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Gambaran
Notebook ini mendemonstrasikan cara menggunakan Moving Average Optimizer bersama dengan Model Average Checkpoint dari paket addons tensorflow.
Rata-rata Bergerak
Keuntungan dari Moving Averaging adalah bahwa mereka kurang rentan terhadap pergeseran kerugian yang merajalela atau representasi data yang tidak teratur dalam batch terbaru. Ini memberikan ide yang halus dan lebih umum dari pelatihan model sampai beberapa titik.
Rata-rata Stochastic
Stochastic Weight Averaging menyatu ke optima yang lebih luas. Dengan demikian, itu menyerupai ensembeling geometris. SWA adalah metode sederhana untuk meningkatkan kinerja model saat digunakan sebagai pembungkus di sekitar pengoptimal lain dan rata-rata hasil dari berbagai titik lintasan pengoptimal dalam.
Pos Pemeriksaan Rata-Rata Model
callbacks.ModelCheckpointtidak memberikan pilihan untuk menyimpan bergerak bobot rata-rata di tengah-tengah pelatihan, yang mengapa Model rata pengoptimalan diperlukan callback kustom. Menggunakanupdate_weightsparameter,ModelAverageCheckpointmemungkinkan Anda untuk:
- Tetapkan bobot rata-rata bergerak ke model, dan simpan.
- Pertahankan bobot non-rata-rata lama, tetapi model yang disimpan menggunakan bobot rata-rata.
Mempersiapkan
pip install -U tensorflow-addons
import tensorflow as tf
import tensorflow_addons as tfa
import numpy as np
import os
Membangun Model
def create_model(opt):
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer=opt,
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return model
Siapkan Kumpulan Data
#Load Fashion MNIST dataset
train, test = tf.keras.datasets.fashion_mnist.load_data()
images, labels = train
images = images/255.0
labels = labels.astype(np.int32)
fmnist_train_ds = tf.data.Dataset.from_tensor_slices((images, labels))
fmnist_train_ds = fmnist_train_ds.shuffle(5000).batch(32)
test_images, test_labels = test
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz 32768/29515 [=================================] - 0s 0us/step 40960/29515 [=========================================] - 0s 0us/step Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz 26427392/26421880 [==============================] - 0s 0us/step 26435584/26421880 [==============================] - 0s 0us/step Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz 16384/5148 [===============================================================================================] - 0s 0us/step Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz 4423680/4422102 [==============================] - 0s 0us/step 4431872/4422102 [==============================] - 0s 0us/step
Kami akan membandingkan tiga pengoptimal di sini:
- SGD terbuka
- SGD dengan Rata-Rata Pergerakan
- SGD dengan Stochastic Weight Averaging
Dan lihat bagaimana mereka tampil dengan model yang sama.
#Optimizers
sgd = tf.keras.optimizers.SGD(0.01)
moving_avg_sgd = tfa.optimizers.MovingAverage(sgd)
stocastic_avg_sgd = tfa.optimizers.SWA(sgd)
Kedua MovingAverage dan StocasticAverage optimers menggunakan ModelAverageCheckpoint .
