代码示例 / 计算机视觉 / 使用Reptile进行小样本学习

使用Reptile进行小样本学习

作者: ADMoreau
创建日期 2020/05/21
上次修改 2023/07/20
描述:使用Reptile在Omniglot数据集上进行小样本分类。

ⓘ 此示例使用Keras 3

在Colab中查看 GitHub源代码


简介

OpenAI开发了Reptile算法来执行模型无关的元学习。具体来说,该算法旨在快速学习如何在进行最少训练的情况下执行新任务(小样本学习)。该算法通过使用在从未见过的新数据的小批量上训练的权重与在固定数量的元迭代次数之前模型权重之间的差异执行随机梯度下降来工作。

import os

os.environ["KERAS_BACKEND"] = "tensorflow"

import keras
from keras import layers

import matplotlib.pyplot as plt
import numpy as np
import random
import tensorflow as tf
import tensorflow_datasets as tfds

定义超参数

learning_rate = 0.003
meta_step_size = 0.25

inner_batch_size = 25
eval_batch_size = 25

meta_iters = 2000
eval_iters = 5
inner_iters = 4

eval_interval = 1
train_shots = 20
shots = 5
classes = 5

准备数据

Omniglot数据集是一个包含1623个字符的数据集,这些字符来自50种不同的字母表,每个字符有20个示例。每个字符的20个样本是通过亚马逊的Mechanical Turk在线获取的。对于小样本学习任务,从n个随机选择的类别中随机抽取k个样本(或“样本”)。这些n个数值用于创建一组新的临时标签,用于测试模型在给定少量示例的情况下学习新任务的能力。换句话说,如果您正在训练5个类别,则您的新类别标签将是0、1、2、3或4。Omniglot非常适合此任务,因为它有许多不同的类别可供选择,并且每个类别的样本数量合理。

class Dataset:
    # This class will facilitate the creation of a few-shot dataset
    # from the Omniglot dataset that can be sampled from quickly while also
    # allowing to create new labels at the same time.
    def __init__(self, training):
        # Download the tfrecord files containing the omniglot data and convert to a
        # dataset.
        split = "train" if training else "test"
        ds = tfds.load("omniglot", split=split, as_supervised=True, shuffle_files=False)
        # Iterate over the dataset to get each individual image and its class,
        # and put that data into a dictionary.
        self.data = {}

        def extraction(image, label):
            # This function will shrink the Omniglot images to the desired size,
            # scale pixel values and convert the RGB image to grayscale
            image = tf.image.convert_image_dtype(image, tf.float32)
            image = tf.image.rgb_to_grayscale(image)
            image = tf.image.resize(image, [28, 28])
            return image, label

        for image, label in ds.map(extraction):
            image = image.numpy()
            label = str(label.numpy())
            if label not in self.data:
                self.data[label] = []
            self.data[label].append(image)
        self.labels = list(self.data.keys())

    def get_mini_dataset(
        self, batch_size, repetitions, shots, num_classes, split=False
    ):
        temp_labels = np.zeros(shape=(num_classes * shots))
        temp_images = np.zeros(shape=(num_classes * shots, 28, 28, 1))
        if split:
            test_labels = np.zeros(shape=(num_classes))
            test_images = np.zeros(shape=(num_classes, 28, 28, 1))

        # Get a random subset of labels from the entire label set.
        label_subset = random.choices(self.labels, k=num_classes)
        for class_idx, class_obj in enumerate(label_subset):
            # Use enumerated index value as a temporary label for mini-batch in
            # few shot learning.
            temp_labels[class_idx * shots : (class_idx + 1) * shots] = class_idx
            # If creating a split dataset for testing, select an extra sample from each
            # label to create the test dataset.
            if split:
                test_labels[class_idx] = class_idx
                images_to_split = random.choices(
                    self.data[label_subset[class_idx]], k=shots + 1
                )
                test_images[class_idx] = images_to_split[-1]
                temp_images[
                    class_idx * shots : (class_idx + 1) * shots
                ] = images_to_split[:-1]
            else:
                # For each index in the randomly selected label_subset, sample the
                # necessary number of images.
                temp_images[
                    class_idx * shots : (class_idx + 1) * shots
                ] = random.choices(self.data[label_subset[class_idx]], k=shots)

        dataset = tf.data.Dataset.from_tensor_slices(
            (temp_images.astype(np.float32), temp_labels.astype(np.int32))
        )
        dataset = dataset.shuffle(100).batch(batch_size).repeat(repetitions)
        if split:
            return dataset, test_images, test_labels
        return dataset


import urllib3

urllib3.disable_warnings()  # Disable SSL warnings that may happen during download.
train_dataset = Dataset(training=True)
test_dataset = Dataset(training=False)
 Downloading and preparing dataset 17.95 MiB (download: 17.95 MiB, generated: Unknown size, total: 17.95 MiB) to /home/fchollet/tensorflow_datasets/omniglot/3.0.0...

