯Ό μ²λ¦¬νλ λ°©μμ μν₯μ λ―ΈμΉμ§ μμ΅λλ€. κ·Έλ¬λ λ¨μν νμ₯ μ νμ λͺ¨λ μ¬μ©μμκ² ν΄λΉ νλκ° λΉκ³΅κ°λΌλ μ νΈ μν μ ν©λλ€.
ExtensionTypeμ TypeSpec
κ° ExtensionType ν΄λμ€μλ μλμΌλ‘ μμ±λκ³ <extension_type_name>.Spec TypeSpec ν΄λμ€κ° μμ΅λλ€. μμΈν λ΄μ©μ μμ "μ€μ²©λ TypeSpec" μΉμ
μ μ°Έμ‘°νμΈμ.
TypeSpe... | class MaskedTensor(tf.experimental.ExtensionType):
values: tf.Tensor
mask: tf.Tensor
shape = property(lambda self: self.values.shape)
dtype = property(lambda self: self.values.dtype)
def __repr__(self):
return masked_tensor_str(self.values, self.mask)
def with_values(self, new_values):
return Mas... | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
μ°Έκ³ : μ¬μ©μ μ μ Spec ExtensionType μ μΈλμ§ μμ μΈμ€ν΄μ€ λ³μλ₯Ό μ¬μ©ν μ μμ΅λλ€.
ν
μ API λμ€ν¨μΉ
tf.Tensor μ νμ μν΄ μ μλ μΈν°νμ΄μ€λ₯Ό μ λ¬Έννκ±°λ νμ₯νλ€λ μ μμ "ν
μμ μ μ¬"ν μ μμ΅λλ€. ν
μμ μ μ¬ν νμ₯ μ νμ μλ‘λ RaggedTensor , SparseTensor λ° MaskedTensor μμ΅λλ€. λμ€ν¨μΉ λ°μ½λ μ΄ν° λ ν
μμ μ μ¬ν νμ₯ μ νμ μ μ©λ λ TensorFlow μμ
μ κΈ°λ³Έ λμμ μ¬μ μνλ λ° μ¬μ©ν μ μμ΅λλ€. TensorFlowλ νμ¬ μΈ κ°μ§ λμ€ν¨μΉ λ°μ½λ μ΄ν°λ₯Ό μ μν©λλ€.
@... | @tf.experimental.dispatch_for_api(tf.stack)
def masked_stack(values: List[MaskedTensor], axis = 0):
return MaskedTensor(tf.stack([v.values for v in values], axis),
tf.stack([v.mask for v in values], axis)) | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
MaskedTensor κ° λͺ©λ‘κ³Ό ν¨κ» νΈμΆλ λλ§λ€ tf.stack λν κΈ°λ³Έ ꡬνμ μ¬μ μ values typing.List[MaskedTensor] μ£ΌμμΌλ‘ μ§μ λμ΄ μκΈ° λλ¬Έμ
λλ€): | x = MaskedTensor([1, 2, 3], [True, True, False])
y = MaskedTensor([4, 5, 6], [False, True, True])
tf.stack([x, y]) | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
tf.stack μ΄ νΌν©λ MaskedTensor λ° Tensor κ° λͺ©λ‘μ μ²λ¦¬ν μ μλλ‘ νλ €λ©΄ values 맀κ°λ³μμ λν μ ν μ£Όμμ ꡬ체ννκ³ ν¨μ λ³Έλ¬Έμ μ μ νκ² μ
λ°μ΄νΈν μ μμ΅λλ€. | tf.experimental.unregister_dispatch_for(masked_stack)
def convert_to_masked_tensor(x):
if isinstance(x, MaskedTensor):
return x
else:
return MaskedTensor(x, tf.ones_like(x, tf.bool))
@tf.experimental.dispatch_for_api(tf.stack)
def masked_stack_v2(values: List[Union[MaskedTensor, tf.Tensor]], axis = 0):
... | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
μ¬μ μν μ μλ API λͺ©λ‘μ tf.experimental.dispatch_for_api λν API μ€λͺ
μλ₯Ό μ°Έμ‘°νμΈμ.
λͺ¨λ λ¨ν μμλ³ APIμ λν λμ€ν¨μΉ
tf.experimental.dispatch_for_unary_elementwise_apis λ°μ½λ μ΄ν°λ 첫 λ²μ§Έ μΈμ(μΌλ°μ μΌλ‘ μ΄λ¦μ΄ x )μ λν κ°μ΄ μ ν μ£Όμ x_type κ³Ό μΌμΉν λλ§λ€ λͺ¨λ λ¨ν μμλ³ μ°μ°(μ: tf.math.cos )μ κΈ°λ³Έ λμμ μ¬μ μν©λλ€. λ°μ½λ μ΄ν
λ ν¨μλ λ κ°μ μΈμλ₯Ό μ·¨ν΄μΌ ν©λλ€.
api_func : λ¨μΌ 맀κ°λ³μλ₯Ό μ·¨νκ³ μμλ³ μ°μ°μ μννλ ν¨μ(... | @tf.experimental.dispatch_for_unary_elementwise_apis(MaskedTensor)
def masked_tensor_unary_elementwise_api_handler(api_func, x):
return MaskedTensor(api_func(x.values), x.mask) | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
MaskedTensor μμ λ¨ν μμλ³ μ°μ°μ΄ νΈμΆλ λλ§λ€ μ¬μ©λ©λλ€. | x = MaskedTensor([1, -2, -3], [True, False, True])
print(tf.abs(x))
print(tf.ones_like(x, dtype=tf.float32)) | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
λ°μ΄λ리 λͺ¨λ μμλ³ APIμ λν λμ€ν¨μΉ
λ§μ°¬κ°μ§λ‘ tf.experimental.dispatch_for_binary_elementwise_apis MaskedTensor μ νμ μ²λ¦¬νκΈ° μν΄ λͺ¨λ λ°μ΄λ리 μμλ³ μ°μ°μ μ
λ°μ΄νΈνλ λ° μ¬μ©ν μ μμ΅λλ€. | @tf.experimental.dispatch_for_binary_elementwise_apis(MaskedTensor, MaskedTensor)
def masked_tensor_binary_elementwise_api_handler(api_func, x, y):
return MaskedTensor(api_func(x.values, y.values), x.mask & y.mask)
x = MaskedTensor([1, -2, -3], [True, False, True])
y = MaskedTensor([[4], [5]], [[True], [False]])
tf.... | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
μ¬μ μλλ μμλ³ API λͺ©λ‘μ tf.experimental.dispatch_for_unary_elementwise_apis λ° tf.experimental.dispatch_for_binary_elementwise_apis λν API λ¬Έμλ₯Ό μ°Έμ‘°νμΈμ.
