 TensorFlow.org에서 보기 |
 Google Colab에서 실행하기 |
 GitHub에서 소스 보기 |
 노트북 다운로드하기 |
개요
이 노트북은 가중치 정규화 레이어를 사용하는 방법과 수렴을 향상할 수 있는 방법을 보여줍니다.
WeightNormalization
심층 신경망의 훈련을 가속하기 위한 간단한 재매개변수화:
Tim Salimans, Diederik P. Kingma (2016)
이러한 방식으로 가중치를 재매개변수화함으로써 최적화 문제의 처리를 개선하고 확률적 경사 하강의 수렴을 가속합니다. 재매개변수화는 배치 정규화에서 영감을 얻었지만, 미니 배치의 예제 간에 종속성을 도입하지는 않습니다. 이는 이 방법이 배치 정규화가 덜 적합한 LSTM과 같은 반복 모델과 심층 강화 학습 또는 생성 모델과 같은 노이즈에 민감한 애플리케이션에 성공적으로 적용될 수 있음을 의미합니다. 이 방법은 훨씬 간단하지만, 전체 배치 정규화의 속도를 크게 향상합니다. 또한, 이 방법의 계산 오버헤드가 더 적으므로 같은 시간에 더 많은 최적화 단계를 수행할 수 있습니다.
설정
pip install -q -U tensorflow-addonsimport tensorflow as tf
import tensorflow_addons as tfa
import numpy as np
from matplotlib import pyplot as plt
# Hyper Parameters
batch_size = 32
epochs = 10
num_classes=10
모델 빌드하기
# Standard ConvNet
reg_model = tf.keras.Sequential([
tf.keras.layers.Conv2D(6, 5, activation='relu'),
tf.keras.layers.MaxPooling2D(2, 2),
tf.keras.layers.Conv2D(16, 5, activation='relu'),
tf.keras.layers.MaxPooling2D(2, 2),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(120, activation='relu'),
tf.keras.layers.Dense(84, activation='relu'),
tf.keras.layers.Dense(num_classes, activation='softmax'),
])
# WeightNorm ConvNet
wn_model = tf.keras.Sequential([
tfa.layers.WeightNormalization(tf.keras.layers.Conv2D(6, 5, activation='relu')),
tf.keras.layers.MaxPooling2D(2, 2),
tfa.layers.WeightNormalization(tf.keras.layers.Conv2D(16, 5, activation='relu')),
tf.keras.layers.MaxPooling2D(2, 2),
tf.keras.layers.Flatten(),
tfa.layers.WeightNormalization(tf.keras.layers.Dense(120, activation='relu')),
tfa.layers.WeightNormalization(tf.keras.layers.Dense(84, activation='relu')),
tfa.layers.WeightNormalization(tf.keras.layers.Dense(num_classes, activation='softmax')),
])
데이터 로드하기
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
# Convert class vectors to binary class matrices.
y_train = tf.keras.utils.to_categorical(y_train, num_classes)
y_test = tf.keras.utils.to_categorical(y_test, num_classes)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
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모델 훈련하기
reg_model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
reg_history = reg_model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
validation_data=(x_test, y_test),
shuffle=True)
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4s 3ms/step - loss: 1.3673 - accuracy: 0.5106 - val_loss: 1.3043 - val_accuracy: 0.5369 Epoch 3/10 1/1563 [..............................] - ETA: 0s - loss: 1.2936 - accuracy: 0.5938 22/1563 [..............................] - ETA: 3s - loss: 1.3158 - accuracy: 0.5398 43/1563 [..............................] - ETA: 3s - loss: 1.2771 - accuracy: 0.5531 63/1563 [>.............................] - ETA: 3s - loss: 1.2656 - accuracy: 0.5456 83/1563 [>.............................] - ETA: 3s - loss: 1.2595 - accuracy: 0.5444 103/1563 [>.............................] - ETA: 3s - loss: 1.2551 - accuracy: 0.5498 124/1563 [=>............................] - ETA: 3s - loss: 1.2555 - accuracy: 0.5509 146/1563 [=>............................] - ETA: 3s - loss: 1.2609 - accuracy: 0.5484 167/1563 [==>...........................] - ETA: 3s - loss: 1.2585 - accuracy: 0.5492 189/1563 [==>...........................] - ETA: 3s - loss: 1.2556 - accuracy: 0.5504 210/1563 [===>..........................] - ETA: 3s - 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ETA: 0s - loss: 1.2465 - accuracy: 0.5556 1465/1563 [===========================>..] - ETA: 0s - loss: 1.2454 - accuracy: 0.5560 1486/1563 [===========================>..] - ETA: 0s - loss: 1.2453 - accuracy: 0.5563 1505/1563 [===========================>..] - ETA: 0s - loss: 1.2445 - accuracy: 0.5568 1524/1563 [============================>.] - ETA: 0s - loss: 1.2448 - accuracy: 0.5571 1544/1563 [============================>.] - ETA: 0s - loss: 1.2439 - accuracy: 0.5570 1563/1563 [==============================] - 4s 3ms/step - loss: 1.2437 - accuracy: 0.5571 - val_loss: 1.2662 - val_accuracy: 0.5441 Epoch 4/10 1/1563 [..............................] - ETA: 0s - loss: 1.0693 - accuracy: 0.6250 21/1563 [..............................] - ETA: 3s - loss: 1.1372 - accuracy: 0.6101 41/1563 [..............................] - ETA: 3s - loss: 1.1006 - accuracy: 0.6220 62/1563 [>.............................] - ETA: 3s - loss: 1.1154 - accuracy: 0.6129 84/1563 [>.............................] - 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ETA: 0s - loss: 1.1562 - accuracy: 0.5917 1563/1563 [==============================] - 4s 3ms/step - loss: 1.1561 - accuracy: 0.5916 - val_loss: 1.1837 - val_accuracy: 0.5858 Epoch 5/10 1/1563 [..............................] - ETA: 0s - loss: 1.0137 - accuracy: 0.6875 21/1563 [..............................] - ETA: 3s - loss: 1.0516 - accuracy: 0.6324 41/1563 [..............................] - ETA: 3s - loss: 1.0583 - accuracy: 0.6258 62/1563 [>.............................] - ETA: 3s - loss: 1.0616 - accuracy: 0.6326 83/1563 [>.............................] - ETA: 3s - loss: 1.0569 - accuracy: 0.6363 104/1563 [>.............................] - ETA: 3s - loss: 1.0743 - accuracy: 0.6301 125/1563 [=>............................] - ETA: 3s - loss: 1.0710 - accuracy: 0.6285 146/1563 [=>............................] - ETA: 3s - loss: 1.0627 - accuracy: 0.6280 167/1563 [==>...........................] - ETA: 3s - loss: 1.0652 - accuracy: 0.6252 187/1563 [==>...........................] - ETA: 3s - 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ETA: 0s - loss: 0.9127 - accuracy: 0.6790 1398/1563 [=========================>....] - ETA: 0s - loss: 0.9131 - accuracy: 0.6790 1419/1563 [==========================>...] - ETA: 0s - loss: 0.9139 - accuracy: 0.6787 1440/1563 [==========================>...] - ETA: 0s - loss: 0.9135 - accuracy: 0.6789 1461/1563 [===========================>..] - ETA: 0s - loss: 0.9136 - accuracy: 0.6788 1481/1563 [===========================>..] - ETA: 0s - loss: 0.9140 - accuracy: 0.6783 1501/1563 [===========================>..] - ETA: 0s - loss: 0.9144 - accuracy: 0.6781 1522/1563 [============================>.] - ETA: 0s - loss: 0.9151 - accuracy: 0.6776 1542/1563 [============================>.] - ETA: 0s - loss: 0.9156 - accuracy: 0.6778 1563/1563 [==============================] - ETA: 0s - loss: 0.9167 - accuracy: 0.6772 1563/1563 [==============================] - 4s 3ms/step - loss: 0.9167 - accuracy: 0.6772 - val_loss: 1.1309 - val_accuracy: 0.6132 Epoch 10/10 1/1563 [..............................] - ETA: 0s - loss: 0.7836 - accuracy: 0.6562 22/1563 [..............................] - ETA: 3s - loss: 0.8456 - accuracy: 0.6989 42/1563 [..............................] - ETA: 3s - loss: 0.8456 - accuracy: 0.6927 63/1563 [>.............................] - ETA: 3s - loss: 0.8461 - accuracy: 0.7024 83/1563 [>.............................] - ETA: 3s - loss: 0.8429 - accuracy: 