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Visión general
Este cuaderno demostrará cómo utilizar la capa de Normalización de peso y cómo puede mejorar la convergencia.
Peso Normalización
Una simple reparametrización para acelerar el entrenamiento de redes neuronales profundas:
Tim Salimans, Diederik P. Kingma (2016)
Al volver a parametrizar los pesos de esta manera, se mejora el acondicionamiento del problema de optimización y se acelera la convergencia del descenso de gradiente estocástico. Nuestra reparametrización se inspira en la normalización por lotes, pero no introduce ninguna dependencia entre los ejemplos de un minibatch. Esto significa que nuestro método también se puede aplicar con éxito a modelos recurrentes como LSTM y a aplicaciones sensibles al ruido, como el aprendizaje por refuerzo profundo o los modelos generativos, para los que la normalización por lotes es menos adecuada. Aunque nuestro método es mucho más simple, aún proporciona gran parte de la aceleración de la normalización por lotes completa. Además, la sobrecarga computacional de nuestro método es menor, lo que permite tomar más pasos de optimización en la misma cantidad de tiempo.
Configuración
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
Construir modelos
# 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')),
])
Cargar datos
(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
Modelos de trenes
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()