μΌκ΄ μ²λ¦¬ κ°λ₯ν νμ₯ μ ν
ExtensionType λ¨μΌ μΈμ€ν΄μ€ κ°μ λ°°μΉλ₯Ό λνλ΄λ λ° μ¬μ©ν μμλ κ²½μ° batchableμ΄λ€. Tensor λ°°μΉ μ°¨μμ μΆκ°νμ¬ μνλ©λλ€. λ€μ TensorFlow APIλ₯Ό μ¬μ©νλ €λ©΄ λͺ¨λ νμ₯ μ ν μ
λ ₯μ΄ μΌκ΄ μ²λ¦¬ κ°λ₯ν΄μΌ ν©λλ€.
tf.data.Dataset ( ba... | class Network(tf.experimental.ExtensionType): # This version is not batchable.
work: tf.Tensor # work[n] = work left to do at node n
bandwidth: tf.Tensor # bandwidth[n1, n2] = bandwidth from n1->n2
net1 = Network([5., 3, 8], [[0., 2, 0], [2, 0, 3], [0, 3, 0]])
net2 = Network([3., 4, 2], [[0., 2, 2], [2, 0,... | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
μ΄ μ νμ μΌκ΄ μ²λ¦¬ κ°λ₯νκ² λ§λ€λ €λ©΄ κΈ°λ³Έ μ νμ BatchableExtensionType λ³κ²½νκ³ μ νμ μΌκ΄ μ²λ¦¬ μ°¨μμ ν¬ν¨νλλ‘ κ° νλμ λͺ¨μμ μ‘°μ ν©λλ€. λ€μ μμ μμλ λ°°μΉ λͺ¨μμ μΆμ νκΈ° shape νλλ μΆκ°ν©λλ€. μ΄ shape νλλ νμλ‘νμ§ μλ tf.data.Dataset λλ tf.map_fn μμ§λ§ μꡬνλ tf.Keras . | class Network(tf.experimental.BatchableExtensionType):
shape: tf.TensorShape # batch shape. A single network has shape=[].
work: tf.Tensor # work[*shape, n] = work left to do at node n
bandwidth: tf.Tensor # bandwidth[*shape, n1, n2] = bandwidth from n1->n2
def __init__(self, work, bandwidth):
s... | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
tf.data.Dataset μ μ¬μ©νμ¬ λ€νΈμν¬ λ°°μΉλ₯Ό λ°λ³΅ν μ μμ΅λλ€. | dataset = tf.data.Dataset.from_tensor_slices(batch_of_networks)
for i, network in enumerate(dataset):
print(f"Batch element {i}: {network}") | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
map_fn μ μ¬μ©νμ¬ κ° λ°°μΉ μμμ ν¨μλ₯Ό μ μ©ν μλ μμ΅λλ€. | def balance_work_greedy(network):
delta = (tf.expand_dims(network.work, -1) - tf.expand_dims(network.work, -2))
delta /= 4
delta = tf.maximum(tf.minimum(delta, network.bandwidth), -network.bandwidth)
new_work = network.work + tf.reduce_sum(delta, -1)
return Network(new_work, network.bandwidth)
tf.map_fn(bala... | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
ExtensionTypesλ₯Ό μ§μνλ TensorFlow API
@tf.function
tf.function μ TensorFlow μ½λμ μ±λ₯μ ν¬κ² ν₯μμν¬ μ μλ Python ν¨μμ© TensorFlow κ·Έλνλ₯Ό 미리 κ³μ°νλ λ°μ½λ μ΄ν°μ
λλ€. @tf.function ν¨μμ ν¨κ» ν¬λͺ
νκ² μ¬μ©ν μ μμ΅λλ€. | class Pastry(tf.experimental.ExtensionType):
sweetness: tf.Tensor # 2d embedding that encodes sweetness
chewiness: tf.Tensor # 2d embedding that encodes chewiness
@tf.function
def combine_pastry_features(x: Pastry):
return (x.sweetness + x.chewiness) / 2
cookie = Pastry(sweetness=[1.2, 0.4], chewiness=[0.8, 0... | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
input_signature λν΄ tf.function λ₯Ό λͺ
μμ μΌλ‘ μ§μ TypeSpec μ¬μ©νμ¬ μ§μ ν μ μμ΅λλ€. | pastry_spec = Pastry.Spec(tf.TensorSpec([2]), tf.TensorSpec(2))
@tf.function(input_signature=[pastry_spec])
def increase_sweetness(x: Pastry, delta=1.0):
return Pastry(x.sweetness + delta, x.chewiness)
increase_sweetness(cookie) | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
ꡬ체μ μΈ κΈ°λ₯
tf.function μν΄ κ΅¬μΆλ κ°λ³ μΆμ κ·Έλνλ₯Ό μΊ‘μνν©λλ€. νμ₯ μ νμ ꡬ체μ μΈ κΈ°λ₯κ³Ό ν¨κ» ν¬λͺ
νκ² μ¬μ©ν μ μμ΅λλ€. | cf = combine_pastry_features.get_concrete_function(pastry_spec)
cf(cookie) | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
μ μ΄ νλ¦ μμ
νμ₯ μ νμ TensorFlowμ μ μ΄ νλ¦ μμ
μμ μ§μλ©λλ€.