0.7044 103/1563 [>.............................] - ETA: 3s - loss: 0.8383 - accuracy: 0.7039 124/1563 [=>............................] - ETA: 3s - loss: 0.8362 - accuracy: 0.7077 145/1563 [=>............................] - ETA: 3s - loss: 0.8435 - accuracy: 0.7032 166/1563 [==>...........................] - ETA: 3s - loss: 0.8443 - accuracy: 0.7026 187/1563 [==>...........................] - ETA: 3s - loss: 0.8514 - accuracy: 0.6977 208/1563 [==>...........................] - ETA: 3s - loss: 0.8544 - accuracy: 0.6988 228/1563 [===>..........................] - ETA: 3s - loss: 0.8535 - accuracy: 0.6998 248/1563 [===>..........................] - 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ETA: 1s - loss: 0.8695 - accuracy: 0.6920 1008/1563 [==================>...........] - ETA: 1s - loss: 0.8699 - accuracy: 0.6916 1028/1563 [==================>...........] - ETA: 1s - loss: 0.8710 - accuracy: 0.6917 1048/1563 [===================>..........] - ETA: 1s - loss: 0.8705 - accuracy: 0.6919 1069/1563 [===================>..........] - ETA: 1s - loss: 0.8704 - accuracy: 0.6919 1090/1563 [===================>..........] - ETA: 1s - loss: 0.8703 - accuracy: 0.6922 1112/1563 [====================>.........] - ETA: 1s - loss: 0.8729 - accuracy: 0.6912 1133/1563 [====================>.........] - ETA: 1s - loss: 0.8735 - accuracy: 0.6910 1154/1563 [=====================>........] - ETA: 1s - loss: 0.8736 - accuracy: 0.6914 1175/1563 [=====================>........] - ETA: 0s - loss: 0.8756 - accuracy: 0.6904 1197/1563 [=====================>........] - ETA: 0s - loss: 0.8762 - accuracy: 0.6900 1218/1563 [======================>.......] - ETA: 0s - loss: 0.8765 - accuracy: 0.6898 1240/1563 [======================>.......] - ETA: 0s - loss: 0.8770 - accuracy: 0.6899 1261/1563 [=======================>......] - ETA: 0s - loss: 0.8773 - accuracy: 0.6897 1283/1563 [=======================>......] - ETA: 0s - loss: 0.8762 - accuracy: 0.6902 1305/1563 [========================>.....] - ETA: 0s - loss: 0.8778 - accuracy: 0.6896 1327/1563 [========================>.....] - ETA: 0s - loss: 0.8766 - accuracy: 0.6897 1348/1563 [========================>.....] - ETA: 0s - loss: 0.8780 - accuracy: 0.6892 1369/1563 [=========================>....] - ETA: 0s - loss: 0.8784 - accuracy: 0.6890 1389/1563 [=========================>....] - ETA: 0s - loss: 0.8783 - accuracy: 0.6891 1410/1563 [==========================>...] - ETA: 0s - loss: 0.8772 - accuracy: 0.6893 1431/1563 [==========================>...] - ETA: 0s - loss: 0.8764 - accuracy: 0.6897 1452/1563 [==========================>...] - ETA: 0s - loss: 0.8763 - accuracy: 0.6896 1473/1563 [===========================>..] - ETA: 0s - loss: 0.8770 - accuracy: 0.6893 1494/1563 [===========================>..] - ETA: 0s - loss: 0.8768 - accuracy: 0.6895 1515/1563 [============================>.] - ETA: 0s - loss: 0.8764 - accuracy: 0.6895 1536/1563 [============================>.] - ETA: 0s - loss: 0.8778 - accuracy: 0.6890 1557/1563 [============================>.] - ETA: 0s - loss: 0.8780 - accuracy: 0.6890 1563/1563 [==============================] - 4s 3ms/step - loss: 0.8779 - accuracy: 0.6891 - val_loss: 1.1575 - val_accuracy: 0.6045
wn_model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
wn_history = wn_model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
validation_data=(x_test, y_test),
shuffle=True)