#Callback
checkpoint_path = "./training/cp-{epoch:04d}.ckpt"
checkpoint_dir = os.path.dirname(checkpoint_path)
cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_dir,
save_weights_only=True,
verbose=1)
avg_callback = tfa.callbacks.AverageModelCheckpoint(filepath=checkpoint_dir,
update_weights=True)
Model Kereta
Pengoptimal SGD Vanila
#Build Model
model = create_model(sgd)
#Train the network
model.fit(fmnist_train_ds, epochs=5, callbacks=[cp_callback])
Epoch 1/5 1875/1875 [==============================] - 4s 2ms/step - loss: 0.8031 - accuracy: 0.7282 Epoch 00001: saving model to ./training Epoch 2/5 1875/1875 [==============================] - 3s 2ms/step - loss: 0.5049 - accuracy: 0.8240 Epoch 00002: saving model to ./training Epoch 3/5 1875/1875 [==============================] - 3s 2ms/step - loss: 0.4591 - accuracy: 0.8375 Epoch 00003: saving model to ./training Epoch 4/5 1875/1875 [==============================] - 3s 2ms/step - loss: 0.4328 - accuracy: 0.8492 Epoch 00004: saving model to ./training Epoch 5/5 1875/1875 [==============================] - 3s 2ms/step - loss: 0.4128 - accuracy: 0.8561 Epoch 00005: saving model to ./training <keras.callbacks.History at 0x7fc9d0262250>
#Evalute results
model.load_weights(checkpoint_dir)
loss, accuracy = model.evaluate(test_images, test_labels, batch_size=32, verbose=2)
print("Loss :", loss)
print("Accuracy :", accuracy)
313/313 - 0s - loss: 95.4645 - accuracy: 0.7796 Loss : 95.46446990966797 Accuracy : 0.7796000242233276
Rata-Rata Bergerak SGD
#Build Model
model = create_model(moving_avg_sgd)
#Train the network
model.fit(fmnist_train_ds, epochs=5, callbacks=[avg_callback])
Epoch 1/5 1875/1875 [==============================] - 4s 2ms/step - loss: 0.8064 - accuracy: 0.7303 2021-09-02 00:35:29.787996: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them. INFO:tensorflow:Assets written to: ./training/assets Epoch 2/5 1875/1875 [==============================] - 4s 2ms/step - loss: 0.5114 - accuracy: 0.8223 INFO:tensorflow:Assets written to: ./training/assets Epoch 3/5 1875/1875 [==============================] - 4s 2ms/step - loss: 0.4620 - accuracy: 0.8382 INFO:tensorflow:Assets written to: ./training/assets Epoch 4/5 1875/1875 [==============================] - 4s 2ms/step - loss: 0.4345 - accuracy: 0.8470 INFO:tensorflow:Assets written to: ./training/assets Epoch 5/5 1875/1875 [==============================] - 4s 2ms/step - loss: 0.4146 - accuracy: 0.8547 INFO:tensorflow:Assets written to: ./training/assets <keras.callbacks.History at 0x7fc8e16f30d0>
#Evalute results
model.load_weights(checkpoint_dir)
loss, accuracy = model.evaluate(test_images, test_labels, batch_size=32, verbose=2)
print("Loss :", loss)
print("Accuracy :", accuracy)
313/313 - 0s - loss: 95.4645 - accuracy: 0.7796 Loss : 95.46446990966797 Accuracy : 0.7796000242233276
Berat Stocastic Rata-rata SGD
#Build Model
model = create_model(stocastic_avg_sgd)
#Train the network
model.fit(fmnist_train_ds, epochs=5, callbacks=[avg_callback])
Epoch 1/5 1875/1875 [==============================] - 5s 2ms/step - loss: 0.7896 - accuracy: 0.7350 INFO:tensorflow:Assets written to: ./training/assets Epoch 2/5 1875/1875 [==============================] - 5s 2ms/step - loss: 0.5670 - accuracy: 0.8065 INFO:tensorflow:Assets written to: ./training/assets Epoch 3/5 1875/1875 [==============================] - 5s 2ms/step - loss: 0.5345 - accuracy: 0.8142 INFO:tensorflow:Assets written to: ./training/assets Epoch 4/5 1875/1875 [==============================] - 5s 2ms/step - loss: 0.5194 - accuracy: 0.8188 INFO:tensorflow:Assets written to: ./training/assets Epoch 5/5 1875/1875 [==============================] - 5s 2ms/step - loss: 0.5089 - accuracy: 0.8235 INFO:tensorflow:Assets written to: ./training/assets <keras.callbacks.History at 0x7fc8e0538790>
#Evalute results
model.load_weights(checkpoint_dir)
loss, accuracy = model.evaluate(test_images, test_labels, batch_size=32, verbose=2)
print("Loss :", loss)
print("Accuracy :", accuracy)
313/313 - 0s - loss: 95.4645 - accuracy: 0.7796 Loss : 95.46446990966797 Accuracy : 0.7796000242233276