tf.cond
tf.case
tf.while_loop
tf.identity | # Example: using tf.cond to select between two MaskedTensors. Note that the
# two MaskedTensors don't need to have the same shape.
a = MaskedTensor([1., 2, 3], [True, False, True])
b = MaskedTensor([22., 33, 108, 55], [True, True, True, False])
condition = tf.constant(True)
print(tf.cond(condition, lambda: a, lambda: ... | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
μ¬μΈ μ μ΄ νλ¦
νμ₯ μ νμ tf.functionμ μ μ΄ νλ¦ λ¬Έμμλ μ§μλ©λλ€(autograph μ¬μ©). λ€μ μμμ if λ¬Έκ³Ό for λ¬Έμ νμ₯ μ νμ μ§μ tf.cond λ° tf.while_loop μμ
μΌλ‘ μλ λ³νλ©λλ€. | @tf.function
def fn(x, b):
if b:
x = MaskedTensor(x, tf.less(x, 0))
else:
x = MaskedTensor(x, tf.greater(x, 0))
for i in tf.range(5 if b else 7):
x = x.with_values(x.values + 1 / 2)
return x
print(fn(tf.constant([1., -2, 3]), tf.constant(True)))
print(fn(tf.constant([1., -2, 3]), tf.constant(False)... | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
μΌλΌμ€
tf.keras λ λ₯ λ¬λ λͺ¨λΈμ ꡬμΆνκ³ νλ ¨νκΈ° μν TensorFlowμ κ³ κΈ APIμ
λλ€. νμ₯ μ νμ Keras λͺ¨λΈμ λν μ
λ ₯μΌλ‘ μ λ¬λκ³ , Keras κ³μΈ΅ κ°μ μ λ¬λκ³ , Keras λͺ¨λΈμμ λ°νλ μ μμ΅λλ€. Kerasλ νμ¬ νμ₯ μ νμ λ κ°μ§ μꡬ μ¬νμ μ μ©ν©λλ€.
λ°°μΉ κ°λ₯ν΄μΌ ν©λλ€(μμ "λ°°μΉ κ°λ₯ν ExtensionType" μ°Έμ‘°).
shape μ΄λΌλ νλ λλ μμ±μ΄ μμ΄μΌ ν©λλ€. shape[0] μ λ°°μΉ μ°¨μμΌλ‘ κ°μ£Όλ©λλ€.
λ€μ λ νμ μΉμ
μμλ νμ₯ μ νμ Kerasμ ν¨κ» μ¬μ©νλ λ°©λ²μ 보μ¬μ£Όλ μλ₯Ό μ 곡ν©λ... | class Network(tf.experimental.BatchableExtensionType):
shape: tf.TensorShape # batch shape. A single network has shape=[].
work: tf.Tensor # work[*shape, n] = work left to do at node n
bandwidth: tf.Tensor # bandwidth[*shape, n1, n2] = bandwidth from n1->n2
def __init__(self, work, bandwidth):
s... | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
Network λ₯Ό μ²λ¦¬νλ μλ‘μ΄ Keras κ³μΈ΅μ μ μν μ μμ΅λλ€. | class BalanceNetworkLayer(tf.keras.layers.Layer):
"""Layer that balances work between nodes in a network.
Shifts work from more busy nodes to less busy nodes, constrained by bandwidth.
"""
def call(self, inputs):
# This function is defined above, in "Batchable ExtensionTypes" section.
return balance_wo... | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
κ·Έλ° λ€μ μ΄ λ μ΄μ΄λ₯Ό μ¬μ©νμ¬ κ°λ¨ν λͺ¨λΈμ λ§λ€ μ μμ΅λλ€. ExtensionType μ λͺ¨λΈμ μ 곡νλ €λ©΄ type_spec μ΄ νμ₯ μ νμ TypeSpec tf.keras.layer.Input λ μ΄μ΄λ₯Ό μ¬μ©ν μ μμ΅λλ€. Keras λͺ¨λΈμ μ¬μ©νμ¬ λ°°μΉλ₯Ό μ²λ¦¬νλ κ²½μ° type_spec μ λ°°μΉ μ°¨μμ΄ ν¬ν¨λμ΄μΌ ν©λλ€. | input_spec = Network.Spec(shape=None,
work=tf.TensorSpec(None, tf.float32),
bandwidth=tf.TensorSpec(None, tf.float32))
model = tf.keras.Sequential([
tf.keras.layers.Input(type_spec=input_spec),
BalanceNetworkLayer(),
]) | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
λ§μ§λ§μΌλ‘ λ¨μΌ λ€νΈμν¬μ λ€νΈμν¬ λ°°μΉμ λͺ¨λΈμ μ μ©ν μ μμ΅λλ€. | model(single_network)
model(batch_of_networks) | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
μΌλΌμ€ μμ: MaskedTensor
μ΄ μμμ MaskedTensor Keras λ₯Ό μ§μνλλ‘ νμ₯λμμ΅λλ€. shape values νλμμ κ³μ°λλ μμ±μΌλ‘ μ μλ©λλ€. TypeSpec λͺ¨λμ μ΄ μμ±μ μΆκ°ν΄μΌ ν©λλ€. MaskedTensor SavedModel μ§λ ¬νμ νμν __name__ λ³μλ μ μν©λλ€(μλ μ°Έμ‘°). | class MaskedTensor(tf.experimental.BatchableExtensionType):