Epoch 1/10 1/1563 [..............................] - ETA: 0s - loss: 2.7097 - accuracy: 0.0312 13/1563 [..............................] - ETA: 6s - loss: 2.3695 - accuracy: 0.1827 26/1563 [..............................] - ETA: 6s - loss: 2.2602 - accuracy: 0.2007 39/1563 [..............................] - ETA: 6s - loss: 2.1882 - accuracy: 0.2123 51/1563 [..............................] - ETA: 6s - loss: 2.1187 - accuracy: 0.2267 64/1563 [>.............................] - ETA: 6s - loss: 2.0897 - accuracy: 0.2383 76/1563 [>.............................] - ETA: 6s - loss: 2.0744 - accuracy: 0.2434 89/1563 [>.............................] - ETA: 6s - loss: 2.0486 - accuracy: 0.2518 102/1563 [>.............................] - ETA: 5s - loss: 2.0291 - accuracy: 0.2583 115/1563 [=>............................] - ETA: 5s - loss: 2.0058 - accuracy: 0.2679 127/1563 [=>............................] - ETA: 5s - loss: 1.9880 - accuracy: 0.2734 140/1563 [=>............................] - ETA: 5s - 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ETA: 0s - loss: 1.1289 - accuracy: 0.5981 1463/1563 [===========================>..] - ETA: 0s - loss: 1.1293 - accuracy: 0.5977 1476/1563 [===========================>..] - ETA: 0s - loss: 1.1288 - accuracy: 0.5978 1489/1563 [===========================>..] - ETA: 0s - loss: 1.1284 - accuracy: 0.5981 1502/1563 [===========================>..] - ETA: 0s - loss: 1.1280 - accuracy: 0.5983 1515/1563 [============================>.] - ETA: 0s - loss: 1.1278 - accuracy: 0.5982 1528/1563 [============================>.] - ETA: 0s - loss: 1.1275 - accuracy: 0.5982 1541/1563 [============================>.] - ETA: 0s - loss: 1.1282 - accuracy: 0.5980 1554/1563 [============================>.] - ETA: 0s - loss: 1.1281 - accuracy: 0.5981 1563/1563 [==============================] - 8s 5ms/step - loss: 1.1279 - accuracy: 0.5982 - val_loss: 1.1730 - val_accuracy: 0.5866 Epoch 5/10 1/1563 [..............................] - ETA: 0s - loss: 0.8235 - accuracy: 0.7188 13/1563 [..............................] - 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7s 5ms/step - loss: 1.0139 - accuracy: 0.6412 - val_loss: 1.1333 - val_accuracy: 0.6025 Epoch 7/10 1/1563 [..............................] - ETA: 0s - loss: 0.9956 - accuracy: 0.6250 12/1563 [..............................] - ETA: 6s - loss: 0.9680 - accuracy: 0.6641 23/1563 [..............................] - ETA: 6s - loss: 0.9483 - accuracy: 0.6739 35/1563 [..............................] - ETA: 6s - loss: 0.9527 - accuracy: 0.6661 46/1563 [..............................] - ETA: 6s - loss: 0.9640 - accuracy: 0.6610 58/1563 [>.............................] - ETA: 6s - loss: 0.9544 - accuracy: 0.6600 70/1563 [>.............................] - ETA: 6s - loss: 0.9350 - accuracy: 0.6656 82/1563 [>.............................] - ETA: 6s - loss: 0.9408 - accuracy: 0.6616 94/1563 [>.............................] - ETA: 6s - loss: 0.9398 - accuracy: 0.6642 106/1563 [=>............................] - ETA: 6s - loss: 0.9339 - accuracy: 0.6698 118/1563 [=>............................] - ETA: 6s - 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ETA: 0s - loss: 0.9632 - accuracy: 0.6585 1555/1563 [============================>.] - ETA: 0s - loss: 0.9639 - accuracy: 0.6584 1563/1563 [==============================] - 8s 5ms/step - loss: 0.9641 - accuracy: 0.6584 - val_loss: 1.1257 - val_accuracy: 0.6100 Epoch 8/10 1/1563 [..............................] - ETA: 0s - loss: 0.5843 - accuracy: 0.8438 13/1563 [..............................] - ETA: 6s - loss: 0.8826 - accuracy: 0.6803 25/1563 [..............................] - ETA: 6s - loss: 0.8696 - accuracy: 0.6925 38/1563 [..............................] - ETA: 6s - loss: 0.8965 - accuracy: 0.6883 51/1563 [..............................] - ETA: 6s - loss: 0.8851 - accuracy: 0.6881 63/1563 [>.............................] - ETA: 6s - loss: 0.8960 - accuracy: 0.6806 76/1563 [>.............................] - ETA: 6s - loss: 0.8781 - accuracy: 0.6879 88/1563 [>.............................] - ETA: 6s - loss: 0.8836 - accuracy: 0.6868 100/1563 [>.............................] - ETA: 6s - 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val_loss: 1.1275 - val_accuracy: 0.6057 Epoch 9/10 1/1563 [..............................] - ETA: 0s - loss: 0.7620 - accuracy: 0.7500 13/1563 [..............................] - ETA: 6s - loss: 0.8068 - accuracy: 0.7260 25/1563 [..............................] - ETA: 6s - loss: 0.8056 - accuracy: 0.7150 38/1563 [..............................] - ETA: 6s - loss: 0.8357 - accuracy: 0.7039 51/1563 [..............................] - ETA: 6s - loss: 0.8332 - accuracy: 0.7028 63/1563 [>.............................] - ETA: 6s - loss: 0.8228 - accuracy: 0.7063 75/1563 [>.............................] - ETA: 6s - loss: 0.8232 - accuracy: 0.7071 87/1563 [>.............................] - ETA: 6s - loss: 0.8190 - accuracy: 0.7091 100/1563 [>.............................] - ETA: 6s - loss: 0.8191 - accuracy: 0.7106 113/1563 [=>............................] - ETA: 6s - loss: 0.8307 - accuracy: 0.7088 126/1563 [=>............................] - ETA: 5s - loss: 0.8330 - accuracy: 0.7068 139/1563 [=>............................] - 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ETA: 0s - loss: 0.8777 - accuracy: 0.6894 1546/1563 [============================>.] - ETA: 0s - loss: 0.8784 - accuracy: 0.6891 1558/1563 [============================>.] - ETA: 0s - loss: 0.8776 - accuracy: 0.6895 1563/1563 [==============================] - 8s 5ms/step - loss: 0.8774 - accuracy: 0.6897 - val_loss: 1.1257 - val_accuracy: 0.6120 Epoch 10/10 1/1563 [..............................] - ETA: 0s - loss: 0.9646 - accuracy: 0.6250 13/1563 [..............................] - ETA: 6s - loss: 0.8207 - accuracy: 0.7043 25/1563 [..............................] - ETA: 6s - loss: 0.8328 - accuracy: 0.7088 37/1563 [..............................] - ETA: 6s - loss: 0.8320 - accuracy: 0.7086 49/1563 [..............................] - ETA: 6s - loss: 0.8319 - accuracy: 0.7085 62/1563 [>.............................] - ETA: 6s - loss: 0.8142 - accuracy: 0.7142 74/1563 [>.............................] - ETA: 6s - loss: 0.8126 - accuracy: 0.7141 86/1563 [>.............................] - ETA: 6s - 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ETA: 0s - loss: 0.8381 - accuracy: 0.7039 1563/1563 [==============================] - 8s 5ms/step - loss: 0.8379 - accuracy: 0.7040 - val_loss: 1.1223 - val_accuracy: 0.6183
reg_accuracy = reg_history.history['accuracy']
wn_accuracy = wn_history.history['accuracy']
plt.plot(np.linspace(0, epochs, epochs), reg_accuracy,
color='red', label='Regular ConvNet')
plt.plot(np.linspace(0, epochs, epochs), wn_accuracy,
color='blue', label='WeightNorm ConvNet')
plt.title('WeightNorm Accuracy Comparison')
plt.legend()
plt.grid(True)
plt.show()