Dl Completed...: 0 url [00:00, ? url/s]

Dl Size...: 0 MiB [00:00, ? MiB/s]

Extraction completed...: 0 file [00:00, ? file/s]

Generating splits...:   0%|          | 0/4 [00:00<?, ? splits/s]

Generating train examples...:   0%|          | 0/19280 [00:00<?, ? examples/s]

Shuffling /home/fchollet/tensorflow_datasets/omniglot/3.0.0.incomplete1MPXME/omniglot-train.tfrecord*...:   0%…

Generating test examples...:   0%|          | 0/13180 [00:00<?, ? examples/s]

Shuffling /home/fchollet/tensorflow_datasets/omniglot/3.0.0.incomplete1MPXME/omniglot-test.tfrecord*...:   0%|…

Generating small1 examples...:   0%|          | 0/2720 [00:00<?, ? examples/s]

Shuffling /home/fchollet/tensorflow_datasets/omniglot/3.0.0.incomplete1MPXME/omniglot-small1.tfrecord*...:   0…

Generating small2 examples...:   0%|          | 0/3120 [00:00<?, ? examples/s]

Shuffling /home/fchollet/tensorflow_datasets/omniglot/3.0.0.incomplete1MPXME/omniglot-small2.tfrecord*...:   0…

 Dataset omniglot downloaded and prepared to /home/fchollet/tensorflow_datasets/omniglot/3.0.0. Subsequent calls will reuse this data.

可视化数据集中的某些示例

_, axarr = plt.subplots(nrows=5, ncols=5, figsize=(20, 20))

sample_keys = list(train_dataset.data.keys())

for a in range(5):
    for b in range(5):
        temp_image = train_dataset.data[sample_keys[a]][b]
        temp_image = np.stack((temp_image[:, :, 0],) * 3, axis=2)
        temp_image *= 255
        temp_image = np.clip(temp_image, 0, 255).astype("uint8")
        if b == 2:
            axarr[a, b].set_title("Class : " + sample_keys[a])
        axarr[a, b].imshow(temp_image, cmap="gray")
        axarr[a, b].xaxis.set_visible(False)
        axarr[a, b].yaxis.set_visible(False)
plt.show()

png


构建模型

def conv_bn(x):
    x = layers.Conv2D(filters=64, kernel_size=3, strides=2, padding="same")(x)
    x = layers.BatchNormalization()(x)
    return layers.ReLU()(x)


inputs = layers.Input(shape=(28, 28, 1))
x = conv_bn(inputs)
x = conv_bn(x)
x = conv_bn(x)
x = conv_bn(x)
x = layers.Flatten()(x)
outputs = layers.Dense(classes, activation="softmax")(x)
model = keras.Model(inputs=inputs, outputs=outputs)
model.compile()
optimizer = keras.optimizers.SGD(learning_rate=learning_rate)