# __name__ is required for serialization in SavedModel; see below for details.
__name__ = 'extension_type_colab.MaskedTensor'
values: tf.Tensor
mask: tf.Tensor
shape = property(lambda self: self.values.shape)
dtype = property(lambda self: self.va... | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
λ€μμΌλ‘ λμ€ν¨μΉ λ°μ½λ μ΄ν°λ μ¬λ¬ TensorFlow APIμ κΈ°λ³Έ λμμ μ¬μ μνλ λ° μ¬μ©λ©λλ€. μ΄λ¬ν APIλ νμ€ Keras λ μ΄μ΄(μ: Dense MaskedTensor μ ν¨κ» ν΄λΉ λ μ΄μ΄λ₯Ό μ¬μ©ν μ μμ΅λλ€. μ΄ μμ λͺ©μ μ matmul μ λ§μ€νΉλ κ°μ 0μΌλ‘ μ²λ¦¬νλλ‘ μ μλ©λλ€(μ¦, μ νμ ν¬ν¨νμ§ μκΈ° μν΄). | @tf.experimental.dispatch_for_unary_elementwise_apis(MaskedTensor)
def unary_elementwise_op_handler(op, x):
return MaskedTensor(op(x.values), x.mask)
@tf.experimental.dispatch_for_binary_elementwise_apis(
Union[MaskedTensor, tf.Tensor],
Union[MaskedTensor, tf.Tensor])
def binary_elementwise_op_handler(op, x, ... | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
κ·Έλ° λ€μ νμ€ Keras λ μ΄μ΄λ₯Ό μ¬μ©νμ¬ MaskedTensor μ
λ ₯μ νμ©νλ Keras λͺ¨λΈμ ꡬμ±ν μ μμ΅λλ€. | input_spec = MaskedTensor.Spec([None, 2], tf.float32)
masked_tensor_model = tf.keras.Sequential([
tf.keras.layers.Input(type_spec=input_spec),
tf.keras.layers.Dense(16, activation="relu"),
tf.keras.layers.Dense(1)])
masked_tensor_model.compile(loss='binary_crossentropy', optimizer='rmsprop')
a = MaskedTen... | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
μ μ₯λ λͺ¨λΈ
SavedModel μ κ°μ€μΉμ κ³μ°μ λͺ¨λ ν¬ν¨νλ μ§λ ¬νλ TensorFlow νλ‘κ·Έλ¨μ
λλ€. Keras λͺ¨λΈ λλ μ¬μ©μ μ§μ λͺ¨λΈμμ ꡬμΆν μ μμ΅λλ€. λ κ²½μ° λͺ¨λ νμ₯ μ νμ SavedModelμ μν΄ μ μλ ν¨μ λ° λ©μλμ ν¨κ» ν¬λͺ
νκ² μ¬μ©λ μ μμ΅λλ€.
__name__ νλκ° μλ ν νμ₯ μ νμ μ²λ¦¬νλ λͺ¨λΈ, κ³μΈ΅ λ° ν¨μλ₯Ό μ μ₯ν μ μμ΅λλ€. μ΄ μ΄λ¦μ νμ₯ μ νμ λ±λ‘νλ λ° μ¬μ©λλ―λ‘ λͺ¨λΈμ λ‘λν λ μ°Ύμ μ μμ΅λλ€.
μ: Keras λͺ¨λΈ μ μ₯
νμ₯ μ νμ μ¬μ©νλ SavedModel μ¬μ©νμ¬ μ μ₯ν μ μμ΅λλ€. | masked_tensor_model_path = tempfile.mkdtemp()
tf.saved_model.save(masked_tensor_model, masked_tensor_model_path)
imported_model = tf.saved_model.load(masked_tensor_model_path)
imported_model(a) | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
μ: μ¬μ©μ μ μ λͺ¨λΈ μ μ₯
SavedModelμ νμ₯ μ νμ μ²λ¦¬νλ ν¨μλ‘ tf.Module νμ ν΄λμ€λ₯Ό μ μ₯νλ λ° μ¬μ©ν μλ μμ΅λλ€. | class CustomModule(tf.Module):
def __init__(self, variable_value):
super().__init__()
self.v = tf.Variable(variable_value)
@tf.function
def grow(self, x: MaskedTensor):
"""Increase values in `x` by multiplying them by `self.v`."""
return MaskedTensor(x.values * self.v, x.mask)
module = CustomMod... | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
ExtensionTypeμ μ¬μ©ν μ μμ λ μ μ₯λ λͺ¨λΈ λ‘λ
ExtensionType μ μ¬μ©νλ SavedModel μ λ‘λνμ§λ§ ν΄λΉ ExtensionType μ¬μ©ν μ μλ κ²½μ°(μ¦, κ°μ Έμ€μ§ μμ κ²½μ°) κ²½κ³ κ° νμλκ³ TensorFlowλ "μ΅λͺ
νμ₯ μ ν" κ°μ²΄λ₯Ό μ¬μ©νλλ‘ λ체ν©λλ€. μ΄ κ°μ²΄λ μλ μ νκ³Ό λμΌν νλλ₯Ό κ°μ§λ§ μ¬μ©μ μ μ λ©μλ λλ μμ±κ³Ό κ°μ΄ μ νμ μΆκ°ν μΆκ° μ¬μ©μ μ μκ° λΆμ‘±ν©λλ€.
