tqdm_progress_bar.ipynb

Kernel: Python 3

Copyright 2020 The TensorFlow Authors.

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TensorFlow 애드온 콜백: TQDM 진행률 표시줄

TensorFlow.org에서 보기

Google Colab에서 실행하기

GitHub에서 소스 보기

노트북 다운로드하기

개요

이 노트북은 TensorFlow 애드온에서 TQDMCallback을 사용하는 방법을 보여줍니다.

설정

!pip install -U tensorflow-addons

!pip install -q "tqdm>=4.36.1"

import tensorflow as tf
import tensorflow_addons as tfa

from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten

import tqdm

# quietly deep-reload tqdm
import sys
from IPython.lib import deepreload 

stdout = sys.stdout
sys.stdout = open('junk','w')
deepreload.reload(tqdm)
sys.stdout = stdout

tqdm.__version__

데이터 가져오기 및 정규화

# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# normalize data
x_train, x_test = x_train / 255.0, x_test / 255.0

간단한 MNIST CNN 모델 빌드하기

# build the model using the Sequential API
model = Sequential()
model.add(Flatten(input_shape=(28, 28)))
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(10, activation='softmax'))

model.compile(optimizer='adam',
              loss = 'sparse_categorical_crossentropy',
              metrics=['accuracy'])

기본 TQDMCallback 사용법

# initialize tqdm callback with default parameters
tqdm_callback = tfa.callbacks.TQDMProgressBar()

# train the model with tqdm_callback
# make sure to set verbose = 0 to disable
# the default progress bar.
model.fit(x_train, y_train,
          batch_size=64,
          epochs=10,
          verbose=0,
          callbacks=[tqdm_callback],
          validation_data=(x_test, y_test))

아래는 위의 셀을 실행할 때 예상되는 출력입니다.

# TQDMProgressBar() also works with evaluate()
model.evaluate(x_test, y_test, batch_size=64, callbacks=[tqdm_callback], verbose=0)

아래는 위의 셀을 실행할 때 예상되는 출력입니다.