训练模型

training = []
testing = []
for meta_iter in range(meta_iters):
    frac_done = meta_iter / meta_iters
    cur_meta_step_size = (1 - frac_done) * meta_step_size
    # Temporarily save the weights from the model.
    old_vars = model.get_weights()
    # Get a sample from the full dataset.
    mini_dataset = train_dataset.get_mini_dataset(
        inner_batch_size, inner_iters, train_shots, classes
    )
    for images, labels in mini_dataset:
        with tf.GradientTape() as tape:
            preds = model(images)
            loss = keras.losses.sparse_categorical_crossentropy(labels, preds)
        grads = tape.gradient(loss, model.trainable_weights)
        optimizer.apply_gradients(zip(grads, model.trainable_weights))
    new_vars = model.get_weights()
    # Perform SGD for the meta step.
    for var in range(len(new_vars)):
        new_vars[var] = old_vars[var] + (
            (new_vars[var] - old_vars[var]) * cur_meta_step_size
        )
    # After the meta-learning step, reload the newly-trained weights into the model.
    model.set_weights(new_vars)
    # Evaluation loop
    if meta_iter % eval_interval == 0:
        accuracies = []
        for dataset in (train_dataset, test_dataset):
            # Sample a mini dataset from the full dataset.
            train_set, test_images, test_labels = dataset.get_mini_dataset(
                eval_batch_size, eval_iters, shots, classes, split=True
            )
            old_vars = model.get_weights()
            # Train on the samples and get the resulting accuracies.
            for images, labels in train_set:
                with tf.GradientTape() as tape:
                    preds = model(images)
                    loss = keras.losses.sparse_categorical_crossentropy(labels, preds)
                grads = tape.gradient(loss, model.trainable_weights)
                optimizer.apply_gradients(zip(grads, model.trainable_weights))
            test_preds = model.predict(test_images)
            test_preds = tf.argmax(test_preds).numpy()
            num_correct = (test_preds == test_labels).sum()
            # Reset the weights after getting the evaluation accuracies.
            model.set_weights(old_vars)
            accuracies.append(num_correct / classes)
        training.append(accuracies[0])
        testing.append(accuracies[1])
        if meta_iter % 100 == 0:
            print(
                "batch %d: train=%f test=%f" % (meta_iter, accuracies[0], accuracies[1])
            )
batch 0: train=0.600000 test=0.200000
batch 100: train=0.800000 test=0.200000
batch 200: train=1.000000 test=1.000000
batch 300: train=1.000000 test=0.800000
batch 400: train=1.000000 test=0.600000
batch 500: train=1.000000 test=1.000000
batch 600: train=1.000000 test=0.600000
batch 700: train=1.000000 test=1.000000
batch 800: train=1.000000 test=0.800000
batch 900: train=0.800000 test=0.600000
batch 1000: train=1.000000 test=0.600000
batch 1100: train=1.000000 test=1.000000
batch 1200: train=1.000000 test=1.000000
batch 1300: train=0.600000 test=1.000000
batch 1400: train=1.000000 test=0.600000
batch 1500: train=1.000000 test=1.000000
batch 1600: train=0.800000 test=1.000000
batch 1700: train=0.800000 test=1.000000
batch 1800: train=0.800000 test=1.000000
batch 1900: train=1.000000 test=1.000000

可视化结果

# First, some preprocessing to smooth the training and testing arrays for display.
window_length = 100
train_s = np.r_[
    training[window_length - 1 : 0 : -1],
    training,
    training[-1:-window_length:-1],
]
test_s = np.r_[
    testing[window_length - 1 : 0 : -1], testing, testing[-1:-window_length:-1]
]
w = np.hamming(window_length)
train_y = np.convolve(w / w.sum(), train_s, mode="valid")
test_y = np.convolve(w / w.sum(), test_s, mode="valid")

# Display the training accuracies.
x = np.arange(0, len(test_y), 1)
plt.plot(x, test_y, x, train_y)
plt.legend(["test", "train"])
plt.grid()

train_set, test_images, test_labels = dataset.get_mini_dataset(
    eval_batch_size, eval_iters, shots, classes, split=True
)
for images, labels in train_set:
    with tf.GradientTape() as tape:
        preds = model(images)
        loss = keras.losses.sparse_categorical_crossentropy(labels, preds)
    grads = tape.gradient(loss, model.trainable_weights)
    optimizer.apply_gradients(zip(grads, model.trainable_weights))
test_preds = model.predict(test_images)
test_preds = tf.argmax(test_preds).numpy()

_, axarr = plt.subplots(nrows=1, ncols=5, figsize=(20, 20))

sample_keys = list(train_dataset.data.keys())

for i, ax in zip(range(5), axarr):
    temp_image = np.stack((test_images[i, :, :, 0],) * 3, axis=2)
    temp_image *= 255
    temp_image = np.clip(temp_image, 0, 255).astype("uint8")
    ax.set_title(
        "Label : {}, Prediction : {}".format(int(test_labels[i]), test_preds[i])
    )
    ax.imshow(temp_image, cmap="gray")
    ax.xaxis.set_visible(False)
    ax.yaxis.set_visible(False)
plt.show()

png

png