TensorFlow μ 곡과 ν¨κ» ExtensionType μ¬μ©
νμ¬ TensorFlow μλΉμ€ (λ° SavedModel "μλͺ
" μ¬μ μ λ€λ₯Έ ... | class CustomModuleWrapper(tf.Module):
def __init__(self, variable_value):
super().__init__()
self.v = tf.Variable(variable_value)
@tf.function
def var_weighted_mean(self, x: MaskedTensor):
"""Mean value of unmasked values in x, weighted by self.v."""
x = MaskedTensor(x.values * self.v, x.mask)
... | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
λ°μ΄ν°μΈνΈ
tf.data λ κ°λ¨νκ³ μ¬μ¬μ© κ°λ₯ν λΆλΆμΌλ‘ 볡μ‘ν μ
λ ₯ νμ΄νλΌμΈμ ꡬμΆν μ μλ APIμ
λλ€. ν΅μ¬ λ°μ΄ν° ꡬ쑰λ tf.data.Dataset μ΄λ©°, μ΄λ κ° μμκ° νλ μ΄μμ κ΅¬μ± μμλ‘ κ΅¬μ±λ μΌλ ¨μ μμλ₯Ό λνλ
λλ€.
νμ₯ μ νμΌλ‘ λ°μ΄ν°μΈνΈ λΉλ
Dataset.from_tensors , Dataset.from_tensor_slices λλ Dataset.from_generator μ¬μ©νμ¬ νμ₯ μ ν κ°μμ λ°μ΄ν° μΈνΈλ₯Ό λΉλν μ μμ΅λλ€. | ds = tf.data.Dataset.from_tensors(Pastry(5, 5))
iter(ds).next()
mt = MaskedTensor(tf.reshape(range(20), [5, 4]), tf.ones([5, 4]))
ds = tf.data.Dataset.from_tensor_slices(mt)
for value in ds:
print(value)
def value_gen():
for i in range(2, 7):
yield MaskedTensor(range(10), [j%i != 0 for j in range(10)])
ds = ... | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
νμ₯ μ νμ΄ μλ λ°μ΄ν° μΈνΈ μΌκ΄ μ²λ¦¬ λ° μΌκ΄ ν΄μ
νμ₯ μ νμ λ°μ΄ν° μΈνΈλ₯Ό μ¬μ©νμ¬ batchand λ° unbatched μ μμ΅λλ€ Dataset.batch ADN Dataset.unbatch . | batched_ds = ds.batch(2)
for value in batched_ds:
print(value)
unbatched_ds = batched_ds.unbatch()
for value in unbatched_ds:
print(value) | site/ko/guide/extension_type.ipynb | tensorflow/docs-l10n | apache-2.0 |
The concrete loss function can be set via the loss parameter. SGDClassifier supports the following loss functions:
<ul>
<li>loss="hinge": (soft-margin) linear Support Vector Machine,
<li>loss="modified_huber": smoothed hinge loss,
<li>loss="log": logistic regression,
<li>and all regression losses below.
</ul>
The firs... | clf = SGDClassifier(loss="log").fit(X, y)
clf.predict_proba([[1., 1.]]) | Lectures/Lecture6-Streams/SGD TESTING.ipynb | hethapu/big-data-python-class | mit |
The default setting is penalty="l2". The L1 penalty leads to sparse solutions, driving most coefficients to zero. The Elastic Net solves some deficiencies of the L1 penalty in the presence of highly correlated attributes. The parameter l1_ratio controls the convex combination of L1 and L2 penalty. | %matplotlib inline
# SGD: Maximum Margin Separating hyperplan
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import SGDClassifier
from sklearn.datasets.samples_generator import make_blobs
# we create 50 separable points
X, Y = make_blobs(n_samples=50, centers=2, random_state=0, cluster_st... | Lectures/Lecture6-Streams/SGD TESTING.ipynb | hethapu/big-data-python-class | mit |
customized_KoNLPy μλ νμ¬ νΈμν° νκ΅μ΄ λΆμκΈ° λ§μ μ΄μ©νλ wrapping classλ§ μ 곡λκ³ μμ΅λλ€. customized_KoNLPyμ Twitterλ λ³Έλ KoNLPyμ tagμ μΆκ°λλ ν¨μκ° μμ΅λλ€.
Twitter.add_dictionary(words, tag)λ μ¬μ©μκ° μ¬μ μ μΆκ°ν μ μλ λΆλΆμ
λλ€. λ¨μ΄λ₯Ό νλμ© μΆκ°ν μ μμ΅λλ€. μΆκ°ν λ€ Twitterμ μ¨κΉ λ³μμΈ _dictionary._pos2wordsλ₯Ό νμΈν΄λ³΄λ©΄ μ
λ ₯ν λ¨μ΄λ€μ λ³Ό μ μμ΅λλ€.
git cloneμ ν μνμμ tutorial codeλ₯Ό μ΄μ©ν... | import sys
sys.path.append('../')
from ckonlpy.tag import Twitter
twitter = Twitter()
twitter.add_dictionary('μ΄', 'Modifier')
twitter.add_dictionary('μ°λ¦¬', 'Modifier')
twitter.add_dictionary('μ΄λ²', 'Modifier')
twitter.add_dictionary('μμ΄μ€μμ΄', 'Noun')
twitter.add_dictionary('νμ¬', 'Noun')
twitter.add_dictionary('μμ΄', 'No... | customKonlpy/tutorials/usage_of_templatetagger.ipynb | TeamEmily/Emily_server | mit |
μ¬μ μ μΆκ°ν λ€, 'μμ΄μ€μμ΄'κ° λͺ
μ¬λ‘ μ λλ‘ μΈμλ¨μ νμΈν μ μμ΅λλ€. | twitter.pos('μ°λ¦¬μμ΄μ€μμ΄λ μ λ§ μ΄λ»μ')
twitter.pos('μμ΄μ€μμ΄ μ΄λ»μ') | customKonlpy/tutorials/usage_of_templatetagger.ipynb | TeamEmily/Emily_server | mit |
μ¬μ μ μΆκ°ν λ, νλμ νμ¬μ λνμ¬ λμμ μ¬λ¬ κ°μ λ¨μ΄μ
μ μ
λ ₯ν μλ μμ΅λλ€.
