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Przegląd
Ten notatnik pokaże, jak korzystać z warstwy normalizacji wagi i jak może ona poprawić zbieżność.
Normalizacja wagi
Prosta reparametryzacja w celu przyspieszenia uczenia głębokich sieci neuronowych:
Tim Salimans, Diederik P. Kingma (2016)
Poprzez przeparametryzowanie wag w ten sposób poprawiasz uwarunkowanie problemu optymalizacji i przyspieszasz zbieżność stochastycznego opadania gradientu. Nasza reparametryzacja jest inspirowana normalizacją partii, ale nie wprowadza żadnych zależności między przykładami w minipartii. Oznacza to, że nasza metoda może być również z powodzeniem stosowana w modelach powtarzalnych, takich jak LSTM, oraz w aplikacjach wrażliwych na hałas, takich jak głębokie uczenie się ze wzmocnieniem lub modele generatywne, dla których normalizacja wsadowa jest mniej odpowiednia. Chociaż nasza metoda jest znacznie prostsza, nadal zapewnia znaczne przyspieszenie pełnej normalizacji wsadowej. Ponadto narzut obliczeniowy naszej metody jest niższy, co pozwala na wykonanie większej liczby kroków optymalizacyjnych w tym samym czasie.
Ustawiać
pip install -q -U tensorflow-addons
import 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
Budowanie modeli
# 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')),
])
Załaduj dane
(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
Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz 170500096/170498071 [==============================] - 11s 0us/step
Modele pociągów
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)
Epoch 1/10 1563/1563 [==============================] - 9s 4ms/step - loss: 1.8336 - accuracy: 0.3253 - val_loss: 1.4039 - val_accuracy: 0.4957 Epoch 2/10 1563/1563 [==============================] - 5s 3ms/step - loss: 1.3773 - accuracy: 0.5039 - val_loss: 1.3419 - val_accuracy: 0.5309 Epoch 3/10 1563/1563 [==============================] - 5s 3ms/step - loss: 1.2510 - accuracy: 0.5497 - val_loss: 1.2108 - val_accuracy: 0.5710 Epoch 4/10 1563/1563 [==============================] - 5s 3ms/step - loss: 1.1606 - accuracy: 0.5858 - val_loss: 1.2134 - val_accuracy: 0.5687 Epoch 5/10 1563/1563 [==============================] - 5s 3ms/step - loss: 1.0971 - accuracy: 0.6100 - val_loss: 1.1534 - val_accuracy: 0.5880 Epoch 6/10 1563/1563 [==============================] - 5s 3ms/step - loss: 1.0420 - accuracy: 0.6296 - val_loss: 1.1944 - val_accuracy: 0.5865 Epoch 7/10 1563/1563 [==============================] - 5s 3ms/step - loss: 1.0014 - accuracy: 0.6445 - val_loss: 1.1386 - val_accuracy: 0.6012 Epoch 8/10 1563/1563 [==============================] - 5s 3ms/step - loss: 0.9550 - accuracy: 0.6623 - val_loss: 1.1659 - val_accuracy: 0.6020 Epoch 9/10 1563/1563 [==============================] - 5s 3ms/step - loss: 0.9196 - accuracy: 0.6737 - val_loss: 1.1539 - val_accuracy: 0.6027 Epoch 10/10 1563/1563 [==============================] - 5s 3ms/step - loss: 0.8768 - accuracy: 0.6889 - val_loss: 1.1509 - val_accuracy: 0.6029
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 1563/1563 [==============================] - 14s 8ms/step - loss: 1.8195 - accuracy: 0.3319 - val_loss: 1.4563 - val_accuracy: 0.4721 Epoch 2/10 1563/1563 [==============================] - 10s 7ms/step - loss: 1.4049 - accuracy: 0.4937 - val_loss: 1.3051 - val_accuracy: 0.5301 Epoch 3/10 1563/1563 [==============================] - 10s 6ms/step - loss: 1.2669 - accuracy: 0.5461 - val_loss: 1.2858 - val_accuracy: 0.5425 Epoch 4/10 1563/1563 [==============================] - 10s 6ms/step - loss: 1.1622 - accuracy: 0.5868 - val_loss: 1.2278 - val_accuracy: 0.5587 Epoch 5/10 1563/1563 [==============================] - 10s 6ms/step - loss: 1.0782 - accuracy: 0.6175 - val_loss: 1.1755 - val_accuracy: 0.5825 Epoch 6/10 1563/1563 [==============================] - 10s 6ms/step - loss: 1.0280 - accuracy: 0.6383 - val_loss: 1.1772 - val_accuracy: 0.5827 Epoch 7/10 1563/1563 [==============================] - 10s 6ms/step - loss: 0.9705 - accuracy: 0.6527 - val_loss: 1.1542 - val_accuracy: 0.5895 Epoch 8/10 1563/1563 [==============================] - 10s 6ms/step - loss: 0.9291 - accuracy: 0.6695 - val_loss: 1.1680 - val_accuracy: 0.5924 Epoch 9/10 1563/1563 [==============================] - 10s 6ms/step - loss: 0.8837 - accuracy: 0.6884 - val_loss: 1.1302 - val_accuracy: 0.6039 Epoch 10/10 1563/1563 [==============================] - 10s 6ms/step - loss: 0.8437 - accuracy: 0.7029 - val_loss: 1.1593 - val_accuracy: 0.6018
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()
