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概述
此笔记本将演示如何使用 Addons 包中的 Lazy Adam 优化器。
LazyAdam
LazyAdam 是 Adam 优化器的一种变体，可以更高效地处理稀疏更新。原始的 Adam 算法为每个可训练变量维护两个移动平均累加器，这些累加器在每一步都会更新。此类为稀疏变量提供了更加懒惰的梯度更新处理。它仅更新当前批次中出现的稀疏变量索引的移动平均累加器，而不是更新所有索引的累加器。与原始的 Adam 优化器相比，它可以大幅提高某些应用的模型训练吞吐量。但是，它的语义与原始的 Adam 算法略有不同，这可能会产生不同的实验结果。
设置
pip install -q -U tensorflow-addons
import tensorflow as tf
import tensorflow_addons as tfa
# Hyperparameters
batch_size=64
epochs=10
构建模型
model = tf.keras.Sequential([
tf.keras.layers.Dense(64, input_shape=(784,), activation='relu', name='dense_1'),
tf.keras.layers.Dense(64, activation='relu', name='dense_2'),
tf.keras.layers.Dense(10, activation='softmax', name='predictions'),
])
准备数据
# Load MNIST dataset as NumPy arrays
dataset = {}
num_validation = 10000
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
# Preprocess the data
x_train = x_train.reshape(-1, 784).astype('float32') / 255
x_test = x_test.reshape(-1, 784).astype('float32') / 255
训练和评估
只需用新的 TFA 优化器替换典型的 Keras 优化器
# Compile the model
model.compile(
optimizer=tfa.optimizers.LazyAdam(0.001), # Utilize TFA optimizer
loss=tf.keras.losses.SparseCategoricalCrossentropy(),
metrics=['accuracy'])
# Train the network
history = model.fit(
x_train,
y_train,
batch_size=batch_size,
epochs=epochs)
Epoch 1/10 938/938 [==============================] - 2s 2ms/step - loss: 0.3188 - accuracy: 0.9079 Epoch 2/10 938/938 [==============================] - 2s 2ms/step - loss: 0.1316 - accuracy: 0.9607 Epoch 3/10 938/938 [==============================] - 2s 2ms/step - loss: 0.0970 - accuracy: 0.9706 Epoch 4/10 938/938 [==============================] - 2s 2ms/step - loss: 0.0783 - accuracy: 0.9760 Epoch 5/10 938/938 [==============================] - 2s 2ms/step - loss: 0.0622 - accuracy: 0.9805 Epoch 6/10 938/938 [==============================] - 2s 2ms/step - loss: 0.0516 - accuracy: 0.9843 Epoch 7/10 938/938 [==============================] - 2s 2ms/step - loss: 0.0431 - accuracy: 0.9863 Epoch 8/10 938/938 [==============================] - 2s 2ms/step - loss: 0.0392 - accuracy: 0.9873 Epoch 9/10 938/938 [==============================] - 2s 2ms/step - loss: 0.0332 - accuracy: 0.9893 Epoch 10/10 938/938 [==============================] - 2s 2ms/step - loss: 0.0283 - accuracy: 0.9909
# Evaluate the network
print('Evaluate on test data:')
results = model.evaluate(x_test, y_test, batch_size=128, verbose = 2)
print('Test loss = {0}, Test acc: {1}'.format(results[0], results[1]))
Evaluate on test data: 79/79 - 0s - loss: 0.0843 - accuracy: 0.9765 Test loss = 0.08429064601659775, Test acc: 0.9764999747276306