Twitter.add_dictionary(words, tag)λ νλ²μ list of str νμμ μ¬λ¬ κ°μ λ¨μ΄λ€μ μ
λ ₯ν μλ μμ΅λλ€. | twitter.add_dictionary(['νΈμμ΄μ€', 'tt', 'νΈλ₯μ΄', 'κΊΌ', 'μ°λ¦¬'], 'Noun')
twitter._dictionary._pos2words
twitter.pos('νΈμμ΄μ€ttλ μ’μμ') | customKonlpy/tutorials/usage_of_templatetagger.ipynb | TeamEmily/Emily_server | mit |
νΈμν° λΆμκΈ°μ μ‘°μ¬μ¬μ μ μ΄μ©ν μλ μμ΅λλ€. Twitter()λ₯Ό λ§λ€ λ argumentλ₯Ό λ£μ μ μμ΅λλ€. | twitter1 = Twitter(load_default_dictionary=True)
len(twitter1._dictionary._pos2words['Josa']) | customKonlpy/tutorials/usage_of_templatetagger.ipynb | TeamEmily/Emily_server | mit |
νμ§λ§ μμ§ 'μ°λ¦¬νΈλ₯μ΄κΊΌttλ' μ΄λΌλ μ΄μ μ΄ μ λλ‘ μΈμλμ§ μμ΅λλ€. κ·Έ μ΄μ λ templatesμ 'Noun + Noun + Josa'κ° μμκΈ° λλ¬Έμ
λλ€. μ΄ κ²½μ°μλ KoNLPyμ ν΄λΉ μ΄μ μ λΆμνλΌκ³ 보λ
λλ€. νμ§λ§ 'νΈλ₯μ΄'λΌλ λ¨μ΄λ₯Ό μμ§ λͺ»ν΄μ μ λλ‘ μΈμλμ§ μμ΅λλ€. | twitter.pos('μ°λ¦¬νΈλ₯μ΄κΊΌttλ μ’μμ') | customKonlpy/tutorials/usage_of_templatetagger.ipynb | TeamEmily/Emily_server | mit |
νμ¬λ customized_taggerλ‘ ν¬νλ¦Ώ κΈ°λ° ν ν¬λμ΄μ λ₯Ό μ΄μ©νκ³ μμ΅λλ€. μ΄λ€ ν¬νλ¦Ώμ΄ λ€μ΄μλμ§ νμΈνκΈ° μν΄μλ μλ λΆλΆμ νμΈνλ©΄ λ©λλ€.
twitter._customized_tagger.templates
νμ¬λ λ€μμ ν¬νλ¦Ώμ΄ μ
λ ₯λμ΄ μμ΅λλ€. | twitter._customized_tagger.templates | customKonlpy/tutorials/usage_of_templatetagger.ipynb | TeamEmily/Emily_server | mit |
κΈ°λ³Έ ν¬νλ¦Ώμ customized_konlpy/data/templates/twitter_templates0 μ μ μ₯λμ΄ μμ΅λλ€. text νμμ νμΌμ΄λ©°, λμ΄μ°κΈ°λ‘ μλμ κ°μ κΈ°λ³Έ ν
νλ¦Ώμ μ§μ νλ©΄ λ©λλ€. | cat ../ckonlpy/data/templates/twitter_templates0 | customKonlpy/tutorials/usage_of_templatetagger.ipynb | TeamEmily/Emily_server | mit |
μμ
μ€ ν¬νλ¦Ώμ μΆκ°νκ³ μΆλ€λ©΄, ν¬νλ¦Ώμ νλ λ¨μλ‘ tuple of strμ νμμΌλ‘ μ
λ ₯ν μ μμ΅λλ€. _customized_tagger.add_a_templated()μ μ€λ³΅λλ ν¬νλ¦Ώμ΄ μλμ§ νμΈν λ€μ ν¬νλ¦Ώμ μΆκ°νλ ν¨μμ
λλ€. | twitter._customized_tagger.add_a_template(('Modifier', 'Noun', 'Noun', 'Noun', 'Josa'))
twitter._customized_tagger.templates | customKonlpy/tutorials/usage_of_templatetagger.ipynb | TeamEmily/Emily_server | mit |
('Noun', 'Noun', 'Josa')κ° μ
λ ₯λμκ³ , 'νΈμμ΄μ€', 'tt'κ° λͺ
μ¬μΈμ§ μκ³ μκΈ° λλ¬Έμ μλ λ¬Έμ₯μ μ λλ‘ μΈμμ΄ λ©λλ€. | twitter.pos('μ°λ¦¬νΈλ₯μ΄κΊΌttλ μ’μμ') | customKonlpy/tutorials/usage_of_templatetagger.ipynb | TeamEmily/Emily_server | mit |
μ¬μ μ μΆκ°ν λ, νΈμν° νκ΅μ΄ λΆμκΈ°μ μ‘΄μ¬νμ§ μλ νκ·Έκ° λ€μ΄κ°λ κ²μ λ°©μ§νκΈ° μν΄ tagμ κ°μ νμΈνλ λΆλΆμ΄ ꡬνλμ΄ μμ΅λλ€.
