mnist数据集经过50次pgd攻击生成对抗样本，并转化为npy文件

作为AI助手，我无法直接生成对抗样本。不过，下面是一些可以生成对抗样本的Python代码，您可以参考一下：

import numpy as np
import tensorflow as tf
import keras
from keras.datasets import mnist

# Load MNIST dataset
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.

# Define the model
model = keras.models.Sequential([
    keras.layers.Flatten(input_shape=[28, 28]),
    keras.layers.Dense(300, activation="relu"),
    keras.layers.Dense(100, activation="relu"),
    keras.layers.Dense(10, activation="softmax")
])

# Compile the model
model.compile(loss="sparse_categorical_crossentropy",
              optimizer="adam", metrics=["accuracy"])

# Train the model
model.fit(x_train, y_train, epochs=50, validation_data=(x_test, y_test))

# Generate adversarial examples using Projected Gradient Descent (PGD) attack
def pgd_attack(model, x, y, eps=0.1, alpha=0.01, iter=40):
    delta = tf.zeros_like(x, dtype=tf.float32)

    for t in range(iter):
        with tf.GradientTape() as tape:
            tape.watch(delta)
            loss = keras.losses.sparse_categorical_crossentropy(y, model(x + delta))
        
        # Compute gradients
        g = tape.gradient(loss, delta)
        
        # Add a small perturbation to the delta using the gradient
        g = tf.sign(g)
        delta = tf.clip_by_value(delta + alpha * g, -eps, eps)
        delta = tf.clip_by_value(x + delta, 0, 1) - x
    
    return delta.numpy()

adv_x_train = np.zeros_like(x_train)
adv_x_test = np.zeros_like(x_test)

for i in range(len(x_train)):
    adv_x_train[i] = x_train[i] + pgd_attack(model, x_train[i][np.newaxis], np.array([y_train[i]]))

for i in range(len(x_test)):
    adv_x_test[i] = x_test[i] + pgd_attack(model, x_test[i][np.newaxis], np.array([y_test[i]]))

# Save the adversarial examples
np.save("adv_x_train.npy", adv_x_train)
np.save("adv_x_test.npy", adv_x_test)

这段代码会加载MNIST数据集，并对其进行训练。然后，使用PGD攻击生成对抗样本，并保存为npy文件。需要使用TensorFlow2.x和Keras库来运行此代码。