使用Python实现深度学习模型：元学习与模型无关优化（MAML）

import numpy as np
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras.optimizers import Adam

def create_model():
    model = tf.keras.Sequential([
        layers.Dense(64, activation='relu', input_shape=(784,)),
        layers.Dense(64, activation='relu'),
        layers.Dense(10, activation='softmax')
    ])
    return model

class MAML:
    def __init__(self, model, meta_lr=0.001, inner_lr=0.01, inner_steps=1):
        self.model = model
        self.meta_optimizer = Adam(learning_rate=meta_lr)
        self.inner_lr = inner_lr
        self.inner_steps = inner_steps

    def inner_update(self, x, y):
        with tf.GradientTape() as tape:
            logits = self.model(x)
            loss = tf.reduce_mean(tf.losses.sparse_categorical_crossentropy(y, logits))
        grads = tape.gradient(loss, self.model.trainable_variables)
        k = 0
        for v in self.model.trainable_variables:
            v.assign_sub(self.inner_lr * grads[k])
            k += 1
        return loss

    def meta_update(self, tasks):
        total_grads = [tf.zeros_like(v) for v in self.model.trainable_variables]
        for task in tasks:
            x, y = task
            original_weights = self.model.get_weights()
            for _ in range(self.inner_steps):
                self.inner_update(x, y)
            with tf.GradientTape() as tape:
                logits = self.model(x)
                loss = tf.reduce_mean(tf.losses.sparse_categorical_crossentropy(y, logits))
            grads = tape.gradient(loss, self.model.trainable_variables)
            total_grads = [total_grads[i] + grads[i] for i in range(len(grads))]
            self.model.set_weights(original_weights)
        total_grads = [g / len(tasks) for g in total_grads]
        self.meta_optimizer.apply_gradients(zip(total_grads, self.model.trainable_variables))

from tensorflow.keras.datasets import mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(-1, 784) / 255.0
x_test = x_test.reshape(-1, 784) / 255.0

def sample_tasks(x, y, num_tasks, num_shots):
    tasks = []
    for _ in range(num_tasks):
        indices = np.random.choice(len(x), num_shots)
        tasks.append((x[indices], y[indices]))
    return tasks

meta_model = create_model()
maml = MAML(meta_model, meta_lr=0.001, inner_lr=0.01, inner_steps=1)

num_tasks = 10
num_shots = 5
num_meta_iterations = 1000

for iteration in range(num_meta_iterations):
    tasks = sample_tasks(x_train, y_train, num_tasks, num_shots)
    maml.meta_update(tasks)
    if iteration % 100 == 0:
        print(f"Iteration {iteration}: Meta Update Completed")

def evaluate_model(model, x, y, num_steps=1):
    model_copy = tf.keras.models.clone_model(model)
    model_copy.set_weights(model.get_weights())
    for _ in range(num_steps):
        with tf.GradientTape() as tape:
            logits = model_copy(x)
            loss = tf.reduce_mean(tf.losses.sparse_categorical_crossentropy(y, logits))
        grads = tape.gradient(loss, model_copy.trainable_variables)
        k = 0
        for v in model_copy.trainable_variables:
            v.assign_sub(0.01 * grads[k])
            k += 1
    logits = model_copy(x)
    predictions = tf.argmax(logits, axis=1)
    accuracy = tf.reduce_mean(tf.cast(predictions == y, tf.float32))
    return accuracy.numpy()

# 在新任务上进行评估
new_task_x, new_task_y = sample_tasks(x_test, y_test, 1, 10)[0]
accuracy = evaluate_model(meta_model, new_task_x, new_task_y, num_steps=5)
print(f"Accuracy on new task: {accuracy:.2f}")