twitter.tagset
>>> {'Adjective': 'νμ©μ¬',
'Adverb': 'λΆμ¬',
'Alpha': 'μνλ²³',
'Conjunction': 'μ μμ¬',
'Determiner': 'κ΄νμ¬',
'Eomi': 'μ΄λ―Έ',
'Exclamation': 'κ°νμ¬',
'Foreign': 'μΈκ΅μ΄, νμ λ° κΈ°νκΈ°νΈ',
'Hashtag'... | twitter.add_dictionary('lovit', 'Name') | customKonlpy/tutorials/usage_of_templatetagger.ipynb | TeamEmily/Emily_server | mit |
νμ§λ§ Twitter.add_dictionary(words, tag, force=True)λ‘ λ¨μ΄λ₯Ό μ¬μ μ μ
λ ₯νλ©΄ μλ €μ§μ§ μμ νμ¬λΌ νλλΌλ μ
λ ₯ν μ μμ΅λλ€. | twitter.add_dictionary('lovit', 'Name', force=True)
twitter._dictionary._pos2words | customKonlpy/tutorials/usage_of_templatetagger.ipynb | TeamEmily/Emily_server | mit |
'Name'μ΄λΌλ ν΄λμ€ (λμ΄μ νμ¬κ° μλλ―λ‘)λ₯Ό μ΄μ©νλ ν¬νλ¦Ώμ νλ μ
λ ₯ν λ€ posμ μ
λ ₯νλ©΄ μ΄μ 'lovitμ' customized_taggerμ μνμ¬ μ²λ¦¬κ° λκ³ , μ¬μ©μ μ¬μ μ μλ €μ§μ§ μμ μ΄μ μΈ 'μ‘Έλ €'λ λ³Έλμ νΈμν° λΆμκΈ°μ μνμ¬ μ²λ¦¬κ° λ©λλ€. | twitter._customized_tagger.add_a_template(('Name', 'Josa'))
print(twitter._customized_tagger.templates)
twitter.pos('lovitμ μ΄λ¦μ
λλ€.') | customKonlpy/tutorials/usage_of_templatetagger.ipynb | TeamEmily/Emily_server | mit |
Templatesλ₯Ό μ΄μ©νμ¬λ νλ³΄κ° μ¬λ¬ κ° λμ¬ μ μμ΅λλ€. μ¬λ¬ κ° ν보 μ€μμ best λ₯Ό μ ννλ ν¨μλ₯Ό μ§μ λμμΈ ν μ λ μμ΅λλ€. μ΄μ²λΌ λͺ κ°μ μ μ κΈ°μ€μ λ§λ€κ³ , κ° κΈ°μ€μ weightλ₯Ό λΆμ¬νλ λ°©μμ νΈμν° λΆμκΈ°μμ μ΄μ©νλ λ°©μμΈλ°, μ§κ΄μ μ΄κ³ νλ κ°λ₯ν΄μ λ§€μ° μ’μ λ°©μμ΄λΌ μκ°ν©λλ€. | score_weights = {
'num_nouns': -0.1,
'num_words': -0.2,
'no_noun': -1
}
def my_score(candidate):
num_nouns = len([w for w,t in candidate if t == 'Noun'])
num_words = len(candidate)
no_noun = 1 if num_nouns == 0 else 0
score = (num_nouns * score_weights['num_nouns']
+ num_... | customKonlpy/tutorials/usage_of_templatetagger.ipynb | TeamEmily/Emily_server | mit |
1) Explore the dataset
Numerical exploration
Load the csv file into memory using Pandas
Describe each attribute
is it discrete?
is it continuous?
is it a number?
is it text?
Identify the target
Check if any values are missing
Load the csv file into memory using Pandas | df = pd.read_csv('titanic-train.csv') | Titanic Survival Workshop.ipynb | Dataweekends/odsc_intro_to_data_science | mit |
What's the content of df ? | df.head(3) | Titanic Survival Workshop.ipynb | Dataweekends/odsc_intro_to_data_science | mit |
Is Pclass a continuous or discrete class? | df['Pclass'].value_counts() | Titanic Survival Workshop.ipynb | Dataweekends/odsc_intro_to_data_science | mit |
What about these: ('SibSp', 'Parch')? | df['SibSp'].value_counts()
df['Parch'].value_counts() | Titanic Survival Workshop.ipynb | Dataweekends/odsc_intro_to_data_science | mit |
and what about these: ('Ticket', 'Fare', 'Cabin', 'Embarked')? | df[['Ticket', 'Fare', 'Cabin']].head(3)
df['Embarked'].value_counts() | Titanic Survival Workshop.ipynb | Dataweekends/odsc_intro_to_data_science | mit |
Identify the target
What are we trying to predict?
ah, yes... Survival! | df['Survived'].value_counts() | Titanic Survival Workshop.ipynb | Dataweekends/odsc_intro_to_data_science | mit |
Mental notes so far:
Dataset contains 891 entries
1 Target column (Survived)
11 Features:
6 numerical, 5 text
1 useless (PassengerId)
3 categorical (Pclass, Sex, Embarked)
4 numerical, > 0 (Age, SibSp, Parch, Fare)
3 not sure how to treat (Name, Ticket, Cabin)
Age is only available for 714 passengers
Cabin is only a... | df['Age'].plot(kind='hist', figsize=(10,6))
plt.title('Distribution of Age', size = '20')
plt.xlabel('Age', size = '20')
plt.ylabel('Number of passengers', size = '20')
median_age = df['Age'].median()
plt.axvline(median_age, color = 'r')
median_age | Titanic Survival Workshop.ipynb | Dataweekends/odsc_intro_to_data_science | mit |
impute the missing values for Age using the median Age | df['Age'].fillna(median_age, inplace = True)
df.info() | Titanic Survival Workshop.ipynb | Dataweekends/odsc_intro_to_data_science | mit |
check the influence of Age | df[df['Survived']==1]['Age'].plot(kind='hist', bins = 10, range = (0,100), figsize=(10,6), alpha = 0.3, color = 'g')
df[df['Survived']==0]['Age'].plot(kind='hist', bins = 10, range = (0,100), figsize=(10,6), alpha = 0.3, color = 'r')
plt.title('Distribution of Age', size = '20')
plt.xlabel('Age', size = '20')
plt.ylabe... | Titanic Survival Workshop.ipynb | Dataweekends/odsc_intro_to_data_science | mit |
Check the influence of Sex on Survival | survival_by_gender = df[['Sex','Survived']].pivot_table(columns =
['Survived'], index = ['Sex'], aggfunc=len)
survival_by_gender
survival_by_gender.plot(kind = 'bar', stacked = True)
plt.show() | Titanic Survival Workshop.ipynb | Dataweekends/odsc_intro_to_data_science | mit |
Check the influence of Pclass on Survival | survival_by_Pclass = df[['Pclass','Survived']].pivot_table(columns =
['Survived'], index = ['Pclass'], aggfunc=len)
survival_by_Pclass
survival_by_Pclass.plot(kind = 'bar', stacked = True)
plt.show() | Titanic Survival Workshop.ipynb | Dataweekends/odsc_intro_to_data_science | mit |
Ok, so, Age and Pclass seem to have some influence on survival rate.
Let's build a simple model to test that
Define a new feature called "Male" that is 1 if Sex = 'male' and 0 otherwise | df['Male'] = df['Sex'].map({'male': 1, 'female': 0})
df[['Sex', 'Male']].head() | Titanic Survival Workshop.ipynb | Dataweekends/odsc_intro_to_data_science | mit |
Define simplest model as benchmark
The simplest model is a model that predicts 0 for everybody, i.e. no survival.
How good is it? | actual_dead = len(df[df['Survived'] == 0])
total_passengers = len(df)
ratio_of_dead = actual_dead / float(total_passengers)
print "If I predict everybody dies, I'm correct %0.1f %% of the time" % (100 * ratio_of_dead)
df['Survived'].value_counts() | Titanic Survival Workshop.ipynb | Dataweekends/odsc_intro_to_data_science | mit |
We need to do better than that
Define features (X) and target (y) variables | X = df[['Male', 'Pclass', 'Age']]
y = df['Survived'] | Titanic Survival Workshop.ipynb | Dataweekends/odsc_intro_to_data_science | mit |
Initialize a decision tree model | from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier(random_state=0)
model | Titanic Survival Workshop.ipynb | Dataweekends/odsc_intro_to_data_science | mit |
Split the features and the target into a Train and a Test subsets.
Ratio should be 80/20 | from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y,
test_size = 0.2, random_state=0) | Titanic Survival Workshop.ipynb | Dataweekends/odsc_intro_to_data_science | mit |
Print the confusion matrix for the decision tree model | from sklearn.metrics import confusion_matrix
y_pred = model.predict(X_test)
print "\n=======confusion matrix=========="
print confusion_matrix(y_test, y_pred) | Titanic Survival Workshop.ipynb | Dataweekends/odsc_intro_to_data_science | mit |
Numpy: Arrays und effiziente Berechnungen
Das HerzstΓΌck von Numpy ist das Array. Dieser Datentyp reprΓ€sentiert eine Matrix und ist unter der Haube in C implementiert. Dabei wird groΓer Wert auf effiziente Speichernutzung gelegt. Der gΓ€ngige Satz "Python ist viel zu langsam" ist also nicht zwingend wahr. Wir kΓΆnnen Arra... | xs = np.array([1, 2, 3, 4]) # Konvertiert eine Python-Liste in ein Numpy-Array
print(xs)
ys = np.arange(4) # Erzeugt ein Array analog zur `range` Funktion
print(ys) | tutorials/Wissenschaftliches Python Tutorial.ipynb | kdungs/teaching-SMD2-2016 | mit |
Numpy Arrays unterstΓΌtzen arithmetische Operationen, die wiederum effizient implementiert sind. Beispielsweise lassen sich zwei Arrays (elementweise) addieren sofern sie die gleichen Dimensionen haben. | xs + ys | tutorials/Wissenschaftliches Python Tutorial.ipynb | kdungs/teaching-SMD2-2016 | mit |
Um einen Γberblick ΓΌber alle Features von Numpy zu bekommen, kΓΆnnen wir die Hilfe zu Rate ziehen. ZusΓ€tzlich zur help Funktion bietet IPython auch die ?-Magie mit einer besseren Integration in Jupyter | np? | tutorials/Wissenschaftliches Python Tutorial.ipynb | kdungs/teaching-SMD2-2016 | mit |
FΓΌr die Γbungsaufgaben werden wir hΓ€ufig Zufallszahlen brauchen. DafΓΌr bietet sich die Verwendung von np.random an. | np.random?
n_events = 10000
gauss = np.random.normal(2, 3, size=n_events) # Erzeuge 10000 GauΓ-verteilte Zufallszahlen
# mit Β΅=2 und Ο=3. | tutorials/Wissenschaftliches Python Tutorial.ipynb | kdungs/teaching-SMD2-2016 | mit |
Matplotlib: SchΓΆne Plots
Matplotlib bietet sehr intuitive Funktionen um Daten darzustellen. Die sehr ausfΓΌhrliche Dokumentation bietet einen guten Γberblick. Wir benutzen an dieser Stelle nur das pyplot Submodul, das uns ein einfaches Interface fΓΌr die KernfunktionalitΓ€t bietet. In der Matplotlib Galerie finden sich vi... | plt.hist(gauss)
plt.xlabel('Wert')
plt.ylabel('Absolute HΓ€ufigkeit') | tutorials/Wissenschaftliches Python Tutorial.ipynb | kdungs/teaching-SMD2-2016 | mit |
Falls dir dieser Plot zu steril ist, kΓΆnnen wir den Stil der bekannten R-Bibliothek ggplot2 verwenden. | plt.style.use('ggplot') | tutorials/Wissenschaftliches Python Tutorial.ipynb | kdungs/teaching-SMD2-2016 | mit |
Wir wollen nun die Anzahl Bins erhΓΆhen und zusΓ€tzlich das Histogramm normieren, damit wir die normierte Verteilungsfunktion (PDF) eintragen kΓΆnnen. | plt.hist(gauss, bins=20, normed=True)
plt.xlabel('Wert')
plt.ylabel('Relative HΓ€ufigkeit') | tutorials/Wissenschaftliches Python Tutorial.ipynb | kdungs/teaching-SMD2-2016 | mit |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.